A large-scale hydropower station work ticket handling intelligent guidance method, system, device and medium

Abstract

This invention relates to the field of work permit processing technology, specifically to an intelligent guidance method, system, equipment, and medium for work permit processing in large hydropower stations. The method provided by this invention mainly includes constructing a comprehensive evaluation model; obtaining evaluation values ​​based on the calculation factor coefficients of pending work permits through the comprehensive evaluation model; sorting and outputting the evaluation values ​​of all pending work permits; obtaining the average working time for each pending work permit; and sending reminder notifications to the staff executing the next work permit based on the average working time of the currently executing work permit. Through this method, pending work permit tasks are remotely queued and managed, allowing for a direct understanding of workload distribution, advance preparation, and rational arrangement of working hours, tasks, and staff. This optimizes resource allocation and workflow, improves production scheduling capabilities, and thus more effectively ensures the safe, orderly, and efficient operation of power safety production.

Description

Technical Field

[0001] This invention relates to the field of work permit processing technology, and more specifically, to an intelligent guidance method, system, equipment, and medium for processing work permits for large hydropower stations. Background Technology

[0002] In the daily operation and maintenance management of hydropower stations, the processing of work permits is a crucial measure to ensure the lean maintenance and safe and stable operation of power system equipment. Currently, in power plants, work permit processing mostly involves the work supervisor going to the permit processing office at the scheduled time and waiting for processing. However, due to factors such as unclear scheduled times and the uncertainty of the work supervisor's arrival time, when there are many people processing permits, they can only mechanically wait, resulting in long waiting times and wasted time. In addition, since the shift supervisor cannot grasp the overall workload distribution for the day in advance, they can only rely on human judgment and professional knowledge to arrange the overall work order, which is not conducive to proactively planning resource allocation and improving work efficiency, and is prone to work congestion or delays. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent guidance method, system, equipment, and medium for handling work permits at large hydropower stations, in order to solve the aforementioned problems in the prior art.

[0004] This invention is achieved through the following technical solution: A smart guidance method for processing work permits at large hydropower stations includes: Obtain all pending work orders and their keywords, and construct a work order attribute database, which includes keywords and the calculated factor coefficients mapped to those keywords. Based on the keywords located in the work ticket attribute database, all calculated factor coefficients of the current pending work tickets are retrieved. A comprehensive evaluation model is constructed. The evaluation value is obtained through the calculation factor coefficient of the pending work tickets. The evaluation values ​​of all pending work tickets are sorted and output. The system obtains the average working time for each pending work order. When it receives a signal that the business of the previous work order is being executed, it sends a reminder notification to the next staff member to execute the next work order based on the average working time of the work order being executed.

[0005] Preferably, the keywords for obtaining the work order include: Establish a keyword database, and use OCR to scan the work order data based on the data in the keyword database; The keyword library includes a device keyword library, a work type keyword library, an urgency level keyword library, and a safety measure keyword library.

[0006] Preferably, the scanning of the work order data using OCR based on the keyword library includes: The image obtained from scanning the work order to be done is preprocessed, and OCR text recognition is performed on the preprocessed image to obtain the original text string; The original text string is searched within the keyword database. Characters in the original text string that match those in the keyword database are marked and output as keywords.

[0007] Preferably, the preprocessing of the image obtained from scanning the work order to be done includes: Perform Gaussian filtering on the image to remove noise:

[0008] In the formula, Image coordinates Gaussian filter weighting coefficients at the location, The standard deviation is Gaussian.

[0009] Preferably, the construction of the comprehensive evaluation model includes:

[0010] In the formula, For the first The evaluation value of a work order. , , The weights are calculated separately, and their sum is 1. , , , These are the calculation factor coefficients for equipment, work type, urgency level, and safety measures, respectively.

[0011] Preferably, the step of sending a reminder notification to the next worker to execute the next work order based on the average working time of the currently executing work order includes: Set a reminder time interval, which is the advance notice period for reminding staff of the next work order, including:

[0012] In the formula, To remind you of the time interval, To calculate the coefficients, This represents the average processing time for the current work order. , , , These are the average calculation factor coefficients for all current work orders regarding equipment, work type, urgency, and safety measures.

[0013] Preferred options also include: Check if the reminder time interval is greater than 1. If it is greater than 1, then... If the reminder interval is not greater than 1, then the original value of the reminder interval will be retained.

[0014] Secondly, the present invention also provides an intelligent guidance system for handling work permits at large hydropower stations, used in the aforementioned intelligent guidance method for handling work permits at large hydropower stations, comprising: The data processing module is configured to: acquire all pending work orders and their keywords; construct a work order attribute database, which includes keywords and the calculated factor coefficients mapped to those keywords; retrieve all calculated factor coefficients of the pending work orders based on the keywords being located within the attribute database; construct a comprehensive evaluation model; obtain evaluation values ​​based on the calculated factor coefficients of the pending work orders using the comprehensive evaluation model; and sort and output the evaluation values ​​of all pending work orders. The reminder module is configured to obtain the average working time of each pending work order, and when it receives a signal that the business of the previous work order is being executed, it sends a reminder notification to the next staff member to execute the next work order based on the average working time of the work order being executed.

[0015] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described intelligent guidance method for handling work permits for large hydropower stations.

[0016] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described intelligent guidance method for handling work permits for large hydropower stations.

[0017] The technical solution of the present invention has at least the following advantages and beneficial effects: The method provided by this invention mainly includes constructing a comprehensive evaluation model, obtaining evaluation values ​​based on the calculation factor coefficients of pending work orders through the comprehensive evaluation model, and sorting and outputting the evaluation values ​​of all pending work orders; obtaining the average working time of each pending work order; and when receiving a signal that the business of the previous work order is being executed, sending a reminder notification to the next staff member executing the next work order based on the average working time of the currently executing work order. Through the above method, pending work order tasks are remotely queued and managed, enabling automatic push notifications of processing order, processing time, and message reminders. This not only helps the duty manager to understand the workload distribution more efficiently, quickly, and intuitively, prepare in advance, rationally arrange working hours, tasks, and staff, optimize resource allocation and workflow, improve production scheduling capabilities, and prevent potential congestion or delays, but also helps the work manager make full use of time, avoid unnecessary waiting, improve work efficiency, and thus more effectively ensure the safe, orderly, and efficient operation of power safety production. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] The module division in this application is a logical division. In actual application, there may be other division methods. For example, multiple modules may be combined into or integrated into another system, or some features may be ignored or not executed.

[0022] The independently described modules or sub-modules may or may not be physically separated; they may be implemented in software or hardware, and some modules or sub-modules may be implemented in software, with the processor calling the software to implement the function of these modules or sub-modules, while other modules or sub-modules may be implemented in hardware, such as through hardware circuits. Furthermore, some or all of the modules can be selected to achieve the purpose of this application's solution according to actual needs.

[0023] Please refer to Figures 1-2 The present invention provides an intelligent guidance method for processing work permits for large hydropower stations, comprising: S1: Obtain all pending work orders and their keywords, and construct a work order attribute database, which includes keywords and the calculated factor coefficients mapped to the keywords; In this embodiment, a keyword library is established, and the work order data is scanned using OCR based on the data in the keyword library; The keyword library includes a device keyword library, a work type keyword library, an urgency level keyword library, and a safety measure keyword library.

[0024] In the work order attribute database, different calculated factor coefficients are assigned to each keyword. All calculated factor coefficients range from 1 to 5. For example: The calculated factor coefficients for equipment keywords are: 5 for power generation equipment, 4 for maintenance equipment, and 2 for detection equipment. Work type: Hot work is 4, Earthwork is 2; Urgency level: 5 for extremely urgent, 4 for urgent, 3 for moderately urgent, 2 for normal, and 1 for non-urgent; Safety measures: power outage is 5, grounding is 1, fencing is 3, signage is 3, isolation is 4.

[0025] The above settings are not the only ones; you can set them according to the specific circumstances on site.

[0026] S2: Based on the keywords located in the work ticket attribute database, retrieve all calculated factor coefficients of the current pending work ticket; S3: Construct a comprehensive evaluation model, obtain the evaluation value based on the calculation factor coefficients of the pending work tickets through the comprehensive evaluation model, and output the evaluation values ​​of all pending work tickets after sorting them. S4: Obtain the average working time for each pending work order. When a signal is received indicating that the business of the previous work order is being executed, send a reminder notification to the next staff member to execute the next work order based on the average working time of the work order being executed.

[0027] The method provided by this invention mainly includes constructing a comprehensive evaluation model, obtaining evaluation values ​​based on the calculation factor coefficients of pending work orders through the comprehensive evaluation model, and sorting and outputting the evaluation values ​​of all pending work orders; obtaining the average working time of each pending work order; and when receiving a signal that the business of the previous work order is being executed, sending a reminder notification to the next staff member executing the next work order based on the average working time of the currently executing work order. Through the above method, pending work order tasks are remotely queued and managed, enabling automatic push notifications of processing order, processing time, and message reminders. This not only helps the duty manager to understand the workload distribution more efficiently, quickly, and intuitively, prepare in advance, rationally arrange working hours, tasks, and staff, optimize resource allocation and workflow, improve production scheduling capabilities, and prevent potential congestion or delays, but also helps the work manager make full use of time, avoid unnecessary waiting, improve work efficiency, and thus more effectively ensure the safe, orderly, and efficient operation of power safety production.

[0028] In one exemplary embodiment of the present invention, the scanning of work order data using OCR based on data from a keyword database includes: The image obtained from scanning the work order to be done is preprocessed, and OCR text recognition is performed on the preprocessed image to obtain the original text string; The original text string is searched within the keyword database. Characters in the original text string that match those in the keyword database are marked and output as keywords.

[0029] Preferably, the preprocessing of the image obtained from scanning the work order to be done includes: Perform Gaussian filtering on the image to remove noise:

[0030] In the formula, Image coordinates Gaussian filter weighting coefficients at the location, The standard deviation is Gaussian.

[0031] In one exemplary embodiment of the present invention, constructing a comprehensive evaluation model includes:

[0032] In the formula, For the first The evaluation value of a work order. , , The weights are calculated separately, and their sum is 1. , , , These are the calculation factor coefficients for equipment, work type, urgency level, and safety measures, respectively.

[0033] Based on a pre-set algorithm model, the system automatically sorts all scheduled work orders and provides a processing order and suggested processing time slots. The sorting is based on a built-in multi-factor database, comprehensively considering various influencing factors and weights, such as work order type, work type, equipment importance, number of safety measures, and planned start and end times. The weights of each influencing factor in the sorting algorithm can be flexibly set and dynamically adjusted according to actual management needs. Based on the sorting results, a relatively reasonable processing order is provided for staff reference.

[0034] In one exemplary embodiment of the present invention, sending a reminder notification to the next worker to execute the next work order based on the average working time of the currently executing work order includes: Set a reminder time interval, which is the advance notice period for reminding staff of the next work order, including:

[0035] In the formula, To remind you of the time interval, To calculate the coefficients, This represents the average processing time for the current work order. , , , These are the average calculation factor coefficients for all current work orders regarding equipment, work type, urgency, and safety measures.

[0036] Check if the reminder time interval is greater than 1. If it is greater than 1, then... If the reminder interval is not greater than 1, then the original value of the reminder interval will be retained.

[0037] Based on the above, a dynamic reminder interval is provided to accommodate work orders in different situations, avoiding insufficient reminder intervals that could result in some important work orders not being prepared in time. One option is 5, which means that in the initial state, a reminder is sent in advance based on one-fifth of the average processing time of the current work order. That is, when the previous work order reaches four-fifths of the average processing time of the current work order, a reminder is sent.

[0038] In specific implementation, it includes: Work appointment: Based on appointment logic, the system allows the person in charge of the work to initiate the appointment for work order processing and collects all pending work order data.

[0039] Reservation logic settings: Work order process status configuration, which allows reservations for work orders and hot work orders that are pending issuance, pending permission, pending termination.

[0040] Work order reservation time configuration: reservations can only be made within the planned start and end time range of the work order.

[0041] The work order reservation role can be configured so that the person in charge of the work order can make the reservation, or the person in charge of the work team to which the work order was issued can make the reservation.

[0042] Application Results: When making an appointment for a work order, the applicant needs to fill in their contact information, service type, and appointment time. All appointments are automatically added to the next day's work plan and sorted in real time, resulting in the following effect: Convenience: Supports reservations via both computer and mobile devices, ensuring a quick and smooth reservation process.

[0043] Real-time performance: All scheduled tasks are added to the planning process in real time, allowing for easy monitoring of the overall work status.

[0044] Intuitive Features: Supports appointments for work permit services across multiple scenarios, including work permit authorization, adding new work content, changing work supervisors, work permit extensions, terminating work permits, and cooperating with experiments, providing a clear and intuitive reflection of the needs for handling work permit services.

[0045] Planning: Focuses on work planning and arrangement, making it easier for the duty officer to understand the workload and make reasonable arrangements and preparations in advance.

[0046] Mitigation: Set up appointment time logic to prevent situations such as work being permitted before the scheduled start time of the work order or work being terminated after the scheduled end time.

[0047] Intelligent sorting: The system has a built-in multi-factor database that considers various influencing factors such as job type, urgency, equipment importance, and number of safety measures, and supports weight adjustment. Based on a preset algorithm model, it automatically sorts all pending work orders and provides the processing order and suggested processing time for each order.

[0048] Review and Release: The suggested sorting results generated by the system will first be submitted to the duty officer for review. The officer can make adjustments and optimizations before releasing the results through the system platform.

[0049] Message push: The system will automatically push the sorted work order tasks and suggested processing time to the corresponding work person's APP in the form of to-do items, for instant notification and reminder of business processing, and supports push of different message types in various scenarios.

[0050] Processing guidance: The duty supervisor has the authority to manually adjust the processing time of work orders based on the on-site processing progress, emergencies, or special needs, enabling real-time control and adjustment of the processing progress of each work order for the day. In addition, the system can send reminder notifications to the next work supervisor in advance based on the current work order sorting and the average processing time of similar historical work.

[0051] Large-screen display: Provides electronic dashboards on the web and mobile app, displaying the real-time appointment order of work orders, including queue order, task name, person in charge, planned time, processing time, business type, and supporting personnel.

[0052] Secondly, the present invention also provides an intelligent guidance system for handling work permits at large hydropower stations, used in the aforementioned intelligent guidance method for handling work permits at large hydropower stations, comprising: The data processing module is configured to: acquire all pending work orders and their keywords; construct a work order attribute database, which includes keywords and the calculated factor coefficients mapped to those keywords; retrieve all calculated factor coefficients of the pending work orders based on the keywords being located within the attribute database; construct a comprehensive evaluation model; obtain evaluation values ​​based on the calculated factor coefficients of the pending work orders using the comprehensive evaluation model; and sort and output the evaluation values ​​of all pending work orders. The reminder module is configured to obtain the average working time of each pending work order, and when it receives a signal that the business of the previous work order is being executed, it sends a reminder notification to the next staff member to execute the next work order based on the average working time of the work order being executed.

[0053] Specifically, it consists of modules such as job appointment, intelligent sorting, review and release, message push, processing guidance, and large-screen display (e.g., Figure 1 The system supports both web and mobile app platforms, both of which have corresponding functionalities. It can collect pending work order data through the appointment module, and utilize intelligent sorting logic and a multi-factor weighting adjustment mechanism to comprehensively analyze data such as task type, urgency, equipment importance, and number of safety measures. It automatically pushes pending work order tasks, provides suggested processing order and time periods, and publishes them after review by the duty manager. It also features real-time adjustment, message reminders, and dashboard display functions.

[0054] The status of work order processing can be pushed to users in real time via a mobile app. The app provides multi-faceted message reminders for different business scenarios, ensuring timely, accurate, and prominent message delivery. It also supports real-time viewing of the day's work progress and tomorrow's planned work on the app, allowing the person in charge to keep track of the work progress and carry out the processing at any time.

[0055] Appointment success notification: Push notifications will be sent in real time after the appointment is reviewed and published.

[0056] Processing reminder notification: The next person to process will be reminded 30 minutes in advance based on the processing progress.

[0057] Cancellation notification: Notifications for canceled appointments are pushed in real time.

[0058] Time adjustment notification: Push notifications will be sent in real time after the time is adjusted according to the actual work schedule.

[0059] Appointment reminder notification: For work tickets with a planned start time of tomorrow and no appointment yet, and work tickets with a planned end time of tomorrow and no appointment yet, an appointment reminder will be sent out at 6 pm every day, and the work supervisor and team leader will be notified simultaneously.

[0060] Data dashboard setup: The system provides electronic dashboard functions on the web and mobile app. The dashboard will display the real-time processing status of work orders, enabling a comprehensive and real-time grasp of work details, processing status, and processing progress.

[0061] The intelligent guidance system for work permit processing of this invention supports work appointments for all business types in the entire work permit processing chain, including work permit authorization, adding new work content, changing work supervisor, equipment power-on trial operation, work permit extension, and work permit termination, making it convenient and intuitive to understand the specific business to be carried out.

[0062] The intelligent guidance system for work ticket processing of the present invention is based on a preset multi-factor algorithm model to achieve comprehensive intelligent sorting of work tickets to be processed, automatically generate processing order and processing time period. The algorithm model supports custom multi-factor sorting weights and also supports preset multi-scenario algorithm models.

[0063] The intelligent guidance system for work permit processing provides data statistics and queries, supporting historical work volume queries and workload distribution statistics. Based on the distribution map, it can further optimize and improve the maintenance plan for power plant equipment and facilities. It provides electronic dashboards on both web and mobile apps, displaying the real-time appointment order and processing status of work permits, improving the transparency and openness of the work permit processing process.

[0064] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0065] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. This computer software product, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent guidance in processing work permits for large hydropower stations, characterized in that, include: Obtain all pending work orders and their keywords, and construct a work order attribute database, which includes keywords and the calculated factor coefficients mapped to those keywords. Based on the keywords located in the work ticket attribute database, all calculated factor coefficients of the current pending work tickets are retrieved. A comprehensive evaluation model is constructed. The evaluation value is obtained through the calculation factor coefficient of the pending work tickets. The evaluation values ​​of all pending work tickets are sorted and output. The system obtains the average working time for each pending work order. When it receives a signal that the business of the previous work order is being executed, it sends a reminder notification to the next staff member to execute the next work order based on the average working time of the work order being executed.

2. The intelligent guidance method for processing work permits for large hydropower stations according to claim 1, characterized in that, The keywords for obtaining a work order include: Establish a keyword database, and use OCR to scan the work order data based on the data in the keyword database; The keyword library includes a device keyword library, a work type keyword library, an urgency level keyword library, and a safety measure keyword library.

3. The intelligent guidance method for processing work permits for large hydropower stations according to claim 2, characterized in that, The scanning of work order data using OCR based on data from the keyword database includes: The image obtained from scanning the work order to be done is preprocessed, and OCR text recognition is performed on the preprocessed image to obtain the original text string; The original text string is searched within the keyword database. Characters in the original text string that match those in the keyword database are marked and output as keywords.

4. The intelligent guidance method for processing work permits for large hydropower stations according to claim 3, characterized in that, The preprocessing of the image obtained from scanning the pending work order includes: Perform Gaussian filtering on the image to remove noise: In the formula, Image coordinates Gaussian filter weighting coefficients at the location, The standard deviation is Gaussian.

5. The intelligent guidance method for processing work permits for large hydropower stations according to claim 4, characterized in that, The construction of the comprehensive evaluation model includes: In the formula, For the first The evaluation value of a work order. , , The weights are calculated separately, and their sum is 1. , , , These are the calculation factor coefficients for equipment, work type, urgency level, and safety measures, respectively.

6. The intelligent guidance method for processing work permits at a large hydropower station according to claim 5, characterized in that, The step of sending a reminder notification to the next worker to execute a work order based on the average working time of the currently executing work order includes: Set a reminder time interval, which is the advance notice period for reminding staff of the next work order, including: In the formula, To remind you of the time interval, To calculate the coefficients, This represents the average processing time for the current work order. , , , These are the average calculation factor coefficients for all current work orders regarding equipment, work type, urgency, and safety measures.

7. The intelligent guidance method for processing work permits at a large hydropower station according to claim 6, characterized in that, Also includes: Check if the reminder time interval is greater than 1. If it is greater than 1, then... If the reminder interval is not greater than 1, then the original value of the reminder interval will be retained.

8. An intelligent guidance system for processing work permits at a large hydropower station, used to execute the intelligent guidance method for processing work permits at a large hydropower station as described in any one of claims 1-7, comprising: The data processing module is configured to obtain all current pending work orders, obtain the keywords of the work orders, construct a work order attribute database, the work order attribute database including keywords and the calculation factor coefficients mapped by the keywords; and retrieve all calculation factor coefficients of the current pending work orders based on the keywords being located in the work order attribute database. A comprehensive evaluation model is constructed. The evaluation value is obtained through the calculation factor coefficient of the pending work tickets. The evaluation values ​​of all pending work tickets are sorted and output. The reminder module is configured to obtain the average working time of each pending work order, and when it receives a signal that the business of the previous work order is being executed, it sends a reminder notification to the next staff member to execute the next work order based on the average working time of the work order being executed.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the intelligent guidance method for handling work permits for large hydropower stations as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the intelligent guidance method for handling work tickets for large hydropower stations as described in any one of claims 1-7.

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