Green remote manufacturing supervision platform and method for intelligent Internet of Things of electrical equipment

Abstract

The invention discloses a green remote manufacturing supervision platform and method for an electrician equipment intelligent Internet of Things, and belongs to the technical field of electric power, and the platform comprises a task generation module which obtains a purchase order contract which is completed in ECP2.0 circulation and meets the requirements of the electrician equipment intelligent Internet of Things, forms a manufacturing supervision task according to material purchase information, and carries out the task generation and issuing; the task production scheduling module is used for confirming filling of sales orders, production scheduling plans, production work orders and parameter specifications hung by suppliers according to the purchase order information; the task sensing module is used for checking raw material and component inspection, production process and process inspection, predelivery test and finished product warehousing data which are produced and uploaded by suppliers; the task verification module is used for judging and verifying the data quality sensed by the task sensing module; and the task reporting module is used for generating a manufacturing supervision report after key point witness and data verification are completed on the manufacturing supervision task. According to the invention, the equipment network access production process is managed and controlled, and a solid guarantee is provided for improving the quality of power grid materials.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, and more specifically, relates to a green remote monitoring platform and method for intelligent Internet of Things of electrical equipment. Background Technology

[0002] To promote the digital transformation and supply chain upgrade of the electrical equipment industry, the construction of the Electrical Equipment Intelligent Internet of Things (EIP) platform aims to achieve interconnectivity across the entire supply chain through technologies such as the Internet of Things and big data. Digitalization will strengthen quality and schedule control over supplier production processes, and optimize internal material management and external industry collaboration.

[0003] However, in existing technologies, when managing and comprehensively evaluating the entire lifecycle of power equipment, including but not limited to the procurement, production, processing, inspection, testing, installation, commissioning, and operation and maintenance of electrical equipment, it is difficult to accurately, objectively, and scientifically assess the quality of electrical equipment, the production capacity of suppliers, and their technical level. This requires repetitive data entry, which is complex and inefficient. Furthermore, after data entry, manual review is required, which is prone to errors and omissions due to fatigue or professional blind spots. Because data correlation is weak, it is difficult to quickly uncover cross-data logic between different reports, and potential quality hazards cannot be detected in a timely manner.

[0004] Therefore, there is an urgent need for a cloud-based monitoring and control platform for the intelligent Internet of Things (IoT) of electrical equipment to uniformly manage the entire process of power equipment manufacturing supervision. Summary of the Invention

[0005] In view of the shortcomings of the above-mentioned or existing technologies, this invention proposes a green remote monitoring platform and method for intelligent IoT of electrical equipment. By implementing intelligent IoT for electrical equipment, it enables full-process monitoring and management of electrical equipment from order placement, production scheduling, and use, thereby improving the accuracy and reliability of monitoring reports.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a green remote monitoring platform for intelligent IoT of electrical equipment, comprising:

[0008] The task generation module obtains purchase order contracts that have been processed through ECP2.0 and meet the requirements of smart IoT for electrical equipment, generates and issues supervision tasks based on material procurement information;

[0009] The task scheduling module confirms the sales orders, production plans, production work orders, and parameter specifications linked to the supplier based on the purchase order information.

[0010] The task awareness module verifies the data on raw materials, component inspection, production process and in-process inspection, factory testing, and finished product warehousing uploaded by suppliers.

[0011] The task verification module is used to evaluate and verify the quality of the data perceived by the task awareness module.

[0012] The task report module is used to generate a supervision report after witnessing key points and verifying data for the completion of the supervision task.

[0013] The AI ​​review module is used to automate and intelligently review the supervision reports and output review opinions and credibility assessments.

[0014] As a further technical solution of the present invention, it also includes:

[0015] The task monitoring module is used to monitor the execution progress and status of the construction supervision tasks in real time, and to provide relevant users with visual early warnings and prompts.

[0016] As a further technical solution of the present invention, the task generation module includes:

[0017] The task selection module is used to select the entire purchase order or a portion of line items in the purchase order to create a supervision task.

[0018] The task confirmation module is used to generate and distribute tasks after the project is confirmed in the supervision task line.

[0019] The task selection module is specifically as follows:

[0020] Upload contract / order;

[0021] Extract contract and order information, and generate pre-monitoring tasks based on the extracted entire purchase order or part of the line items.

[0022] As a further technical solution of the present invention, the contract order information content shown includes: project name, provincial company name, project unit, contract type, requesting unit, material category, material type, supplier name, contract number / agreement number, purchase order number, task code, task status, task issuance date, planned delivery date, confirmed delivery date, order status, line project status, contract effective date, and actual contract delivery date.

[0023] As a further technical solution of the present invention, the task awareness module includes:

[0024] The self-collected data module is used to automatically collect raw material data information based on the supplier's test reports;

[0025] The manual push module is used by suppliers to manually push production test data through their self-built system and submit it to the data platform. The data platform is used to obtain the test data of the current equipment.

[0026] As a further technical solution of the present invention, the task verification module includes:

[0027] The supervision task query module is used by supervisors to query the assigned supervision tasks;

[0028] The parameter specification module is used to view, modify, enable, and confirm the parameter specifications related to the construction supervision task;

[0029] The key point witnessing module is used to confirm key point information for supervision tasks with a status of "in progress" or "report rejected".

[0030] The data verification module is used to verify the data of supervision tasks that are in "in progress" or "report rejected".

[0031] As a further technical solution of the present invention, the task verification module further includes a data verification review module, the data verification review module comprising:

[0032] The reconsideration module is used by suppliers to raise objections to the results of manual verification and to initiate a reconsideration by submitting a description of the reconsideration application and attachments.

[0033] The reconsideration response module is used by the supervisors to view the reconsideration application and related records, determine whether to modify the original verification result, and form the preliminary review result.

[0034] The review and confirmation module is used by the provincial company's quality supervision administrator to review the data verification applications that have been completed in the initial review and generate review results.

[0035] As a further technical solution of the present invention, the artificial intelligence review module includes: a compliance review unit, used to automatically compare and verify the test data, key parameters and document integrity in the supervision report based on preset supervision specifications and technical standards; and an anomaly detection unit, used to identify abnormal information in the report such as data logic conflicts, deviations from historical patterns or typical defect patterns through machine learning models.

[0036] The credibility scoring unit is used to quantitatively score the overall quality and consistency of the supervision report and output the credibility level of the audit.

[0037] The audit comment generation unit is used to automatically generate structured audit comments based on compliance review and anomaly detection results, and locate specific chapters or data entries in the report.

[0038] Secondly, the present invention provides a green remote monitoring method for smart IoT of electrical equipment, comprising:

[0039] Obtain purchase order contracts that have been processed through ECP2.0 and meet the requirements of smart IoT for electrical equipment; generate and issue supervision tasks based on material procurement information;

[0040] Confirm the sales orders, production plans, production work orders, and parameter specifications linked to the supplier based on the purchase order information;

[0041] Verify the data on raw materials, component inspection, production process and in-process inspection, factory testing, and finished product warehousing uploaded by the supplier.

[0042] Evaluate and verify the quality of the data perceived by the task awareness module;

[0043] A construction supervision report is generated after witnessing and verifying key points and data related to the completion of the construction supervision task.

[0044] The system performs automated and intelligent review of the supervision reports and outputs review comments and credibility assessments.

[0045] As a further technical solution of the present invention, the automated intelligent review of the supervision report using artificial intelligence technology includes:

[0046] Based on the pre-set supervision specifications and technical standards, a compliance review is conducted on the test data, key parameters, and document completeness in the report;

[0047] Machine learning models are used to identify anomalous information in reports, such as data logic conflicts, deviations from historical patterns, or typical defect patterns.

[0048] The overall quality and consistency of the supervision report are quantitatively scored, and the credibility level of the audit is output.

[0049] Based on the review and testing results, structured review comments are automatically generated and located to specific chapters or data entries in the report.

[0050] The beneficial effects of this invention are as follows:

[0051] This invention, based on cloud-based manufacturing supervision operation specifications and platform operation specifications, addresses cloud-based manufacturing supervision business processes, operational requirements, key material witnessing requirements, system operation, and common issues. It constructs a unified and standardized power grid material quality management system, facilitating quality supervision administrators to manage and execute supervision projects according to actual business needs. This effectively improves the professional competence of practitioners, ensures the quality of production data, and leverages the control efficiency of cloud-based manufacturing supervision over the production process of equipment entering the grid. It provides a solid guarantee for improving the quality of power grid materials. Furthermore, this invention uses an artificial intelligence review module to automatically review the generated supervision reports, avoiding subjective biases and oversights inherent in manual review. It automatically compares the report data with standard thresholds and compliance requirements in the database. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This invention provides a structural diagram of a green remote monitoring platform for smart IoT of electrical equipment.

[0054] Figure 2 A business process diagram of a green remote monitoring platform for smart IoT of electrical equipment provided in an embodiment of the present invention;

[0055] Figure 3 A flowchart of a green remote monitoring method for smart IoT of electrical equipment provided in an embodiment of the present invention. Detailed Implementation

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0057] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0058] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the term covers the element or object listed after the term and its equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Connections can be fixed, detachable, or integral; they can be mechanical or electrical; they can be direct or indirect through an intermediate medium; they can be internal communication between two elements or an interaction between two elements, unless otherwise expressly defined. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances. "Up," "down," "left," and "right" are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0059] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments. It should be noted that the embodiments of the present invention can be applied to any applicable scenario.

[0060] Example 1

[0061] See Figure 1 A green remote monitoring platform for smart IoT of electrical equipment, provided in one embodiment of the present invention, includes:

[0062] The task generation module 101 obtains the purchase order contract that has been completed by ECP2.0 and meets the requirements of smart IoT for electrical equipment, forms the supervision task based on the material procurement information, and generates and issues the task.

[0063] The task scheduling module 102 confirms the sales orders, production plans, production work orders, and parameter specifications linked to the supplier based on the purchase order information.

[0064] The task perception module 103 verifies the data on raw materials, component inspection, production process and in-process inspection, factory test and finished product warehousing uploaded by the supplier.

[0065] The task verification module 104 is used to evaluate and verify the quality of the data perceived by the task perception module.

[0066] Task report module 105 is used to generate a supervision report after witnessing key points and verifying data for the completion of the supervision task;

[0067] The AI ​​review module 106 is used to perform automated intelligent review of the supervision report and output review opinions and credibility assessment.

[0068] This invention provides a green remote monitoring platform for electrical equipment using the Internet of Things (IoT). The platform acquires material procurement information for monitoring tasks, generates orders, and performs tasks such as contract signing, task confirmation, supplier selection, parameter specification confirmation and activation, key point verification, data verification, alarm handling, collaborative problem resolution, and monitoring report generation and review. This effectively improves the professional competence of personnel, ensures the quality of production data, builds a unified and standardized power grid material monitoring and management system, and fully leverages the IoT's production process control capabilities for equipment access quality, providing a solid guarantee for improving the quality of power grid materials.

[0069] This invention introduces the cloud construction system workflow of the State Grid Electric Equipment Smart IoT Platform, covering order access, task issuance, production supervision, data collection, report generation and review mechanism. Based on the material procurement target of the supervision task, orders are signed with suppliers. Supervisors can view order details through the system, including issuance date, purchase order number, contract agreement number, material category and other information, and distinguish between supply orders, demand orders or framework agreements according to the contract type.

[0070] See Figure 2 The present invention provides a green remote monitoring platform for smart IoT of electrical equipment, the business process of which is as follows:

[0071] Quality supervision and management personnel create new supervision tasks and issue them to the supervision units. Before the supervision units confirm the tasks, the provincial company's quality supervision administrator can withdraw the tasks.

[0072] After the supervising unit reviews and confirms the supervision task, it assigns the task to the supervision personnel under the same organization. The supervising unit may also choose to return the supervision task after reviewing it.

[0073] Supervisors can review their assigned supervision tasks and conduct "cloud supervision." a) Confirm the parameter specifications uploaded by suppliers; if there are any issues, make modifications. Once confirmed, activate the parameter specifications. b) Review relevant data from production trials. c) Complete witnessing of all key points. Quality supervision administrators and the supervising unit can also view these witnessings. d) After all key point witnessings are completed, supervisors can generate / upload a supervision report.

[0074] The supervision report is first internally reviewed by the supervising unit. Once the supervision unit confirms its accuracy, the quality supervision administrator then approves it. Upon approval, the task is completed. The quality supervision administrator can reject the report after the task is completed.

[0075] The supervising unit and the provincial company's quality supervision administrator will review and confirm the report. If the report is rejected, it will be returned to the supervising personnel, who can then re-witness the key points, upload the report, and resubmit it for review.

[0076] The material company administrator, material department administrator, and provincial company quality supervision administrator can cancel tasks at any stage. After a task is canceled, its status will be 'Task canceled', and the related order line items will be released. Tasks can be recreated and issued for the line items. The "reason for cancellation" can be viewed in the list of each role for canceled tasks. Cancelled tasks cannot be issued for supervision.

[0077] In this embodiment of the invention, the task monitoring module monitors the execution progress and status of the supervision task in real time and provides visual early warnings and prompts to relevant users. When a problem is found in the supervision management system, the quality supervision administrator can initiate a collaboration through the collaboration module. If a problem is found during the supervision process (such as quality alarms, data exceeding limits, etc.), and the task status is "Pending confirmation of parameter specifications", "In progress", "Report internal review", "Report pending confirmation", "Report rejected", or "Task completed", clicking "Initiate collaboration" will initiate a collaboration operation. Based on the current task's line item information, the user will be redirected to the collaborative quality control page to publish a new issue.

[0078] Quality supervision and management personnel can view the collaboration history and collaboration details. When the task status is "Parameter specification to be confirmed", "In progress", "Report internal review", "Report to be confirmed", "Report rejected", or "Task completed", clicking "Collaboration Record" will allow them to view the collaboration history and collaboration details.

[0079] In this embodiment of the invention, the supervision task creation module enables quality supervision personnel to create supervision tasks based on the project content. The task generation module includes:

[0080] The task selection module is used to select the entire purchase order or a portion of line items in the purchase order to create a supervision task.

[0081] The task confirmation module is used to generate and distribute tasks after the project is confirmed in the supervision task line.

[0082] The task selection module specifically involves uploading the contract order, extracting information from the contract order, and generating a pre-monitoring task based on the extracted entire purchase order or selected line items under the purchase order.

[0083] Quality supervision and management personnel can access the corresponding page by clicking "Intelligent Supervision -> Supervision Task Management" in the right-hand menu. This page provides functions for adding and viewing supervision tasks. Users can search the supervision task list using multiple dimensions and combinations of search criteria, including project name, provincial company name, project unit, contract type, requesting unit, material category, material type, supplier name, contract number / agreement number, purchase order number, task code, task status, supervision unit, task issuance date, planned delivery date, confirmed delivery date, order status, line project status, contract effective date, and actual contract delivery date.

[0084] In this embodiment of the invention, the task awareness module refers to the process from raw material production to the final product shipment. The raw materials are defined by the batch number of the supplier and the specifications and testing time. Raw material data is obtained through supplier-uploaded testing reports. These reports are divided into two categories: one is the testing conducted by the downstream supplier, and the other is an initial entry testing report and a self-inspection report. This invention obtains raw material equipment information by identifying these testing reports.

[0085] In this embodiment of the invention, the task awareness module includes:

[0086] The self-collected data module is used to automatically collect raw material data information based on the supplier's test reports;

[0087] The manual push module is used by suppliers to manually push production test data through their self-built system and submit it to the data platform. The data platform is used to obtain the test data of the current equipment.

[0088] The contract order process in this embodiment of the invention is divided into three stages: signing, scheduling, and production. In the signing stage, the supplier needs to upload parameter specifications, which, after review and approval, will proceed to scheduling. The scheduling plan reflects whether the delivery date meets the project commissioning requirements. In the production stage, progress, quality alarms, and industry benchmarking scores can be monitored. Production data is divided into two categories: automatic collection and manual push. Automatically collected production data is obtained automatically from the supplier platform, while manually pushed data is entered locally and is affected by network conditions and the data platform's capture cycle. The factory testing stage requires uploading experimental videos, steps, parameters, start and end times, and raw data line graphs to determine whether the test is qualified and ensure that the product meets standards.

[0089] Task signing refers to the creation and issuance of tasks. Task creation involves quality management personnel identifying suppliers and signing contracts based on the material procurement information for the supervised task. Each supervised task corresponds to one or more suppliers. Multiple suppliers upload reports based on the purchase order information. Quality management personnel can manage the reports uploaded by suppliers through "batch confirmation" and "batch withdrawal".

[0090] In this embodiment of the invention, quality management personnel can access the corresponding page to add and view supervision tasks. Specifically, they can query received tasks based on a combination of multiple dimensions and query conditions, including project name, provincial company name, project unit, contract type, requesting unit, material category, material type, supplier name, contract number / agreement number, purchase order number, task code, task status, order status, task issuance date, planned delivery date, confirmed delivery date, project status, contract effective date, and actual contract delivery date. After a task is signed, the order status includes: pending parameter specification confirmation, in progress, internal review of report, pending confirmation of report, and rejected report.

[0091] In this embodiment of the invention, the task verification module 104 includes:

[0092] The supervision task query module 141 allows quality management personnel to query signed orders based on a combination of multiple dimensions and query conditions, including project name, provincial company name, project unit, contract type, requesting unit, material category, material type, supplier name, contract number / agreement number, purchase order number, task code, task status, order status, task issuance date, planned delivery date, confirmed delivery date, project status, contract effective date, and actual contract delivery date.

[0093] The parameter specification module 142 is used to view, modify, enable, and confirm the parameter specifications of the supervision task; among them, the parameter specifications can be set for tasks with statuses such as "parameter specifications to be confirmed", "in progress", and "report rejected".

[0094] When the parameter specification is "not enabled", the quality management personnel need to check and enable the parameter specification first. Only after enabling can the parameter specification be confirmed and transferred to the next node for key point witnessing and data verification. When the supplier's parameter specification is "not submitted", the task monitoring module can be used to urge the supplier to submit the parameter specification as soon as possible.

[0095] The key point witnessing module 143 is used to confirm key point information for tasks in the "in progress" or "report rejected" status of the supervision task. If the supplier uploads data for the key witnessing points normally and the data meets the parameter specification requirements, select "Yes"; otherwise, select "No". When selecting "No", a note must be filled in. It also supports selecting all yes / all no and can be modified one by one. A supervision summary must be filled in for key point witnessing.

[0096] The data verification module 144 proposed in this invention is used to verify the data of tasks whose task status is "in progress" or "report rejected".

[0097] The data verification page includes: basic information, data verification results, data verification details, save, and submit; among them,

[0098] ① Basic information, including: purchase order number, line item number, material category, material type, project unit, requesting unit, and project name;

[0099] ② Data verification results, including: data quality, data timeliness, and data integrity;

[0100] ③ Data verification details include three categories of information: data quality, data timeliness, and data integrity. Each category of information has two verification methods: manual and automatic. Supervisors only need to verify and confirm the data items that are manually verified.

[0101] In this embodiment of the invention, the raw material stage requires verification of batch number, supplier, specifications, testing time, and attachments (incoming inspection report and self-inspection report). Key witnessing points, such as the resistivity of metal rods, will be highlighted in red if they do not meet the requirements. After the key points are witnessed, the supervisor needs to write a summary to evaluate the supplier's cooperation, platform data quality, and the timeliness of data push. Data verification includes completeness, timeliness, and quality assessment, divided into automatic judgment (system-completed) and manual judgment (attachment matching). The results serve as the basis for report generation.

[0102] In this embodiment of the invention, the task verification module 104 further includes: a data verification review module 145, the data verification review module comprising:

[0103] The reconsideration module 1451 is used for suppliers to raise objections to the results of manual verification and to initiate a reconsideration by submitting a reconsideration application description and attachments.

[0104] The reconsideration response module 1452 is used by the supervisors to view the reconsideration application and related records, determine whether to modify the original verification results, and form the preliminary review results.

[0105] The review and confirmation module 1453 is used by the provincial company's quality supervision administrator to review the data verification applications that have been completed in the initial review and form a review result.

[0106] Suppliers can view order details in two ways: ① Through the "Order Tracking - Order Details" path, click the "View Details" button, and the page will jump to the "Order Tracking - Order Inquiry - Purchase Order Details" page; ② Click the "View Details" button for the line item list data verification, and enter the "Intelligent Supervision - Supervision Task Management - Data Verification" page to view the data verification information. The "View Details" button is grayed out by default. When the line item status is "Completed", the "View Details" button will be lit up and can be clicked.

[0107] If a supplier has any doubts about the verification results manually verified by the quality supervision administrator, they can click the "Data Verification Review" button as needed. The page will then redirect to the "Operations Management - Supplier Evaluation - Data Verification Review Management - Data Verification Review Audit" page. Based on the verification results of the quality supervision administrator, the supplier can fill in the review application description and upload the review description attachments, and then click submit to conduct the review.

[0108] Suppliers can appeal within 90 days after the project status is "Completed" and the supervision task status is "Task Completed". Suppliers can initiate multiple appeals for each supervision task.

[0109] This button will not be displayed 90 days after the supervision task status is "Task Completed" or when the supervision task status is "Cancelled". Reconsideration is not allowed again.

[0110] After a supervision task is cancelled, the data verification and review initiated by the supplier based on the supervision task will not be displayed synchronously, and the related data verification and review management will also not be displayed synchronously.

[0111] Quality supervision administrators can conduct preliminary reviews of supplier-submitted appeals on the "Operations Management - Supplier Evaluation - Data Verification Review Management" page.

[0112] Quality supervision administrators can review the appeals submitted by suppliers on the "Operations Management - Supplier Evaluation - Data Verification Review Management" page.

[0113] The quality supervision administrator can view the data verification information submitted by suppliers through the path "Operations Management - Supplier Evaluation - Data Verification Review Management", and conduct preliminary reviews of data verification applications with the review status of "Pending Preliminary Review" and "Review Rejected". The quality supervision administrator clicks the "Review" button to enter the "Data Verification Review Management" page, views the description and attachments of the review application submitted by the supplier, operation records, determines whether the results of manual verification need to be modified, and fills in the preliminary review results.

[0114] The preliminary review results include: review conclusions and remarks; the review conclusions include: re-evaluation required and no re-evaluation required.

[0115] After completing the preliminary review, click the "Submit" button to submit. The quality supervision administrator will then conduct a second review. If the quality supervision administrator makes any adjustments to the submitted application after the preliminary review, the data verification review with the status "Preliminary Review Completed" can be withdrawn. After withdrawal, it can be submitted again.

[0116] The quality supervision administrator can click the "Review" button to enter the "Data Verification Review Management" page, where they can view ① the description and attachments of the review application submitted by the supplier and the operation record, ② the preliminary review results of the supervisors, and then determine whether the results of the manual verification by the supervisors need to be modified based on the relevant information, and fill in the review results.

[0117] The review results include: review conclusions and remarks; the review conclusions include: passed and failed.

[0118] If the quality supervision administrator selects "Pass" and clicks "Submit", the task status will change to "Review Completed"; if the quality supervision administrator selects "Fail" and clicks "Submit", the task status will change to "Review Rejected", and the supervisor will need to conduct the initial review again.

[0119] After the review is approved:

[0120] ① If the status of the supervision task is "in progress", "report rejected", or "report under internal review", the quality supervision administrator does not need to take any action;

[0121] ② If the supervision task status is "Report Pending Confirmation", the quality supervision personnel need to reject the report. After rejection, the quality supervision administrator will modify the data verification information and continue the supervision.

[0122] ③ If the supervision task status is "task completed", the quality supervision personnel need to cancel the supervision task. After cancellation, the supervision task will be reissued and the quality supervision administrator will carry out the supervision again. Before canceling the task, the quality supervision administrator can notify the supervision unit and the quality supervision administrator to download and save the generated supervision report to avoid missing the corresponding supervision information when re-supervising.

[0123] The materials department administrator and the materials company administrator can view the data verification information submitted by all suppliers on the platform, as well as the review content of the suppliers' submitted reconsideration by the provincial companies and supervision personnel, through the path "Operations Management - Supplier Evaluation - Data Verification and Review Management".

[0124] In this embodiment of the invention, the construction supervision task reporting module 105 includes:

[0125] The report generation module 151 is used to generate a supervision report after the supervision task is completed and the supervision summary is finished.

[0126] The report download module 152 is used to download supervision reports that are in the task status of "internal review of report", "report pending confirmation", "report rejected" or "task completed".

[0127] After the quality supervision administrator has completed the key point witnessing (i.e., witnessing all key points and filling in the supervision summary) and data verification (i.e., manual verification items have been verified and submitted, and automatic verification items have been automatically generated by the system), click "Generate Report" to generate a report for tasks with the status of "In Progress" or "Report Rejected".

[0128] When you click the "Generate Report" button, the system needs to perform the following checks:

[0129] ① Have all key points been completed? That is, complete the key point witnessing and fill in the construction supervision summary;

[0130] ② Whether all data verification items have been verified, i.e., whether manual verification items have been verified and submitted, and whether automatic verification items have been automatically generated by the system;

[0131] If the key point witnessing and data verification have been completed, a "Generate Supervision Report" prompt box will pop up. You can choose to generate the supervision report automatically by the system or manually upload the report.

[0132] If the key witness points are not completed but the data verification is completed, a message will pop up at the top of the page saying "There are incomplete key witness points, please confirm".

[0133] If the key witness point has been completed but the data verification has not been completed, a message will pop up at the top of the page saying "Data verification result not submitted, please confirm".

[0134] If neither the key witness points nor the data verification is completed, a message will pop up at the top of the page: "Neither the key witness points nor the data verification is completed. Please confirm."

[0135] Select "Automatically generate report" and click the "Confirm" button. The system will then need to determine the following:

[0136] ①The work order for this project has been completed and supervised (same as the conditions for generating quality files: physical ID, shipment, and quality evaluation completed). If it is not completed, the message "There are incomplete production work orders for this project" will be displayed.

[0137] ② On the key point witness page, the supervision summary has been filled in (if not filled in, the message "Please go to the key point witness page and fill in the supervision summary" will appear);

[0138] ③ If the judgment condition "Report is being automatically generated" is met, the radio button name will change from "System automatically generates report" to "Report generating". The report generation success status will proceed to the next step of internal report review (if automatic report generation fails, the radio button name will be "System automatically generates report" and the report can be regenerated).

[0139] ④ While the report is being automatically generated, manually upload the report. After successful upload, the status will change to the "Internal Review of Report" stage. If the report is automatically generated successfully, it cannot overwrite the manually uploaded report that has already been moved to the internal review stage (e.g., after a successful manual upload, the status will move to the next stage, and although the report is automatically generated successfully at this time, it will not overwrite the content of the manually uploaded report).

[0140] ⑤ For tasks with a status of "In Progress", after the system automatically generates a report successfully, the status will change to the "Internal Review of Report" stage. If the supervising unit does not review or rejects the report, the quality supervision administrator can re-upload the report. At this time, the re-uploaded report will overwrite the generated report (e.g., after the automatic generation of the report is successful, the status will change to the next stage, and at this time the report can be re-uploaded to overwrite the content of the automatically generated report).

[0141] If the conditions are met, the system will automatically generate a report or the supervision report will be successfully uploaded manually and submitted to the supervision unit for internal review (before the organization reviews the report, the quality supervision administrator can manually upload the report again). Then, the report will be submitted to the quality supervision administrator for confirmation. If the confirmation is successful, the task will be terminated directly. If the confirmation is unsuccessful, the report will be rejected and returned to the quality supervision administrator, who can then re-witness the key points, regenerate the report, and submit it for review.

[0142] When the task status is "Report under internal review", "Report pending confirmation", "Report rejected", or "Task completed", click "Download Report" to download the supervision report for a single task.

[0143] Click "Report Review" to perform an internal review of the task whose status is "Report Internal Review"; if the review is successful, click "Confirm" and the status will change to "Report Pending Confirmation"; if you click "Reject", you need to enter a maximum of 200 characters for the rejection reason.

[0144] When the task status is "Report rejected", click "Rejection Reason" to view the reason for the report rejection.

[0145] Quality supervision administrators can confirm reports for approved tasks. Clicking "Report Confirmation" allows them to confirm reports for tasks whose status is "Report Pending Confirmation." If the review is successful, clicking the "Confirm" button will change the task completion status to "Task Completed." Clicking "Reject" requires entering a maximum of 200 characters for the rejection reason.

[0146] When the task status is "Report rejected", the quality supervision administrator can click "Reason for rejection" to view the reason for the report rejection.

[0147] For tasks with a status of "Task Completed," the quality supervision administrator can still reject the report. Click the "Report Rejected" button in the upper right corner of the list, and you must fill in a rejection reason of up to 200 characters. After rejection, the task will move to the "Report Rejected" status.

[0148] The quality supervision administrator can click the "Bulk Cancel Tasks" button in the upper left corner of the list to cancel tasks with statuses of "Pending Issuance", "Pending Confirmation by Supervision Unit", "Pending Assignment of Supervision Personnel", "Pending Confirmation of Parameter Specifications", "In Progress", "Report Internal Review", "Report Pending Confirmation", "Report Rejected", and "Task Completed" in bulk. After cancellation, the related order line items will be released, and the task can be recreated and issued.

[0149] Click "Reason for Cancellation" to view the reason for task cancellation when the task status is "Task Cancelled".

[0150] Quality supervision and management personnel can click the "Batch Export" button in the upper right corner of the list. If they select all tasks, the exported task information will be the selected tasks on the current page. If they do not select any tasks and click Batch Export directly, all task information in the list will be exported in Excel format.

[0151] This invention tracks the entire process from contract task creation, procurement, production, installation, and operation and maintenance. Based on contract information, procurement information, equipment information, production information, etc., it ultimately presents the results in the form of a supervision report. The process involves task generation, task scheduling, task awareness, and task verification to generate the final task report. This process achieves the generation of the supervision report through both automatic and manual data collection.

[0152] To verify the compliance and anomalies of the generated supervision report, this invention also includes an artificial intelligence review module 106, comprising: a compliance review unit 161, used to automatically compare and verify the test data, key parameters and document integrity in the supervision report based on preset supervision specifications and technical standards; and an anomaly detection unit 162, used to identify abnormal information in the report such as data logic conflicts, deviations from historical patterns or typical defect patterns through machine learning models.

[0153] Credibility scoring unit 163 is used to quantitatively score the overall quality and consistency of the supervision report and output the audit credibility level;

[0154] The audit comment generation unit 164 is used to automatically generate structured audit comments based on compliance review and anomaly detection results, and locate the specific chapter or data item in the report.

[0155] The AI-powered audit module constructs a power equipment supervision standard database by analyzing core technical documents related to power engineering quality. This database covers key information such as equipment raw material inspection, production process compliance, and performance parameter testing, as well as multiple standards including the State Grid Q / GDW standard, industry DL / T standard, and equipment technical agreements. Based on machine learning algorithms, a standard matching model is trained. The module automatically compares the parsed report data with the standard thresholds and compliance requirements in the power equipment supervision standard database. It judges and evaluates the compliance and abnormal data of the supervision report, achieving automated and intelligent auditing, saving labor costs, improving audit efficiency, and enhancing the accuracy of the audit through comprehensive database comparison.

[0156] For example, for transformer oil dielectric loss test data, an AI-powered review module can automatically match the standard limits at the corresponding temperature, determine whether the test results are qualified, and mark the specific locations of out-of-tolerance data. By using correlation analysis algorithms to uncover potential logical relationships between report data, such as the correlation between equipment insulation resistance and ambient humidity, or the correlation between raw material batches and test pass rates, hidden risks that are difficult to detect from a single review dimension can be identified in a timely manner. Simultaneously, a risk level assessment system is constructed, with credibility levels including: High-risk level: indicating that the report has missing key data, mandatory test items were not performed, or key parameters deviate significantly from the standard, casting doubt on the report's authenticity or validity; Medium-risk level: indicating that the report has inconsistent data logic, non-critical parameters exceed allowable deviations, or document incompleteness, requiring intensive manual review; Low-risk level: indicating that the report content is complete, the data conforms to specifications and is logically consistent, and the AI ​​review did not find any obvious anomalies, allowing for rapid approval or requiring only sampling review. This credibility level system effectively guides manual review resources, prioritizing medium and low-credibility reports, thereby improving overall review efficiency.

[0157] For abnormal data, the system automatically labels the data as "general hidden danger", "major hidden danger" or "emergency hidden danger" according to the severity of the violation, and generates a visual review report that lists the violation, the standard basis, rectification suggestions and reference cases to help reviewers make quick decisions.

[0158] In this embodiment of the invention, the AI ​​review module strictly follows the review specifications in the standard database, avoiding issues such as subjective bias and oversight in manual review, thus improving the review accuracy rate to over 99%. Through a standardized review process, it ensures consistency in review standards across different reviewers and projects, effectively enhancing the standardization and authority of the supervision report review.

[0159] Example 2

[0160] See Figure 3 This invention provides a green remote monitoring method for smart IoT of electrical equipment, comprising:

[0161] Step S1: Obtain the purchase order contract that has been processed by ECP2.0 and meets the requirements of smart IoT for electrical equipment; generate and issue the supervision task based on the material procurement information.

[0162] Step S2: Confirm the sales orders, production schedules, production work orders, and parameter specifications linked to the supplier based on the purchase order information;

[0163] Step S3: Verify the data on raw materials, component inspection, production process and in-process inspection, factory test and finished product warehousing uploaded by the supplier.

[0164] Step S4 is used to evaluate and verify the quality of the data perceived by the task awareness module;

[0165] Step S5: After witnessing the key points and verifying the data for the construction supervision task, generate a construction supervision report;

[0166] Step S6: Perform automated intelligent review of the supervision report and output review comments and credibility assessment.

[0167] In this embodiment of the invention, the supervision report is automatically and intelligently reviewed using artificial intelligence technology, including:

[0168] Based on the pre-set supervision specifications and technical standards, a compliance review is conducted on the test data, key parameters, and document completeness in the report;

[0169] Machine learning models are used to identify anomalous information in reports, such as data logic conflicts, deviations from historical patterns, or typical defect patterns.

[0170] The overall quality and consistency of the supervision report are quantitatively scored, and the credibility level of the audit is output.

[0171] Based on the review and testing results, structured review comments are automatically generated and located to specific chapters or data entries in the report.

[0172] The various variations and specific examples of the green remote monitoring platform for smart IoT of electrical equipment in the foregoing embodiments are also applicable to the green remote monitoring method for smart IoT of electrical equipment in this embodiment. Through the foregoing detailed description of the green remote monitoring platform for smart IoT of electrical equipment, those skilled in the art can clearly understand the green remote monitoring method for smart IoT of electrical equipment in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0173] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A green remote monitoring platform for intelligent IoT of electrical equipment, characterized in that, include: The task generation module obtains purchase order contracts that have been processed through ECP2.0 and meet the requirements of smart IoT for electrical equipment, generates and issues supervision tasks based on material procurement information; The task scheduling module confirms the sales orders, production plans, production work orders, and parameter specifications linked to the supplier based on the purchase order information. The task awareness module verifies the data on raw materials, component inspection, production process and in-process inspection, factory testing, and finished product warehousing uploaded by suppliers. The task verification module is used to evaluate and verify the quality of the data perceived by the task awareness module. The task report module is used to generate a supervision report after witnessing key points and verifying data for the completion of the supervision task. The AI ​​review module is used to automate and intelligently review the supervision reports and output review opinions and credibility assessments.

2. The green remote monitoring platform for intelligent IoT of electrical equipment according to claim 1, characterized in that, Also includes: The task monitoring module is used to monitor the execution progress and status of the construction supervision tasks in real time, and to provide relevant users with visual early warnings and prompts.

3. A green remote monitoring platform for intelligent IoT of electrical equipment as described in claim 1, characterized in that, The task generation module includes: The task selection module is used to select the entire purchase order or a portion of line items in the purchase order to create a supervision task. The task confirmation module is used to generate and distribute tasks after the project is confirmed in the supervision task line. The task selection module is specifically as follows: Upload contract / order; Extract contract and order information, and generate pre-monitoring tasks based on the extracted entire purchase order or part of the line items.

4. A green remote monitoring platform for intelligent IoT of electrical equipment according to claim 3, characterized in that, The contract order information shown includes: project name, provincial company name, project unit, contract type, requesting unit, material category, material type, supplier name, contract number / agreement number, purchase order number, task code, task status, task issuance date, planned delivery date, confirmed delivery date, order status, line project status, contract effective date, and actual contract delivery date.

5. A green remote monitoring platform for intelligent IoT of electrical equipment according to claim 1, characterized in that, The task awareness module includes: The self-collected data module is used to automatically collect raw material data information based on the supplier's test reports; The manual push module is used by suppliers to manually push production test data through their self-built system and submit it to the data platform. The data platform is used to obtain the test data of the current equipment.

6. A green remote monitoring platform for intelligent IoT of electrical equipment according to claim 1, characterized in that, The task verification module includes: The supervision task query module is used by supervisors to query the assigned supervision tasks; The parameter specification module is used to view, modify, enable, and confirm the parameter specifications related to the construction supervision task; The key point witnessing module is used to confirm key point information for supervision tasks with a status of "in progress" or "report rejected". The data verification module is used to verify the data of supervision tasks with a status of "in progress" or "report rejected".

7. A green remote monitoring platform for intelligent IoT of electrical equipment according to claim 6, characterized in that, The task verification module also includes a data verification review module, which includes: The reconsideration module is used by suppliers to raise objections to the results of manual verification and to initiate a reconsideration by submitting a description of the reconsideration application and attachments. The reconsideration response module is used by the supervisors to view the reconsideration application and related records, determine whether to modify the original verification result, and form the preliminary review result. The review and confirmation module is used by the provincial company's quality supervision administrator to review the data verification applications that have been completed in the initial review and generate review results.

8. A green remote monitoring platform for intelligent IoT of electrical equipment according to claim 1, characterized in that, The AI ​​audit module includes: a compliance review unit, used to automatically compare and verify the test data, key parameters and document integrity in the supervision report based on preset supervision specifications and technical standards; and an anomaly detection unit, used to identify abnormal information in the report such as data logic conflicts, deviations from historical patterns or typical defect patterns through machine learning models. The credibility scoring unit is used to quantitatively score the overall quality and consistency of the supervision report and output the credibility level of the audit. The audit comment generation unit is used to automatically generate structured audit comments based on compliance review and anomaly detection results, and locate specific chapters or data entries in the report.

9. A green remote manufacturing monitoring method for smart IoT of electrical equipment, characterized in that, The green remote monitoring platform for smart IoT of electrical equipment, as described in any one of claims 1-8, comprises: Obtain purchase order contracts that have been processed through ECP2.0 and meet the requirements of smart IoT for electrical equipment; generate and issue supervision tasks based on material procurement information; Confirm the sales orders, production plans, production work orders, and parameter specifications linked to the supplier based on the purchase order information; Verify the data on raw materials, component inspection, production process and in-process inspection, factory testing, and finished product warehousing uploaded by suppliers. Evaluate and verify the quality of the data perceived by the task awareness module; A supervision report is generated after witnessing and verifying key points and data of the completion of the supervision task. The system performs automated and intelligent review of the supervision reports and outputs review comments and credibility assessments.

10. A green remote manufacturing supervision method for intelligent IoT of electrical equipment according to claim 9, characterized in that, The automated intelligent review of the supervision report using artificial intelligence technology includes: Based on the pre-set supervision specifications and technical standards, a compliance review is conducted on the test data, key parameters, and document completeness in the report; Machine learning models are used to identify anomalous information in reports, such as data logic conflicts, deviations from historical patterns, or typical defect patterns. The overall quality and consistency of the supervision report are quantitatively scored, and the credibility level of the audit is output. Based on the review and testing results, structured review comments are automatically generated and located to specific chapters or data entries in the report.

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