Protection inspection intelligent identification method based on operation inspection mobile terminal

Abstract

The invention discloses a protection check intelligent identification method based on an operation check mobile terminal. The method comprises the following steps: 1, completing basic configuration of field check, and ensuring data synchronization of a mobile terminal and a cloud terminal; 2, quickly positioning a to-be-checked pressing plate, and obtaining cloud standard data; 3, the current state of the pressing plate is accurately collected, and evidence information is attached; 4, uploading on-site collected data to a cloud end, and verifying the authenticity and integrity of the data through a block chain and a knowledge base; 5, on the basis of the intelligent knowledge base data, multi-dimensionally comparing the check data with the standard data, and automatically judging that the state of the protection pressing plate is normal / abnormal; 6, the abnormal information is accurately pushed to the corresponding personnel according to the early warning level, and the handling responsibility and time limit are determined; 7, exception handling is completed, the effect is verified, and a problem closed loop is ensured; 8, updating the knowledge base and the block chain data, precipitating the verification experience, and optimizing the subsequent process; the method has the advantages of being high in field checking efficiency, high in recognition and judgment precision, good in exception handling effect, credible in data and capable of continuously increasing value.

Description

Technical Field

[0001] This invention belongs to the field of relay protection pressure plate operation and maintenance verification technology, specifically involving an intelligent identification method for protection verification based on a mobile operation and maintenance terminal. Background Technology

[0002] Currently, the status verification of relay protection circuit boards in power systems mainly relies on manual inspection by maintenance personnel. In large substations, there can be hundreds of circuit boards, and a single comprehensive verification takes 2-3 hours, which is inefficient and easily affected by personnel fatigue. The main problems are as follows: 1. The error rate of manual verification is as high as 15%, which may lead to the failure or malfunction of protection devices; 2. The status records of pressure plates are mostly recorded in paper ledgers or scattered electronic spreadsheets, lacking a unified management platform. The records are scattered and data traceability is difficult. During fault investigation, it is difficult to quickly retrieve historical data for comparison and analysis, which affects the efficiency of accident handling. For example, the fault cause analysis of a certain substation was delayed by 4 hours due to the lack of pressure plate status records. 3. The traditional model relies on periodic inspections, which cannot monitor changes in the status of the pressure plate in real time, resulting in insufficient risk warnings and significant safety hazards. For example, a power plant once experienced a power outage caused by the protection device failing to operate due to a loose pressure plate, resulting in direct economic losses of over one million yuan. To address the aforementioned issues, it is essential to develop an intelligent identification method for protection verification based on mobile operation and maintenance terminals. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a protection verification intelligent identification method based on operation and maintenance mobile terminals, which has high on-site verification efficiency, high identification and judgment accuracy, good anomaly handling effect, reliable data and continuous value-added. It is designed around the whole process of preliminary preparation, on-site verification, cloud collaboration, anomaly handling and closed-loop archiving. Combined with the convenience of mobile terminals and the intelligent capabilities of cloud, it can realize the standardization and intelligentization of protection pressure plate verification.

[0004] The objective of this invention is achieved as follows: a smart identification method for protection verification based on a mobile operation and maintenance terminal, comprising the following steps: Step 1, Preparation before verification: Complete the basic configuration for on-site verification, ensure that the mobile terminal and cloud data are synchronized, and adapt to the on-site scenario; Step 2, On-site pressure plate location and information retrieval: Quickly locate the pressure plate to be checked and obtain standard data from the cloud to provide a basis for on-site judgment; Step 3, On-site verification of data collection: Accurately collect the current status of the pressure plate, along with supporting information, to ensure that the data is authentic and traceable; Step 4, verify data synchronization and cloud verification: Upload the data collected on-site to the cloud, and verify the authenticity and completeness of the data through blockchain and knowledge base; Step 5, Cloud-based intelligent comparison and result determination: Based on intelligent knowledge base data, the data is compared and verified with standard data from multiple dimensions, and the status of the protection pressure plate is automatically determined as normal / abnormal; Step 6, Intelligent Anomaly Push and Task Assignment: Accurately push anomaly information to the corresponding personnel according to the warning level, and clarify the handling responsibilities and time limits; Step 7, Collaborative Anomaly Handling and Result Verification: Complete anomaly handling and verify the effect to ensure problem closure; Step 8, Closed-loop archiving and data reuse: Update the knowledge base and blockchain data, accumulate verification experience, and optimize subsequent processes.

[0005] Furthermore, step 1 is completed on the mobile device, and specifically includes the following steps: Step 11, Region and Permission Confirmation: The operation and maintenance personnel log in to the mobile APP, select the target region for this verification, and the system automatically verifies the user's permissions. User permissions are set based on the blockchain role mechanism. Step 12, Offline Data Download: If the on-site signal is weak, download the basic data of the pressure plate in the target area to the local machine in advance, including the pressure plate ID, standard status, QR code association information, and historical verification records. Data storage is encrypted using AES-256. Step 13, Device check: Confirm that the mobile camera and positioning function are normal and the battery is sufficient. If batch verification is required, enable continuous scanning mode in the APP settings.

[0006] Furthermore, step 2 is completed on the mobile device, and specifically includes the following steps: Step 21, Scan to Locate: Maintenance personnel use a mobile app to scan the unique QR code next to the pressure plate. The app automatically parses the pressure plate ID associated with the QR code and retrieves the key information of the pressure plate from the cloud-based intelligent knowledge base in real time, including basic information: pressure plate name, model, region, and standard status; and associated information: a list of logically associated pressure plates and the last 3 historical verification records. Step 22, Information Confirmation: The APP interface displays the above information intuitively. The maintenance personnel check that the physical identification of the pressure plate is consistent with the information in the cloud. If the location is correct, proceed to the next step of verification. If the information is inconsistent, mark the location as abnormal and upload it to the cloud, triggering a level 3 warning.

[0007] Furthermore, in step 21, the pressure plate name includes a five-level management data structure of "substation-compartment-cabinet-device-pressure plate".

[0008] Furthermore, step 3 is completed on the mobile device, and specifically includes the following steps: Step 31, Status Acquisition: First, based on the actual status of the on-site pressure plate, select closing or opening with one click in the APP; then, automatically take supporting photos. After selecting the status, the APP automatically retrieves the camera and takes a clear image of the pressure plate. The photo is automatically associated with "pressure plate ID + timestamp + mobile terminal location information". Step 32, Anomaly Labeling (if anomalies exist): If the circuit breaker status does not match the cloud standard status, select the anomaly type in the APP; Status anomaly: Opening / closing error, status unclear and cannot be judged; Physical anomaly: blurry label, damaged casing, loose wiring; Additional notes: Fill in the anomaly details through voice input or short text. Step 33, Batch Verification Optimization: If multiple pressure plates in the same area need to be verified continuously, after confirming that the current pressure plate has been collected, the APP will automatically jump to the scanning interface. There is no need to return to the homepage; you can directly scan the QR code of the next pressure plate.

[0009] Furthermore, step 4 is completed collaboratively by the mobile device and the cloud, specifically including the following steps: Step 41, Data Synchronization: This includes online scenarios: After data collection is completed, the APP automatically uploads "Pressure Plate ID + Verification Status + Supporting Photos + Timestamp + Location" to the cloud, and the synchronization progress is displayed in real time; and offline scenarios: Data is temporarily stored in an encrypted directory on the mobile device. After the device is connected to the network, the APP automatically triggers offline data re-upload, and the data is uploaded in the order of collection time during the re-upload. Step 42, Cloud Verification: This includes blockchain verification: Connecting to a blockchain platform in the cloud to verify the authenticity of the "timestamp + location" of the uploaded data. If the verification fails, the app will be notified that the data is abnormal and needs to be collected again; and format verification: Checking the clarity of the photos and the completeness of the abnormal annotations. If the photos are blurry or have no annotations, the app will be prompted to supplement supporting information.

[0010] Furthermore, step 5 is completed in the cloud, and specifically includes the following steps: Step 51, Basic Status Comparison: Retrieve the current standard status of the pressure plate from the intelligent knowledge base in the cloud and directly compare and verify the status: If they match, the basic status is determined to be normal; if they do not match, proceed to the next step of in-depth comparison. Step 52, Historical Trend Comparison: If the pressure plate has no clear standard status, retrieve the three most recent historical verification statuses from the knowledge base and calculate the consistency rate between the current verification status and the historical status: if the consistency rate is ≥80%, the historical status is judged to be normal; if the consistency rate is <80%, the historical status is marked as abnormal. Step 53, Logical association comparison: Retrieve the list of logical association boards for this board in the knowledge base and compare the status of the association boards with the concurrent verification status: If the association status meets the preset rules, the logic is judged to be normal; if there is a conflict, the logic is marked as abnormal. Step 54, final judgment: Based on the above 3 comparison results, the final conclusion is output: if all 3 items are normal → verification is normal; if any 1 item is abnormal → verification is abnormal, and a three-level warning level is matched according to the abnormality type.

[0011] Furthermore, step 6 is completed collaboratively by the cloud and the mobile terminal, specifically including the following steps: Step 61, Warning Level Matching: The cloud matches warning levels based on the anomaly type, using the three-level warning mechanism: Level 1 warning: critical pressure plate status error, batch pressure plate anomaly; Level 2 warning: non-critical pressure plate status anomaly, logical association conflict; Level 3 warning: appearance anomaly, single non-critical pressure plate historical anomaly; Step 62, Targeted Push Notification: Push targets: Level 1 → Operations manager + regional operations personnel; Level 2 → Regional operations personnel; Level 3 → On-site operations personnel; Push methods: Level 1 → APP pop-up + SMS + phone notification; Level 2 → APP pop-up + SMS; Level 3 → APP message; Push content: Abnormal pressure plate ID, location, verification status, standard status, supporting photos, processing time limit, requiring Level 1 ≤ 30 minutes, Level 2 ≤ 2 hours, Level 3 ≤ 24 hours; Step 63, Task Assignment: The operations and maintenance manager can view the list of level 1 / level 2 anomalies in the mobile APP and assign tasks to specific operations and maintenance personnel. The assigned personnel will receive a strong reminder of the pending tasks, including a red dot on the APP icon and a pop-up window.

[0012] Furthermore, step 7 is completed collaboratively by the mobile terminal and the cloud, specifically including the following steps: Step 71, On-site handling: The assigned maintenance personnel bring a mobile device to the site of the abnormal pressure plate and investigate the cause based on the pushed abnormal information; Step 72, Feedback on Processing Results: After processing is complete, submit the processing results in the mobile app, including selecting the processing type: status correction, label replacement, physical repair, or error correction; and uploading supporting evidence: taking a photo of the processed pressure plate and adding processing notes. Step 73, Cloud Verification: After receiving the processing result, the cloud automatically completes two verifications: status verification: compare the pressure plate status in the processed photo with the standard status in the knowledge base to confirm whether they are consistent; and logic verification: if there is a logic anomaly, verify whether the associated pressure plate status has been corrected synchronously. Verification passed → mark as exception handling completed; verification failed → feedback to APP that the processing did not meet the standards, requiring reprocessing and an extended processing time.

[0013] Furthermore, step 8 is completed in the cloud and specifically includes the following steps: Step 81, Data Archiving: This includes normal verification results: updating the latest verification time, verifier, and status of the pressure plate in the intelligent knowledge base and synchronizing it to the blockchain for evidence storage; and abnormal handling results: storing the abnormal type, handling process, handling result, and supporting photos in the knowledge base's abnormal case database, associating them with the corresponding pressure plate ID, and forming a historical archive. Step 82, Statistical Analysis: The cloud periodically generates closed-loop verification reports, including verification completion rate, anomaly rate, and timely handling rate of early warnings at each level. The reports can be viewed in the mobile APP data center for managers to optimize operation and maintenance plans. Step 83, Knowledge Base Optimization: Based on high-frequency anomaly types, update the key points for checking such pressure plates in the knowledge base, and automatically display the prompts when checking on mobile devices in the future.

[0014] Due to the adoption of the above technical solution, the beneficial effects of the present invention are: (1) This invention eliminates the need for manual input of the pressure plate ID and repeated switching of the interface by “scanning the code for positioning + batch collection + offline adaptation”, reducing the time consumption of single pressure plate verification by more than 50%, and can still operate normally in weak signal scenarios. The efficiency of batch verification is increased by 3 times, significantly improving the efficiency of on-site verification. (2) This invention compares data from multiple dimensions, including “basic, historical and logical” in the cloud and verifies the authenticity of data using blockchain, avoiding subjective human error. The accuracy of anomaly identification is over 95%, and the false alarm rate is controlled within 5%, thus achieving dual protection of identification and judgment accuracy. (3) This invention accurately pushes the three-level early warning to the responsible role, clarifies the processing time limit and verification standard, improves the response speed of abnormal handling by 60% (the response rate of the first-level early warning reaches 100% within 30 minutes), eliminates the problem of no one following up after verification, and realizes closed-loop management of abnormal handling; (4) This invention verifies and processes data by storing it on the blockchain throughout the entire process, making it traceable and tamper-proof; at the same time, it accumulates data in an intelligent knowledge base to form an "abnormal case library + verification optimization prompts". The efficiency and accuracy of subsequent verifications continue to improve with the accumulation of data, making the data reliable and continuously increasing in value. In summary, this invention has the advantages of high efficiency in on-site verification, high accuracy in identification and judgment, good anomaly handling effect, reliable data and continuous value-added. Its design revolves around the entire process of preliminary preparation, on-site verification, cloud collaboration, anomaly handling and closed-loop archiving. Combined with the convenience of mobile terminals and the intelligent capabilities of the cloud, it can realize the standardization and intelligentization of protection pressure plate verification. Detailed Implementation

[0015] The technical solution of the present invention will be further described in detail below through embodiments.

[0016] A protection verification intelligent identification method based on a mobile operation and maintenance terminal includes the following steps: Step 1, Preparation before verification: Complete the basic configuration for on-site verification on the mobile device, ensuring that the mobile device and cloud data are synchronized and adapted to the on-site scenario.

[0017] 1. Area and permission confirmation: Maintenance personnel log in to the mobile APP, select the target area for this verification (such as control cabinet A-1 of the substation), and the system automatically verifies user permissions (based on the blockchain role mechanism, only authorized personnel are allowed to view the corresponding area's pressure plate data).

[0018] 2. Offline data download: If the signal is weak on site, download the basic data of the pressure plate in the target area to the local machine in advance, including the pressure plate ID, standard status, QR code association information, and historical verification records. Data storage is encrypted with AES-256 to avoid data leakage.

[0019] 3. Equipment check: Confirm that the mobile camera and positioning function are working properly (used for scanning, taking pictures and recording the verification location), and that the battery is sufficient. If batch verification is required, enable continuous scanning mode (check the box in the APP settings).

[0020] Step 2, On-site pressure plate location and information retrieval: Completed on the mobile device, quickly locate the pressure plate to be checked, obtain standard data from the cloud, and provide a basis for on-site judgment.

[0021] 1. QR code location: Maintenance personnel use a mobile app to scan the unique QR code next to the pressure plate. The app automatically parses the pressure plate ID associated with the QR code and retrieves the key information of the pressure plate from the cloud-based intelligent knowledge base in real time (or offline, by retrieving local cache): Basic information: Pressure plate name (five-level management data structure of "substation-compartment-cabinet-device-pressure plate"), model, region, standard status; Related information: list of logically related pressure plates, and records of the last 3 historical checks.

[0022] 2. Information Confirmation: The APP interface displays the above information intuitively. The maintenance personnel check that the physical identification of the pressure plate is consistent with the information in the cloud. If the location is correct, they can proceed to the next step of verification. If the information is inconsistent, the location is marked as abnormal and uploaded to the cloud, triggering a level 3 warning.

[0023] Step 3, On-site verification of data collection: Completed on a mobile device, accurately collecting the current status of the pressure plate, along with supporting information, to ensure the data is authentic and traceable.

[0024] 1. Status Acquisition: Based on the actual status of the on-site pressure plate, the closing and opening functions can be selected with one click in the APP; Automatically take supporting photos: After selecting the status, the APP automatically accesses the camera and takes a clear image of the pressure plate. The photo is automatically associated with "pressure plate ID + timestamp + mobile device location information".

[0025] 2. Anomaly labeling (if anomalies exist): If the pressure plate status does not match the standard status on the cloud, select the exception type in the APP; Abnormal status: Opening / closing error, status is unclear and cannot be determined; Physical abnormalities: blurred markings, damaged casing, loose wiring; Additional notes: Fill in the details of the abnormality via voice input (to avoid typing on site) or short text (e.g., at 10:30, the pressure plate markings were found to be covered with dust, and the status was suspected to be open).

[0026] 3. Batch verification optimization: If multiple pressure plates in the same area need to be verified continuously, after confirming that the current pressure plate has been collected, the APP will automatically jump to the scanning interface. There is no need to return to the homepage, and you can directly scan the QR code of the next pressure plate.

[0027] Step 4, Data Synchronization and Cloud Verification: This is completed collaboratively by the mobile terminal and the cloud, uploading the data collected on-site to the cloud, and verifying the authenticity and completeness of the data through blockchain and knowledge base.

[0028] 1. Data synchronization: In network scenarios: After data collection is completed, the APP automatically uploads the "plate ID + verification status + supporting photos + timestamp + location" to the cloud, and the progress is displayed in real time. Offline scenario: Data is temporarily stored in an encrypted local directory on the mobile device. Once the device is connected to the network (e.g., when returning to the control room), the APP automatically triggers offline data retransmission. During retransmission, data is uploaded in the order of collection time to avoid omissions.

[0029] 2. Cloud verification: Blockchain verification: The cloud connects to the blockchain platform to verify the authenticity of the "timestamp + location" of the uploaded data (compare the reasonableness of the device location range and time stored on the chain). If the verification fails (such as timestamp tampering or location not being in the target area), the system will report "APP data is abnormal and needs to be collected again". Format verification: Check the clarity of the photo (resolution ≥ 1080P) and the completeness of the abnormal label. If the photo is blurry or has no label, prompt the APP to supplement supporting information.

[0030] Step 5, Cloud-based intelligent comparison and result determination: Completed in the cloud, based on intelligent knowledge base data, the data is compared and verified with standard data from multiple dimensions, and the status of the protection pressure plate is automatically determined as normal / abnormal.

[0031] 1. Basic Status Comparison: Retrieve the current standard status of the pressure plate from the intelligent knowledge base in the cloud and directly compare and verify the status: If the results are consistent (e.g., the standard "closed", check "closed"), the foundation is deemed normal; If there is no discrepancy, proceed to the next step of in-depth comparison.

[0032] 2. Historical Trend Comparison: If the pressure plate does not have a clear standard status (such as a spare pressure plate), retrieve the three most recent historical verification statuses from the knowledge base and calculate the consistency rate between the current verification status and the historical status: If the consistency rate is ≥80% (configurable threshold), the historical data is considered normal. If the consistency rate is less than 80%, mark it as a historical anomaly.

[0033] 3. Logical Relationship Comparison: Retrieve the list of logically related boards for this board from the knowledge base and compare the concurrent verification status of the related boards: If the associated status meets the preset rules (e.g., both "trip output pressure plate" and "protection input pressure plate" are "closed"), the judgment logic is normal; If there is a conflict (such as the former "opening" and the latter "closing"), mark it as a logical anomaly.

[0034] 4. Final Judgment: Based on the above three comparison results, the final conclusion is output as follows: If all 3 items are normal → verification is normal; If any one item is abnormal → check for abnormality and match the three-level warning level according to the abnormality type.

[0035] Step 6, Intelligent Anomaly Push and Task Assignment: This is completed collaboratively by the cloud and mobile terminals. Anomaly information is accurately pushed to the corresponding personnel according to the warning level, clarifying the handling responsibilities and time limits.

[0036] 1. Warning Level Matching: The cloud-based system matches warning levels based on the type of anomaly (using the three-level warning mechanism): Level 1 warning: Critical pressure plate status error (e.g., tripped output circuit breaker), batch pressure plate abnormality (e.g., ≥10 pressure plates in the same area are abnormal); Level 2 warning: Abnormal status of non-critical circuit breakers (e.g., backup power supply tripping), logical conflict; Level 3 warning: Appearance abnormalities (e.g., blurred markings), historical anomalies of a single non-critical pressure plate.

[0037] 2. Targeted push notifications: Push notification recipients: Level 1 → Operations Manager + Regional Operations Staff; Level 2 → Regional Operations Staff; Level 3 → On-site Operations Staff; Push notification methods: Level 1 → App pop-up + SMS + phone notification; Level 2 → App pop-up + SMS; Level 3 → App message; Push notification content: Abnormal pressure plate ID, location, verification status, standard status, supporting photos, and processing time limit (Level 1 ≤ 30 minutes, Level 2 ≤ 2 hours, Level 3 ≤ 24 hours).

[0038] 3. Task Assignment: The operations and maintenance manager can view the list of level 1 / level 2 anomalies in the mobile APP and assign tasks to specific operations and maintenance personnel. The assigned personnel will receive a strong reminder of the pending tasks (red dot on the APP icon + pop-up window).

[0039] Step 7, Collaborative Anomaly Handling and Result Verification: This step is completed collaboratively by the mobile device and the cloud to handle anomalies and verify the results, ensuring a closed-loop problem resolution process.

[0040] 1. On-site handling: The assigned maintenance personnel carry a mobile device to the site of the faulty pressure plate and investigate the cause based on the pushed abnormal information (e.g., status error → check operation records, blurry labels → clean / repaste labels). 2. Processing Result Feedback: After processing is complete, submit the processing result in the mobile app: Select the processing type: Status Correction, Identifier Replacement, Physical Repair, Misjudgment Elimination; Upload supporting evidence: Take a photo of the processed pressure plate (such as the corrected "closed" status and new label), and add processing notes (such as "11:20 manually switch the pressure plate to close, status is normal").

[0041] 3. Cloud Verification: After receiving the processing results in the cloud, two verifications are automatically completed: Status verification: Compare the status of the pressure plate in the processed photo with the standard status in the knowledge base to confirm whether they are consistent; Logical verification: If there is a logical anomaly, verify whether the status of the associated pressure plate has been corrected synchronously; Verification passed → mark as exception handling completed; verification failed → feedback to APP that the processing did not meet the standards, requiring reprocessing and an extended processing time.

[0042] Step 8, Closed-loop archiving and data reuse: Completed in the cloud, updating the knowledge base and blockchain data, accumulating verification experience, and optimizing subsequent processes.

[0043] 1. Data archiving: Normal verification result: Update the latest verification time, verifier, and status of the pressure plate in the intelligent knowledge base, and synchronize it to the blockchain for evidence storage (record the verification trajectory to ensure traceability); Anomaly handling results: Store the anomaly type, handling process, handling result, and supporting photos in the anomaly case library of the knowledge base, associate them with the corresponding pressure plate ID, and form a historical archive.

[0044] 2. Statistical Analysis: The cloud generates closed-loop verification reports regularly (e.g., daily / weekly), including verification completion rate, anomaly rate, and timely handling rate of warnings at each level. The reports can be viewed in the mobile APP data center, allowing administrators to optimize operation and maintenance plans.

[0045] 3. Knowledge base optimization: Based on high-frequency anomaly types (such as "blurred identification" frequently occurring for a certain type of pressure plate), update the key verification prompts for this type of pressure plate in the knowledge base (such as "the integrity of the identification should be checked first in each verification"), and automatically display the prompts during subsequent mobile verification to reduce the anomaly occurrence rate.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A protection and verification intelligent identification method based on operation inspection mobile terminal, characterized in that, The method comprises the following steps: Step 1, pre-preparation for checking: complete the basic configuration of the on-site check, ensure that the mobile terminal and the cloud data are synchronized, and adapt to the on-site scene; Step 2, positioning and information retrieval of the on-site pressing plate: quickly locate the pressing plate to be checked, obtain the standard data in the cloud, and provide a basis for on-site judgment; Step 3, on-site check data collection: accurately collect the current state of the pressing plate, with supporting information, to ensure that the data is real and traceable; Step 4, synchronization and cloud verification of check data: upload the on-site collected data to the cloud, and verify the authenticity and integrity of the data through the blockchain and knowledge base; Step 5, cloud intelligent comparison and result determination: based on the intelligent knowledge base data, compare the check data and standard data in multiple dimensions, and automatically determine whether the protection pressing plate state is normal or abnormal; Step 6, abnormal intelligent pushing and task assignment: according to the warning level, the abnormal information is accurately pushed to the corresponding personnel, and the responsibility and time limit for processing are clearly defined; Step 7, abnormal collaborative processing and result verification: complete the abnormal processing and verify the effect to ensure problem closure; Step 8, closed-loop archiving and data reuse: update the knowledge base and blockchain data, deposit the check experience, and optimize the subsequent process. 2.The protection and verification intelligent identification method based on the operation inspection mobile terminal according to claim 1, characterized in that, The step 1 is completed on the mobile terminal, and specifically comprises the following steps: Step 11, area and permission confirmation: the operation and maintenance personnel log in the mobile terminal APP, select the target area for this check, and the system automatically verifies the user's permission, which is set based on the blockchain role mechanism; Step 12, offline data download: if the on-site signal is weak, download the pressing plate basic data of the target area to the local in advance, including the pressing plate ID, standard state, two-dimensional code associated information, and historical check records, and the data storage adopts AES-256 encryption; Step 13, equipment check: confirm that the camera and positioning function of the mobile terminal are normal, and the power is sufficient, and if batch checking is needed, the continuous scanning mode is started by checking in the APP settings. 3.The protection and verification intelligent identification method based on the operation inspection mobile terminal according to claim 1, characterized in that, The step 2 is completed on the mobile terminal, and specifically comprises the following steps: Step 21, code scanning positioning: the operation and maintenance personnel scan the unique two-dimensional code beside the pressing plate with the mobile terminal APP, the APP automatically analyzes the pressing plate ID associated with the two-dimensional code, and real-time retrieves the key information of the pressing plate from the cloud intelligent knowledge base, including basic information: pressing plate name, model, belonging area, standard state; And associated information: logically associated pressing plate list, and the last three historical check records; Step 22, information confirmation: the APP interface directly displays the above information, the operation and maintenance personnel check that the pressing plate physical identifier is consistent with the cloud information, confirm that the positioning is correct, and enter the next step of checking; If the information is inconsistent, mark the positioning exception and upload it to the cloud to trigger a three-level warning.

4. The protection and verification intelligent identification method based on the operation inspection mobile terminal according to claim 3, characterized in that: In the step 21, the pressing plate name contains a five-level management data structure of "substation - cubicle - screen cabinet - device - pressing plate".

5. The protection verification intelligent identification method based on the operation inspection mobile terminal according to claim 1, characterized in that, The step 3 is completed on the mobile terminal, and specifically comprises the following steps: Step 31, state collection: first, based on the actual state of the on-site pressing plate, select the closed and open states in the APP; then, automatically take supporting photos, after selecting the state, the APP automatically retrieves the camera, takes clear images of the pressing plate, and the photos are automatically associated with "pressing plate ID + timestamp + mobile terminal positioning information"; Step 32, Abnormal Labeling (if there is an abnormality): If the pressboard state does not match the cloud standard state, select the abnormal type in the APP; state abnormality: closing / opening error, state ambiguity cannot be judged; Physical abnormalities: identification ambiguity, shell damage, loose wiring; supplementary remarks: fill in the abnormal details through voice input or short text; Step 33, Batch Verification Optimization: If multiple pressboards in the same area need to be continuously verified, after confirming that the current pressboard collection is complete, the APP automatically jumps to the code scanning interface without returning to the home page, and directly scans the two-dimensional code of the next pressboard. 6.The protection and verification intelligent identification method based on the operation inspection mobile terminal according to claim 1, characterized in that, The step 4 is completed by the mobile terminal and the cloud, and specifically includes the following steps: Step 41, Data Synchronization: Including networked scenarios: after collection is complete, the APP automatically uploads "pressboard ID + verification state + supporting photos + timestamp + positioning" to the cloud, and the progress is displayed in real time; and offline scenarios: data is temporarily stored in the local encrypted directory of the mobile terminal, and after the device is connected to the network, the APP automatically triggers offline data retransmission, which is uploaded in the order of collection time; Step 42, Cloud Verification: Including blockchain verification: the cloud is connected to the blockchain platform to verify the authenticity of "timestamp + positioning" of the uploaded data, and if the verification fails, the APP data is abnormal and needs to be collected again; And Format verification: check the photo clarity and abnormal labeling completeness, if the photo is blurred or without labeling, prompt the APP to supplement the supporting information. 7.The protection verification intelligent identification method based on the operation inspection mobile terminal according to claim 1, characterized in that, The step 5 is completed in the cloud, and specifically includes the following steps: Step 51, Basic State Comparison: The cloud retrieves the current standard state of the pressboard in the intelligent knowledge base, and directly compares the verification state: if consistent, it is determined to be normal; if not consistent, go to the next step of deep comparison; Step 52, Historical Trend Comparison: If there is no clear standard state for the pressboard, retrieve the last three historical verification states in the knowledge base, calculate the consistency rate of the current verification state and the historical state: if the consistency rate is ≥80%, it is determined to be historically normal; if the consistency rate is <80%, mark the historical abnormality; Step 53, Logical Association Comparison: Retrieve the list of logically associated pressboards of the pressboard in the knowledge base, and compare the verification states of the associated pressboards at the same period: if the associated state meets the preset rules, it is determined to be logically normal; If there is a conflict, mark the logical abnormality; Step 54, Final Determination: Based on the results of the above three comparisons, output the final conclusion: if all three are normal → verification is normal; if any one is abnormal → verification is abnormal, and match the three-level warning level according to the abnormal type. 8.The protection and verification intelligent identification method based on the operation inspection mobile terminal according to claim 1, characterized in that, The step 6 is completed by the cloud and the mobile terminal, and specifically includes the following steps: Step 61, Warning Level Matching: The cloud matches the warning level based on the abnormal type, and follows the three-level warning mechanism: first-level warning: key pressboard state error, batch pressboard abnormality; Second-level warning: non-key pressboard state abnormality, logical association conflict; third-level warning: appearance abnormality, single non-key pressboard historical abnormality; Step 62, Precise Push: Push object: first-level → operation and maintenance responsible person + regional operation and maintenance personnel; second-level → regional operation and maintenance personnel; third-level → on-site operation and maintenance personnel; push method: first-level → APP pop-up window + SMS + phone notification; second-level → APP pop-up window + SMS; third-level → APP message; Push content: abnormal platen ID, location, verification status, standard state, evidence photo, processing time limit, first level ≤ 30 minutes, second level ≤ 2 hours, third level ≤ 24 hours; Step 63, task assignment: the operation and maintenance person in charge can view the first / second abnormal list in the mobile terminal APP, assign tasks to specific operation and maintenance personnel, and the assignee receives a strong reminder of the pending task, including APP icon red dot + pop-up window. 9.The protection verification intelligent identification method based on the operation inspection mobile terminal according to claim 1, characterized in that, The step 7 is completed by the mobile terminal and the cloud, specifically including the following steps: Step 71, on-site processing: the assigned operation and maintenance personnel carries the mobile terminal to the abnormal platen site, and investigates the cause based on the pushed abnormal information; Step 72, processing result feedback: after processing, submit the processing result in the mobile terminal APP: including selecting the processing type: state correction, identification replacement, physical maintenance, and false judgment exclusion; and uploading processing evidence: taking photos of the processed platen, and supplementing processing notes; Step 73, cloud verification: after receiving the processing result, the cloud automatically completes two verifications: including state verification: comparing the platen state in the processed photo with the knowledge base standard state to confirm whether they are consistent; and logic verification: if it is a logical exception, verify whether the associated platen state is corrected synchronously; Verification passed → mark the abnormal processing completed; verification not passed → feedback APP processing is not up to standard, needs to be reprocessed, and the processing time limit is extended. 10.The protection and verification intelligent identification method based on the operation inspection mobile terminal according to claim 1, characterized in that, The step 8 is completed in the cloud, specifically including the following steps: Step 81, data archiving: including normal verification results: updating the latest verification time, verifier, and state of the platen in the intelligent knowledge base, and synchronizing to the blockchain storage; and abnormal processing results: storing the abnormal type, processing process, processing result, and evidence photo into the knowledge base exception case library, associating the corresponding platen ID, and forming a historical archive; Step 82, statistical analysis: the cloud regularly generates a verification closed-loop report, including verification completion rate, abnormal rate, and timely rate of each level of early warning processing, which can be viewed in the mobile terminal APP data center for management personnel to optimize operation and maintenance plans; Step 83, knowledge base optimization: based on high-frequency abnormal types, update the verification focus prompts of this type of platen in the knowledge base, which will be automatically displayed when subsequent mobile terminal verification is performed.