Abnormal inspection result process management and control method and system and storage medium

Abstract

The invention relates to the technical field of abnormal patrol result process management and control, in particular to an abnormal patrol result process management and control method and system and a storage medium. The abnormal patrol result process management and control method comprises the following steps: data acquisition: acquiring patrol data by using a mobile terminal or other equipment; through the functions of automatic data acquisition, intelligent abnormity identification, flow assignment and the like, the efficiency of patrol work is greatly improved, meanwhile, through real-time monitoring, risk early warning and a perfect tracing mechanism, the risk management and control capability is effectively enhanced, safe operation is guaranteed, and through construction of a unified data platform, the risk management and control capability is effectively improved. A large amount of inspection data is collected and stored, data support is provided for managers through data visualization and report generation functions, scientific decision making is assisted, in addition, fine management is supported, different inspection schemes and processing flows can be formulated according to different inspection objects and risk levels, and the fine degree of management is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of abnormal patrol result process management and control, in particular to an abnormal patrol result process management and control method, system and storage medium. BACKGROUND

[0002] Patrol management refers to a system for supervising and checking leaders and departments, aiming to ensure the smooth progress of various work, maintain the authority and image of the organization, and improve work efficiency and quality. Patrol management is indispensable in any company or workshop, and good patrol management can ensure the production of the company and the safety of the workshop, which is an essential part of the operation process of modern enterprises.

[0003] However, the existing patrol management method mainly relies on manual inspection, recording and processing, which has the following shortcomings:

[0004] 1. Low efficiency: manual inspection is inefficient, and data recording relies on manual work, which is prone to errors and time-consuming.

[0005] 2. Information silos: patrol data is scattered among various patrol personnel, and there is a lack of unified data platform, making it difficult to analyze and share data.

[0006] 3. Slow response: after discovering abnormal situations, the processing procedure is not standardized, and the response speed is slow, which may cause delays.

[0007] 4. Lack of early warning: potential risks cannot be warned, and only abnormal situations can be dealt with passively.

[0008] 5. Difficulty in tracing: there is a lack of complete abnormal handling records, making it difficult to trace responsibilities and improve the process.

[0009] Therefore, there is an urgent need for an efficient, standardized and traceable abnormal patrol result process management and control method and system. SUMMARY

[0010] The present application aims to provide an abnormal patrol result process management and control method, system and storage medium to solve the problems of low efficiency, information silos, slow response, lack of early warning and difficulty in tracing in the existing patrol management method.

[0011] In the first aspect, to solve the above technical problems, the present application provides an abnormal patrol result process management and control method, including the following steps:

[0012] S1, data collection: using a mobile terminal or other equipment to collect patrol data, including time, location, patrol personnel, patrol items and discovered abnormal situations;

[0013] S2, data upload: upload the collected data to the cloud server or local database;

[0014] S3, anomaly identification: analyze the uploaded data, automatically identify abnormal situations, and perform preliminary risk assessment;

[0015] S4, process assignment: according to the risk level of abnormal situation, automatically assign to relevant responsible personnel for processing;

[0016] S5, result feedback: after processing the abnormal situation, feedback the processing result and related attachments in the system;

[0017] S6, report generation: the system automatically generates a patrol report, including patrol results, abnormal situations, processing procedures and statistical analysis information.

[0018] In an optional embodiment, the specific operation steps of data collection in step S1 are:

[0019] S11, start the inspection task: select or create the task that needs to be inspected;

[0020] S12, record inspection information: fill in the time, place, inspector's name or ID, and inspection project name;

[0021] S13, record abnormal situation: record the specific description information for the discovered abnormal situation, which can be recorded by text input, photographing and video recording, and classified according to the preset abnormal type.

[0022] In an optional embodiment, the specific operation steps of data upload in step S2 are:

[0023] S21, start data upload: upload the recorded abnormal situation data;

[0024] S22, data verification: integrity check of uploaded abnormal situation data;

[0025] S23, data transmission: abnormal situation data is transmitted to the server through the network.

[0026] In an optional embodiment, the specific operation steps of anomaly identification in step S3 are:

[0027] S31, data reading: read the abnormal situation data inside the server;

[0028] S32, data preprocessing: data cleaning and conversion operation;

[0029] S33, anomaly identification: apply algorithm or model to analyze data and identify abnormal situation;

[0030] S34, risk assessment: automatically assess the risk level R of the identified abnormal situation risk , the risk level R risk is calculated as follows:

[0031] R risk = a·S severity + b·I impact + g·Pprobability

[0032] Where S severity represents the severity of the abnormality, obtained by pre-set classification, reflecting the urgency and importance of the abnormality, I impact represents the influence range coefficient, obtained by text description analysis, used to measure the influence range of the abnormality on the system or business, P probability represents the probability of occurrence, obtained by statistical analysis of historical similar abnormal data, used to reflect the possibility of abnormality, a, b, g represent the preset weight coefficient, and the sum of the three is 1, used to adjust the proportion of severity, influence range and probability of occurrence in the calculation of risk level.

[0033] In an optional embodiment, the specific operation steps of assigning the process in step S4 are as follows:

[0034] S41, assign the person in charge: according to the pre-set rules, and combined with the dynamic priority formula Calculate the priority of abnormal situation, and automatically assign the abnormal situation to the corresponding responsible department or personnel according to the priority order of abnormal situation, wherein T occur is the time of abnormal situation occurrence, which is the time point collected by step S1, T current is the current system time, that is, the system time when the calculation is performed, K is the risk value, representing the risk degree of a task or abnormality, and e is the base number of natural logarithm;

[0035] S42, notify the person in charge: notify the assigned personnel by SMS or email.

[0036] In an optional embodiment, the specific operation steps of result feedback in step S5 are as follows:

[0037] S51, handle the abnormality: the person in charge handles the scene or other necessary operations;

[0038] S52, feedback processing result: fill in the processing result and upload the relevant attachments.

[0039] In an optional embodiment, the specific operation steps of report generation in step S6 are as follows:

[0040] S61, data aggregation: automatically collect all patrol data and processing results;

[0041] S62, report generation: automatically generate various types of reports according to the preset template, including the abnormal occurrence rate and the abnormal solution rate Wherein, N total is the total number of inspection items, N abnormal is the number of abnormal occurrences, N resolved is the number of solved abnormalities;

[0042] S63, report output: the report is output in PDF or Excel format.

[0043] In the second aspect, the embodiments of the present application also provide an abnormal patrol result process management and control system, comprising:

[0044] A server for data storage, processing, security and sharing, and overall operation and management of the system;

[0045] A data acquisition module for acquiring various data during the patrol process;

[0046] A data upload module for uploading the data collected by the data acquisition module to the system server;

[0047] An abnormality identification module for analyzing and processing the uploaded data to identify abnormal situations therein;

[0048] A process assignment module for automatically or manually assigning corresponding processing procedures and responsible persons according to the results of abnormality identification;

[0049] A result feedback module for collecting and recording the processing results of abnormal events, and evaluating and feeding back the processing results;

[0050] And a report generation module for automatically generating various types of reports according to the data and processing results collected by the system.

[0051] In an alternative embodiment, the output end of the data collection module is connected to a data uploading module, for transmitting the collected inspection data to the data uploading module, the output end of the data uploading module is connected to a server, for uploading the inspection data to the server, the input end of the abnormality identification module is connected to the server, for reading the inspection data from the server, the output end of the abnormality identification module is connected to the process assignment module, for transmitting the identified abnormality and its related information to the process assignment module, the output end of the process assignment module is connected to the result feedback module, for outputting the abnormal event information, and the output end of the result feedback module is connected to the report generation module, for transmitting the processing result data, together with the original data and abnormal information provided by the previous data collection module and abnormality identification module, to the report generation module.

[0052] In a third aspect, the embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of any of the embodiments of the first aspect.

[0053] Compared with the prior art, the present application has the following beneficial effects:

[0054] 1、The automatic data collection, intelligent abnormality identification and process assignment functions in the present application greatly improve the efficiency of inspection work. The previous manual inspection, recording and processing method is not only time-consuming and laborious, but also prone to errors, resulting in delayed response and high processing cost. The present application effectively solves these problems. The automatic data collection module eliminates the tedious process of manual recording, the intelligent abnormality identification module can quickly and accurately identify abnormal conditions and automatically perform risk assessment and process assignment, reducing manual intervention and shortening processing time. The standardization and normalization of the process also reduce the probability of human error, ultimately achieving a significant improvement in efficiency and a significant reduction in operating costs. The statistical report generated by the system can clearly display the inspection data and processing efficiency, providing data support for managers to further optimize resource allocation and achieve cost reduction and efficiency improvement.

[0055] 2、The real-time monitoring, risk warning and perfect traceability mechanism in the present application effectively enhance the risk control capability and ensure safe operation. The real-time monitoring function can timely discover abnormal conditions and remind the relevant responsible personnel to handle them in time through the warning mechanism, avoiding potential risks from evolving into accidents. The intelligent abnormality identification algorithm can effectively improve the accuracy and timeliness of abnormality identification, reduce false positives and false negatives, and reduce safety hazards. The complete processing records and process tracking function in the system facilitate responsibility tracing, provide a basis for accident analysis and responsibility identification, and promote process improvement, thereby effectively reducing safety risks and ensuring safe operation. The risk level assessment function enables resources to be allocated to high-risk areas or events in priority, thereby minimizing losses.

[0056] 3. This invention constructs a unified data platform to collect and store a large amount of inspection data. Through data visualization and report generation functions, it provides data support for managers and assists in scientific decision-making. In the past, data was scattered and lacked effective analysis methods, making it difficult to have a comprehensive understanding of the inspection situation. This system, through its data analysis function, can deeply mine the inspection data, such as analyzing the frequency and distribution patterns of abnormal events, providing managers with valuable information to help them formulate more effective inspection strategies and risk control measures. The various reports generated by the system, such as trend analysis charts and risk distribution charts, can intuitively display the inspection situation and risk level, facilitating managers to make scientific and reasonable decisions.

[0057] 4. This invention supports refined management, enabling the formulation of different inspection plans and processing procedures based on different inspection objects and risk levels, thereby improving the level of management refinement. The system's access control function can assign different operating permissions according to the responsibilities and permissions of different personnel, ensuring data security. Through the analysis of historical data, the inspection process can be continuously optimized, management methods improved, and the overall management level enhanced. At the same time, the system can flexibly adjust parameters according to actual conditions to adapt to different application scenarios, improving the system's applicability and stability. Ultimately, it achieves refined, intelligent, and efficient inspection management. Attached Figure Description

[0058] Figure 1 This is a schematic diagram illustrating the steps of the abnormal inspection result process control method of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] like Figure 1 As shown, this embodiment of the invention provides a method for managing the process of abnormal inspection results, including the following steps:

[0061] Step 1: Data Collection: Use mobile terminals or other devices to collect patrol data, including time, location, patrol personnel, patrol items, and any abnormalities found.

[0062] 1. Start an inspection task: Select or create a task that needs to be inspected.

[0063] Login System: The inspector logs into the inspection management system using a pre-assigned account and password. This system is usually deployed on a mobile device (such as a smartphone or tablet) or accessed through a web browser.

[0064] Select Inspection Plan: The system lists available inspection plans, each containing information such as inspection route, inspection items, inspection frequency, etc. The inspector can choose an existing plan or create a new one as needed. Creating a new plan may require filling in the plan name, description, inspection route (which can be mapped or imported GPS coordinates), inspection item list, inspection frequency, and responsible person, etc.

[0065] Confirm Task: After selecting or creating, the inspector needs to confirm the task. The system will generate a unique task ID and record the task start time.

[0066] Offline Mode (Optional): Some systems support offline mode, allowing inspectors to collect data without network connection, and synchronize data when network connection is restored.

[0067] 2、Record Inspection Information: Fill in the time, location, inspector's name or ID, and inspection item name.

[0068] Time Record: The system usually automatically records the timestamp of data collection, accurate to seconds.

[0069] Location Record: GPS Positioning - The system uses the GPS module to obtain the inspector's longitude and latitude coordinates (longitude: λ, latitude: φ), with accuracy depending on GPS signal strength. WGS84 coordinate system or other geographic coordinate systems can be used. Manual Input - If the GPS signal is weak or unavailable, the inspector can manually input location information, such as address or building name.

[0070] Inspector Information: The system may record the inspector's name or ID through automatic login, or may require manual selection or input.

[0071] Inspection Item Name: The system will display the inspection item list corresponding to the current task, and the inspector needs to confirm or check each completed item one by one. Each item may correspond to different data collection methods and indicators.

[0072] 3、Record Abnormal Situations: For discovered abnormal situations, record specific description information, which can be recorded using text input, photos, and videos, and classified according to pre-set abnormal types.

[0073] Abnormality Type Selection: The system provides a list of preset abnormality types, such as "Equipment Failure", "Safety Hazard", "Environmental Abnormality", etc. The inspector can choose the matching type. If the preset types are insufficient to cover all cases, custom abnormality types can be added.

[0074] Text Description: The inspector needs to provide a detailed text description of the abnormality, including the specific location, phenomenon, impact range, etc.

[0075] Photograph: Use the mobile device's camera to take a photo of the abnormality, which will be automatically associated with the abnormality record.

[0076] Video Recording: For abnormality cases that require more detailed recording, the mobile device's camera can be used to record a video.

[0077] Attachment Upload: In addition to photos and videos, the system may also allow the upload of other types of attachments, such as relevant documents or reports.

[0078] Abnormality Severity Level: Assign a severity level to each abnormality, such as: 1 (minor), 2 (general), 3 (serious), 4 (urgent). A simple formula can be used to calculate the severity level, such as: Severity Level = a × Impact Range + b × Duration + c × Repair Difficulty, where a, b, c are weight coefficients determined according to actual conditions.

[0079] Associated Project: Associate the abnormality with the relevant inspection project.

[0080] Through the above steps, the completeness and accuracy of data collection can be ensured, providing a reliable data foundation for subsequent abnormality identification, process assignment, and report generation. In actual application, adjustments need to be made according to specific inspection tasks and system design.

[0081] Step 2, Data Upload: Upload the collected data to the cloud server or local database.

[0082] 1. Start data upload: Upload the recorded abnormality data.

[0083] Network connection confirmation: The system first checks the network connection status of the mobile device. If the network connection is unstable or unavailable, the system will prompt the user to connect to the network or try again later. This can be achieved by checking the network connection type (such as: Wi-Fi, 4G, 5G) and signal strength.

[0084] Data preparation: The system automatically packages the local cached inspection data, including all information collected in the above steps (time, location, inspector, inspection project, abnormality description, pictures, videos, etc.). This may involve data compression and encryption operations to improve transmission efficiency and security.

[0085] Upload operation initiation: The inspector initiates the data upload process by clicking the "Upload Data" or similar button. The system displays the upload progress, such as the percentage of completion.

[0086] Batch upload: The system should support batch upload, allowing multiple inspection task data to be uploaded at once, improving efficiency. Multi-threading or asynchronous upload techniques can be used.

[0087] Resume interrupted upload (optional): If the network is interrupted during the upload process, the system should support the resume interrupted upload function to avoid data loss and repeated upload.

[0088] 2、Data verification: Perform integrity checks on abnormal situation data uploaded.

[0089] Data integrity check: The server checks the received data packet to ensure data integrity and consistency. This can be achieved through checksum, cyclic redundancy check (CRC), or other data integrity algorithms.

[0090] Data format check: The server checks whether the data format conforms to the predefined specifications, such as data field type, length, range, etc. Data that does not meet the specifications will be rejected.

[0091] Data consistency check: The server checks the consistency of the data, such as the consistency of the timestamp, the rationality of the latitude and longitude coordinates, etc. This may involve the verification of data logic rules.

[0092] Data duplication check: The server checks whether there are duplicate data submissions to avoid duplicate records.

[0093] Exception handling: If data verification fails, the system will record error information and may send an error prompt to the inspector.

[0094] 3、Data transmission: Abnormal situation data is transmitted to the server through the network.

[0095] Secure transmission: The data transmission process needs to use a secure communication protocol, such as HTTPS, to protect the security and confidentiality of data during transmission.

[0096] Encrypted transmission (optional): To further improve security, data encryption technology such as AES encryption can be used.

[0097] Network protocol: TCP or UDP protocol can be used for data transmission, TCP protocol guarantees reliable data transmission, and UDP protocol guarantees transmission efficiency. Choose which protocol depends on the specific requirements of the system.

[0098] Transmission speed optimization: Various techniques can be used to optimize data transmission speed, such as data compression, packet transmission, etc.

[0099] Transmission progress monitoring: The server monitors the progress of data transmission and feeds back to the client in a timely manner.

[0100] Transmission log recording: The server records the log of each data transmission, including transmission time, data size, transmission speed, status, etc. information, which is convenient for troubleshooting.

[0101] Through the above steps, combined with appropriate error handling and security mechanisms, the safety, reliability and efficiency of the data upload process can be ensured. In practical applications, it needs to be adjusted according to the specific network environment and system design. For example, message queues can be used to handle high-concurrency data upload requests.

[0102] Step 3, anomaly identification: Analyze the uploaded data, automatically identify abnormal situations, and conduct preliminary risk assessment.

[0103] 1、Data reading: Read the abnormal situation data inside the server.

[0104] Database connection: The system connects to the database server that stores abnormal situation data. This may require the use of a database connection pool to manage database connections, improve efficiency and stability.

[0105] Data query: The system reads abnormal situation data from the database according to predefined query conditions. Query conditions may include time range, inspection area, abnormal type, etc.

[0106] Data filtering (optional): After reading the data, it can be filtered according to certain rules, such as removing duplicate data or invalid data.

[0107] Data caching (optional): To improve efficiency, the read data can be cached in memory, reducing database access times.

[0108] 2、Data preprocessing: Data cleaning and conversion operations.

[0109] Data cleaning: Handle missing values, outliers and noise data. Missing value handling methods include deletion, interpolation or using mean / median filling. Outlier detection methods include boxplot method, 3σ principle, etc.

[0110] Data conversion: Convert data into a format suitable for algorithm or model processing. For example, convert text descriptions to numerical features, convert timestamps to time series data. This may involve feature engineering, such as One-hot encoding, word embedding, etc.

[0111] Data Standardization / Normalization: Standardize or normalize the data to the same range, such as [0, 1] or [-1, 1], to avoid certain features having too much influence on the algorithm or model. Common methods include Z-score standardization and Min-Max normalization.

[0112] Data Dimensionality Reduction (Optional): If the data dimension is too high, principal component analysis (PCA) or other dimensionality reduction techniques can be used to reduce the dimension, improve algorithm efficiency and reduce the risk of overfitting.

[0113] 3. Anomaly Identification: Apply algorithms or models to analyze data and identify anomalies.

[0114] Algorithm / Model Selection: Select appropriate algorithms or models based on data characteristics and application scenarios, such as statistical-based anomaly detection methods (e.g., 3σ rule, IQR method), machine learning methods (e.g., support vector machine SVM, isolation forest Isolation Forest, One-Class SVM) or deep learning methods.

[0115] Model Training (if necessary): If using machine learning or deep learning methods, historical data needs to be used to train the model.

[0116] Anomaly Detection: Use the trained model or algorithm to detect anomalies in preprocessed data and identify anomalies. The model will output anomaly scores or probabilities, and data points above the set threshold are considered abnormal.

[0117] Result Filtering (Optional): Anomalies identified can be filtered based on anomaly scores or probabilities, such as focusing only on high-risk anomalies.

[0118] 4. Risk Assessment: Automatically assess the risk level R risk of the identified anomalies based on their types, severity, impact range, etc.

[0119] Risk Level Definition: Predefine different risk levels, such as low, medium, high, and critical.

[0120] Risk Assessment Model: Design a risk assessment model to automatically assess the risk level based on the types, severity, impact range, etc. of the identified anomalies. This may be a simple rule engine or a complex machine learning model. Formula example: Risk Level = a × Severity + b × Impact Range + c × Occurrence Probability (where a, b, c are weight coefficients determined according to actual situation). Specifically, the calculation formula of risk level R risk is:

[0121] R risk = α·S severity + β·Iimpact + γ · Pprobability

[0122] where S severity represents the severity of the anomaly, obtained through pre-set classification, reflecting the urgency and importance of the anomaly, I impact represents the impact range coefficient, obtained through text description analysis, measuring the impact range of the anomaly on the system or business, P probability represents the occurrence probability, obtained through historical similar anomaly data statistics, reflecting the possibility of the anomaly, α, β, γ represent pre-set weight coefficients, and the sum of the three is 1, used to adjust the proportion of severity, impact range and occurrence probability in risk level calculation.

[0123] Risk level output: the system will output each identified anomaly and its corresponding risk level, facilitating subsequent processing.

[0124] Through the above steps, combined with appropriate algorithms and models, effective identification and risk assessment of abnormal situations can be achieved. In actual application, it needs to be adjusted and optimized according to specific business needs and data characteristics. For example, according to the spatio-temporal distribution characteristics of the anomaly, a spatio-temporal statistical model can be applied to improve the accuracy of anomaly identification.

[0125] Fourth step, process assignment: according to the risk level of the abnormal situation, automatically assign to the relevant responsible person for processing.

[0126] 1. Assign responsibility person: according to the pre-set rules, and combined with the dynamic priority formula calculate the priority of the abnormal situation, and automatically assign the abnormal situation to the corresponding responsible department or personnel according to the priority order of the abnormal situation, where T occur is the time of the abnormal situation, indicating the time point collected through step S1, T current is the current system time, i.e. the system time when the calculation is performed, K is the risk value, representing the risk degree of a task or anomaly, e is the base number of natural logarithm.

[0127] Responsibility person rule base: the system maintains a responsibility person rule base, which defines the corresponding responsible department or personnel under different types of abnormal situations, different geographical locations or other conditions. Rules can exist in various forms, such as:

[0128] Rule table - database table, containing fields such as abnormal type, geographical location, responsible department / personnel ID, etc.

[0129] Decision tree - rules based on decision tree algorithm, automatically determine the responsibility person according to the attributes of the abnormal situation.

[0130] Rule Engine - A more flexible rule engine that supports complex logical judgments and rule combinations.

[0131] Anomaly Matching: The system searches for matching rules in the responsible person rule base based on the attributes of the anomaly identified in the previous steps (e.g., anomaly type, geographic location, device ID, etc.).

[0132] Responsible Person Determination: Based on the matched rules, the corresponding responsible department or person is determined. If there are multiple matching rules, the system may need to select the appropriate responsible person according to priority or other strategies.

[0133] Responsible Person Record: The system records the correspondence between the anomaly and the responsible person for subsequent tracking and management. This is usually stored in a database, with fields including: anomaly ID, responsible person ID, assignment time, etc.

[0134] Conflict Resolution (Optional): If multiple rules match the same anomaly and point to different responsible persons, the system needs a conflict resolution mechanism, such as priority sorting, manual intervention, etc.

[0135] 2. Notify the Responsible Person: Notify the assigned personnel through SMS or email.

[0136] Get Contact Information: The system obtains the contact information of the assigned personnel from the personnel information library or organizational structure, such as mobile phone number, email address, etc.

[0137] Message Content Generation: The system generates a notification message according to a predefined template, and the message content should include: a brief description of the anomaly; the severity or risk level of the anomaly; the time and location of the anomaly; relevant links or attachments (e.g., detailed information page of the anomaly).

[0138] Message Sending: The system sends the notification message to the assigned personnel through an SMS gateway or email server.

[0139] Sending Status Record: The system records the message sending status, such as sending success, sending failure, read or unread, etc., to monitor the notification effect.

[0140] Multi-channel Notification (Optional): The system can support multiple notification channels, such as SMS, email, APP push, etc., to ensure that the message can be delivered in time.

[0141] Message Priority (Optional): According to the severity of the anomaly, the system can set the priority of the message, such as high priority messages can be sent first.

[0142] Through the above steps, combined with a flexible rules engine and a reliable message notification mechanism, it can ensure that abnormal situations are handled in a timely and effective manner. In practical applications, message templates, rule libraries, and notification methods can be customized according to actual needs. For example, internal communication tools such as WeChat for Enterprise and DingTalk can be integrated for message pushing, improving efficiency and convenience.

[0143] Step 5, Result Feedback: After handling the abnormal situation, feedback the processing result and related attachments in the system.

[0144] 1. Handle the exception: the person in charge handles the scene or other necessary operations.

[0145] Abnormal information viewing: the person in charge views the detailed information of the abnormal situation assigned to himself through the system or other ways, including the type of abnormality, the time of occurrence, the place, the severity, etc.

[0146] On-site treatment (if necessary): If the abnormal situation needs on-site treatment, the person in charge needs to go to the scene for treatment. This may include: equipment maintenance, troubleshooting, safety hazard elimination, etc.

[0147] Remote processing (if necessary): Some abnormal situations can be handled remotely, such as software configuration modification, remote restart of equipment, etc.

[0148] Treatment record: no matter what way of treatment is taken, the person in charge needs to record the treatment process in detail, including: treatment time, treatment method, treatment result, tools and materials used, etc. This can be recorded in text form or filled in through the forms provided by the system.

[0149] Evidence collection (if necessary): To ensure the reliability of the treatment result, the person in charge needs to collect relevant evidence, such as photos, videos, maintenance records, etc.

[0150] Treatment time tracking: the system may need to track the time of handling the abnormality, such as the time from assignment to completion of treatment, for subsequent performance evaluation or process optimization. The treatment time can be calculated using the formula: treatment time = treatment completion time - assignment time.

[0151] 2. Feedback on treatment results: fill in the treatment results and upload relevant attachments.

[0152] Result filling: the person in charge fills in the treatment result information through the forms or interfaces provided by the system, which may include:

[0153] Treatment status - handled, in progress, cannot be handled, etc.

[0154] Treatment result description - detailed description of the treatment process and result.

[0155] Treatment Measures - Specific measures taken.

[0156] Impact Assessment - Assess the impact of the treatment results.

[0157] Attachment Upload: Upload photos, videos, maintenance reports, and other attachments.

[0158] Attachment Upload: The system provides file upload functionality to facilitate the upload of relevant attachments during the treatment process. The system may impose restrictions on attachment types and sizes to ensure system security and efficiency.

[0159] Result Verification (Optional): The system can verify the feedback results, such as checking whether the attachments are complete and the result description is clear. This can improve data quality and avoid false or incomplete results.

[0160] Status Update: The system updates the status of the abnormal situation based on the feedback results from the responsible person, such as updating from "to be processed" to "processed".

[0161] Result Audit (Optional): The system can design an audit process to audit the treatment results to ensure the accuracy and reliability of the results. This may require assigning a specific person responsible for auditing.

[0162] Through the above steps, combined with the perfect system function, the treatment process of abnormal situation can be effectively managed, and the accuracy and timeliness of the treatment results can be ensured. In actual application, it can be adjusted and optimized according to specific business needs and system capabilities. For example, a workflow engine can be introduced to achieve more complex process control and automation. Other systems such as device management system, personnel management system, etc. can also be integrated to improve data integration and information sharing capabilities.

[0163] Step 6, Report Generation: The system automatically generates a patrol report containing patrol results, abnormal situations, treatment processes, and statistical analysis information.

[0164] 1. Data aggregation: Automatically collect all patrol data and treatment results.

[0165] Data source identification: The system identifies all relevant data sources, including patrol data and treatment result data. These data may be stored in different database tables or files, such as: patrol record table, abnormal treatment record table, device information table, etc.

[0166] Data extraction: The system extracts the required data from the identified data sources.

[0167] Data cleaning and conversion: The extracted data may need to be cleaned and converted to ensure data quality and consistency. This includes: handling missing values, outliers, data type conversion, etc.

[0168] Data integration: The data extracted from different data sources is integrated to form a unified dataset. This may require data connection, data merging, etc.

[0169] Data summary calculation (optional): The system can perform summary calculations on the data as needed, such as calculating the frequency of abnormal situations, average processing time, number of different types of abnormal situations, etc.

[0170] 2. Report generation: Various types of reports are automatically generated according to pre-set templates.

[0171] Report template management: The system maintains a report template library containing various types of report templates, such as daily reports, weekly reports, monthly reports, and abnormal situation statistics reports. Templates can be designed using report design tools such as JasperReports, BIRT, etc.

[0172] Template selection: Select the appropriate report template according to the needs. This can be manually selected by the user or automatically selected according to pre-set rules.

[0173] Data filling: Fill the data summarized in the above steps into the selected report template.

[0174] Report formatting: Format the report data according to the format requirements of the report template, such as number format, date format, cell style, etc.

[0175] Report verification (optional): The system can verify the generated report, such as checking whether the data is complete and the format is correct.

[0176] 3. Report output: The report is output in PDF or Excel format.

[0177] Output format selection: The system supports two output formats: PDF and Excel. Users can choose the desired output format.

[0178] File generation: The system generates a report file according to the selected output format.

[0179] File naming: The system names the generated report file according to pre-set rules or user-defined rules, such as "Inspection Report-20240229.pdf".

[0180] File storage and download: The system stores the generated report file in the specified directory and provides file download function, which is convenient for users to view and save the report.

[0181] Through the above steps, combined with report design tools and data processing technology, the automatic report generation function can be realized, the work efficiency is improved, and the accuracy and reliability of the report data are ensured. In practical applications, report templates and output formats can be customized according to actual needs, and other systems such as data visualization tools can be integrated to provide more intuitive report presentation. For example, the generated report can be automatically sent to relevant personnel by email.

[0182] Based on the same inventive concept, the embodiments of the present application also provide an abnormal patrol result process management and control system, which can realize the functions corresponding to the abnormal patrol result process management and control method described above. The abnormal patrol result process management and control system can be a hardware structure, a software module, or a hardware structure plus a software module. The abnormal patrol result process management and control system can be implemented by a chip system, which can be composed of a chip or can include a chip and other discrete devices. The abnormal patrol result process management and control system includes a server, a data acquisition module, a data uploading module, an abnormality identification module, a process assignment module, a result feedback module, and a report generation module.

[0183] The output end of the data acquisition module is connected to the data uploading module, for transmitting the collected patrol data to the data uploading module. The output end of the data uploading module is connected to the server, for uploading the patrol data to the server. The input end of the abnormality identification module is connected to the server, for reading the patrol data from the server. The output end of the abnormality identification module is connected to the process assignment module, for transmitting the identified abnormal situation and its related information to the process assignment module. The output end of the process assignment module is connected to the result feedback module, for outputting the abnormal event information. The output end of the result feedback module is connected to the report generation module, for transmitting the processing result data, together with the original data and abnormal information provided by the data acquisition module and the abnormality identification module, to the report generation module.

[0184] In the actual application process, for example, Figure 1As shown, first, the data collection module is used to inspect the inspection object and collect various data, such as pictures, videos, text descriptions, sensor data, etc. The data type depends on the specific situation and needs of the inspection object. Then the data collection module transmits the collected data to the data upload module, which checks and processes the uploaded data to ensure data integrity and correctness, and stores it in the server. The anomaly recognition module reads the inspection data from the server and analyzes it according to the pre-set rules, algorithms or models to identify the abnormal situations, and the identified abnormal situations are recorded, including abnormal type, severity, location, etc. Then the anomaly recognition module transmits the identified abnormal situations and related information to the process assignment module, which automatically or manually assigns the corresponding processing process and responsible person, automatic assignment is usually based on pre-set rules, such as automatically assigning to the corresponding department or personnel according to the abnormal type and severity, manual assignment allows administrators to intervene manually according to actual conditions, such as in complex or special situations. The responsible person assigned according to the system assignment tasks handles the abnormal situation, and in the process of handling, the responsible person can upload the processing process information (such as pictures, videos, processing reports, etc.) and the processing result through the result feedback module. At the same time, the result feedback module collects and records the processing result, and evaluates and feeds back the processing result, which may include processing efficiency, processing quality, processing cost, etc. Finally, the report generation module automatically generates various types of reports, such as daily report, weekly report, monthly report, etc., according to the data and processing result collected by the result feedback module, and the report content can include abnormal event statistics, processing efficiency analysis, processing quality evaluation, etc., providing data support for management decision-making.

[0185] Based on the same inventive concept, the embodiments of the present application also provide a computer readable storage medium storing a computer program, which realizes the functions corresponding to the aforementioned abnormal inspection result process control method when executed by a processor.

[0186] In summary, the present application greatly improves the efficiency of inspection work through automatic data collection, intelligent anomaly recognition and process assignment, etc., and effectively enhances the risk control ability through real-time monitoring, risk early warning and perfect traceability mechanism, ensuring safe operation. In addition, by building a unified data platform, a large amount of inspection data is collected and stored, and through data visualization and report generation functions, data support is provided for managers to assist scientific decision-making. In addition, the present application supports fine management, which can develop different inspection schemes and processing processes according to different inspection objects and risk levels, and improve the degree of fine management.

[0187] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Various changes and improvements can be made to the present application without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the process of abnormal inspection results, characterized in that, Includes the following steps: S1. Data Collection: Use mobile terminals or other devices to collect patrol data, including time, location, patrol personnel, patrol items, and any abnormalities found. S2. Data Upload: Upload the collected data to the cloud server or local database; S3. Anomaly Detection: Analyze the uploaded data, automatically identify anomalies, and conduct a preliminary risk assessment; S4. Process Assignment: Based on the risk level of the abnormal situation, it is automatically assigned to the relevant responsible personnel for handling; S5. Result Feedback: After handling the abnormal situation, the system will provide feedback on the handling result and related attachments. S6. Report Generation: The system automatically generates inspection reports, which include inspection results, abnormal situations, handling processes, and statistical analysis information.

2. The abnormal inspection result process control method according to claim 1, characterized in that, The specific steps for data acquisition in step S1 are as follows: S11. Start Inspection Task: Select or create a task that needs to be inspected. S12. Record inspection information: Fill in the time, location, name or ID of the inspector, and name of the inspection item. S13. Record abnormal situations: For any abnormal situations discovered, record specific descriptive information. This can be done by text input, taking photos, or recording videos, and the abnormal situations can be categorized according to preset abnormality types.

3. The abnormal inspection result process control method according to claim 1, characterized in that, The specific steps for uploading data in step S2 are as follows: S21. Start data upload: Upload the recorded abnormal data; S22. Data Verification: Perform integrity verification on uploaded abnormal data; S23. Data transmission: Abnormal data is transmitted to the server via the network.

4. The abnormal inspection result process control method according to claim 1, characterized in that, The specific steps for anomaly identification in step S3 are as follows: S31. Data Reading: Read abnormal situation data from inside the server; S32. Data preprocessing: Cleaning and transforming the data; S33. Anomaly Detection: Using algorithms or models to analyze data and identify abnormal situations; S34. Risk Assessment: Based on the identified anomalies, automatically assess their risk level R. risk The risk level R risk The calculation formula is: R risk =α·S severity +β·I impact +γ·Pprobability Among them, S severity Indicates the severity of the anomaly, obtained through preset classifications, and is used to reflect the urgency and importance of the anomaly. impact The impact range coefficient is obtained through text description analysis and is used to measure the scope of an anomaly's impact on the system or business. The probability of occurrence is obtained through statistical analysis of historical data on similar anomalies and is used to reflect the likelihood of an anomaly occurring. α, β, and γ represent preset weight coefficients, and the sum of the three is 1. They are used to adjust the weight of severity, impact range, and probability of occurrence in the risk level calculation.

5. The abnormal inspection result process control method according to claim 4, characterized in that, The specific steps for process assignment in step S4 are as follows: S41. Assigning Responsible Person: Based on preset rules and combined with dynamic priority formula. Calculate the priority of abnormal situations and automatically assign them to the corresponding responsible departments or personnel according to their priority order. Where T... occur The time of occurrence of the abnormal situation refers to the time point collected through step S1, T. current The current system time is the system time at the time of the calculation; K is the risk value, representing the risk level of a task or anomaly; and e is the base of the natural logarithm. S42. Notify the responsible person: Notify the assigned personnel via SMS or email.

6. The abnormal inspection result process control method according to claim 1, characterized in that, The specific steps for providing result feedback in step S5 are as follows: S51. Handling Abnormalities: The responsible person shall handle the situation on-site or perform other necessary operations. S52. Feedback on processing results: Fill in the processing results and upload relevant attachments.

7. The abnormal inspection result process control method according to claim 1, characterized in that, The specific steps for generating the report in step S6 are as follows: S61. Data Summary: Automatically collects all inspection data and processing results; S62. Report Generation: Automatically generate various types of reports based on preset templates, including anomaly occurrence rates. and exception resolution rate Where, N total N represents the total number of inspection items. abnormal N represents the number of times the anomaly occurred. resolved Number of resolved anomalies; S63. Report Output: The report is output in PDF or Excel format.

8. An abnormal inspection result process control system, characterized in that, include: Servers are used for data storage, processing, security, and sharing, as well as the overall operation and management of the system; The data acquisition module is used to collect various data during the inspection process; The data upload module is used to upload the data collected by the data acquisition module to the system server; The anomaly detection module is used to analyze and process the uploaded data and identify any abnormal situations. The workflow assignment module is used to automatically or manually assign corresponding processing procedures and responsible persons based on the results of anomaly identification. The results feedback module is used to collect and record the processing results of abnormal events, and to evaluate and provide feedback on the processing results; It also includes a report generation module, which automatically generates various types of reports based on the data collected and processed by the system.

9. The abnormal inspection result process control system according to claim 8, characterized in that, The output of the data acquisition module is connected to the data upload module to transmit the acquired inspection data. The output of the data upload module is connected to the server to upload the inspection data. The input of the anomaly identification module is connected to the server to read the inspection data from the server. The output of the anomaly identification module is connected to the process assignment module to transmit the identified anomalies and related information to the process assignment module. The output of the process assignment module is connected to the result feedback module to output anomaly event information. The output of the result feedback module is connected to the report generation module to transmit the processed result data, along with the raw data and anomaly information provided by the previous data acquisition module and anomaly identification module, to the report generation module.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the abnormal inspection result process control method as described in any one of claims 1 to 7.

Images