RFID-based intelligent pipeline management system and exception handling method

Abstract

The invention discloses an RFID-based intelligent pipeline management system and an exception handling method, and belongs to the technical field of pipeline management, and the system comprises an RFID tag module which is used for collecting pipeline state data and storing position identification information; the reader-writer module is used for receiving and rectifying signals of the RFID tag module and uploading the signals; the data processing terminal is used for receiving the signal uploaded by the reader-writer module, processing the signal and establishing a life cycle database; the exception handling module is used for identifying pipeline exception, positioning an exception position, grading and generating a handling scheme; and the user interaction module is used for displaying the data and the processing progress. Real-time sensing and data collection of the pipeline state are achieved through the RFID technology, the multi-dimensional anomaly recognition algorithm and the cooperative positioning technology are combined, the problems that in traditional pipeline management, the inspection efficiency is low, the anomaly positioning precision is poor, and the processing response lags are solved, and the intelligent level and the anomaly processing efficiency of pipeline management can be remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of pipeline management technology, and in particular to an RFID-based intelligent pipeline management system and anomaly handling method. Background Technology

[0002] Pipelines serve as core transportation carriers in energy, water conservancy, and chemical industries, and their safe and stable operation directly impacts industrial production and people's livelihoods. Traditional pipeline management relies primarily on manual inspections and periodic maintenance, which has the following shortcomings:

[0003] Low inspection efficiency: Manual inspection requires checking each section along the pipeline route. For long-distance pipelines in complex terrain (such as underground or mountainous areas), the inspection cycle can take up to several weeks, which is difficult to meet the needs of real-time monitoring.

[0004] Anomaly location is difficult: anomalies such as pipeline leaks and corrosion are often judged by indirect parameters such as sudden pressure drops and flow changes, lacking direct location markers, and the location error often exceeds 10 meters.

[0005] Data fragmentation: Inspection data is mostly recorded on paper or stored locally, making it difficult to integrate data throughout the entire lifecycle, and historical data has a weak supporting role in anomaly prediction;

[0006] Delayed response: After an anomaly is discovered, it needs to be manually verified and reported level by level. The response time from discovery to handling usually exceeds 24 hours, which can easily amplify the impact of the fault.

[0007] In existing technologies, some solutions use fiber optic sensing or GPS positioning to assist pipeline management. However, fiber optic sensing is expensive (approximately 20,000 yuan per kilometer to lay) and is easily damaged during construction. GPS signals are weak underground or in enclosed spaces, and its positioning accuracy cannot meet pipeline-level requirements. RFID technology has advantages such as non-contact identification, resistance to harsh environments, and the ability to read data in batches. However, existing RFID-based pipeline management solutions are only used for static asset identification and do not achieve closed-loop management for status monitoring and anomaly handling.

[0008] Therefore, there is an urgent need for an intelligent pipeline management system and anomaly handling method that integrates RFID technology to achieve real-time pipeline status perception, accurate anomaly location, and rapid response. Summary of the Invention

[0009] The purpose of this invention is to provide an RFID-based intelligent pipeline management system and an anomaly handling method to solve the problems existing in the background technology.

[0010] To achieve the above objectives, the present invention provides an RFID-based intelligent pipeline management system and an anomaly handling method, comprising:

[0011] The RFID tag module is used to collect pipeline status data and store location identification information;

[0012] The reader module is used to receive and rectify signals from the RFID tag module and upload them.

[0013] The data processing terminal is used to receive signals uploaded by the reader module, process the signals, and establish a lifecycle database.

[0014] The anomaly handling module is used to identify pipeline anomalies, locate the anomaly location, classify them, and generate a response plan.

[0015] The user interaction module is used to display data and processing progress;

[0016] It also includes a boost amplifier circuit and a signal amplifier circuit located between the reader module and the data processing terminal.

[0017] Preferably, the RFID tag module includes several passive RFID status tags and several active RFID positioning tags.

[0018] Preferably, the fixed reader adopts an industrial-grade ultra-high frequency reader, which is installed at fixed monitoring points such as inspection wells and valve wells along the pipeline. It supports simultaneous reading of multiple tags (maximum reading volume ≥200 tags per second) and communicates with the data processing terminal through a LoRa (long-range radio) network.

[0019] Mobile readers, integrated into inspection robots or drones, support handheld or automatic mode switching, and use Bluetooth + 4G dual-mode communication for data supplementation during manual inspections or in complex terrain areas.

[0020] Preferably, the data processing terminal includes:

[0021] Edge computing units, deployed near fixed readers, perform real-time preprocessing (including noise filtering and data encryption) on the collected temperature, pressure, and vibration data, and trigger local early warnings when data exceeds limits.

[0022] The cloud platform adopts a distributed architecture, receives processed data uploaded by edge computing units, establishes a pipeline lifecycle database (storing tag IDs, historical status data, and maintenance records), and supports data query and trend analysis.

[0023] Preferably, the exception handling module includes:

[0024] The anomaly identification unit, based on the random forest algorithm, performs multi-dimensional analysis of pipeline status data (comparing real-time data with historical thresholds and differences between adjacent label data) to identify anomaly types such as leakage, corrosion, and displacement.

[0025] The positioning unit combines the RFID tag ID coordinates with the received signal strength (RSSI) of multiple readers and uses a triangulation algorithm to calculate abnormal locations with a positioning accuracy of ≤1 meter.

[0026] The classification unit categorizes anomalies into three levels based on their type and impact range (e.g., leakage estimation): Level I: Emergency, requiring response within 1 hour; Level II: Important, requiring response within 6 hours; Level III: General, requiring response within 24 hours.

[0027] The response unit automatically generates response plans based on the classification results (such as triggering an emergency repair order for a Level I anomaly, and simultaneously pushing pipeline drawings and historical maintenance records), and tracks the response progress.

[0028] Preferably, the user interaction module accesses the Internet via a wired or wireless communication link and then establishes a connection with the cloud platform.

[0029] An anomaly handling method for an RFID-based intelligent pipeline management system includes the following steps:

[0030] S1. RFID status tags collect pipeline temperature and pressure data in real time, and RFID positioning tags collect vibration and displacement data. The data is uploaded to the edge computing unit for filtering (removing high-frequency noise) and encryption (using AES-128 algorithm). If the data exceeds the preset threshold (such as temperature > 80℃, pressure fluctuation > 10%), a local audible and visual alarm is triggered.

[0031] S2. The cloud platform receives preprocessed data, calls the anomaly identification unit, and compares the real-time data with historical data of the same period (average of the last 3 months) and adjacent tag data (average of 5 tags upstream and downstream). If the data deviation exceeds 20% for 3 consecutive collection cycles (10 minutes per cycle), or the data exceeds the threshold by 30% in a single time, it is judged as an anomaly and the anomaly type is marked.

[0032] S3. The positioning unit extracts the initial coordinates corresponding to the abnormal tag ID; combines the RSSI values ​​of the tag signals received by 3 or more adjacent readers, corrects the coordinates through the triangulation algorithm, and outputs the positioning result (accuracy ≤ 1 meter).

[0033] S4. The classification unit determines the level based on the anomaly type, the importance of the pipeline associated with the location results (e.g., main pipe, branch pipe), and environmental risk (e.g., proximity to residential areas, water sources); Level I anomalies include leaks in main pipes, displacement of joints and valves greater than 5mm, any type of anomaly occurring in sensitive areas, and leaks exceeding the safety threshold; Level II anomalies include branch pipe leaks less than the safety threshold, slight corrosion of main pipes, anomalies in main pipes or branch pipes occurring in non-core areas of sensitive areas, and displacement of joints and valves of 3-5mm; Level III anomalies include slight leaks in user access pipes, uniform corrosion of main pipes or branch pipes, displacement of joints and valves less than 3mm, and branch pipe anomalies in non-sensitive areas;

[0034] S5. Generate handling plans based on the level: For Level I anomalies, dispatch information (including location, anomaly type, and historical maintenance records) is automatically pushed to the nearest emergency repair team (via mobile GPS positioning) and management personnel are notified simultaneously; for Level II and Level III anomalies, maintenance work orders are generated and prioritized for inclusion in the maintenance plan; emergency repair personnel provide feedback on the handling progress (arrival, in progress, completed) via mobile devices, and the cloud platform updates the status in real time; after the handling is completed, the anomaly handling module automatically records the handling results (such as repair method and replaced parts) and updates the pipeline database for subsequent preventive maintenance analysis.

[0035] Preferably, the multi-dimensional analysis in the anomaly detection unit includes:

[0036] The Random Forest algorithm constructs 100 decision trees (the optimal number after training and optimization) to perform parallel analysis on the input data. Each decision tree makes independent judgments based on different feature subsets (such as temperature change rate, pressure fluctuation frequency, vibration duration, etc.), and finally outputs a comprehensive result through a voting mechanism.

[0037] Comparing over time, the deviation rate between real-time data and historical thresholds is calculated using the formula: Deviation rate = (Real-time value - Historical average) / Historical average × 100%.

[0038] Spatial dimension comparison: calculate the real-time data difference between the target label and its five adjacent labels. If the difference exceeds the normal range (e.g., the temperature difference between adjacent labels is usually ≤2℃, but the current difference is 8℃), it is marked as spatial dimension abnormal.

[0039] Therefore, the present invention employs the above-mentioned RFID-based intelligent pipeline management system and anomaly handling method, which has the following beneficial effects:

[0040] (1) Automatic RFID data collection replaces manual inspection, increasing the data collection frequency to 10 minutes / time, reducing the cost of long-distance pipeline inspection.

[0041] (2) Combining tag ID with RSSI triangulation, the anomaly positioning accuracy is improved from the traditional 10-meter level to the 1-meter level, shortening the maintenance preparation time;

[0042] (3) Establish a pipeline status database. By analyzing historical data, potential anomalies can be predicted in advance, reducing the failure rate.

[0043] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the structure of an RFID-based intelligent pipeline management system according to the present invention;

[0045] Figure 2 This is an overall flowchart of an RFID-based intelligent pipeline management system and anomaly handling method according to the present invention.

[0046] Figure 3 This is a design diagram of a boost amplifier circuit according to an embodiment of the present invention;

[0047] Figure 4 This is a design diagram of a signal amplification circuit according to an embodiment of the present invention. Detailed Implementation

[0048] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0049] Please see Figure 1 An RFID-based intelligent pipeline management system includes:

[0050] The RFID tag module is used to collect pipeline status data and store location identification information.

[0051] The RFID tag module includes several passive RFID status tags and several active RFID positioning tags. Passive RFID status tags have no built-in battery and rely on the radio frequency signal emitted by the reader to convert into electrical energy to operate. When the tag is at the edge of the reader's coverage area (e.g., 8-10 meters away), the voltage converted from the received radio frequency energy may be as low as 1-2V, insufficient to drive temperature and pressure sensors. A boost amplifier circuit (such as...) Figure 3This low voltage can be boosted to 3-5V (the threshold required for tag operation), ensuring that the tag can still complete data acquisition and transmission over long distances. The signals transmitted by FID tags (such as temperature and pressure data) are attenuated during transmission due to distance and obstructions (such as corrosion on the outer wall of pipes), resulting in insufficient signal energy received by the reader. The boost amplifier circuit reduces the impact of transmission loss on data integrity by enhancing the signal energy intensity (rather than simply amplifying the amplitude), making it particularly suitable for scenarios with severe signal attenuation, such as underground pipelines.

[0052] The signals transmitted back by passive RFID tags (such as pressure fluctuation data) have extremely weak original amplitudes and are easily masked by environmental noise. Signal amplification circuits (such as...) Figure 4 This can amplify the signal amplitude by 10-100 times (the amplification factor can be adjusted according to the noise level), making it exceed the noise threshold and providing a clear raw signal for the filtering processing of the edge computing unit. Vibration and displacement data collected by active RFID positioning tags may exhibit slight fluctuations (e.g., vibration signal amplitude deviation ±5%) when converted into electrical signals due to sensor accuracy limitations. The signal amplification circuit amplifies these minute changes linearly, enabling the anomaly detection unit to more accurately capture "displacement > 3mm" and "vibration amplitude > 15mm / s". 2 "Critical characteristics such as "signal attenuation may occur during signal transmission from the reader to the edge computing unit due to distance (e.g., more than 1 kilometer). The signal amplification circuit pre-amplifies the signal at the transmission node to avoid data distortion caused by insufficient amplitude in the signal received by the edge computing unit, thus ensuring the accuracy of subsequent multi-dimensional analyses such as "temperature deviation rate calculation" and "pressure trend analysis".

[0053] The reader module receives and rectifies signals from the RFID tag module and uploads them. The fixed reader uses an industrial-grade UHF reader, installed at fixed monitoring points such as inspection wells and valve wells along the pipeline. It supports simultaneous reading of multiple tags (maximum reading volume ≥200 tags per second) and communicates with the data processing terminal via a LoRa (long-range radio) network.

[0054] Mobile readers, integrated into inspection robots or drones, support handheld or automatic mode switching, and use Bluetooth + 4G dual-mode communication for data supplementation during manual inspections or in complex terrain areas.

[0055] The data processing terminal receives and processes signals uploaded by the reader module, establishing a lifecycle database. It includes an edge computing unit deployed near the fixed reader to perform real-time preprocessing (including noise filtering and data encryption) of collected temperature, pressure, and vibration data, triggering a local alert when data exceeds limits.

[0056] The cloud platform adopts a distributed architecture, receives processed data uploaded by edge computing units, establishes a pipeline lifecycle database (storing tag IDs, historical status data, and maintenance records), and supports data query and trend analysis.

[0057] The anomaly handling module is used to identify pipeline anomalies, locate their positions, classify them, and generate response plans. It includes:

[0058] The anomaly identification unit, based on the random forest algorithm, performs multi-dimensional analysis of pipeline status data (comparing real-time data with historical thresholds and differences between adjacent label data) to identify anomaly types such as leakage, corrosion, and displacement.

[0059] The positioning unit combines the RFID tag ID coordinates with the received signal strength (RSSI) of multiple readers and uses a triangulation algorithm to calculate abnormal locations with a positioning accuracy of ≤1 meter.

[0060] The classification unit categorizes anomalies into three levels based on their type and impact range (e.g., leakage estimation): Level I: Emergency, requiring response within 1 hour; Level II: Important, requiring response within 6 hours; Level III: General, requiring response within 24 hours.

[0061] The response unit automatically generates response plans based on the classification results and tracks the response progress. For example, if a Level I anomaly triggers an emergency repair order, pipeline drawings and historical maintenance records are simultaneously pushed to the system.

[0062] The user interaction module is used to display data and processing progress. It connects to the internet via wired or wireless communication links and then establishes a connection with the cloud platform. It includes web and mobile applications, allowing administrators to view real-time pipeline status, anomaly alerts, and processing progress, and to manually adjust anomaly thresholds and handling strategies.

[0063] like Figure 2 An anomaly handling method for an RFID-based intelligent pipeline management system includes the following steps:

[0064] S1. RFID status tags collect pipeline temperature and pressure data in real time, and RFID positioning tags collect vibration and displacement data. The data is uploaded to the edge computing unit for filtering and encryption. If the data exceeds the preset threshold (such as temperature > 80℃, pressure fluctuation > 10%), a local audible and visual alarm is triggered.

[0065] S2. The cloud platform receives preprocessed data, calls the anomaly identification unit, and compares the real-time data with historical data of the same period (average of the last 3 months) and adjacent tag data (average of 5 tags upstream and downstream). If the data deviation exceeds 20% for 3 consecutive collection cycles, each cycle is 10 minutes, or the data exceeds the threshold by 30% in a single time, it is judged as an anomaly and the anomaly type is marked.

[0066] S3. The positioning unit extracts the initial coordinates corresponding to the abnormal tag ID; combines the RSSI values ​​of the tag signals received by 3 or more adjacent readers, corrects the coordinates through a triangulation algorithm, and outputs the positioning result.

[0067] S4. The classification unit determines the level based on the anomaly type, the pipeline importance associated with the location results, and the environmental risk. Level I anomalies include leaks in main pipelines, displacement of joints and valves greater than 5mm, any type of anomaly occurring in sensitive areas, and leaks exceeding safety thresholds. Level II anomalies include branch pipe leaks less than safety thresholds, minor corrosion of main pipelines, anomalies in main or branch pipelines occurring in non-core areas of sensitive regions (e.g., within 50-100 meters of residential areas), and displacement of joints and valves of 3-5mm. Level III anomalies include minor leaks in user access pipes, uniform corrosion of main or branch pipelines, displacement of joints and valves less than 3mm, and branch pipeline anomalies in non-sensitive areas. Sensitive areas are defined as those less than 50 meters from residential areas, within drinking water source protection zones, and in the core areas of chemical industrial parks.

[0068] For example: A leak occurred in a city's main natural gas pipeline (800mm in diameter) within 30 meters of a residential area. A passive RFID tag detected a pressure drop from 0.8MPa to 0.3MPa (a decrease of 62.5%), and the location indicated it was near a residential building, thus classifying it as Level I. A chemical branch pipeline (300mm in diameter) experienced corrosion at the edge of an industrial area. The temperature rose from 28℃ to 32℃ (a 14% increase in 30 minutes), while the pressure remained relatively stable, classifying it as Level II. A 200mm in diameter irrigation branch pipeline in suburban farmland showed slight corrosion. A passive tag detected a temperature increase of 2℃ compared to the historical average, with pressure fluctuations <5%, classifying it as Level III.

[0069] S5. Generate handling plans based on the severity level: For Level I anomalies, dispatch information is automatically pushed to the nearest emergency repair team via mobile GPS location. The information includes location, anomaly type, and historical maintenance records, and management personnel are notified simultaneously. For Level II and Level III anomalies, maintenance work orders are generated and prioritized for inclusion in the maintenance plan. Emergency repair personnel provide feedback on the handling progress via mobile devices, and the cloud platform updates the status in real time. After the handling is completed, the anomaly handling module automatically records the handling results (such as repair method and replaced parts) and updates the pipeline database for subsequent preventive maintenance analysis.

[0070] The multi-dimensional analysis in the anomaly detection unit includes:

[0071] The Random Forest algorithm constructs 100 decision trees to perform parallel analysis on the input data. Each decision tree makes independent judgments based on different feature subsets (such as temperature change rate, pressure fluctuation frequency, vibration duration, etc.), and finally outputs a comprehensive result through a voting mechanism.

[0072] Comparing over time, the deviation rate between real-time data and historical thresholds is calculated using the formula: Deviation rate = (Real-time value - Historical average) / Historical average × 100%.

[0073] Spatial dimension comparison: calculate the real-time data difference between the target label and its five adjacent labels. If the difference exceeds the normal range (e.g., the temperature difference between adjacent labels is usually ≤2℃, but the current difference is 8℃), it is marked as spatial dimension abnormal.

[0074] Therefore, the present invention adopts the above-mentioned RFID-based intelligent pipeline management system and anomaly handling method to realize real-time pipeline status perception, accurate anomaly location and rapid response.

[0075] 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 them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An RFID-based intelligent pipeline management system, characterized in that, include: The RFID tag module is used to collect pipeline status data and store location identification information; The reader module is used to receive and rectify signals from the RFID tag module and upload them. The data processing terminal is used to receive signals uploaded by the reader module, process the signals, and establish a lifecycle database. The anomaly handling module is used to identify pipeline anomalies, locate the anomaly location, classify them, and generate a response plan. The user interaction module is used to display data and processing progress; It also includes a boost amplifier circuit and a signal amplifier circuit located between the reader module and the data processing terminal.

2. The RFID-based intelligent pipeline management system according to claim 1, characterized in that: The RFID tag module includes several passive RFID status tags and several active RFID positioning tags.

3. The RFID-based intelligent pipeline management system according to claim 2, characterized in that, The reader module includes: The fixed reader / writer adopts an industrial-grade ultra-high frequency reader / writer and is installed at fixed monitoring points in inspection wells and valve wells along the pipeline. It communicates with the data processing terminal through a LoRa network. Mobile readers, integrated into inspection robots or drones, are used for data supplementation during manual inspections or in complex terrain areas.

4. The RFID-based intelligent pipeline management system according to claim 3, characterized in that, The data processing terminal includes: The edge computing unit, deployed near the fixed reader, performs real-time preprocessing of the collected temperature, pressure, and vibration data, and triggers a local early warning when data exceeds limits. The cloud platform adopts a distributed architecture, receives processed data uploaded by edge computing units, establishes a pipeline lifecycle database, and supports data query and trend analysis.

5. The RFID-based intelligent pipeline management system according to claim 1, characterized in that, The exception handling module includes: The anomaly identification unit, based on the random forest algorithm, performs multi-dimensional analysis of pipeline status data to identify leakage, corrosion, and displacement anomaly types. The positioning unit combines the RFID tag ID coordinates with the signal strength received by multiple readers and uses a triangulation algorithm to calculate abnormal locations, with a positioning accuracy of ≤1 meter. The hierarchical unit classifies anomalies into three levels based on their anomaly type and scope of impact. The disposal unit automatically generates disposal plans based on the classification results and tracks the disposal progress.

6. The RFID-based intelligent pipeline management system according to claim 1, characterized in that: The user interaction module connects to the Internet via wired or wireless communication links and then establishes a connection with the cloud platform.

7. An anomaly handling method for an RFID-based intelligent pipeline management system, characterized by the application of the RFID-based intelligent pipeline management system as described in any one of claims 1-6. Includes the following steps: S1. RFID status tags collect pipeline temperature and pressure data in real time, and RFID positioning tags collect vibration and displacement data. The data is uploaded to the edge computing unit for filtering and encryption. If the data exceeds the preset threshold, a local audible and visual alarm is triggered. S2. The cloud platform receives the preprocessed data, calls the anomaly identification unit, and compares the real-time data with historical data from the same period and adjacent label data. S3. The positioning unit extracts the initial coordinates corresponding to the abnormal tag ID; combines the RSSI values ​​of the tag signals received by 3 or more adjacent readers, corrects the coordinates through a triangulation algorithm, and outputs the positioning result. S4. The grading unit determines the level based on the anomaly type, the pipeline importance associated with the location results, and the environmental risk. Level I anomalies include leaks in main pipelines, displacement of joints and valves greater than 5 mm, any type of anomaly occurring in sensitive areas, and leaks exceeding the safety threshold. Level II anomalies include branch pipe leaks less than the safety threshold, minor corrosion in main pipelines, anomalies in main or branch pipelines occurring in non-core areas of sensitive regions, and displacement of joints and valves of 3-5 mm. Level III anomalies include minor leaks in user access pipes, uniform corrosion in main or branch pipelines, displacement of joints and valves less than 3 mm, and branch pipeline anomalies in non-sensitive areas. S5. Generate handling plans based on the level: Level I anomalies automatically push dispatch information to the nearest emergency repair team and simultaneously notify management personnel; Level II and Level III anomalies generate maintenance work orders and are scheduled into the maintenance plan according to priority; emergency repair personnel provide feedback on handling progress via mobile devices, and the cloud platform updates the status in real time; after handling is completed, the anomaly handling module automatically records the handling results and updates the pipeline database for subsequent preventive maintenance analysis.

8. The anomaly handling method of an RFID-based intelligent pipeline management system according to claim 7, characterized in that, The multi-dimensional analysis in the anomaly detection unit includes: The Random Forest algorithm constructs 100 decision trees to perform parallel analysis on the input data. Each decision tree makes independent judgments based on different feature subsets, and finally outputs a comprehensive result through a voting mechanism. Comparing data over time, the deviation rate between real-time data and historical thresholds is calculated using the following formula: Deviation rate = (real-time value - historical average) / historical average × 100%; Spatial dimension comparison: calculate the real-time data difference between the target label and the five adjacent labels. If the difference exceeds the normal range, it is marked as spatial dimension abnormality.

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