Pipeline equipment dynamic data acquisition system and method based on Internet of Things

Abstract

The invention provides a pipeline equipment dynamic data acquisition system and method based on the Internet of Things, and relates to the technical field of pipeline equipment management.The pipeline equipment dynamic data acquisition system comprises a data acquisition module, a data transmission module, a data processing module and a data display and alarm module; the data transmission module realizes data secure transmission through wireless and near field communication technologies, the data processing module supports high-concurrency data service through cloud hybrid storage and distributed cache, and the data display and alarm module realizes real-time visual monitoring of pipeline operation states and instant early warning pushing of abnormal conditions through a multi-terminal platform. According to the invention, real-time acquisition and fusion of multi-modal sensor data, efficient and safe data processing and remote management, automatic early warning and risk prevention and control of abnormal working conditions, and traceability and analysis of full-life-cycle data of pipelines can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline equipment management, in particular to a pipeline equipment dynamic data acquisition system and method based on Internet of Things. BACKGROUND

[0002] Pipeline equipment, as the "blood vessels" of the petroleum, natural gas, chemical industry, water supply and other industrial fields, is the core infrastructure to ensure the safe transportation of energy and resources. Its long-term operation under complex working conditions such as high pressure, corrosion, temperature difference change, the structural integrity and reliability are directly related to production safety, public safety and environmental protection.

[0003] The existing pipeline equipment management relies on manual transcription of equipment information, which is not only prone to errors, but also wastes time. The equipment state update is lagging behind, which cannot meet the dynamic allocation and real-time monitoring requirements. In addition, the account, maintenance record and location information scattered in different systems are difficult to correlate and analyze, and there is a lack of real-time early warning capability for pipeline corrosion, pressure abnormalities and other risks. SUMMARY

[0004] The purpose of the present application is to solve the problems of low efficiency, lack of real-time, serious information island and prominent safety hazards in the existing pipeline equipment data acquisition based on Internet of Things.

[0005] In order to achieve the above-mentioned purpose of the application, the present application provides the following technical solutions:

[0006] A pipeline equipment dynamic data acquisition system and method based on Internet of Things, comprising:

[0007] A data acquisition module for acquiring pipeline physical and environmental data and realizing real-time monitoring and preprocessing with high precision and low error;

[0008] A data transmission module for realizing safe data transmission through wireless and near field communication technology, and ensuring reliable storage and privacy protection of key operations by using blockchain technology;

[0009] A data processing module for supporting high-concurrency data services through cloud hybrid storage and distributed cache, and realizing life prediction and dynamic rule approval relying on intelligent analysis engine;

[0010] A data display and alarm module for realizing real-time visual monitoring of pipeline operation state and instant early warning push of abnormal situation through multi-terminal platform.

[0011] As a preferred technical solution of the present application, the data acquisition module comprises hardware components and software functions.

[0012] As a preferred technical solution of this application, the hardware components include anti-metal RFID, a handheld scanner, and a multimodal sensor array, used to collect data on pipeline equipment identification, geometric dimensions, spatial location, and operating environment.

[0013] As a preferred technical solution of this application, the software functions include dynamic data fusion algorithms and edge computing acceleration, which are used for real-time noise reduction calibration and preprocessing of sensor data to ensure high accuracy and reliability of the collected data.

[0014] As a preferred technical solution of this application, the data transmission module includes a communication protocol and blockchain evidence storage. The communication protocol combines wireless and near-field communication technologies to ensure high-speed and secure data transmission and reliable device connection in different scenarios. The blockchain evidence storage utilizes smart contracts and privacy protection technologies to ensure the immutability and reliable traceability of key operational data.

[0015] As a preferred technical solution of this application, the data processing module includes a cloud database and an intelligent analysis engine. The cloud database adopts a hybrid storage architecture and distributed caching technology to provide the system with efficient and reliable structured and unstructured data management services. The intelligent analysis engine realizes accurate prediction of pipeline life and intelligent processing of approval processes through neural network prediction and dynamic rule management.

[0016] This invention also discloses an IoT-based dynamic data acquisition method for pipeline equipment, applicable to any of the aforementioned IoT-based dynamic data acquisition systems for pipeline equipment, including a pipeline allocation data acquisition method and an anomaly early warning data acquisition method.

[0017] As a preferred technical solution of this application, the pipeline allocation data acquisition method includes the following steps:

[0018] Use a handheld terminal to scan the RFID tag on the pipeline to trigger a laser thickness gauge to automatically measure the wall thickness.

[0019] Data is uploaded to the edge gateway via Bluetooth, and the edge terminal calculates the remaining lifespan of the pipeline and marks anomalies in real time.

[0020] The edge gateway transmits encrypted data to the cloud via the 4G network, triggering the allocation approval process.

[0021] Once approved, the system automatically generates a QR code label.

[0022] As a preferred technical solution of this application, the early warning data collection method includes the following steps:

[0023] Pipeline physical and environmental data are collected using a multimodal sensor array;

[0024] When the buried pipeline sensor detects a sudden increase in pressure, the GNSS module reports the pipeline coordinates to locate the location of the anomaly.

[0025] The system pushes alarm information to administrators via SMS / APP, along with a fault simulation video;

[0026] Automatically link historical maintenance records to generate fault diagnosis reports.

[0027] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0028] 1. A handheld terminal scans the RFID tag on the pipeline, triggering a laser thickness gauge to automatically measure the wall thickness. The data is then uploaded to the edge gateway via Bluetooth. The edge terminal can calculate the remaining lifespan of the pipeline in real time and mark any abnormalities. The edge gateway also encrypts and transmits the data to the cloud via a 4G network, triggering a transfer approval process. Once approved, the system automatically generates a QR code tag (containing information such as the transferor, time, and equipment status). Therefore, the data collection efficiency is high, eliminating the need for manual recording of equipment information. It also provides high real-time performance, preventing delays in equipment status updates. This meets the needs of dynamic transfer and real-time monitoring, and can also perform correlation analysis on ledgers, maintenance records, and location information scattered across different systems.

[0029] 2. By collecting physical and environmental data of pipelines through a multi-modal sensor array, when the buried pipeline sensors detect a sudden increase in pressure, the GNSS module reports the pipeline coordinates to locate the location of the anomaly. The system will push alarm information to the management personnel via SMS / APP, along with a fault simulation video, automatically linking historical maintenance records to generate a fault diagnosis report. This enables real-time early warning of risks such as pipeline corrosion and abnormal pressure, thereby improving safety. Attached Figure Description

[0030] Figure 1 This is a diagram of the overall system architecture of the present invention;

[0031] Figure 2 This is a flowchart of the pipeline allocation data acquisition method of the present invention;

[0032] Figure 3 This is a flowchart of the abnormal early warning data acquisition method of the present invention;

[0033] Figure 4 This is a flowchart of the data acquisition process of the present invention;

[0034] Figure 5 This is a block diagram of the abnormal early warning logic of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0036] like Figures 1 to 5 As shown, this embodiment proposes an IoT-based dynamic data acquisition system for pipeline equipment, including a data acquisition module, a data transmission module, a data processing module, and a data display and alarm module. The data acquisition module is used to collect pipeline physical and environmental data and achieve high-precision, low-error real-time monitoring and preprocessing. The data transmission module achieves secure data transmission through wireless and near-field communication technologies and uses blockchain technology to ensure reliable evidence storage and privacy protection for critical operations. The data processing module supports high-concurrency data services through cloud hybrid storage and distributed caching, and relies on an intelligent analysis engine to achieve lifespan prediction and dynamic rule approval. The data display and alarm module achieves real-time visual monitoring of pipeline operation status and instant early warning push of abnormal situations through a multi-terminal platform.

[0037] The data acquisition module collaboratively collects pipeline physical and environmental data through anti-metal RFID, multimodal sensor arrays, and handheld scanning terminals. It leverages dynamic fusion algorithms and edge computing technology to achieve high-precision, low-error real-time monitoring and preprocessing. The data transmission module ensures efficient and secure data transmission through 4G / 5G dual-mode encrypted communication and BLE near-field connection, and utilizes blockchain smart contracts for immutable evidence storage of critical operations. The data processing module employs hybrid cloud storage and intelligent analysis technology, supporting millions of concurrent connections through MySQL+MongoDB layered storage and Redis high-performance caching. It also uses an LSTM model and rule engine to achieve accurate pipeline life prediction and dynamic hot updates of approval rules. The data display and alarm module displays pipeline status data in real-time via a web interface and mobile app, and proactively pushes early warning information when anomalies are detected, achieving visualized monitoring and instant alarms.

[0038] In summary, this invention enables real-time acquisition and fusion of multimodal sensor data, efficient and secure data processing and remote management, automatic early warning and risk prevention of abnormal operating conditions, and traceability and analysis of pipeline lifecycle data. This eliminates the need for manual recording of equipment information, prevents delays in equipment status updates, meets the needs of dynamic allocation and real-time monitoring, and can also perform correlation analysis on ledgers, maintenance records, and location information scattered across different systems to provide real-time early warnings for risks such as pipeline corrosion and abnormal pressure, thereby improving safety.

[0039] Specifically, the data acquisition module includes hardware components and software functions;

[0040] The hardware components include anti-metal RFID, handheld scanners, and multimodal sensor arrays, used to collect data on pipeline equipment identification, geometric dimensions, spatial location, and operating environment.

[0041] Hardware components:

[0042] Anti-metal RFID tags: embedded on the surface of pipeline equipment, storing unique identifiers such as equipment number and manufacturing information, and supporting the ISO / IEC 15693 protocol.

[0043] Handheld scanner: integrates Bluetooth / WiFi module, equipped with laser rangefinder (accuracy ±0.1mm) and QR code scanning function, and supports offline data caching (capacity ≥1GB).

[0044] In addition, multimodal sensor arrays:

[0045] Ultrasonic thickness sensor: Non-contact real-time monitoring of pipeline wall thickness, sampling frequency ≥1Hz;

[0046] GNSS positioning module: GPS / BeiDou dual-mode positioning, accuracy ≤1m, supports trajectory tracking;

[0047] MEMS sensors: integrate pressure, temperature, and vibration sensors to monitor the operating environment of equipment.

[0048] The software features include dynamic data fusion algorithms and edge computing acceleration, used for real-time noise reduction, calibration and preprocessing of sensor data to ensure high accuracy and reliability of the acquired data;

[0049] Software features:

[0050] Dynamic data fusion algorithm: Sensor noise is eliminated by Kalman filtering, and the thickness measurement results are calibrated by combining historical data (error rate <2%).

[0051] Edge computing acceleration: FPGA acceleration cards are deployed in handheld terminals to support real-time data preprocessing (such as data cleaning and format conversion).

[0052] Specifically, the data transmission module includes a communication protocol and blockchain evidence storage;

[0053] The communication protocol combines wireless and near-field communication technologies to ensure high-speed, secure data transmission and reliable device connectivity in different scenarios.

[0054] Communication protocol:

[0055] Wireless transmission: 4G Cat.1 / 5G dual-mode communication, supports the national standard SM4 encryption algorithm, and has a transmission rate of ≥10Mbps;

[0056] Near Field Communication: The BLE 5.0 protocol enables rapid pairing between handheld devices and electronic tags (pairing success rate > 99%).

[0057] The blockchain-based evidence storage utilizes smart contracts and privacy protection technologies to ensure the immutability and reliable traceability of critical operational data.

[0058] Blockchain-based evidence storage:

[0059] Key operations (such as data modification and approval records) are stored on the blockchain through smart contracts, supporting zero-knowledge proof privacy protection.

[0060] Specifically, the data processing module includes a cloud database and an intelligent analysis engine;

[0061] The cloud database employs a hybrid storage architecture and distributed caching technology to provide the system with efficient and reliable structured and unstructured data management services.

[0062] Cloud database:

[0063] Hybrid storage architecture: MySQL stores structured data (ledgers, approval records), and MongoDB stores unstructured data (images, logs);

[0064] Distributed caching: Redis clusters support millions of concurrent queries with a response latency of ≤50ms.

[0065] The intelligent analysis engine achieves accurate prediction of pipeline life and intelligent processing of approval processes through neural network prediction and dynamic rule management.

[0066] Intelligent Analysis Engine:

[0067] LSTM neural network model: predicts pipeline remaining lifetime (prediction error < 5%), supports online incremental training;

[0068] Rules engine: Dynamically loads approval rules (such as "Amount > 100,000 yuan requires three levels of approval"), and supports hot rule updates.

[0069] like Figures 1 to 5As shown, this invention also discloses an IoT-based dynamic data acquisition method M10 for pipeline equipment, applied to the IoT-based dynamic data acquisition system for pipeline equipment in any of the above embodiments. It includes a pipeline allocation data acquisition method M100 and an anomaly warning data acquisition method M100'. M10 includes steps S100, S200, S300, and S400. In pipeline allocation scenarios, it can acquire real-time pipeline physical and environmental data, saving time and avoiding errors, and updating equipment status to meet the needs of dynamic allocation and real-time monitoring. M10' includes steps S100', S200', S300', and S400'. In anomaly warning scenarios, it can provide real-time warnings for risks such as pipeline corrosion and abnormal pressure to improve safety.

[0070] Specifically, the pipeline allocation data acquisition method M10 includes:

[0071] S100: Engineers use a handheld terminal to scan the pipeline RFID tag, triggering a laser thickness gauge to automatically measure the wall thickness, with a data accuracy error of ±0.1mm;

[0072] Engineers use handheld terminals to scan pipeline RFID tags to automatically obtain equipment identification information. The terminal then triggers a connected laser thickness gauge to perform non-contact, precise measurements at preset measurement points with an accuracy of ±0.1mm. The measured wall thickness data is automatically bound to equipment ID, time, location, and other information, and stored encrypted on the terminal. The terminal indicates that the measurement is complete and the data packet is in a pending upload state, waiting for network connection to synchronize to the cloud. This process realizes a fully automated, high-precision, and error-proof closed-loop operation from "identity recognition" to "data readiness".

[0073] S200: Uploads data to the edge gateway via Bluetooth. The edge terminal calculates the remaining lifespan of the pipeline in real time and marks anomalies, such as remaining lifespan <30%.

[0074] The handheld terminal sends encrypted thickness measurement data packets to the edge gateway via Bluetooth. After receiving the data, the gateway calls the preset life prediction model to perform real-time analysis and calculation. The model outputs the remaining life percentage and automatically compares it with the safety threshold, marking abnormal states. The edge terminal feeds back the calculation results and abnormal markings to the terminal interface in real time and uploads them to the cloud simultaneously. This process realizes a second-level closed loop of "data-analysis-decision", ensuring that on-site personnel can perceive risks in real time.

[0075] S300: The edge gateway transmits encrypted data to the cloud via the 4G network, triggering the allocation approval process (linking with the OA system);

[0076] The edge gateway transmits encrypted abnormal data packets to the cloud platform via the 4G network. The cloud receives and decrypts the data, parses out the device information, abnormality level, and required approval type, calls the OA system interface, automatically creates and initiates a "Spare Parts Transfer Approval Form" containing all details, and returns the approval form number and process link to the edge system to complete the operation loop. This process realizes fully automated, paperless, and efficient collaboration from "on-site abnormality" to "process initiation".

[0077] S400: After approval, the system automatically generates a QR code label (containing information such as the transferor, time, and equipment status);

[0078] After the OA system approves the application, it automatically sends a generation instruction and allocation data to the label printing system. The system encodes and encrypts information such as the transferor, time, equipment status, and destination to generate a unique QR code. The instruction is transmitted to the networked printer, which automatically prints a physical label containing the QR code and plaintext information. The QR code data is permanently bound to the allocation task in the database for subsequent scanning and traceability. This process realizes fully automated and traceable closed-loop management from "approval decision" to "physical label".

[0079] Specifically, the abnormal early warning data acquisition method M10' includes:

[0080] S100': Acquires pipeline physical and environmental data through a multimodal sensor array;

[0081] By simultaneously activating ultrasonic thickness measurement, GNSS positioning, and pressure / temperature / vibration sensors, the system collects data on pipeline wall thickness, location, and operating environment. The built-in processor performs real-time timestamp alignment, unit unification, and preliminary filtering on the multi-source data. The processed data is packaged in a standard format, labeled with the device ID and acquisition time, and output through a wired or wireless interface for reception by an edge gateway or handheld terminal. This process achieves a high-precision, synchronized data acquisition closed loop from "multi-source sensing" to "standard output".

[0082] S200': The buried pipeline pressure sensor detected a sudden increase in pressure (exceeding the safety threshold by 20%), and the GNSS module reported the pipeline coordinates (error ≤ 1m) to locate the location of the anomaly;

[0083] The buried pipeline pressure sensor detects a sudden increase in internal pressure in real time. When the data instantly exceeds the safety threshold by 20%, an abnormal signal is immediately generated. At this time, the GNSS module is triggered simultaneously. Through GPS / BeiDou dual-mode positioning, the geographic coordinates of the abnormality point are accurately reported with a positioning error of no more than 1 meter. The edge gateway automatically binds the pressure anomaly data with the precise location information and generates an alarm message containing the pressure exceeding the standard value, the time of occurrence, and the precise coordinates. The alarm message is pushed to the monitoring center in real time through the 4G / 5G network and accurately located and displayed on the GIS map to guide the inspection personnel to respond quickly. This process realizes a rapid response closed loop from "pressure anomaly perception" to "precise location and handling", which greatly improves emergency response efficiency.

[0084] S300': The system pushes alarm information to management personnel via SMS / APP, along with a fault simulation video (generated based on a digital twin model);

[0085] Once the system confirms an anomaly, it automatically generates alarm information including the fault location, type, and level. Based on real-time data, the digital twin engine quickly generates a simulation video of the fault's development trend. The alarm information and simulation video are simultaneously pushed to management personnel via SMS (text summary) and APP (complete information + video). The system records the message delivery status and management personnel's viewing feedback to ensure that the warning is effectively delivered. This process realizes a visual warning closed loop from "fault alarm" to "trend prediction," improving decision-making efficiency.

[0086] S400': Automatically links historical maintenance records to generate fault diagnosis reports (including recommended solutions);

[0087] Based on the current device ID and fault characteristics, the system automatically matches and retrieves historical maintenance records and relevant case libraries, compares historical data with real-time status, analyzes fault modes, root causes and potential impact range, and automatically generates a structured diagnostic report, including fault analysis, priority assessment and recommended solutions (such as replacing parts, adjusting parameters, etc.). The report is pushed to management personnel along with alarm information to provide data support for decision-making. This process realizes an intelligent closed loop from "fault perception" to "precise recommendation", improving maintenance efficiency and accuracy.

[0088] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described herein. Although the present invention has been described in detail with reference to the above embodiments, the present invention is not limited to the specific embodiments described above. Therefore, any modifications or equivalent substitutions to the present invention, as well as all technical solutions and improvements that do not depart from the spirit and scope of the invention, are covered within the scope of the claims of the present invention.

Claims

1. A dynamic data acquisition system for pipeline equipment based on the Internet of Things, characterized in that, include: The data acquisition module is used to collect pipeline physical and environmental data and to achieve high-precision, low-error real-time monitoring and preprocessing. The data transmission module achieves secure data transmission through wireless and near-field communication technologies, and utilizes blockchain technology to ensure reliable evidence storage and privacy protection for critical operations. The data processing module supports high-concurrency data services through cloud hybrid storage and distributed caching, and relies on an intelligent analysis engine to achieve lifespan prediction and dynamic rule approval. The data display and alarm module enables real-time visual monitoring of pipeline operation status and immediate early warning push notifications for abnormal situations through a multi-terminal platform.

2. The IoT-based pipeline equipment dynamic data acquisition system according to claim 1, characterized in that, The data acquisition module includes hardware components and software functions.

3. The IoT-based pipeline equipment dynamic data acquisition system according to claim 2, characterized in that, The hardware components include anti-metal RFID, a handheld scanner, and a multimodal sensor array, used to collect data on pipeline equipment identification, geometry, spatial location, and operating environment.

4. The IoT-based pipeline equipment dynamic data acquisition system according to claim 1, characterized in that, The software features include dynamic data fusion algorithms and edge computing acceleration for real-time noise reduction, calibration, and preprocessing of sensor data.

5. The IoT-based pipeline equipment dynamic data acquisition system according to claim 1, characterized in that, The data transmission module includes a communication protocol and a blockchain notarization. The communication protocol is used for high-speed and secure data transmission and reliable connection to devices in different scenarios. The blockchain notarization is used to ensure the immutability and reliable traceability of critical operational data.

6. The IoT-based pipeline equipment dynamic data acquisition system according to claim 5, characterized in that, The data processing module includes a cloud database and an intelligent analysis engine. The cloud database provides the system with efficient and reliable structured and unstructured data management services. The intelligent analysis engine uses neural network prediction and dynamic rule management to perform intelligent processing for accurate pipeline life prediction and approval processes.

7. A method for dynamic data acquisition of pipeline equipment based on the Internet of Things (IoT), applied to the dynamic data acquisition system for pipeline equipment based on the IoT as described in any one of claims 1-6, characterized in that, This includes methods for collecting pipeline allocation data and methods for collecting abnormal early warning data.

8. The method for dynamic data acquisition of pipeline equipment based on the Internet of Things according to claim 7, characterized in that, The pipeline allocation data acquisition method includes the following steps: Use a handheld terminal to scan the RFID tag on the pipeline to trigger a laser thickness gauge to automatically measure the wall thickness. Data is uploaded to the edge gateway via Bluetooth, and the edge terminal calculates the remaining lifespan of the pipeline and marks anomalies in real time. The edge gateway transmits encrypted data to the cloud via the 4G network, triggering the allocation approval process. Once approved, the system automatically generates a QR code label.

9. The method for dynamic data acquisition of pipeline equipment based on the Internet of Things according to claim 7, characterized in that, The method for collecting early warning data includes the following steps: Pipeline physical and environmental data are collected using a multimodal sensor array; When the buried pipeline sensor detects a sudden increase in pressure, the GNSS module reports the pipeline coordinates to locate the location of the anomaly. The system pushes alarm information to management personnel via SMS / APP, along with a fault simulation video; it automatically links to historical maintenance records and generates a fault diagnosis report.

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