Recognition and early warning system and method for gas pipeline and storage medium
By combining multi-source sensor terminals and deep learning models, all-weather automated intelligent monitoring of gas pipelines has been achieved, solving the problems of low efficiency and high false alarm rate in existing technologies. It has realized proactive early warning and accurate positioning, forming a self-optimizing closed-loop management system.
Patent Information
- Application Number
- CN202511716900.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-21
- Publication Date
- 2026-01-02
AI Technical Summary
Existing technologies cannot achieve all-weather, automated, high-precision, and intelligent safety monitoring of gas pipelines. They suffer from problems such as low efficiency, high false alarm rate, and reliance on manual judgment, and cannot achieve proactive early warning and accurate positioning.
The system employs a multi-source sensor terminal module to collect physical state and visual data. Data preprocessing and lightweight convolutional neural network recognition are performed through an edge intelligent processing gateway. Trend analysis and spatiotemporal alignment are then performed using LSTM and multimodal fusion deep learning models. This drives the digital twin model of the gas pipeline to perform risk visualization and positioning, and generates and pushes early warning information.
It enables proactive early warning, intelligent identification, and precise positioning of gas pipelines, reduces false alarm rates, improves system reliability and scalability, forms a self-optimizing closed-loop management system, and has self-learning capabilities.
Smart Images
Figure CN121251981A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of gas pipeline technology, specifically relating to a gas pipeline identification and early warning system, method, and storage medium. Background Technology
[0002] As the "lifeline" of a city, the safe and stable operation of gas pipelines is directly related to public safety, people's lives and property, and the normal order of the city. However, because gas pipelines are usually buried deep underground and laid in complex environments (crossing roads, buildings, rivers, etc.), they are subject to multiple factors such as soil corrosion, damage from third-party construction, geological subsidence, and material aging. They are prone to safety accidents such as leaks and ruptures, and their risks are characterized by concealment, suddenness, and catastrophicness.
[0003] Currently, the industry mainly relies on the following five technical solutions, but all of them have obvious limitations: The first method is traditional manual inspection, where inspectors periodically patrol the route, checking using visual inspection and handheld leak detectors. This method is inefficient, has limited coverage, cannot provide 24 / 7 real-time monitoring, and is highly dependent on personnel experience and responsibility, resulting in a significant delay in response to sudden damage.
[0004] The second type is a data acquisition and monitoring control system, which indirectly determines leakage by installing pressure and flow sensors at key nodes to monitor data anomalies. This method only has point-based sensing capabilities, resulting in huge pipeline blind spots, inability to accurately locate leak points, high false alarm rates, and susceptibility to interference from normal operating condition adjustments.
[0005] The third type is distributed fiber optic sensing technology, which uses laid optical cables to sense vibration and temperature changes along the line to achieve continuous monitoring. This method is expensive, complex to deploy, and extremely sensitive to environmental noise (such as traffic and wind and rain); the algorithm is complex, making it difficult to accurately identify real threats, and the risk of false alarms and missed alarms is high.
[0006] The fourth method is conventional video surveillance, which involves installing cameras in high-risk areas and having personnel monitor the footage from a control center. This method is essentially "screen inspection," which is labor-intensive and prone to oversights due to staff fatigue, thus failing to achieve automated and intelligent early warning systems.
[0007] The fifth type is a monitoring system based on the fusion of video surveillance and fiber optic vibration detection. After a vibration sensor detects a potential threat (such as excavator vibration), it automatically triggers nearby cameras to capture footage of the scene and pushes it to the monitoring center for final manual confirmation. This method has low intelligence, heavily relies on manual intervention, and the system cannot automatically identify the video content (what equipment is being used and what it is doing), still requiring manual judgment. It is slow to respond, costly, and lacks standardized procedures. It is merely a simple hard correlation of "vibration alarm → video linkage," failing to deeply cross-validate the vibration pattern with the video recognition results. Furthermore, vibration sensors are susceptible to interference, generating numerous false alarms. Frequent false alarms lead to a "boy who cried wolf" effect, causing monitoring personnel to become complacent and potentially miss real dangers. This method is essentially at the "post-event alarm" or "in-event alarm" stage, unable to predict high-risk areas and time periods in advance.
[0008] In summary, existing technologies cannot meet the urgent needs of gas pipeline safety for all-weather, automated, high-precision, and intelligent systems. There is an urgent need in this field for a new intelligent identification and early warning system that can overcome the aforementioned shortcomings and achieve proactive early warning, intelligent identification, precise positioning, and closed-loop management. Summary of the Invention
[0009] To realize a novel intelligent identification and early warning system with proactive warning, intelligent identification, and precise positioning, in a first aspect, the present invention provides a gas pipeline identification and early warning system, the system comprising: Multi-source sensor terminal module is used to collect physical and visual data around gas pipelines; The edge intelligent processing gateway module is used to receive physical state data and visual data collected by the multi-source sensing terminal module, perform data preprocessing on the physical state data to obtain effective physical state data, and perform target recognition on the visual data through a lightweight convolutional neural network model. The recognition objects include construction machinery and its behavioral intentions and personnel behavior. The cloud platform intelligent analysis center module is used to call the LSTM deep learning model to perform trend analysis on the effective physical state data; call the multimodal fusion deep learning model to perform spatiotemporal alignment and correlation on the effective physical state data and target recognition results; and use the output results of the LSTM deep learning model and the multimodal fusion deep learning model to drive the gas pipeline digital twin model to be updated and risk visualization and positioning. The early warning response and human-computer interaction module is used to receive the analysis results and risk positioning of the cloud platform intelligent analysis center module, generate early warning information, and send the early warning information to the terminal device through at least one of message push, SMS, and voice.
[0010] In one possible implementation, the physical state data includes the pressure value of the gas pipeline, the flow rate value of the gas pipeline, the gas concentration value of the gas pipeline, the vibration signal around the gas pipeline, and the sound signal around the gas pipeline. The edge intelligent processing gateway module is also used to judge the valid physical state data according to a preset threshold, generate a preliminary alarm event, and send the preliminary alarm event to the early warning response and human-machine interaction module so that the early warning response and human-machine interaction module sends the preliminary alarm event to multiple terminal devices.
[0011] In one possible implementation, the step of invoking an LSTM deep learning model to perform trend analysis on the effective physical state data, and invoking a multimodal fusion deep learning model to perform spatiotemporal alignment and correlation between the effective physical state data and the target recognition results, includes: Acoustic events are detected based on vibration and sound signals around the gas pipeline. The LSTM deep learning model is used to analyze whether there are any abnormal trends in the pressure, flow rate, and gas concentration values of the gas pipeline. A multimodal fusion deep learning model is invoked to perform spatiotemporal alignment and correlation of acoustic events, trend information, and target recognition results.
[0012] In one possible implementation, the step of using the outputs of an LSTM deep learning model and a multimodal fusion deep learning model to drive the updating and risk visualization localization of the gas pipeline digital twin model includes: Obtain the event type and location output by the multimodal fusion deep learning model, call the 3D GIS engine, and highlight and alarm the corresponding location on the digital twin model of the gas pipeline; The cloud platform's intelligent analysis center module is also used to perform risk level weighted assessments on the output results of the LSTM deep learning model and the multimodal fusion deep learning model, generating a structured early warning report that includes the risk level, a digital twin scenario of the gas pipeline, and disposal recommendations.
[0013] In one possible implementation, the early warning response and human-computer interaction module is also used to call the message push service to send the structured early warning report to the monitoring center's large screen display system or the mobile devices of maintenance personnel, and to display the report list, handling process guidance, and handling feedback entry in a visual manner on multiple terminals; to receive user handling results and feed them back to the cloud platform intelligent analysis center module for incremental learning of the LSTM deep learning model and the multimodal fusion deep learning model.
[0014] Secondly, embodiments of this application provide a method for identifying and issuing early warnings for gas pipelines, the method comprising: Collect physical and visual data about the area surrounding the gas pipeline; The physical state data is preprocessed to obtain effective physical state data. A lightweight convolutional neural network model is then used to perform target recognition on the visual data. The recognized objects include engineering machinery and its behavioral intentions, as well as personnel behavior. The LSTM deep learning model is invoked to perform trend analysis on the effective physical state data; the multimodal fusion deep learning model is invoked to perform spatiotemporal alignment and correlation on the effective physical state data and target recognition results; the output results of the LSTM deep learning model and the multimodal fusion deep learning model are used to drive the gas pipeline digital twin model to be updated and risk visualization and positioning. Generate early warning information; send the early warning information to the terminal device via at least one of push notification, SMS, or voice.
[0015] In one possible implementation, the physical state data includes the pressure value of the gas pipeline, the flow rate value of the gas pipeline, the gas concentration value of the gas pipeline, the vibration signal around the gas pipeline, and the sound signal around the gas pipeline. The method further includes: Based on a preset threshold, the valid physical state data is judged, a preliminary alarm event is generated, and the preliminary alarm event is sent to multiple terminal devices.
[0016] In one possible implementation, the step of invoking an LSTM deep learning model to perform trend analysis on the effective physical state data, and invoking a multimodal fusion deep learning model to perform spatiotemporal alignment and correlation between the effective physical state data and the target recognition results, includes: Acoustic events are detected based on vibration and sound signals around the gas pipeline. The LSTM deep learning model is used to analyze whether there are any abnormal trends in the pressure, flow rate, and gas concentration values of the gas pipeline. A multimodal fusion deep learning model is invoked to perform spatiotemporal alignment and correlation of acoustic events, trend information, and target recognition results.
[0017] In one possible implementation, the step of using the outputs of an LSTM deep learning model and a multimodal fusion deep learning model to drive the updating and risk visualization localization of the gas pipeline digital twin model includes: Obtain the event type and location output by the multimodal fusion deep learning model, call the 3D GIS engine, and highlight and alarm the corresponding location on the digital twin model of the gas pipeline; The method further includes: The outputs of the LSTM deep learning model and the multimodal fusion deep learning model are weighted for risk level assessment, generating a structured early warning report that includes risk level, digital twin scenario of gas pipeline, and disposal recommendations.
[0018] Thirdly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned early warning identification method for any of the gas pipelines.
[0019] This application provides a gas pipeline identification and early warning system, comprising: a multi-source sensor terminal module for collecting physical state data and visual data around the gas pipeline; an edge intelligent processing gateway module for receiving the physical state data and visual data collected by the multi-source sensor terminal module, preprocessing the physical state data to obtain effective physical state data, and performing target recognition on the visual data using a lightweight convolutional neural network model, the recognized objects including construction machinery and its behavioral intentions and personnel behavior; a cloud platform intelligent analysis center module for calling an LSTM deep learning model to perform trend analysis on the effective physical state data; calling a multimodal fusion deep learning model to perform spatiotemporal alignment and correlation on the effective physical state data and target recognition results; using the output results of the LSTM deep learning model and the multimodal fusion deep learning model to drive the gas pipeline digital twin model to be updated and risk visualization and positioning; and an early warning response and human-computer interaction module for receiving the analysis results and risk positioning of the cloud platform intelligent analysis center module, generating early warning information; and sending the early warning information to the terminal device through at least one of message push, SMS, and voice. This system achieves proactive early warning, intelligent identification, and precise positioning. Attached Figure Description
[0020] Figure 1 A schematic diagram of a gas pipeline early warning and identification system provided in an embodiment of the present invention; Figure 2 A schematic diagram of the first result of applying the gas pipeline identification and early warning system provided in the embodiments of the present invention; Figure 3 This is a schematic diagram illustrating a second result of applying the gas pipeline identification and early warning system provided in the embodiments of the present invention. Detailed Implementation
[0021] The present invention will be described in detail below through embodiments.
[0022] As the "lifeline" of a city, the safe and stable operation of gas pipelines is directly related to public safety, people's lives and property, and the normal order of the city. However, because gas pipelines are usually buried deep underground and laid in complex environments (crossing roads, buildings, rivers, etc.), they are subject to multiple factors such as soil corrosion, damage from third-party construction, geological subsidence, and material aging. They are prone to safety accidents such as leaks and ruptures, and their risks are characterized by concealment, suddenness, and catastrophicness.
[0023] Currently, the industry mainly relies on the following five technical solutions, but all of them have obvious limitations: The first method is traditional manual inspection, where inspectors periodically patrol the route, checking using visual inspection and handheld leak detectors. This method is inefficient, has limited coverage, cannot provide 24 / 7 real-time monitoring, and is highly dependent on personnel experience and responsibility, resulting in a significant delay in response to sudden damage.
[0024] The second type is a data acquisition and monitoring control system, which indirectly determines leakage by installing pressure and flow sensors at key nodes to monitor data anomalies. This method only has point-based sensing capabilities, resulting in huge pipeline blind spots, inability to accurately locate leak points, high false alarm rates, and susceptibility to interference from normal operating condition adjustments.
[0025] The third type is distributed fiber optic sensing technology, which uses laid optical cables to sense vibration and temperature changes along the line to achieve continuous monitoring. This method is expensive, complex to deploy, and extremely sensitive to environmental noise (such as traffic and wind and rain); the algorithm is complex, making it difficult to accurately identify real threats, and the risk of false alarms and missed alarms is high.
[0026] The fourth method is conventional video surveillance, which involves installing cameras in high-risk areas and having personnel monitor the footage from a control center. This method is essentially "screen inspection," which is labor-intensive and prone to oversights due to staff fatigue, thus failing to achieve automated and intelligent early warning systems.
[0027] The fifth type is a monitoring system based on the fusion of video surveillance and fiber optic vibration detection. After a vibration sensor detects a potential threat (such as excavator vibration), it automatically triggers nearby cameras to capture footage of the scene and pushes it to the monitoring center for final manual confirmation. This method has low intelligence, heavily relies on manual intervention, and the system cannot automatically identify the video content (what equipment is being used and what it is doing), still requiring manual judgment. It is slow to respond, costly, and lacks standardized procedures. It is merely a simple hard correlation of "vibration alarm → video linkage," failing to deeply cross-validate the vibration pattern with the video recognition results. Furthermore, vibration sensors are susceptible to interference, generating numerous false alarms. Frequent false alarms lead to a "boy who cried wolf" effect, causing monitoring personnel to become complacent and potentially miss real dangers. This method is essentially at the "post-event alarm" or "in-event alarm" stage, unable to predict high-risk areas and time periods in advance.
[0028] In summary, existing technologies cannot meet the urgent needs of gas pipeline safety for all-weather, automated, high-precision, and intelligent systems. There is an urgent need in this field for a new intelligent identification and early warning system that can overcome the aforementioned shortcomings and achieve proactive early warning, intelligent identification, precise positioning, and closed-loop management.
[0029] Firstly, see [the following] Figure 1 This application provides a gas pipeline identification and early warning system, the system comprising: The multi-source sensor terminal module 101 is used to collect physical and visual data around the gas pipeline.
[0030] Physical condition data includes the pressure value of the gas pipeline, the flow rate of the gas pipeline, the gas concentration value of the gas pipeline, the vibration signal around the gas pipeline, and the sound signal around the gas pipeline.
[0031] The multi-source sensing terminal module consists of a physical state sensing unit, a visual acquisition unit, and a data aggregation unit. This module has embedded firmware responsible for driving the sensors, configuring the acquisition frequency, performing analog-to-digital conversion, and data packetization. The physical state sensing unit comprises an array of pressure sensors, flow sensors, gas concentration sensors, vibration sensors, and acoustic sensors deployed along the pipeline. These sensors are connected to the data aggregation unit via wired or wireless means. The visual acquisition unit consists of high-definition network cameras deployed in key areas, providing night vision and fog penetration capabilities. The data aggregation unit consists of an embedded data acquisition unit responsible for aggregating data from the aforementioned sensing units and includes an integrated IoT communication module (such as an NB-IoT / 4G / 5G DTU) to establish a communication connection with the edge intelligent processing gateway module.
[0032] The edge intelligent processing gateway module 102 is used to receive physical state data and visual data collected by the multi-source sensing terminal module, perform data preprocessing on the physical state data to obtain effective physical state data, and perform target recognition on the visual data through a lightweight convolutional neural network model. The recognition objects include engineering machinery and its behavioral intentions and personnel behavior.
[0033] Data preprocessing includes filtering, noise reduction, and standardization. Construction machinery includes excavators, loaders, drilling equipment, etc. The behavioral intentions of construction machinery include stationary, transit, and operational activities, while personnel behaviors include surveying or marking.
[0034] The target recognition results can be: {Object: Excavator, Confidence: 0.95, Location: [x1, y1, x2, y2], Behavior: Excavation, Timestamp: T1}; {Object: Personnel, Confidence: 0.90, Behavior: Surveying, Timestamp: T1}; {Object: Truck, Confidence: 0.98, Behavior: Passing, Timestamp: T1}.
[0035] In one example, the edge intelligent processing gateway module is further configured to judge the valid physical state data according to a preset threshold, generate a preliminary alarm event, and send the preliminary alarm event to the early warning response and human-machine interaction module, so that the early warning response and human-machine interaction module sends the preliminary alarm event to multiple terminal devices.
[0036] The core of the edge intelligent processing gateway module is an edge computing gateway that adopts an ARM architecture or a low-power x86 architecture with certain computing power, and has a built-in AI acceleration chip, such as an NPU. The edge intelligent processing gateway module has multiple interfaces (such as RJ45, RS485, LoRaWAN) to connect to multi-source sensor terminal modules, and also has uplink interfaces such as 5G / fiber to connect to the cloud platform intelligent analysis center module.
[0037] The multi-source sensing terminal module includes a data preprocessing submodule, a lightweight AI inference engine submodule, and an edge rule engine submodule.
[0038] The data preprocessing submodule filters, denoises, and standardizes sensor data. The lightweight AI inference engine submodule embeds a lightweight convolutional neural network model for real-time analysis of video streams, identifying construction machinery, its behavioral intentions, and human behavior. The edge rule engine submodule enables rapid response to sudden sensor anomalies (such as instantaneous concentration exceeding limits) by configuring simple threshold rules.
[0039] Once started, the software program on the edge intelligent processing gateway module continuously utilizes the gateway's CPU and NPU resources to perform data reception, computation, and forwarding tasks. Its output is sent to the cloud platform intelligent analysis center module via the network interface, reducing the data processing pressure and network bandwidth usage of the cloud platform intelligent analysis center module.
[0040] The cloud platform intelligent analysis center module 103 is used to call the LSTM deep learning model to perform trend analysis on the effective physical state data; call the multimodal fusion deep learning model to perform spatiotemporal alignment and correlation on the effective physical state data and target recognition results; and use the output results of the LSTM deep learning model and the multimodal fusion deep learning model to drive the gas pipeline digital twin model to be updated and risk visualization and positioning.
[0041] The above-mentioned method involves calling an LSTM deep learning model to perform trend analysis on the effective physical state data; and calling a multimodal fusion deep learning model to perform spatiotemporal alignment and correlation between the effective physical state data and the target recognition results, including: Acoustic events are detected based on vibration and sound signals around the gas pipeline. The LSTM deep learning model is used to analyze whether there are any abnormal trends in the pressure, flow rate, and gas concentration values of the gas pipeline. A multimodal fusion deep learning model is invoked to perform spatiotemporal alignment and correlation of acoustic events, trend information, and target recognition results.
[0042] Vibration / sound signals are converted into frequency domain graphs (spectrums) using signal processing algorithms and deep learning models specifically designed for time-series data (such as 1D-CNN or LSTM), and their patterns are analyzed. The output results can be: {Type: Heavy machinery impact, confidence level: 0.88, dominant frequency: 85Hz, intensity: 0.7, timestamp: T1}; {Type: Hydraulic pump noise, confidence level: 0.93, timestamp: T1}.
[0043] The LSTM deep learning model is used to analyze whether there are abnormal trends in the pressure, flow rate, and gas concentration values of the gas pipeline within an event window. The output can be: {Parameters: Pressure, Abnormal Trend: Slow Decrease, Trend Confidence: 0.75, Time Window: [T0, T1]}.
[0044] To perform trend analysis, the LSTM model is first trained using normal data from the past few months or even years. The model learns what the data, such as pressure and flow rate, should look like under normal conditions, and what their periodicity should be (e.g., diurnal fluctuations, peak gas consumption periods). Then, based on recent data, the model predicts the normal range for the data at the next point in time. The system continuously compares the model's predictions with the actual measurements from the sensors. If the actual values begin to show a "systematic deviation"—that is, consistently, even slightly, below or above the model's predicted range—the LSTM network identifies this "trend anomaly" and assigns an anomaly probability or score.
[0045] Specific application scenarios in real-world systems include: slow pipeline leakage / corrosion perforation, as shown in the example above, where internal wall corrosion or micro-holes cause a slow pressure drop. Equipment performance degradation, for example, the performance of the pressure regulator in a pressure regulating station begins to decline, leading to a gradual increase in downstream pressure fluctuations (the trend is: decreased stability). Sensor drift, where a pressure sensor itself drifts, causing its readings to slowly and continuously deviate from the theoretical values calculated from the readings of other related sensors.
[0046] The core value of analyzing long-term trend anomalies in sensor data lies in shifting the security defense line "forward." It transforms the system from a mere "hindsight" alarm into a "predictive" early warning system. It can uncover slowly evolving risks hidden beneath the data deluge, providing maintenance personnel with ample response time to resolve small problems before they escalate into major incidents, truly achieving a leap from "passive response" to "proactive protection."
[0047] Finally, a multimodal fusion deep learning model is invoked to perform spatiotemporal alignment and correlation of acoustic events, trend information, and target recognition results. In other words, it aims to ensure that acoustic events, trend information, and target recognition results all point to a preset time period and a preset spatial domain. Specifically, the multimodal fusion deep learning model can be executed as follows: based on vibration (heavy machinery impact), video (excavator identified), sound waves (hydraulic pump noise), and pressure (no significant trend), it determines that the excavator is operating, is in the early stages of operation, and has not yet damaged the pipeline; based on vibration (heavy machinery impact), video (truck identified), sound (vehicle engine sound), and pressure (no trend), it determines that the truck passed over a bumpy road, constituting a traffic disturbance; based on vibration (none), video (no abnormalities), sound (ultrasonic signal), pressure (slowly decreasing trend), and gas concentration (slightly increasing), it outputs a suspected slow leak.
[0048] The above-mentioned use of the output results of the LSTM deep learning model and the multimodal fusion deep learning model to drive the updating and risk visualization and localization of the gas pipeline digital twin model includes: Obtain the event type and location output by the multimodal fusion deep learning model, call the 3D GIS engine, and highlight and alarm the corresponding location on the digital twin model of the gas pipeline; The three examples above correspond to high-risk third-party construction, traffic interference, and suspected slow leaks, respectively. For traffic interference, the warning is filtered out and not reported. For high-risk third-party construction and suspected slow leaks, the incident location can be obtained (calculated during spatiotemporal alignment, so it can be directly obtained). The system calls a 3D GIS engine in the background and quickly finds the corresponding coordinates in its maintained digital twin model of the gas pipeline (a 3D virtual pipeline corresponding one-to-one with the physical world) based on the received incident location. At the found coordinates, the system generates a corresponding visual icon based on the incident type. A dynamic "excavator" icon represents third-party construction, a "flashing flame" icon represents a gas leak, and an "exclamation mark" represents a general warning. If the target is moving (such as an excavator traveling along the pipeline), and the system can continuously obtain its incident location, then this icon will move in real time on the digital twin model, allowing for location tracking.
[0049] The system will also integrate more information to enhance visualization, giving managers a comprehensive understanding of risks. For example, clicking a risk icon will immediately display pipeline information below that location, such as pipe diameter, material, burial depth, transport pressure, and the area of the pipeline network it belongs to. The map itself provides information about the surrounding environment, such as roads, buildings, rivers, schools, and hospitals, facilitating the assessment of the potential consequences of an accident. See [link / reference]. Figure 2 and Figure 3 The image shows a schematic diagram illustrating the results of applying the gas pipeline identification and early warning system provided in this application. It can be seen that suspected excavators are highlighted and alerted in the image because the map includes detailed information about the surrounding environment. Figure 2 and Figure 3 You can clearly understand the potential consequences of third-party construction at a specific location. Clicking the icon will also allow you to directly access live footage from the nearest camera, providing a direct view of the situation. You can also view historical sensor data curves for that location to check for any abnormalities in pressure, vibration, or other parameters.
[0050] In one example, the cloud platform intelligent analysis center module is also used to perform risk level weighted assessment on the output results of the LSTM deep learning model and the multimodal fusion deep learning model, and generate a structured early warning report including risk level, digital twin scenario of gas pipeline and disposal suggestions.
[0051] Recommended actions include: immediately notifying the nearest inspector to verify the situation; if construction is confirmed, inquiring whether the construction permit is valid, issuing a safety warning to the construction company, and supervising the site.
[0052] The core of the cloud platform intelligent analysis center module is a high-performance server cluster (including computing servers, GPU servers, and database servers) deployed in the data center.
[0053] The cloud platform intelligent analysis center module includes a data access and management submodule, a multimodal AI analysis engine submodule, a digital twin and GIS service submodule, and a risk decision-making and early warning generation submodule.
[0054] The data access and management submodule is responsible for receiving and caching data from the edge intelligent processing gateway module, classifying and storing it in a time-series database (for sensor data) and a relational database (for events and metadata). The multimodal AI analysis engine submodule calls an LSTM deep learning model to analyze long-term trend anomalies in sensor data; it runs a multimodal fusion deep learning model to comprehensively judge multi-source information such as video, vibration, and sound waves to achieve accurate identification. The digital twin and GIS service submodule, based on a 3D GIS engine, builds and maintains a digital twin model of gas pipelines; this submodule receives the output results of the AI analysis engine, drives the 3D model update, and realizes visualized risk location. The risk decision-making and early warning generation submodule has a built-in expert knowledge base and rule base, receives AI analysis results and GIS location information, performs weighted risk level assessment, and automatically generates structured early warning reports.
[0055] The early warning response and human-computer interaction module 104 is used to receive the analysis results and risk positioning of the cloud platform intelligent analysis center module, generate early warning information, and send the early warning information to the terminal device through at least one of message push, SMS, and voice.
[0056] The early warning response and human-computer interaction module includes an early warning distribution submodule, a human-computer interaction submodule, and a feedback learning submodule.
[0057] The early warning distribution submodule pushes early warning information through multiple channels, including message push services, SMS gateways, and voice gateways. The human-computer interaction submodule provides users with a visual interface in the form of a web application and mobile app, displaying alarm lists, digital twin scenarios, handling process guidance, and feedback entry points. The feedback learning submodule receives user feedback and sends this labeled data back to the database of the cloud platform's intelligent analysis center module for incremental model learning.
[0058] The early warning response and human-computer interaction module drives devices such as mobile phones and large screens to emit physical prompts such as sound, light, and vibration to attract people's attention and record their interactive operations, thus completing the closed loop.
[0059] The gas pipeline identification and early warning system provided by this invention has the following technical effects.
[0060] 1. Resource optimization has been achieved: The "end-edge-cloud" collaborative architecture rationally allocates computing tasks, reduces cloud load and network transmission pressure, and improves the overall efficiency of the system.
[0061] 2. Improved system reliability: The edge gateway has independent processing capabilities, and can still perform local analysis and alarms even in the event of a short network interruption, ensuring system availability.
[0062] 3. Enhanced system scalability: The modular design allows each functional module to be upgraded and expanded independently (such as adding new sensor types or replacing advanced AI models) without affecting the overall system operation. 4. A self-optimizing closed loop has been formed: Through feedback from the early warning response and human-computer interaction modules, the system can continuously optimize the AI model, so that the early warning accuracy of the entire system can be continuously improved over time, and it has the ability to learn and evolve on its own.
[0063] Compared with the prior art, the present invention achieves improvements in the following aspects through the above technical solution: 1. A breakthrough transformation from "delayed alarm" to "proactive early warning" has been achieved. This invention employs time-series analysis models such as Long Short-Term Memory (LSTM) networks to identify weak, slow-moving trend patterns in sensor data such as pressure and flow. Traditional threshold alarms can only be triggered after data exceeds a certain limit, representing a reactive response; however, the AI model of this invention can capture abnormal signs before exceeding the limit, directly leading to a significant advancement in warning time and providing a valuable window for emergency response.
[0064] 2. Significantly reduced the system's false alarm rate and improved alarm reliability. Traditional single sensors are susceptible to environmental interference. This invention utilizes multi-source information fusion technology to perform spatiotemporal alignment and correlation analysis on various data sources, such as vibration, sound waves, and video recognition results. For example, a vibration alarm might only originate from a passing vehicle, but the multiple chain of evidence—"vibration alarm + video recognition of an excavator + sound wave recognition of specific mechanical noise"—inevitably generates a high degree of certainty regarding the risk of "third-party construction," thus effectively filtering out most false alarms caused by environmental interference.
[0065] 3. It has achieved comprehensive and multi-dimensional accurate perception and assessment of risks. Causal principle: This solution combines two major technological approaches: physical sensing and machine vision, overcoming the limitations of traditional systems that rely on a single perception dimension. By mapping AI recognition results onto a GIS digital twin model, the geographical location of the risk point, its surrounding environment, real-time on-site images, and historical data curves are presented in an integrated manner. This combination of technological features directly leads to more comprehensive, objective, and accurate risk assessment conclusions, enabling managers to quickly understand the nature and severity of the risk.
[0066] In summary, the beneficial effects of this invention are directly or inevitably caused by its unique technical features. These effects are interconnected and mutually supportive, together forming a highly efficient, reliable, and intelligent gas pipeline safety protection system.
[0067] Secondly, embodiments of this application provide a method for identifying and issuing early warnings for gas pipelines, the method comprising: Collect physical and visual data about the area surrounding the gas pipeline; The physical state data is preprocessed to obtain effective physical state data. A lightweight convolutional neural network model is then used to perform target recognition on the visual data. The recognized objects include engineering machinery and its behavioral intentions, as well as personnel behavior. The LSTM deep learning model is invoked to perform trend analysis on the effective physical state data; the multimodal fusion deep learning model is invoked to perform spatiotemporal alignment and correlation on the effective physical state data and target recognition results; the output results of the LSTM deep learning model and the multimodal fusion deep learning model are used to drive the gas pipeline digital twin model to be updated and risk visualization and positioning. Generate early warning information; send the early warning information to the terminal device via at least one of push notification, SMS, or voice.
[0068] In one possible implementation, the physical state data includes the pressure value of the gas pipeline, the flow rate value of the gas pipeline, the gas concentration value of the gas pipeline, the vibration signal around the gas pipeline, and the sound signal around the gas pipeline. The method further includes: Based on a preset threshold, the valid physical state data is judged, a preliminary alarm event is generated, and the preliminary alarm event is sent to multiple terminal devices.
[0069] In one possible implementation, the step of invoking an LSTM deep learning model to perform trend analysis on the effective physical state data, and invoking a multimodal fusion deep learning model to perform spatiotemporal alignment and correlation between the effective physical state data and the target recognition results, includes: Acoustic events are detected based on vibration and sound signals around the gas pipeline. The LSTM deep learning model is used to analyze whether there are any abnormal trends in the pressure, flow rate, and gas concentration values of the gas pipeline. A multimodal fusion deep learning model is invoked to perform spatiotemporal alignment and correlation of acoustic events, trend information, and target recognition results.
[0070] In one possible implementation, the step of using the outputs of an LSTM deep learning model and a multimodal fusion deep learning model to drive the updating and risk visualization localization of the gas pipeline digital twin model includes: Obtain the event type and location output by the multimodal fusion deep learning model, call the 3D GIS engine, and highlight and alarm the corresponding location on the digital twin model of the gas pipeline; The method further includes: The outputs of the LSTM deep learning model and the multimodal fusion deep learning model are weighted for risk level assessment, generating a structured early warning report that includes risk level, digital twin scenario of gas pipeline, and disposal recommendations.
[0071] Thirdly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the identification and early warning method for any of the above-mentioned gas pipelines.
[0072] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).
[0073] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0074] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the method embodiments are basically similar to the system embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the system embodiments.
[0075] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.
Claims
1. A gas pipeline identification and early warning system, characterized in that, The system includes: Multi-source sensor terminal module is used to collect physical and visual data around gas pipelines; The edge intelligent processing gateway module is used to receive physical state data and visual data collected by the multi-source sensing terminal module, perform data preprocessing on the physical state data to obtain effective physical state data, and perform target recognition on the visual data through a lightweight convolutional neural network model. The recognition objects include construction machinery and its behavioral intentions and personnel behavior. The cloud platform intelligent analysis center module is used to call the LSTM deep learning model to perform trend analysis on the effective physical state data; call the multimodal fusion deep learning model to perform spatiotemporal alignment and correlation on the effective physical state data and target recognition results; and use the output results of the LSTM deep learning model and the multimodal fusion deep learning model to drive the gas pipeline digital twin model to be updated and risk visualization and positioning. The early warning response and human-computer interaction module is used to receive the analysis results and risk positioning of the cloud platform intelligent analysis center module, generate early warning information, and send the early warning information to the terminal device through at least one of message push, SMS, and voice.
2. The system according to claim 1, characterized in that, The physical state data includes the pressure value of the gas pipeline, the flow rate of the gas pipeline, the gas concentration value of the gas pipeline, the vibration signal around the gas pipeline, and the sound signal around the gas pipeline. The edge intelligent processing gateway module is also used to judge the valid physical state data according to a preset threshold, generate a preliminary alarm event, and send the preliminary alarm event to the early warning response and human-machine interaction module so that the early warning response and human-machine interaction module sends the preliminary alarm event to multiple terminal devices.
3. The system according to claim 2, characterized in that, The step of calling an LSTM deep learning model to perform trend analysis on the effective physical state data, and calling a multimodal fusion deep learning model to perform spatiotemporal alignment and correlation between the effective physical state data and the target recognition results, includes: Acoustic events are detected based on vibration and sound signals around the gas pipeline. The LSTM deep learning model is used to analyze whether there are any abnormal trends in the pressure, flow rate, and gas concentration values of the gas pipeline. A multimodal fusion deep learning model is invoked to perform spatiotemporal alignment and correlation of acoustic events, trend information, and target recognition results.
4. The system according to claim 1, characterized in that, The process of using the outputs of LSTM deep learning models and multimodal fusion deep learning models to drive the updating and risk visualization of the digital twin model of gas pipelines includes: Obtain the event type and location output by the multimodal fusion deep learning model, call the 3D GIS engine, and highlight and alarm the corresponding location on the digital twin model of the gas pipeline; The cloud platform's intelligent analysis center module is also used to perform risk level weighted assessments on the output results of the LSTM deep learning model and the multimodal fusion deep learning model, generating a structured early warning report that includes the risk level, a digital twin scenario of the gas pipeline, and disposal recommendations.
5. The system according to claim 4, characterized in that, The early warning response and human-computer interaction module is also used to call the message push service to send the structured early warning report to the monitoring center's large screen display system or the mobile devices of maintenance personnel, and to display the report list, handling process guidance and handling feedback entry in a visual manner on multiple terminals; to receive user handling results and feed them back to the cloud platform intelligent analysis center module for incremental learning of the LSTM deep learning model and the multimodal fusion deep learning model.
6. A method for identifying and issuing early warnings for gas pipelines, characterized in that, The method includes: Collect physical and visual data about the area surrounding the gas pipeline; The physical state data is preprocessed to obtain effective physical state data. A lightweight convolutional neural network model is then used to perform target recognition on the visual data. The recognized objects include engineering machinery and its behavioral intentions, as well as personnel behavior. The LSTM deep learning model is invoked to perform trend analysis on the effective physical state data; the multimodal fusion deep learning model is invoked to perform spatiotemporal alignment and correlation on the effective physical state data and target recognition results; the output results of the LSTM deep learning model and the multimodal fusion deep learning model are used to drive the gas pipeline digital twin model to be updated and risk visualization and positioning. Generate early warning information; send the early warning information to the terminal device via at least one of push notification, SMS, or voice.
7. The method according to claim 6, characterized in that, The physical state data includes the pressure value of the gas pipeline, the flow rate of the gas pipeline, the gas concentration value of the gas pipeline, the vibration signal around the gas pipeline, and the sound signal around the gas pipeline. The method further includes: Based on a preset threshold, the valid physical state data is judged, a preliminary alarm event is generated, and the preliminary alarm event is sent to multiple terminal devices.
8. The method according to claim 7, characterized in that, The step of calling an LSTM deep learning model to perform trend analysis on the effective physical state data, and calling a multimodal fusion deep learning model to perform spatiotemporal alignment and correlation between the effective physical state data and the target recognition results, includes: Acoustic events are detected based on vibration and sound signals around the gas pipeline. The LSTM deep learning model is used to analyze whether there are any abnormal trends in the pressure, flow rate, and gas concentration values of the gas pipeline. A multimodal fusion deep learning model is invoked to perform spatiotemporal alignment and correlation of acoustic events, trend information, and target recognition results.
9. The method according to claim 6, characterized in that, The process of using the outputs of LSTM deep learning models and multimodal fusion deep learning models to drive the updating and risk visualization of the digital twin model of gas pipelines includes: Obtain the event type and location output by the multimodal fusion deep learning model, call the 3D GIS engine, and highlight and alarm the corresponding location on the digital twin model of the gas pipeline; The method further includes: The outputs of the LSTM deep learning model and the multimodal fusion deep learning model are weighted for risk level assessment, generating a structured early warning report that includes risk level, digital twin scenario of gas pipeline, and disposal recommendations.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the early warning identification method for any of the gas pipelines described in claims 6-9.