Pipe network gas leakage detection method and system based on multi-modal sensing

Abstract

The invention discloses a pipe network gas leakage detection method and system based on multi-modal sensing, and relates to the technical field of hydrogen energy leakage detection.The method comprises the steps that leakage detection coverage analysis is conducted on a hydrogen energy transmission pipe network, and a leakage detection site is positioned; performing sensor configuration to obtain a multi-mode sensing array, and pre-constructing a hydrogen energy leakage identification module; when the hydrogen energy leakage identification module receives and analyzes the hydrogen concentration change sequence, a hydrogen energy leakage risk node is identified and positioned; positioning an associated sensing node, and receiving environment verification sensing data; and carrying out environment change correlation analysis on the N environment verification sensing data, and outputting a hydrogen energy leakage detection result. According to the invention, the technical problems of inaccurate hydrogen energy leakage point detection and low detection efficiency and speed in the prior art are solved, and the technical effects of accurately positioning the hydrogen energy leakage risk node and the final leakage target node and improving the detection efficiency and speed are achieved.

Description

Technical Field

[0001] This invention relates to the field of hydrogen leak detection technology, specifically to a method and system for detecting gas leaks in pipeline networks based on multimodal sensing. Background Technology

[0002] Against the backdrop of today's energy transition, hydrogen energy, as a clean and efficient energy source, is receiving increasing attention and application. With the continuous development of the hydrogen energy industry, the construction of hydrogen transmission pipeline networks is also expanding rapidly. However, due to the flammable and explosive nature of hydrogen, leaks in hydrogen transmission pipelines pose significant safety risks. Traditional hydrogen leak detection methods often have many limitations. On the one hand, some detection methods lack precision and struggle to accurately detect even minor leaks; on the other hand, some methods have slow response times, failing to detect problems in their early stages, thus increasing the likelihood of accidents. Furthermore, environmental factors significantly interfere with traditional detection methods, leading to false alarms or missed detections. For example, changes in temperature, humidity, and pressure can affect sensor performance, resulting in inaccurate detection results. Simultaneously, as the scale and complexity of hydrogen transmission pipeline networks continue to expand, the requirements for leak detection are becoming increasingly stringent. It is not only necessary to quickly and accurately locate the leak, but also to comprehensively analyze and assess the leak situation in order to take timely and effective countermeasures.

[0003] Existing technologies suffer from inaccurate detection of hydrogen leak locations, as well as low detection efficiency and speed. Summary of the Invention

[0004] This application provides a method and system for detecting gas leaks in pipeline networks based on multimodal sensing, which is intended to address the technical problems of inaccurate detection of hydrogen leak points, low detection efficiency and speed in the prior art.

[0005] In view of the above problems, this application provides a method and system for detecting gas leaks in pipeline networks based on multimodal sensing.

[0006] The first aspect of this application provides a method for detecting gas leaks in pipeline networks based on multimodal sensing, the method comprising: A leak detection coverage analysis is performed on the hydrogen energy transmission pipeline network to locate M leak detection sites, where M is a positive integer. Sensors are configured according to the transmission environment of the hydrogen energy transmission pipeline network to obtain a multimodal sensor, wherein the multimodal sensor integrates a hydrogen sensor and an environmental verification sensing unit. Sensors are configured at the M leak detection sites to obtain a multimodal sensor array, wherein the multimodal sensor array includes M multimodal sensing nodes, and each of the M multimodal sensing nodes is equipped with the multimodal sensor. A pre-constructed hydrogen leak identification module is built, wherein the hydrogen leak identification module is connected to M primary sensors among the M multimodal sensing nodes via low-power wireless transmission technology. A pre-constructed hydrogen leak verification module is also built, wherein the hydrogen... The hydrogen leak detection module connects to M environmental verification sensing units among M multimodal sensing nodes via low-power wireless transmission technology. When the hydrogen leak identification module receives and analyzes the M hydrogen concentration change sequences transmitted back by the M primary sensors, identifies and locates hydrogen leak risk nodes, and then sends the hydrogen leak risk nodes to the hydrogen leak detection module, the hydrogen leak detection module locates N associated sensing nodes in the hydrogen transmission pipeline network based on the hydrogen leak risk nodes, and receives N environmental verification sensing data stored locally by the N associated sensing nodes. The hydrogen leak detection module performs environmental change correlation analysis on the N environmental verification sensing data, verifies the hydrogen leak risk nodes based on the analysis results, and outputs hydrogen leak detection results.

[0007] A second aspect of this application provides a pipeline gas leak detection system based on multimodal sensing, the system comprising: The system comprises the following modules: a leak detection site localization module for performing leak detection coverage analysis on the hydrogen transmission pipeline network to locate M leak detection sites (M being a positive integer); a multimodal sensor acquisition module for configuring sensors based on the transmission environment of the hydrogen transmission pipeline network to obtain multimodal sensors, wherein the multimodal sensors integrate a hydrogen sensor and an environmental verification sensing unit; a multimodal sensor array acquisition module for configuring sensors at the M leak detection sites to obtain a multimodal sensor array, wherein the multimodal sensor array includes M multimodal sensor nodes, each of which is equipped with the multimodal sensor; a pre-built leak identification module for pre-building a hydrogen leak identification module, wherein the hydrogen leak identification module is connected to M primary sensors among the M multimodal sensor nodes via low-power wireless transmission technology; and a pre-built leak verification module for... A pre-built hydrogen leakage verification module is included, wherein the hydrogen leakage verification module is connected to M environmental verification sensing units among M multimodal sensing nodes via low-power wireless transmission technology; a hydrogen concentration change sequence acquisition module is used to, after the hydrogen leakage identification module receives and analyzes the M hydrogen concentration change sequences returned by the M primary sensors, identifies and locates hydrogen leakage risk nodes, and then sends the hydrogen leakage risk nodes to the hydrogen leakage verification module; an environmental verification sensing data acquisition module is used by the hydrogen leakage verification module to locate N associated sensing nodes in the hydrogen transmission pipeline network based on the hydrogen leakage risk nodes, and receive N environmental verification sensing data stored locally by the N associated sensing nodes; a hydrogen leakage detection result acquisition module is used to perform environmental change correlation analysis on the N environmental verification sensing data through the hydrogen leakage verification module, verify the hydrogen leakage risk nodes based on the analysis results, and output hydrogen leakage detection results.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: A leak detection coverage analysis is performed on the hydrogen transmission pipeline network to locate M leak detection sites. Sensors are configured according to the transmission environment of the pipeline network to obtain multimodal sensors. Sensors are then configured at the M leak detection sites to obtain a multimodal sensor array. A hydrogen leak identification module and a hydrogen leak verification module are pre-built. When the hydrogen leak identification module receives and analyzes the M hydrogen concentration change sequences returned by the M primary sensors, identifies and locates hydrogen leak risk nodes, and then sends these risk nodes to the hydrogen leak verification module. The hydrogen leak verification module locates N associated sensor nodes in the hydrogen transmission pipeline network based on the risk nodes and receives N environmental verification sensor data stored locally by these N associated sensor nodes. The hydrogen leak verification module performs environmental change correlation analysis on the N environmental verification sensor data and verifies the hydrogen leak risk nodes based on the analysis results, outputting the hydrogen leak detection result. This achieves the technical effect of accurately locating hydrogen leak risk nodes and the final leak target node, improving detection efficiency and speed. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A schematic flowchart of a pipeline gas leak detection method based on multimodal sensing provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a pipeline gas leak detection system based on multimodal sensing provided in an embodiment of this application.

[0011] Figure labeling: Leak detection site localization module 10, multimodal sensor acquisition module 20, multimodal sensor array acquisition module 30, pre-built leak identification module 40, pre-built leak verification module 50, hydrogen concentration change sequence acquisition module 60, environmental verification sensor data acquisition module 70, hydrogen energy leak detection result acquisition module 80. Detailed Implementation

[0012] This application provides a method and system for detecting gas leaks in pipeline networks based on multimodal sensing, which addresses the technical problems of inaccurate detection of hydrogen leak points, low detection efficiency and speed in existing technologies.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] Example 1, as Figure 1 As shown, this application provides a method for detecting gas leaks in pipeline networks based on multimodal sensing, the method comprising: Step S100: Perform a leak detection coverage analysis on the hydrogen energy transmission pipeline network to locate M leak detection sites, where M is a positive integer.

[0015] Specifically, firstly, the pipeline layout plan of the hydrogen energy transmission network needs to be obtained interactively. Based on this network layout plan, a 3D modeling operation is performed to obtain a transmission network model that accurately reflects the actual structure and spatial relationships of the network. Simultaneously, historical leakage records need to be acquired, including H historical leakage locations and their corresponding H historical leakage time-series data. These historical leakage locations are fitted onto the constructed transmission network model. Then, based on the flow direction characteristics of hydrogen in the hydrogen energy transmission network and the H historical leakage time-series data, two or more leakage locations located on the same pipeline component (such as the same pipe) are merged based on flow direction, retaining only locations with rapidly changing leakage timelines. After processing, W historical leakage locations and W historical leakage time-series data are obtained, where W is a positive integer less than or equal to H. Next, detection scale constraints are obtained interactively again. Based on the detection scale constraints and the W historical leakage locations, a detection area selection operation is performed in the transmission network model, thereby determining M leakage detection areas. Finally, based on the M leak detection areas, the historical leak locations of W are selected as M local leak detection groups. Then, spatial centering is performed on these local leak detection groups to accurately locate the M leak detection sites. These sites will serve as key locations for subsequent sensor configuration and leak detection.

[0016] Step S200: Configure sensors according to the transmission environment of the hydrogen energy transmission pipeline network to obtain a multimodal sensor, wherein the multimodal sensor integrates a hydrogen sensor and an environmental verification sensing unit.

[0017] Specifically, firstly, the leakage velocity is calculated using W historical leakage time-series data, thus obtaining W velocity time-varying data, which reflect the dynamic characteristics of historical leakage events. Next, the maximum values ​​of the W velocity time-varying data are extracted, and the extraction results are used to iterate through a risk level table to determine W secondary concentration response ranges. Then, based on the mapping relationship between M local leakage detection groups and W historical leakage locations, the W secondary concentration response ranges are grouped. Based on this, hydrogen concentration response ranges are selected, thereby obtaining the key ranges for hydrogen concentration detection represented by the M primary concentration responses for different detection areas. Meanwhile, since hydrogen sensors may be affected by interference under certain environmental conditions, such as temperature fluctuations, humidity changes, or the presence of other gases, false alarms may occur. Therefore, environmental parameters such as the ambient temperature variation range, ambient humidity variation range, and leakage pressure variation range of the hydrogen energy transmission pipeline are interactively acquired. Environmental sensors are configured based on these environmental parameters to obtain an environmental verification sensing unit composed of temperature, pressure, and humidity sensors. Finally, by combining the M primary concentration response ranges and W secondary concentration response ranges obtained earlier, hydrogen sensors were configured to produce M primary sensors and W secondary sensors. Then, based on the M primary sensors and W secondary sensors, the environmental verification sensing unit was scheduled and configured, ultimately resulting in M ​​sets of multimodal sensors. These multimodal sensors integrate hydrogen sensors and environmental verification sensing units. Through the monitoring of environmental factors by the environmental verification sensing units, a distinction can be made between genuine hydrogen leaks and false alarms caused by environmental factors, thereby improving the accuracy of hydrogen leak detection.

[0018] Step S300: Configure sensors at the M leakage detection sites to obtain a multimodal sensing array, wherein the multimodal sensing array includes M multimodal sensing nodes, and each of the M multimodal sensing nodes is equipped with the multimodal sensor.

[0019] Specifically, sensors are first configured at M leak detection sites to construct a multimodal sensor array. The multimodal sensor array consists of M multimodal sensor nodes, each equipped with multiple multimodal sensors to detect different types of leak signals. The sensors can simultaneously sense multiple physical and chemical parameters, such as temperature, pressure, and gas concentration, ensuring comprehensive monitoring of the detection sites. Each multimodal sensor node at each detection site will continuously collect data to identify leak risks. When an abnormal signal is detected, a further response mechanism will be triggered. Through the efficient configuration and real-time monitoring of this multimodal sensor array, the leakage situation in the entire area can be fully covered, and leak risks can be responded to quickly, ensuring the safe operation of the system.

[0020] Step S400: Pre-build a hydrogen leak detection module, wherein the hydrogen leak detection module is connected to M primary sensors among M multimodal sensing nodes through low-power wireless transmission technology.

[0021] Specifically, the pre-built hydrogen leak detection module is designed to receive and process hydrogen concentration data from the sensing nodes. It establishes connections with M primary sensors among the M multimodal sensing nodes via low-power wireless transmission technology. This connection method ensures that hydrogen concentration information detected by the primary sensors can be transmitted to the hydrogen leak detection module in a timely and efficient manner. The application of low-power wireless transmission technology satisfies data transmission requirements while reducing energy consumption, making it more energy-efficient and environmentally friendly during long-term operation. After receiving the data, the hydrogen leak detection module analyzes and processes it based on its internal algorithms and logic, laying the foundation for subsequent identification of hydrogen leak risk nodes.

[0022] Step S500: Pre-build a hydrogen leakage verification module, wherein the hydrogen leakage verification module is connected to M environmental verification sensing units among M multimodal sensing nodes through low-power wireless transmission technology.

[0023] Specifically, the pre-built hydrogen leak verification module aims to verify hydrogen leak conditions. It establishes connections with M environmental verification sensing units among M multimodal sensing nodes via low-power wireless transmission technology. This connection enables the hydrogen leak verification module to acquire environmental data collected by the environmental verification sensing units. This environmental data is crucial for accurately determining hydrogen leak conditions because environmental factors can affect the detection results of hydrogen sensors. By receiving data from the environmental verification sensing units, the hydrogen leak verification module can comprehensively consider the relationship between environmental factors and changes in hydrogen concentration, thereby more accurately verifying hydrogen leak risk points and providing strong support for ultimately outputting accurate hydrogen leak detection results.

[0024] Step S600: When the hydrogen leakage identification module receives and analyzes the M hydrogen concentration change sequences returned by the M primary sensors, identifies and locates the hydrogen leakage risk nodes, it sends the hydrogen leakage risk nodes to the hydrogen leakage verification module.

[0025] Specifically, the hydrogen leak detection module receives M hydrogen concentration change sequences from M primary sensors. These sequences contain information on the time-varying hydrogen concentration at each detection site. The module then analyzes these received hydrogen concentration change sequences to identify and locate potential hydrogen leak nodes. These nodes represent locations within the hydrogen transmission network where leaks are possible. Once a potential leak node is identified, the module sends this information to the hydrogen leak verification module. This allows the verification module to further validate the node, comprehensively considering environmental factors and other information to ultimately determine whether a hydrogen leak actually exists, thereby improving the accuracy of the detection results.

[0026] Step S700: The hydrogen leakage verification module locates N associated sensor nodes in the hydrogen transmission pipeline network according to the hydrogen leakage risk node, and receives N environmental verification sensor data stored locally by the N associated sensor nodes.

[0027] Specifically, the intelligent leak verification module begins further verification of hydrogen leak risk nodes. Based on the received information on hydrogen leak risk nodes, it locates N associated sensor nodes within the hydrogen transmission network. These associated sensor nodes are typically located around the risk nodes, and the data they collect provides more comprehensive environmental information for verifying the risk nodes. The hydrogen leak verification module receives N environmental verification sensor data stored locally by these N associated sensor nodes. This environmental verification sensor data, collected by the environmental verification sensor units within the associated sensor nodes, includes information on environmental parameters such as temperature, humidity, and pressure. By acquiring this environmental verification sensor data from the associated sensor nodes, the hydrogen leak verification module can gain a more comprehensive understanding of the environmental conditions surrounding the risk nodes, providing a crucial data foundation for subsequent verification of hydrogen leak risk nodes based on environmental change correlation analysis.

[0028] Step S800: The hydrogen leakage verification module performs environmental change correlation analysis on the N environmental verification sensor data, verifies the hydrogen leakage risk nodes based on the analysis results, and outputs the hydrogen leakage detection results.

[0029] Specifically, the hydrogen leakage verification module performs environmental change correlation analysis on N environmental verification sensor data. First, it aligns the data with the relevant hydrogen concentration data in time series and plots N sets of environmental fusion change curves. When the changes within the curve set are consistent, it calculates the concentration change rate of the relevant hydrogen concentration data. Using the hydrogen concentration change threshold, it screens and locates the hydrogen leakage target node in the target risk area. Finally, it outputs the hydrogen leakage detection result, which clarifies whether the pipeline is leaking and its location, providing a basis for subsequent measures.

[0030] In one possible implementation, step S100 further includes: Step S110: Interact to obtain the pipeline layout plan of the hydrogen energy transmission pipeline network, and perform three-dimensional modeling based on the pipeline layout plan to obtain the transmission pipeline network model.

[0031] Step S120: Interact to obtain historical leakage records, wherein the historical leakage records include H historical leakage locations and H historical leakage time sequence data.

[0032] Step S130: After fitting the H historical leakage locations to the transmission pipeline model, based on the flow direction characteristics of hydrogen in the hydrogen energy transmission pipeline and the H historical leakage time series data, the H historical leakage locations are merged and retained to obtain W historical leakage locations and W historical leakage time series data, where W is a positive integer less than or equal to H.

[0033] Step S140: Interact to obtain detection scale constraints, and select detection areas in the transmission network model according to the detection scale constraints and the W historical leakage locations to locate M leakage detection areas.

[0034] Step S150: Select the W historical leakage locations as M local leakage detection groups based on the M leakage detection areas.

[0035] Step S160: Based on the M local leakage detection groups, perform spatial center positioning to obtain the M leakage detection sites.

[0036] Specifically, the layout plan of the hydrogen energy transmission pipeline network is obtained through interactive means. This is the basic information for locating and detecting leaks. Based on this pipeline layout plan, three-dimensional modeling is performed using relevant technologies to obtain a transmission pipeline network model that can accurately reflect the actual structure and spatial relationship of the pipeline network.

[0037] Historical leak records are obtained interactively, documenting past leaks in the hydrogen transmission pipeline network. These records comprise two crucial parts: H historical leak locations, identifying where leaks have occurred in the network; and H historical leak time-series data, recording the temporal sequence of leaks at each location. This historical leak location and time-series data is essential for subsequent analysis of leak patterns in the hydrogen transmission pipeline network and for determining suitable leak detection sites.

[0038] H historical leak locations are fitted onto the constructed transmission pipeline network model, establishing corresponding positional relationships within the model. Then, analysis is performed based on the flow direction characteristics of hydrogen in the hydrogen energy transmission pipeline network and the time-series data of the H historical leaks. Based on the flow direction, two or more leak locations located on the same pipeline component (such as the same pipe) are merged, retaining only locations with rapidly changing leak times, and removing duplicate or unnecessary information. After this processing, W historical leak locations and W historical leak time-series data are finally obtained, where W is a positive integer less than or equal to H. These retained locations and data will be more beneficial for subsequent determination and analysis of leak detection areas.

[0039] The detection scale constraint is obtained interactively. This constraint is a crucial parameter that determines the scope and accuracy of subsequent leak detection area division. After obtaining the constraint, and based on it and the W historical leak locations obtained in previous steps, a detection area selection operation is performed on the transmission pipeline network model. Using the detection scale constraint as the standard, and combining the distribution of the W historical leak locations, a suitable area is determined in the pipeline network model as the detection area. Ultimately, M leak detection areas are located. These areas will serve as the basis for further refining the leak detection points, providing important location data for subsequent sensor configuration and leak detection work.

[0040] Each leak detection area is grouped into a single group based on its historical leak locations. These W historical leak locations are then grouped into M local leak detection groups according to their corresponding M leak detection areas. This grouping method facilitates more targeted analysis and handling of leaks in different areas.

[0041] For each local leak detection group, it is necessary to determine the spatial coordinates of each historical leak location within the group (these coordinates may be based on the spatial coordinate system previously established for the transmission network model). The average coordinates of all historical leak locations within the group are calculated, i.e., the coordinate values ​​are summed along each coordinate axis, and then divided by the number of historical leak locations within the group. The result is the center coordinate value of the group along each coordinate axis, and these coordinate values ​​collectively determine the spatial center position of the group. This process is repeated for M local leak detection groups to obtain M leak detection points.

[0042] In one possible implementation, step S200 further includes: Step S210: Calculate the leakage rate based on the W historical leakage time-series data to obtain W time-varying velocity data.

[0043] Step S220: Extract the maximum value from the W time-varying velocity data, and use the extraction results to traverse the risk level table to obtain W secondary concentration response ranges.

[0044] Step S230: Based on the mapping relationship between the M local leakage detection groups and the W historical leakage locations, the W secondary concentration response ranges are divided into groups, and then the hydrogen concentration response ranges are selected to obtain the M primary concentration response ranges.

[0045] Step S240: Interact to obtain the ambient temperature change range, ambient humidity change range, and leakage pressure change range of the hydrogen energy transmission pipeline network, and configure the environmental sensors according to the ambient temperature change range, ambient humidity change range, and leakage pressure change range to obtain the environmental verification sensing unit.

[0046] Step S250: Configure hydrogen sensors according to the M primary concentration response ranges and W secondary concentration response ranges to obtain M primary sensors and W secondary sensors.

[0047] Step S260: Configure the environmental verification sensing unit according to the M primary sensors and W secondary sensors to obtain M sets of the multimodal sensors.

[0048] Specifically, it is necessary to acquire W historical leakage time-series data points. These data record the temporal sequence of past leakage events. Leakage velocity is calculated based on this historical leakage time-series data by analyzing factors such as changes in leakage location and the diffusion of leaked substances at different time points. The average leakage velocity is calculated based on the distance and time difference of the leaked substance's diffusion between two different time points. This calculation process is performed for each historical leakage event, resulting in W time-varying velocity data points. These time-varying velocity data reflect the changes in leakage velocity at different time points for different historical leakage events, providing important reference for subsequent steps such as determining the secondary concentration response range and configuring sensors.

[0049] The process involves processing W time-varying velocity data points to extract maximum values ​​from these data points, which reflect the velocity variations over time in different historical leakage events. These maximum values ​​represent the highest possible leakage velocity within a specific time period, highlighting more severe or critical states in the leakage event. Then, this maximum value result is used to iterate through a risk level table. This risk level table pre-defines the correspondence between different leakage velocity ranges and their corresponding risk levels and secondary concentration response ranges. By comparing and matching the extracted maximum value with the velocity ranges in the risk level table, the risk level to which the maximum value belongs is determined, and thus the corresponding secondary concentration response range is obtained. This process is repeated for each time-varying velocity data point, ultimately resulting in W secondary concentration response ranges.

[0050] A mapping relationship is established between the M local leak detection groups and W historical leak locations. This mapping helps determine the historical leak locations corresponding to each local leak detection group. Based on this mapping relationship, the W secondary concentration response ranges are grouped. Since different local leak detection groups may have different leak characteristics and environmental conditions, the secondary concentration response ranges are grouped according to their correspondence with the local leak detection groups to better analyze and process different areas. After completing the grouping, the hydrogen concentration response range is selected. Taking into account the specific circumstances of each local leak detection group, including historical leak data and environmental factors, appropriate hydrogen concentration responses are selected from the divided groups. Through the above steps, M primary concentration response ranges are finally obtained.

[0051] The ambient temperature, humidity, and leakage pressure variation ranges of the hydrogen transmission pipeline network are obtained interactively. Environmental sensors are configured based on the obtained temperature variation ranges. For example, if the temperature variation range is large, a temperature sensor with a wide measurement range and high accuracy is required to ensure accurate monitoring of temperature changes around the hydrogen transmission pipeline network. Similarly, for the humidity variation range, an appropriate humidity sensor is selected based on its range; if humidity changes are frequent or significant, a humidity sensor with fast response and good stability is chosen. Regarding the leakage pressure variation range, an appropriate pressure sensor is configured according to its magnitude and characteristics. For situations with large pressure fluctuations, a pressure sensor capable of withstanding high pressure and possessing good dynamic response characteristics is required. Through the reasonable configuration of temperature, humidity, and pressure sensors, an environmental verification sensing unit is finally obtained. This unit monitors the environmental parameters around the hydrogen transmission pipeline network in real time, providing crucial environmental information support for subsequent hydrogen leakage detection and verification.

[0052] By extracting the maximum leakage rate based on historical leakage data, W secondary hydrogen sensors with different sensitivities are configured for local monitoring. Then, the sensitivities of the secondary sensors within the region are aggregated, and the sensor with the highest sensitivity is selected as M primary sensors. This enables the primary sensors to cover the global monitoring needs and trigger the secondary sensors for more refined local monitoring and data analysis after the response.

[0053] After obtaining M primary sensors and W secondary sensors, the environmental validation sensing unit is configured and scheduled based on these sensors. First, it is determined that the environmental validation sensing unit consists of temperature sensors, pressure sensors, and humidity sensors. For the configuration of each group of multimodal sensors, the environmental characteristics of the detection area corresponding to the hydrogen sensors (primary and secondary sensors) need to be considered. If the temperature variation in a certain area is large, then the configuration of the multimodal sensors in that area needs to place greater emphasis on the accuracy and response speed of the temperature sensor to ensure accurate monitoring of the impact of ambient temperature changes on hydrogen leak detection. Similarly, the configuration of the pressure and humidity sensors also needs to be adjusted according to specific environmental requirements. By rationally scheduling and configuring the environmental validation sensing unit, the multimodal sensors become more adaptable to different detection environments, improving the accuracy and reliability of detection. Ultimately, M groups of multimodal sensors are obtained, which will play an important role in hydrogen leak detection.

[0054] In one possible implementation, step S240 further includes: Step S241: The environmental verification sensing unit consists of a temperature sensor, a pressure sensor, and a humidity sensor.

[0055] Specifically, the composition of the environmental verification sensing unit was clarified. The unit consists of a temperature sensor, a pressure sensor, and a humidity sensor.

[0056] Temperature sensors are used to monitor temperature changes in the environment surrounding hydrogen transmission pipelines. Temperature changes can affect hydrogen transmission; for example, excessively high or low temperatures may affect the pipeline's sealing and the physical properties of hydrogen. By monitoring the temperature, potential leakage risks caused by temperature anomalies can be detected in a timely manner.

[0057] Pressure sensors are responsible for detecting pressure changes in the environment. In hydrogen energy transmission, pressure changes are a crucial indicator of leakage; an abnormal drop in pressure indicates a hydrogen leak, causing a decrease in ambient pressure. Simultaneously, pressure sensors can also monitor pressure changes inside the pipeline, ensuring pressure stability during transmission.

[0058] The function of a humidity sensor is to measure ambient humidity. Changes in humidity are related to hydrogen leaks; for example, leaked hydrogen may react with moisture in the air, thus altering the ambient humidity. Furthermore, changes in humidity affect sensor performance, therefore humidity monitoring is necessary to ensure sensor accuracy.

[0059] These three sensors together constitute the environmental verification sensing unit, which can comprehensively monitor environmental parameters around the hydrogen energy transmission pipeline network and provide important environmental information support for hydrogen leakage detection.

[0060] In one possible implementation, step S600 further includes: Step S610: Configure the M multimodal sensing nodes at the M leakage detection sites according to the mapping of the M groups of multimodal sensors to form the multimodal sensing array.

[0061] Step S620: Configure the equipment according to the mapping of the M groups of multimodal sensors in the M local leakage detection groups to obtain M local detection arrays.

[0062] Step S630: Using the M leakage detection areas as constraints, construct data communication links between the M local detection arrays and the M multimodal sensing nodes to optimize the detection accuracy of the multimodal sensing arrays.

[0063] Specifically, firstly, the M sets of multimodal sensors are configured according to the correspondence between them and the M leak detection sites. The multimodal sensors integrate hydrogen sensors and environmental verification sensing units, enabling comprehensive monitoring of various parameters of the hydrogen transmission network. A multimodal sensing node is installed at each leak detection site, and these nodes are connected to the corresponding multimodal sensor. This configuration ensures that each leak detection site can monitor hydrogen concentration and ambient environmental parameters such as temperature, pressure, and humidity in real time. Once the multimodal sensing nodes at all M leak detection sites are configured, a multimodal sensing array is formed. This array can comprehensively monitor leaks throughout the entire hydrogen transmission network.

[0064] The equipment is configured based on the mapping relationship between M groups of multimodal sensors and M local leak detection groups. Each local leak detection group represents a specific detection area, and corresponding multimodal sensor devices are installed in these areas to form M local detection arrays. These local detection arrays can monitor the leakage situation in specific areas more precisely, and work in conjunction with the multimodal sensor arrays to improve the accuracy and reliability of the entire detection system. For example, for some local leak detection groups with a high historical leakage frequency, higher-precision multimodal sensor devices can be configured to enhance the monitoring of these areas.

[0065] With M leak detection areas as constraints, a data communication link is constructed between M local detection arrays and M multimodal sensing nodes. Establishing a stable data communication link ensures timely and accurate data transmission between the various detection devices. Low-power wireless communication technology is employed to ensure data transmission stability and reliability while reducing energy consumption. During the construction of the data communication link, the characteristics and requirements of the M leak detection areas are fully considered, and the data transmission path and method are optimized to improve data transmission efficiency and speed. After the data communication link is constructed, the detection accuracy of the multimodal sensing array is optimized. The various detection devices collaborate, sharing data and information, enabling more accurate and timely monitoring of hydrogen leaks. Furthermore, optimizing the data communication link also improves the response speed and reliability of the detection system, providing strong support for timely countermeasures.

[0066] In one possible implementation, step S600 further includes: Step S640: Predefine the threshold for hydrogen concentration change.

[0067] Step S650: Calculate the concentration change rate for the M hydrogen concentration change sequences to obtain the M hydrogen concentration change rates.

[0068] Step S660: Using the hydrogen concentration change threshold as a constraint, filter the M hydrogen concentration change rates, and locate H leakage risk areas in the M leakage detection areas, where H is a positive integer less than M.

[0069] Step S670: After calling the H hydrogen concentration change sequences of the H leakage risk areas from the M hydrogen concentration change sequences, perform time-series alignment on the H hydrogen concentration change sequences to obtain H updated concentration change sequences.

[0070] Step S680: Perform regional concentration gradient change analysis on the H updated concentration change sequences for the H leakage risk areas to locate the target risk area, wherein the hydrogen leakage risk node is located in the target risk area.

[0071] Step S690: Send the target risk area and hydrogen leakage risk node to the hydrogen leakage verification module.

[0072] Specifically, before conducting hydrogen leak detection, a threshold for hydrogen concentration variation is predefined. This threshold is determined based on a comprehensive consideration of factors such as safety requirements for hydrogen transmission pipelines, historical data, and relevant standards. It will serve as an important reference standard for subsequently assessing the existence and severity of leak risks.

[0073] M hydrogen concentration change sequences were identified, acquired from hydrogen sensors in a multimodal sensing node. These sequences reflect the changes in hydrogen concentration over time at different detection sites. To further analyze the trend of hydrogen concentration changes and potential leakage risks, the concentration change rate of the M hydrogen concentration change sequences was calculated. A difference method was used, calculating the difference in hydrogen concentration between adjacent time points and dividing by the time interval to obtain the concentration change rate within each time interval. This calculation was performed for each hydrogen concentration change sequence, ultimately yielding M hydrogen concentration change rates. These rates visually reflect the rate of change of hydrogen concentration at different detection sites.

[0074] Using a predefined hydrogen concentration change threshold as a screening criterion, M hydrogen concentration change rates were screened. Defining this threshold is a crucial criterion, reflecting the extent to which hydrogen concentration changes might indicate a leakage risk. When the hydrogen concentration change rate at a detection site exceeds this threshold, it indicates a potentially high leakage risk in that area. Each of the M hydrogen concentration change rates was compared to the threshold. If a rate exceeded the threshold, the corresponding leak detection area was considered a potentially leak-risk area. Through this screening process, H leakage risk areas were located within the M leak detection areas. H is a positive integer less than M, indicating that the number of identified leakage risk areas after screening is less than the total number of detection areas. These H identified leakage risk areas will be the focus of further analysis and processing to more accurately determine the specific location and situation of hydrogen leaks.

[0075] From M hydrogen concentration change sequences, H hydrogen concentration change sequences belonging to H leakage risk areas are retrieved. Since H leakage risk areas have been identified, their corresponding hydrogen concentration change sequences need to be extracted for further analysis. Time-series alignment is then performed on these H hydrogen concentration change sequences. The purpose of time-series alignment is to make the concentration change data from different areas comparable on the time axis, allowing for better analysis of the relationships between different leakage risk areas and their trends over time. Through time-series alignment, H updated concentration change sequences are obtained.

[0076] H updated concentration change sequences were used to analyze the regional concentration gradient changes in H leakage risk areas. These updated concentration change sequences were time-aligned, providing more accurate temporal reference. For each leakage risk area, the changes in hydrogen concentration over time within that area were analyzed using its corresponding updated concentration change sequence. The regional concentration gradient change analysis aims to determine the differences in hydrogen concentration changes between different locations. By comparing the rates and directions of concentration changes at different locations, it identifies areas with more drastic concentration changes and a higher leakage risk. For example, if the hydrogen concentration in a certain area rises rapidly in a short period while the concentration changes in surrounding areas are relatively small, then this area may be a target risk area. Since hydrogen leakage risk nodes are located within target risk areas, this analysis method can accurately pinpoint specific areas where leaks may occur. This provides a clear target for subsequent verification and handling, contributing to improved accuracy and efficiency in hydrogen leakage detection.

[0077] Through the preceding analysis and processing, the target risk area with leakage risk has been identified. This step provides a clear direction for subsequently determining the specific leak location within a smaller area. After the target risk area information is sent to the hydrogen leak verification module, the module can focus its efforts on more in-depth detection and analysis within this specific area. The distribution of hydrogen leak risk nodes further provides the verification module with specific points of focus. This node, located within the target risk area, is likely to be a location with a high probability of leakage. The verification module focuses on this node, combining data from associated sensor nodes, to conduct more refined analysis and verification. In subsequent processes, the hydrogen leak verification module selectively calls relevant sensor data and analysis tools based on the target risk area and hydrogen leak risk nodes. For example, it acquires environmental verification sensor data and local concentration change sequences from associated sensor nodes in the target local array. Through in-depth analysis of this data, the range of leak locations is further narrowed down, ultimately identifying the target hydrogen leak node and improving the accuracy and efficiency of leak detection.

[0078] In one possible implementation, step S700 further includes: Step S710: Locate the target local array in the M local detection arrays according to the hydrogen leakage risk node, wherein the target local array is located in the target risk area, and each of the N associated sensing nodes in the target local array is configured with a secondary sensor and the environmental verification sensing unit.

[0079] Step S720: Receive N environmental verification sensing data stored locally by the N environmental verification sensing units of the N associated sensing nodes.

[0080] Step S730: Receive the N local concentration change sequences stored locally by the N secondary sensors of the N associated sensing nodes.

[0081] Specifically, once the hydrogen leakage risk nodes are identified, a search is conducted among M local detection arrays to locate the target local array. This target local array is located within the already identified target risk area, indicating a high risk of hydrogen leakage in that area. The N associated sensor nodes, corresponding to N multimodal sensors deployed at N historical leakage locations within the target risk area of ​​the M leakage detection areas, play a crucial role. Each associated sensor node is equipped with a secondary sensor and an environmental verification sensor unit. The secondary sensors provide finer local detection, capable of detecting even minute changes in hydrogen concentration, while the environmental verification sensor unit, consisting of temperature, pressure, and humidity sensors, monitors changes in the surrounding environment, as environmental factors influence hydrogen leakage detection. This configuration provides a more comprehensive understanding of the target local array, offering more accurate data support for subsequent analysis and judgment.

[0082] These associated sensor nodes were identified from the target local area array after the hydrogen leakage risk nodes were determined. Each associated sensor node is equipped with an environmental verification sensing unit, which typically consists of temperature, pressure, and humidity sensors. Receiving this environmental verification sensor data is crucial. Environmental factors such as temperature, pressure, and humidity can affect the detection of hydrogen leakage. By acquiring this data, a more comprehensive understanding of the environmental conditions of the target local area array can be obtained, providing more reference for further analysis of hydrogen leakage risks. For example, a sudden increase in ambient temperature can affect the sealing of pipelines, increasing the possibility of hydrogen leakage. Changes in pressure and humidity are also correlated with leakage. By analyzing this environmental verification sensor data, the accuracy of the hydrogen leakage risk nodes can be better determined, and more targeted suggestions can be provided for subsequent handling measures.

[0083] The system receives N local concentration change sequences from secondary sensors located locally on N associated sensor nodes. These associated sensor nodes were identified from the target area array after determining nodes at risk of hydrogen leakage. Each associated sensor node is equipped with a secondary sensor, which provides more refined local concentration detection. Receiving these local concentration change sequences is crucial. By analyzing these sequences, we can understand how the hydrogen concentration changes over time within the target area. This helps to further determine the specific location and extent of a hydrogen leak. For example, if the local concentration change sequence of a particular associated sensor node shows a sharp increase in hydrogen concentration within a short period, then this area may have a high risk of leakage. Combining this with environmental validation sensor data allows for a more comprehensive assessment of the leakage situation.

[0084] In one possible implementation, step S800 further includes: Step S810: After time-series alignment of the N environmental verification sensor data and the N local concentration change sequences, plot the N environmental verification sensor data and the N local concentration change sequences to obtain N sets of environmental fusion change curves.

[0085] Step S820: When the N sets of environmental fusion change curves have consistency in intra-group changes, calculate the concentration change rate of the N local concentration change sequences to obtain the N local concentration change rates.

[0086] Step S830: Using the hydrogen concentration change threshold as a constraint, filter the N local concentration change rates and locate the hydrogen leakage target node in the target risk area.

[0087] Step S840: Output the hydrogen leakage target node as the hydrogen leakage detection result.

[0088] Specifically, time-series alignment is performed on N environmental validation sensor data (including temperature, pressure, humidity, etc.) and N local concentration change sequences. This step ensures the comparability of different types of data on the time axis, facilitating subsequent comprehensive analysis. Based on the aligned N environmental validation sensor data and N local concentration change sequences, graphs are created to visually display the relationship between environmental factors and changes in local hydrogen concentration, resulting in N sets of integrated environmental change curves. These integrated environmental change curves reflect the interrelationship and trends between environmental factors and hydrogen concentration within a specific time period. For example, an increase in ambient temperature will lead to changes in hydrogen concentration, and these changes can be clearly shown in the curves. By analyzing these curves, a better understanding of the impact of environmental factors on hydrogen leakage, as well as the potential location and extent of hydrogen leakage, can be achieved.

[0089] When the N sets of environmental fusion curves are determined to have consistent changes within each set, it means that the possibility of abnormal fluctuations in the sensor due to environmental factors has been ruled out. At this point, the concentration change rate is calculated for the N local concentration change sequences. By calculating the concentration change rate, the speed of hydrogen concentration change within a specific time period is quantified, which helps to further determine the severity and development trend of the leak. A high local concentration change rate indicates a rapid change in hydrogen concentration within a short period, meaning the leak is relatively serious or rapidly expanding, while a lower concentration change rate may indicate a relatively minor leak or a relatively stable state. The obtained N local concentration change rates provide specific data for subsequent analysis and decision-making. These change rates are used to determine whether immediate emergency measures are needed, such as shutting down relevant pipelines and evacuating personnel. They also provide important references for further determining the leak location and developing a remediation plan.

[0090] Using a predefined hydrogen concentration change threshold as a constraint, N local concentration change rates are screened. First, the hydrogen concentration change threshold is a critical value determined based on factors such as the characteristics of the hydrogen transmission pipeline network, safety requirements, and historical data. This threshold is used to determine whether the hydrogen concentration change has reached a level that may pose a leakage risk. By screening the N local concentration change rates, areas with low change rates and unlikely to pose a leakage risk are excluded, focusing only on areas with change rates exceeding the threshold, as these areas are more likely to have hydrogen leaks. Within the target risk area, the operation to locate the hydrogen leak target node is performed. Due to the preceding analysis and screening, the target risk area is a relatively small area with a high potential leakage risk. By further screening the local concentration change rates within this area, the range of leak locations is narrowed down, and the hydrogen leak target node is located more accurately. The determination of this target node is crucial for subsequent leak repair and safety management, providing maintenance personnel with specific location information to take timely repair measures and ensure the safety and stability of hydrogen transmission.

[0091] Following a series of complex analyses and processing steps, including analyzing and mapping environmental validation sensor data and local concentration change sequences, calculating local concentration change rates, screening data, and locating the target risk area, the hydrogen leak target node was finally determined. This node represents the specific location where a hydrogen leak may occur. This information is output as a detection result, providing clear information to relevant personnel so that timely remedial and handling measures can be taken. For example, maintenance personnel can quickly locate the leak location based on this result and carry out repair work, thereby reducing the safety risks and economic losses caused by hydrogen leaks. Simultaneously, this output also provides important data for the management and maintenance of hydrogen transmission pipeline networks, helping to optimize detection systems and prevent future potential leaks.

[0092] Example 2 is based on the same inventive concept as the pipeline gas leak detection method based on multimodal sensing in the previous examples, such as... Figure 2 As shown, this application provides a pipeline gas leak detection system based on multimodal sensing. The method embodiments in this application are based on the same inventive concept. The system includes: Leak detection site location module 10 is used to perform leak detection coverage analysis on the hydrogen energy transmission pipeline network and locate M leak detection sites, where M is a positive integer.

[0093] A multimodal sensor acquisition module 20 is used to configure sensors according to the transmission environment of the hydrogen energy transmission pipeline network to obtain multimodal sensors, wherein the multimodal sensors integrate a hydrogen sensor and an environmental verification sensing unit.

[0094] A multimodal sensor array acquisition module 30 is used to configure sensors at the M leakage detection sites to obtain a multimodal sensor array, wherein the multimodal sensor array includes M multimodal sensor nodes, and each of the M multimodal sensor nodes is equipped with the multimodal sensor.

[0095] A pre-built leak identification module 40 is used to pre-build a hydrogen energy leak identification module, wherein the hydrogen energy leak identification module is connected to M primary sensors among M multimodal sensing nodes through low-power wireless transmission technology.

[0096] A pre-built leakage verification module 50 is used to pre-build a hydrogen energy leakage verification module, wherein the hydrogen energy leakage verification module is connected to M environmental verification sensing units among M multimodal sensing nodes through low-power wireless transmission technology.

[0097] The hydrogen concentration change sequence acquisition module 60 is used to send the hydrogen leakage risk node to the hydrogen leakage verification module after the hydrogen energy leakage identification module receives and analyzes the M hydrogen concentration change sequences returned by the M primary sensors and identifies and locates the hydrogen energy leakage risk node.

[0098] The environmental verification sensor data acquisition module 70 is used by the hydrogen energy leakage verification module to locate N associated sensor nodes in the hydrogen energy transmission pipeline network according to the hydrogen energy leakage risk node, and to receive N environmental verification sensor data stored locally by the N associated sensor nodes.

[0099] The hydrogen leakage detection result acquisition module 80 is used to perform environmental change correlation analysis on the N environmental verification sensor data through the hydrogen leakage verification module, and verify the hydrogen leakage risk nodes based on the analysis results, and output the hydrogen leakage detection result.

[0100] Furthermore, the leak detection site localization module 10 also includes: The pipeline layout planning acquisition unit is used to interactively obtain the pipeline layout plan of the hydrogen energy transmission pipeline, and perform three-dimensional modeling based on the pipeline layout plan to obtain the transmission pipeline model.

[0101] A historical leakage record acquisition unit is used to interactively acquire historical leakage records, wherein the historical leakage records include H historical leakage locations and H historical leakage time sequence data.

[0102] The leakage location acquisition unit is used to fit the H historical leakage locations to the transmission pipeline model, and then merge and retain the H historical leakage locations based on the flow direction characteristics of hydrogen in the hydrogen energy transmission pipeline and the H historical leakage time series data to obtain W historical leakage locations and W historical leakage time series data, where W is a positive integer less than or equal to H.

[0103] The detection scale constraint unit is used to interactively obtain detection scale constraints, and to select detection areas in the transmission network model according to the detection scale constraints and the W historical leakage locations, thereby locating M leakage detection areas.

[0104] A local leakage detection group selection unit is used to select the W historical leakage locations as M local leakage detection groups based on the M leakage detection areas.

[0105] A leak detection site acquisition unit is used to obtain the M leak detection sites by spatial center positioning based on the M local leak detection groups.

[0106] Furthermore, the multimodal sensor acquisition module 20 also includes: A velocity time-varying data acquisition unit is used to calculate the leakage velocity based on the W historical leakage time-series data to obtain W velocity time-varying data.

[0107] The secondary concentration response range acquisition unit is used to extract the maximum value of the W time-varying velocity data and use the extraction results to traverse the risk level table to obtain W secondary concentration response ranges.

[0108] The primary concentration response range acquisition unit is used to select hydrogen concentration response ranges by grouping the W secondary concentration response ranges according to the mapping relationship between the M local leakage detection groups and the W historical leakage locations, thereby obtaining M primary concentration response ranges.

[0109] An environmental verification sensing acquisition unit is used to interactively obtain the environmental temperature change range, environmental humidity change range, and leakage pressure change range of the hydrogen energy transmission pipeline network, and to configure environmental sensors according to the environmental temperature change range, environmental humidity change range, and leakage pressure change range to obtain the environmental verification sensing unit.

[0110] A sensor acquisition unit is configured to acquire M primary sensors and W secondary sensors by configuring hydrogen sensors according to the M primary concentration response ranges and W secondary concentration response ranges.

[0111] A scheduling configuration unit is used to schedule and configure the environmental verification sensing unit according to the M primary sensors and W secondary sensors to obtain M sets of the multimodal sensors.

[0112] Furthermore, the environmental verification sensing acquisition unit also includes: The environmental verification sensing unit consists of a temperature sensor, a pressure sensor, and a humidity sensor.

[0113] Furthermore, the hydrogen concentration change sequence acquisition module 60 also includes: A multimodal sensor array constitutive unit is used to configure the M multimodal sensor nodes according to the mapping of the M groups of multimodal sensors to the M leakage detection sites, thereby constituting the multimodal sensor array.

[0114] A local detection array acquisition unit is used to configure the device according to the mapping of the M groups of multimodal sensors in the M local leakage detection groups, and obtain M local detection arrays.

[0115] The detection accuracy optimization unit is used to construct data communication links between the M local detection arrays and the M multimodal sensing nodes, based on the M leakage detection areas as constraints, and to optimize the detection accuracy of the multimodal sensing arrays.

[0116] Furthermore, the hydrogen concentration change sequence acquisition module 60 also includes: A concentration change threshold predefined unit is used to predefine the hydrogen concentration change threshold. A hydrogen concentration change rate acquisition unit is used to calculate the concentration change rate of the M hydrogen concentration change sequences to obtain M hydrogen concentration change rates.

[0117] A leakage risk area location unit is used to filter the M hydrogen concentration change rates with the hydrogen concentration change threshold as a constraint, and locate H leakage risk areas in the M leakage detection areas, where H is a positive integer less than M.

[0118] An updated concentration change sequence acquisition unit is used to retrieve H hydrogen concentration change sequences from the M hydrogen concentration change sequences of the H leakage risk areas, and then perform time-series alignment on the H hydrogen concentration change sequences to obtain H updated concentration change sequences.

[0119] The target risk area location unit is used to perform regional concentration gradient change analysis on the H updated concentration change sequences for the H leakage risk areas to locate the target risk area, wherein the hydrogen leakage risk node is located in the target risk area.

[0120] The leakage risk node distribution unit is used to distribute the target risk area and hydrogen leakage risk node to the hydrogen leakage verification module.

[0121] Furthermore, the environmental verification sensor data acquisition module 70 also includes: A target local array positioning unit is used to locate a target local array in the M local detection arrays according to the hydrogen leakage risk node, wherein the target local array is located in the target risk area, and each of the N associated sensing nodes in the target local array is configured with a secondary sensor and the environmental verification sensing unit.

[0122] An environmental verification sensor data receiving unit is used to receive N environmental verification sensor data stored locally by the N associated sensor nodes.

[0123] A local concentration change sequence receiving unit is used to receive N local concentration change sequences stored locally by N secondary sensors of the N associated sensing nodes.

[0124] Furthermore, the hydrogen leak detection result acquisition module 80 also includes: An environmental fusion change curve acquisition unit is used to perform time-series alignment on the N environmental verification sensor data and N local concentration change sequences, and then generate N sets of environmental fusion change curves based on the N environmental verification sensor data and N local concentration change sequences.

[0125] The local concentration change rate acquisition unit is used to calculate the concentration change rate of the N local concentration change sequences when the N sets of environmental fusion change curves have intra-group consistency, and obtain N local concentration change rates.

[0126] A hydrogen leak target node location unit is used to filter the N local concentration change rates with the hydrogen concentration change threshold as a constraint, and locate the hydrogen leak target node in the target risk area.

[0127] The detection result output unit is used to output the hydrogen leakage target node as the hydrogen leakage detection result.

[0128] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0129] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0130] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for detecting gas leaks in pipeline networks based on multimodal sensing, characterized in that, The method includes: Leak detection coverage analysis was performed on the hydrogen energy transmission pipeline network to locate M leak detection sites, where M is a positive integer; Based on the transmission environment of the hydrogen energy transmission pipeline network, a multimodal sensor is configured to obtain the sensor, wherein the multimodal sensor integrates a hydrogen sensor and an environmental verification sensing unit. Sensors are configured at the M leakage detection sites to obtain a multimodal sensing array, wherein the multimodal sensing array includes M multimodal sensing nodes, and each of the M multimodal sensing nodes is configured with the multimodal sensor. A pre-built hydrogen leak detection module is provided, wherein the hydrogen leak detection module is connected to M primary sensors among M multimodal sensing nodes via low-power wireless transmission technology. A pre-built hydrogen leakage verification module is provided, wherein the hydrogen leakage verification module is connected to M environmental verification sensing units among M multimodal sensing nodes through low-power wireless transmission technology. When the hydrogen leakage identification module receives and analyzes the M hydrogen concentration change sequences returned by the M primary sensors, identifies and locates the hydrogen leakage risk nodes, it sends the hydrogen leakage risk nodes to the hydrogen leakage verification module. The hydrogen leakage verification module locates N associated sensor nodes in the hydrogen transmission pipeline network based on the hydrogen leakage risk node, and receives N environmental verification sensor data stored locally by the N associated sensor nodes. The hydrogen leakage verification module performs environmental change correlation analysis on the N environmental verification sensor data, verifies the hydrogen leakage risk nodes based on the analysis results, and outputs the hydrogen leakage detection results.

2. The pipeline gas leak detection method based on multimodal sensing as described in claim 1, characterized in that, Leak detection coverage analysis is performed on the hydrogen energy transmission pipeline network to locate M leak detection sites. The method includes: The pipeline layout plan of the hydrogen energy transmission pipeline network is obtained interactively, and a three-dimensional model is performed based on the pipeline layout plan to obtain the transmission pipeline network model. Historical leakage records are obtained interactively, wherein the historical leakage records include H historical leakage locations and H historical leakage time-series data; After fitting the H historical leakage locations to the transmission pipeline network model, based on the flow direction characteristics of hydrogen in the hydrogen energy transmission pipeline network and the H historical leakage time series data, the H historical leakage locations are merged and retained to obtain W historical leakage locations and W historical leakage time series data, where W is a positive integer less than or equal to H; Interactively obtain detection scale constraints, and select detection areas in the transmission network model according to the detection scale constraints and the W historical leakage locations to locate M leakage detection areas; Based on the M leakage detection areas, the W historical leakage locations are selected as M local leakage detection groups; Based on the spatial center positioning of the M local leakage detection groups, the M leakage detection sites are obtained.

3. The pipeline gas leak detection method based on multimodal sensing as described in claim 2, characterized in that, The method involves configuring sensors based on the transmission environment of the hydrogen energy transmission pipeline network to obtain multimodal sensors, and includes: Based on the W historical leakage time-series data, the leakage rate is calculated to obtain W time-varying velocity data. Maximum values ​​are extracted from the W time-varying velocity data, and the extraction results are used to traverse the risk level table to obtain W secondary concentration response ranges; Based on the mapping relationship between the M local leakage detection groups and the W historical leakage locations, the W secondary concentration response ranges are divided into groups, and then the hydrogen concentration response range is selected to obtain the M primary concentration response ranges. The ambient temperature variation range, ambient humidity variation range, and leakage pressure variation range of the hydrogen energy transmission pipeline are obtained interactively, and the environmental sensors are configured according to the ambient temperature variation range, ambient humidity variation range, and leakage pressure variation range to obtain the environmental verification sensing unit. Based on the M primary concentration response ranges and W secondary concentration response ranges, hydrogen sensors are configured to obtain M primary sensors and W secondary sensors; The environmental verification sensing unit is scheduled and configured based on the M primary sensors and W secondary sensors to obtain M sets of the multimodal sensors.

4. The pipeline gas leak detection method based on multimodal sensing as described in claim 3, characterized in that, The environmental verification sensing unit consists of a temperature sensor, a pressure sensor, and a humidity sensor.

5. The pipeline gas leak detection method based on multimodal sensing as described in claim 4, characterized in that, When the hydrogen leakage identification module receives and analyzes the M hydrogen concentration change sequences transmitted back by the M primary sensors, identifies and locates the hydrogen leakage risk nodes, and then sends the hydrogen leakage risk nodes to the hydrogen leakage verification module, the method includes: The M groups of multimodal sensors are mapped to the M leakage detection sites to configure the M multimodal sensing nodes, thereby forming the multimodal sensing array; Based on the mapping of the M groups of multimodal sensors to the M local leakage detection groups, the equipment is configured to obtain M local detection arrays; Using the M leakage detection areas as constraints, a data communication link is constructed between the M local detection arrays and the M multimodal sensing nodes to optimize the detection accuracy of the multimodal sensing arrays.

6. The pipeline gas leak detection method based on multimodal sensing as described in claim 5, characterized in that, When the hydrogen leakage identification module receives and analyzes the M hydrogen concentration change sequences transmitted back by the M primary sensors, identifies and locates the hydrogen leakage risk nodes, and then sends the hydrogen leakage risk nodes to the hydrogen leakage verification module, the method includes: Predefined threshold for hydrogen concentration change; The concentration change rate is calculated for the M hydrogen concentration change sequences to obtain M hydrogen concentration change rates; Using the hydrogen concentration change threshold as a constraint, the M hydrogen concentration change rates are screened, and H leakage risk areas are located in the M leakage detection areas, where H is a positive integer less than M; After calling the H hydrogen concentration change sequences of the H leakage risk areas from the M hydrogen concentration change sequences, the H hydrogen concentration change sequences are time-aligned to obtain H updated concentration change sequences; The H updated concentration change sequences are used to perform regional concentration gradient change analysis on the H leakage risk areas to locate the target risk areas, wherein the hydrogen leakage risk node is located in the target risk area; The target risk area and hydrogen leakage risk node are sent to the hydrogen leakage verification module.

7. The pipeline gas leak detection method based on multimodal sensing as described in claim 6, characterized in that, The hydrogen leakage verification module locates N associated sensor nodes in the hydrogen transmission pipeline network based on the hydrogen leakage risk nodes, and receives N environmental verification sensor data stored locally by the N associated sensor nodes. The method includes: Based on the hydrogen leakage risk node, the target local array is located in the M local detection arrays, wherein the target local array is located in the target risk area, and each of the N associated sensing nodes in the target local array is configured with a secondary sensor and the environmental verification sensing unit. Receive N environmental verification sensing data stored locally by the N environmental verification sensing units of the N associated sensing nodes; Receive N local concentration change sequences stored locally by N secondary sensors of the N associated sensing nodes.

8. The pipeline gas leak detection method based on multimodal sensing as described in claim 7, characterized in that, The method involves performing environmental change correlation analysis on the N environmental verification sensor data using the hydrogen leakage verification module, verifying the hydrogen leakage risk nodes based on the analysis results, and outputting hydrogen leakage detection results. After time-series alignment of the N environmental verification sensor data and N local concentration change sequences, N sets of environmental fusion change curves are obtained based on the N environmental verification sensor data and N local concentration change sequences. When the N sets of environmental fusion change curves have consistency in intra-group changes, the concentration change rate is calculated for the N local concentration change sequences to obtain N local concentration change rates; Using the hydrogen concentration change threshold as a constraint, the N local concentration change rates are screened to locate the hydrogen leakage target node in the target risk area; The hydrogen leakage target node is output as the hydrogen leakage detection result.

9. A pipeline gas leak detection system based on multimodal sensing, characterized in that, The system is used to implement the pipeline gas leak detection method based on multimodal sensing as described in any one of claims 1-8, the system comprising: A leak detection site localization module is used to perform leak detection coverage analysis on the hydrogen energy transmission pipeline network and locate M leak detection sites, where M is a positive integer. A multimodal sensor acquisition module is used to configure sensors according to the transmission environment of the hydrogen energy transmission pipeline network to obtain multimodal sensors, wherein the multimodal sensors integrate a hydrogen sensor and an environmental verification sensing unit. A multimodal sensor array acquisition module is used to configure sensors at the M leakage detection sites to obtain a multimodal sensor array, wherein the multimodal sensor array includes M multimodal sensor nodes, and each of the M multimodal sensor nodes is configured with the multimodal sensor. A pre-built leak identification module is used to pre-build a hydrogen energy leak identification module, wherein the hydrogen energy leak identification module is connected to M primary sensors among M multimodal sensing nodes through low-power wireless transmission technology. A pre-built leak verification module is used to pre-build a hydrogen energy leak verification module, wherein the hydrogen energy leak verification module is connected to M environmental verification sensing units among M multimodal sensing nodes through low-power wireless transmission technology. A hydrogen concentration change sequence acquisition module is used to send the hydrogen leakage risk node to the hydrogen leakage verification module after the hydrogen energy leakage identification module receives and analyzes the M hydrogen concentration change sequences returned by the M primary sensors and identifies and locates the hydrogen energy leakage risk node. An environmental verification sensor data acquisition module is used by the hydrogen energy leakage verification module to locate N associated sensor nodes in the hydrogen energy transmission pipeline network according to the hydrogen energy leakage risk node, and to receive N environmental verification sensor data stored locally by the N associated sensor nodes. The hydrogen leakage detection result acquisition module is used to perform environmental change correlation analysis on the N environmental verification sensor data through the hydrogen leakage verification module, and verify the hydrogen leakage risk nodes based on the analysis results, and output the hydrogen leakage detection result.

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