Gas leak spatiotemporal correlation warning method and system based on multimodal data fusion

Abstract

This invention provides a spatiotemporal gas leak early warning method and system based on multimodal data fusion, relating to the field of data processing technology. The method comprises: collecting real-time concentration data and a historical leakage accident database through an NB-IoT combustible gas sensor network deployed at key nodes of the gas pipeline network; aligning geospatial data using the UTM projected coordinate system based on the real-time concentration data and the historical leakage accident database, establishing a three-dimensional spatial mapping model of the pipeline network topology, and aligning the timestamps of all dynamic monitoring data streams using the NTP clock synchronization protocol to form a standardized data set with spatiotemporal correlation. This invention realizes intelligent management of the entire chain of gas leakage risks, from perception and prediction to disposal.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to a gas leakage spatiotemporal correlation early warning method and system based on multimodal data fusion. Background Art

[0002] Traditional gas leak monitoring relies primarily on single-mode sensors (such as combustible gas detector threshold alarms) and manual inspections, resulting in delayed response times and insufficient spatial coverage. In recent years, the application of IoT technology has led to the widespread adoption of distributed gas sensor networks based on NB-IoT. However, existing systems are often limited to single-point concentration over-limit alarms and lack multi-dimensional correlation analysis of pipeline network topology, environmental variables, and historical leakage patterns.

[0003] For example, some leak location algorithms are based on changes in pressure gradients, but they do not consider the impact of long-term degradation factors such as soil settlement and pipe aging, resulting in an increased false alarm rate under complex working conditions. GIS systems are used to visualize gas concentration distribution, but spatiotemporal data processing is still mainly based on static layer overlays, failing to achieve time synchronization and spatial alignment of dynamic monitoring data streams, making it difficult to capture the spatiotemporal coupling characteristics of leakage diffusion. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a gas leakage spatiotemporal correlation early warning method and system based on multimodal data fusion, which can improve the accuracy of gas leakage risk identification.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] In a first aspect, a gas leak spatiotemporal correlation early warning method based on multimodal data fusion is provided, the method comprising:

[0007] Step S1: Collect real-time concentration data and a historical leakage accident database through the NB-IoT combustible gas sensor network deployed at key nodes of the gas pipeline network;

[0008] Step S2: Based on the real-time concentration data and the historical leakage accident database, the UTM projection coordinate system is used to align the geospatial data, and a three-dimensional spatial mapping model of the pipeline network topology is established. The NTP clock synchronization protocol is used to align the timestamps of all dynamic monitoring data streams to form a standardized data set with temporal and spatial correlation;

[0009] Step S3: Normalize the sensor concentration values, pipe network service life, real-time pressure gradient, cumulative precipitation, and complaint event spatial density distribution characteristics in the standardized data set to extract a multimodal spatiotemporal feature matrix containing 18-dimensional feature vectors;

[0010] Step S4: Based on the multimodal spatiotemporal feature matrix and the spatial distribution characteristics of the historical leakage accident database, two core monitoring points with leakage frequencies higher than the threshold are selected, and their pipe type, service life degradation coefficient, and surrounding population density parameters are obtained. The sensor concentration, accumulated sedimentation, and complaint density dimensions of the multimodal spatiotemporal feature matrix are dynamically corrected using weighting factors.

[0011] Step S5: The corrected multimodal spatiotemporal feature matrix for 12 consecutive hours is input into the LSTM-CNN hybrid neural network model. Through time series feature extraction and spatial correlation analysis, a gas leak probability prediction result for the next two hours is generated, and a leakage heat map with a spatial resolution of 50 meters × 50 meters and a temporal resolution of 15 minutes is output.

[0012] Step S6: Based on the leakage probability value of each grid unit in the thermal map, when the yellow warning threshold of 30% is reached, the regional monitoring enhancement instruction is triggered; when the orange threshold of 70% is reached, the pipeline pressure regulation is started; when the red threshold of 90% is exceeded, the emergency command system is linked to execute the automated disposal process of closing valves and cutting off sources, and evacuation guidance.

[0013] Furthermore, the NB-IoT combustible gas sensor network deployed at key nodes of the gas pipeline network collects real-time concentration data and a historical leakage accident database, including:

[0014] Step S1.1: Receive the gas concentration data stream from each sensor node in real time through the NB-IoT communication protocol, clean up the abnormal values, and obtain standardized time series data;

[0015] Step S1.2: Extract the accident time, geographic coordinates, leakage intensity, and disposal records from the historical leakage accident database and store them by accident type.

[0016] Furthermore, based on real-time concentration data and a historical leak accident database, the UTM projection coordinate system was used to align the geospatial data, establishing a three-dimensional spatial mapping model of the pipe network topology. All dynamic monitoring data streams were timestamped using the NTP clock synchronization protocol, forming a standardized data set with temporal and spatial associations, including:

[0017] Step S2.1: Convert the sensor node coordinates, historical leakage point coordinates, and pipe network topology data into a unified UTM projection coordinate system to eliminate distance calculation errors caused by the curvature of the earth in geographic coordinates. Construct a pipe network topology spatial model containing three-dimensional coordinates based on pipe segment connection relationships and node elevation data.

[0018] Step S2.2: Based on the topological spatial model, the local clocks of all sensor nodes are synchronized at the millisecond level using the NTP protocol. A unified reference timestamp is added to the real-time concentration data stream and pipe network pressure monitoring data. Time information in historical leakage accident records is converted to a different time zone and compensated for time differences to ensure alignment with the real-time data stream.

[0019] Step S2.3: Align the real-time data streams on the same time axis, use the network topology nodes as spatial units, perform sliding window statistics on the real-time concentration data and pressure gradient data in 15-minute time windows, and spatially superimpose the statistical results with the historical leakage events and sedimentation monitoring data in the corresponding time window to obtain a standardized data set with spatiotemporal labels.

[0020] Furthermore, the sensor concentration values, pipe network service life, real-time pressure gradient, cumulative sedimentation, and spatial density distribution characteristics of complaint events in the standardized data set are normalized to extract a multimodal spatiotemporal feature matrix containing 18-dimensional feature vectors, including:

[0021] Step S3.1: Normalize the sensor concentration values, pipe network service life, real-time pressure gradient, cumulative precipitation, and complaint event spatial density distribution characteristics in the standardized data set to obtain normalized data;

[0022] Step S3.2: Extract the following 18-dimensional features from the normalized data: 12-hour mean, variance, gradient change rate, accumulated settlement within a 50-meter radius around the node, complaint density, concentration difference between adjacent nodes, pipe section service life degradation coefficient, pipe corrosion resistance grade, joint sealing score, real-time pressure gradient, temperature impact factor, wind speed diffusion coefficient, historical leakage frequency at the same location, response time for similar accidents, maintenance record coverage, node connectivity in the pipeline network, upstream and downstream pressure difference, and pipeline bending radius;

[0023] Step S3.3: Integrate the above features into a multidimensional matrix according to the spatiotemporal labels, and store the feature matrix in slices according to the pipe network area.

[0024] Furthermore, based on the multimodal spatiotemporal feature matrix and the spatial distribution characteristics of the historical leakage accident database, two core monitoring points with leakage frequencies higher than the threshold were selected, and their pipe type, service life degradation coefficient, and surrounding population density parameters were obtained. The sensor concentration, accumulated sedimentation, and complaint density dimensions of the multimodal spatiotemporal feature matrix were dynamically corrected using weighting factors, including:

[0025] Step S4.1: Based on the spatial distribution characteristics of the historical leakage accident database, the leakage frequency of each pipeline network node in the past five years is counted, and two core monitoring points with leakage frequencies exceeding the preset threshold are selected as high-risk area benchmarks;

[0026] Step S4.2: Based on the high-risk area benchmark, extract the pipe type and service life degradation coefficient of the core monitoring point from the pipe network attribute database, and combine it with the GIS map to obtain the population density parameters within 500 meters around it;

[0027] Step S4.3: Generate a pipe risk weight based on the product of the pipe type's corrosion resistance grade and the degradation coefficient. This weight is used to amplify the sensor concentration values ​​in pipe sections with high corrosion propensity. Based on the linear relationship between population density and accumulated sedimentation, a population-sedimentation coupling weight is calculated. Nonlinear enhancement is performed on the sedimentation characteristics of high-density areas. Combined with the spatiotemporal overlap of historical complaint density and leak events, a complaint credibility weight is generated to enhance the contribution of complaint density characteristics in areas with high complaint frequencies.

[0028] Step S4.4: Apply the above weight factors to the sensor concentration, cumulative sedimentation and complaint density dimensions in the multimodal spatiotemporal feature matrix respectively, and dynamically modify the eigenvalues ​​of the core monitoring point and its adjacent nodes according to the formula: the corrected eigenvalue is equal to the original eigenvalue into dynamic weights.

[0029] Furthermore, the 12-hour continuous and corrected multimodal spatiotemporal feature matrix is ​​input into the LSTM-CNN hybrid neural network model. Through time series feature extraction and spatial correlation analysis, a gas leak probability prediction result for the next two hours is generated. The output is a leakage heat map with a spatial resolution of 50 meters × 50 meters and a temporal resolution of 15 minutes, including:

[0030] Step S5.1: The 12-hour corrected spatiotemporal feature matrix is ​​reorganized into a three-dimensional tensor by time step as the model input. A bidirectional LSTM layer is used to extract time series features to capture the long-term dependencies between concentration fluctuations and pressure gradient changes. A three-dimensional convolutional layer is used to mine the spatial correlation of the pipeline network topology.

[0031] Step S5.2: Perform a weighted fusion of the temporal features output by the LSTM and the spatial features extracted by the CNN using an attention mechanism to generate a leakage probability prediction value for each pipeline network node in the next 2 hours. Based on the 3D coordinates of the pipeline network nodes, a spatial interpolation algorithm is used to map the node-level prediction value to a 50 m × 50 m grid cell. The diffusion range is corrected by combining real-time wind speed and terrain elevation data to generate a leakage probability heat map with a time resolution of 15 minutes.

[0032] Furthermore, based on the leakage probability values ​​of each grid cell in the heat map, when the yellow warning threshold of 30% is reached, the regional monitoring enhancement command is triggered. When the orange threshold of 70% is reached, the pipeline network pressure regulation is initiated. When the red threshold of 90% is exceeded, the emergency command system is linked to execute the automated disposal process of shutting down valves and cutting off the source, and evacuating and guiding, including:

[0033] Step S6.1: Real-time analysis of the leakage probability value for each 50m x 50m grid cell in the thermal map. If the probability value reaches a first preset threshold of 30% or higher, the area is marked as a yellow warning area. Instructions for enhanced regional monitoring are issued, and the sensor sampling frequency in that area is dynamically increased to once per second. Mobile inspection equipment is dispatched for on-site verification.

[0034] Step S6.2: If the probability value reaches the second preset threshold value ≥ 70%, the area is marked as an orange warning zone, and a pipeline network pressure control instruction is obtained. Based on the topological relationship of the upstream pipe section of the leak point, the final pressure attenuation coefficient is calculated, and the pipeline network pressure is reduced to below the safety threshold through the remote control valve;

[0035] Step S6.3: If the probability value exceeds the third preset threshold value ≥90%, it is marked as a red warning area, and an emergency linkage instruction is received, which automatically triggers the valve closing and source isolation operation, closes all main control valves within 500 meters upstream and downstream of the leakage point, and sends an evacuation guidance signal to the emergency command system, and the associated electronic fence system activates the sound and light alarm devices within a radius of 1 km.

[0036] Secondly, the gas leak spatiotemporal correlation early warning system based on multimodal data fusion includes:

[0037] The acquisition module is used to collect real-time concentration data and a historical leakage accident database through the NB-IoT combustible gas sensor network deployed at key nodes in the gas pipeline network. Based on the real-time concentration data and the historical leakage accident database, the UTM projection coordinate system is used to align the geospatial data, establish a three-dimensional spatial mapping model of the pipeline network topology, and align the timestamps of all dynamic monitoring data streams using the NTP clock synchronization protocol to form a standardized data set with temporal and spatial correlation.

[0038] The calculation module is used to normalize the sensor concentration values, pipeline service life, real-time pressure gradient, cumulative sedimentation, and spatial density distribution characteristics of complaint events in the standardized data set, and extract a multimodal spatiotemporal feature matrix containing 18-dimensional feature vectors. Based on the multimodal spatiotemporal feature matrix and the spatial distribution characteristics of the historical leakage accident database, two core monitoring points with leakage frequencies above the threshold are selected, and their pipe type, service life degradation coefficient, and surrounding population density parameters are obtained. The sensor concentration, cumulative sedimentation, and complaint density dimensions of the multimodal spatiotemporal feature matrix are dynamically corrected using weighting factors. The corrected multimodal spatiotemporal feature matrix for 12 consecutive hours is input into the LSTM-CNN hybrid neural network model. Through time series feature extraction and spatial correlation analysis, a gas leakage probability prediction result for the next two hours is generated, and a leakage heat map with a spatial resolution of 50 meters × 50 meters and a temporal resolution of 15 minutes is output.

[0039] The processing module is used to trigger the regional monitoring enhancement command when the leakage probability value of each grid unit in the thermal map reaches the yellow warning threshold of 30%, start the pipeline pressure regulation when it reaches the orange threshold of 70%, and link the emergency command system to execute the automated disposal process of closing valves and cutting off sources and evacuating guidance when it exceeds the red threshold of 90%.

[0040] According to a third aspect, a computing device includes:

[0041] one or more processors;

[0042] The storage device is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0043] In a fourth aspect, a computer-readable storage medium stores a program, which implements the method when executed by a processor.

[0044] The above solution of the present invention includes at least the following beneficial effects:

[0045] Through multimodal data fusion and spatiotemporal correlation analysis, the accuracy and timeliness of gas leak warnings have been significantly improved. The NB-IoT sensor network, combined with UTM-NTP spatiotemporal registration technology, has been used to construct a three-dimensional pipeline network topology model and standardized dataset, effectively eliminating geographical errors and time asynchrony, and achieving precise positioning of the leak source. The multimodal feature matrix (concentration, sedimentation, and complaint density) is corrected using dynamic weighting factors to enhance monitoring sensitivity in high-risk areas. The LSTM-CNN hybrid model is used to simultaneously mine spatiotemporal features and generate high-resolution leak heat maps, achieving a detection rate of over 95% for minor leaks and a 40% reduction in false alarm rates. A graded response mechanism triggers differentiated measures (enhanced monitoring, pressure regulation, and valve closure and evacuation) based on probability thresholds, shortening the warning response time to 15 minutes and increasing the efficiency of handling high-risk events by 60%. Furthermore, closed-loop feedback is used to optimize the pipeline network risk model, adapting to pipeline aging and environmental changes, improving system robustness and urban gas safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a gas leak spatiotemporal correlation warning method based on multimodal data fusion provided by an embodiment of the present invention.

[0047] Figure 2 Schematic diagram of a gas leak spatiotemporal correlation warning system based on multimodal data fusion provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0049] like Figure 1 As shown, an embodiment of the present invention proposes a gas leak spatiotemporal correlation early warning method based on multimodal data fusion, the method comprising the following steps:

[0050] Steps Step S1: Collect real-time concentration data and historical leakage accident database through the NB-IoT combustible gas sensor network deployed at key nodes of the gas pipeline network;

[0051] Step S2: Based on the real-time concentration data and the historical leakage accident database, the UTM projection coordinate system is used to align the geospatial data, and a three-dimensional spatial mapping model of the pipeline network topology is established. The timestamps of all dynamic monitoring data streams are aligned through the NTP clock synchronization protocol to form a standardized data set with temporal and spatial correlation;

[0052] Step S3: Normalize the sensor concentration values, pipe network service years, real-time pressure gradient, cumulative sedimentation, and complaint event spatial density distribution characteristics in the standardized data set to extract a multimodal spatiotemporal feature matrix containing 18-dimensional feature vectors;

[0053] Step S4: According to the multimodal spatiotemporal feature matrix and the spatial distribution characteristics of the historical leakage accident database, two core monitoring points with leakage frequencies higher than the threshold are selected, and their pipe type, service age degradation coefficient, and surrounding population density parameters are obtained. The sensor concentration, sedimentation accumulation, and complaint density dimensions of the multimodal spatiotemporal feature matrix are dynamically corrected by weighting factors;

[0054] Step S5: Input the corrected multimodal spatiotemporal feature matrix for 12 consecutive hours into the LSTM-CNN hybrid neural network model. Through time series feature extraction and spatial correlation analysis, a gas leakage probability prediction result for the next 2 hours is generated, and a leakage heat map with a spatial resolution of 50 meters × 50 meters and a temporal resolution of 15 minutes is output.

[0055] Step S6: According to the leakage probability value of each grid unit in the thermal map, when the yellow warning threshold of 30% is reached, the regional monitoring enhancement instruction is triggered; when the orange threshold of 70% is reached, the pipeline pressure regulation is started; when the red threshold of 90% is exceeded, the emergency command system is linked to execute the automated disposal process of closing the valve and cutting off the source, and evacuating and guiding.

[0056] In an embodiment of the present invention, multimodal data fusion and spatiotemporal correlation analysis technology are used to significantly improve the accuracy and response efficiency of gas leak warnings: a three-dimensional pipeline network topology model is constructed using UTM projection and NTP clock synchronization to eliminate geographical errors and time asynchrony problems, achieve centimeter-level precise positioning of the leak source, and dynamically correct key features (concentration, sedimentation, and complaint density). Combined with the LSTM-CNN hybrid model to mine spatiotemporal correlation patterns, a high-resolution heat map with a 50m×50m grid and a 15-minute interval is generated, achieving a detection rate of over 95% for small leaks and a 40% reduction in false alarm rate. Based on probability threshold-based hierarchical triggering of monitoring enhancement, pressure regulation, and valve closing and evacuation commands, the warning response time is shortened to 15 minutes, and the efficiency of handling high-risk events is increased by 60%. At the same time, the pipeline network risk model is optimized through closed-loop feedback, adapting to pipeline network aging and dynamic environmental changes, significantly improving the safety and reliability of the urban gas system.

[0057] In a preferred embodiment of the present invention, the above step S1 may include:

[0058] Step S1.1: Receive the gas concentration data stream from each sensor node in real time through the NB-IoT communication protocol, clean up the abnormal values, and obtain standardized time series data;

[0059] Step S1.2: Extract the accident time, geographic coordinates, leakage intensity, and disposal records from the historical leakage accident database and store them by accident type.

[0060] In an embodiment of the present invention, the gas concentration data stream is collected in real time through the NB-IoT communication protocol and outliers are cleaned to generate standardized time series data, thereby ensuring the reliability and format uniformity of the real-time monitoring data. At the same time, multi-dimensional information such as accident time, geographic coordinates, leakage intensity and disposal records are extracted from the historical leakage accident database and stored by type. A basic data set containing historical spatiotemporal characteristics and disposal experience is constructed, realizing the standardized integration of real-time monitoring data and historical accident data, effectively solving the compatibility problem of heterogeneous data collection, and improving the standardization and availability of data preprocessing.

[0061] In an embodiment of the present invention, the specific steps include:

[0062] Step S1.1 deploys NB-IoT combustible gas sensors at key nodes of the gas pipeline network (such as valves, elbows, welds and other high-risk locations), uses the NB-IoT communication protocol (narrowband Internet of Things technology) to establish a wireless connection, and receives the gas concentration data stream uploaded periodically by each sensor node in real time (for example, collecting a concentration value every 15 seconds). The received raw data is quality controlled and the cleaned data is converted into a time series in a unified format. Each data point contains a timestamp and gas concentration value, forming a standardized real-time concentration data set arranged in chronological order.

[0063] Step S1.2 retrieves and extracts key information from the historical leakage accident database, including: recording the specific date and time of the accident (accurate to the hour and minute), using the UTM projection coordinate system, recording the latitude and longitude or plane coordinates of the leakage point, quantitative indicators (such as leakage rate, duration, and impact range) or qualitative levels (such as minor, moderate, and severe), including emergency response details such as alarm time, valve closing time, personnel evacuation range, and maintenance measures. The extracted data is classified according to the cause of the accident (such as pipe corrosion, external force damage, construction defects, stress cracking, etc.) or leakage type (such as slow leakage and sudden pipe burst), and a structured storage table is established.

[0064] In a preferred embodiment of the present invention, the above step S2 may include:

[0065] Step S2.1: Convert the sensor node coordinates, historical leakage point coordinates, and pipe network topology data into a unified UTM projection coordinate system to eliminate distance calculation errors caused by the curvature of the earth in geographic coordinates. Construct a pipe network topology spatial model containing three-dimensional coordinates based on pipe segment connection relationships and node elevation data.

[0066] Step S2.2: Based on the topological spatial model, the local clocks of all sensor nodes are synchronized at the millisecond level using the NTP protocol. A unified reference timestamp is added to the real-time concentration data stream and pipe network pressure monitoring data. Time information in historical leakage accident records is converted to a different time zone and compensated for time differences to ensure alignment with the real-time data stream.

[0067] Step S2.3: Align the real-time data streams on the same time axis, use the network topology nodes as spatial units, perform sliding window statistics on the real-time concentration data and pressure gradient data in 15-minute time windows, and spatially superimpose the statistical results with the historical leakage events and sedimentation monitoring data in the corresponding time window to obtain a standardized data set with spatiotemporal labels.

[0068] In an embodiment of the present invention, the geographic coordinates of sensor nodes, historical leakage points and pipe network topology are converted through the UTM projection coordinate system, the distance calculation error caused by the curvature of the earth is eliminated, and a pipe network topology spatial model containing three-dimensional coordinates is constructed, thereby achieving high-precision alignment of geographic spatial data. The sensor node clocks are synchronized at the millisecond level with the help of the NTP protocol, and the historical data are converted into time zones and compensated for time differences to ensure that the real-time data stream and the historical data are aligned on a unified time axis, solving the time asynchrony problem of heterogeneous data. By taking the pipe network topology node as the unit and performing data statistics and spatial superposition in a 15-minute time window, a standardized data set with spatiotemporal labels is generated, thereby achieving precise fusion of multi-source data (real-time concentration, pressure gradient, historical leakage, and sedimentation) in the spatiotemporal dimensions.

[0069] In an embodiment of the present invention, the specific steps include:

[0070] Step S2.1: Project the latitude and longitude coordinates of the sensor nodes, the coordinates of historical leakage points, and the pipeline network topology data (e.g., valve and elbow locations) uniformly to the corresponding zones of the UTM (Universal Transverse Mercator) coordinate system (e.g., the 50N and 51N zones commonly used in China) to eliminate distance calculation errors caused by the curvature of the earth (e.g., converting spherical coordinates to plane rectangular coordinates to reduce the 1 km distance error from several meters to centimeters). For elevation data, use the EGM96 geoid model or a refined regional quasi-geoid model to convert the altitude value of each node to the Z-axis coordinate of the UTM coordinate system to form a complete three-dimensional coordinate system (X, Y, Z). Define the adjacency matrix between nodes based on the connection relationships between pipe sections (e.g., welding and flange connections) to construct the pipeline network topology. Combine the three-dimensional coordinate information with the topological relationships to establish a three-dimensional pipeline network topology model that includes spatial location and connection relationships (e.g., using a graph structure representation, with each node storing the three-dimensional coordinates and adjacent node information).

[0071] Step S2.2: Synchronize the local clocks of all sensor nodes through the NTP (Network Time Protocol) server to achieve millisecond-level time accuracy (e.g., error ≤ 1ms). Perform clock calibration regularly (e.g., every hour) to compensate for time deviations caused by network delays and crystal oscillator drift. Attach a unified reference timestamp (e.g., UTC time) to dynamic data such as real-time concentration data streams and pipeline pressure monitoring data. Perform time zone conversion on the time information in historical leakage accident records (e.g., convert local time to UTC). Correct the time offset caused by device clock differences through a time difference compensation algorithm (e.g., based on GPS timing records).

[0072] Step S2.3: Taking the pipe network topology node as the spatial unit, perform sliding statistics on the real-time concentration data and pressure gradient data in a 15-minute time window. Calculate the mean concentration, where Is the timestamp The average value of gas concentration in the 15-minute sliding window at the time, is the number of data points in the sliding window, is the sum index used to traverse each data point in the sliding window, It is the timestamp of the current calculation, the average concentration within the time window, the concentration change rate and other indicators. The statistical results are spatially superimposed with historical leakage events in the corresponding time window (such as whether leakage has occurred at this node or adjacent nodes) and settlement monitoring data (such as pipeline stress changes caused by geological settlement). A spatiotemporal label (such as node ID, time window, geographic coordinates) is added to each data point to form a standardized spatiotemporal correlation data set (for example, the format is: node ID, timestamp, concentration mean, pressure gradient, historical leakage frequency, settlement).

[0073] In a preferred embodiment of the present invention, the above step S3 may include:

[0074] Step S3.1: Normalize the sensor concentration values, pipe network service life, real-time pressure gradient, cumulative precipitation, and complaint event spatial density distribution characteristics in the standardized data set to obtain normalized data;

[0075] Step S3.2: Extract the following 18-dimensional features from the normalized data: the mean, variance, and gradient change rate of the concentration for 12 consecutive hours; the accumulated settlement within a 50-meter radius of the node; the complaint density; the concentration difference between adjacent nodes; the service life degradation coefficient of the pipe section; the corrosion resistance grade of the pipe material; the sealing score of the joint; the real-time pressure gradient; the temperature influencing factor; the wind speed diffusion coefficient; the historical leakage frequency at the same location; the response time for similar accidents; the coverage of maintenance records; the connectivity of the node in the pipeline network; the upstream and downstream pressure difference; and the pipe bending radius.

[0076] Step S3.3: Integrate the above features into a multidimensional matrix according to the spatiotemporal labels, and store the feature matrix in slices according to the pipe network area.

[0077] In an embodiment of the present invention, multi-source heterogeneous data is normalized to eliminate the interference of different dimensions on feature analysis, thereby ensuring data comparability and model compatibility; 18-dimensional multimodal spatiotemporal features including time series features, spatial distribution features, pipeline network attribute features and environmental history features are extracted to comprehensively cover the key influencing factors of gas leakage risk, breaking through the limitations of traditional single concentration monitoring, integrating features into a multidimensional matrix according to spatiotemporal labels and storing them in partitions, and constructing a structured and hierarchical feature data set, which not only supports the LSTM-CNN hybrid model's deep mining of spatiotemporal correlation laws, but also facilitates the rapid call of high-risk feature data by region, significantly improving feature expression capabilities and model input efficiency, providing a rich and balanced feature vector basis for refined prediction of leakage probability, and effectively enhancing the robustness of the early warning model and the accuracy of risk identification.

[0078] In an embodiment of the present invention, the specific steps include:

[0079] Step S3.1: Determine the normalization object. Normalize the following heterogeneous data in the standardized data set to eliminate dimensional differences: sensor concentration value (unit: ppm or LEL%), pipeline network service life (unit: year), real-time pressure gradient (unit: Pa / min), cumulative precipitation (unit: mm), spatial density of complaint events (unit: times / km2). Min-Max scaling: Applicable to data with clear boundaries (such as concentration values ​​0-100% LEL). The formula is: , Z-score standardization: Applicable to data that obeys normal distribution (such as pressure gradient), the formula is: ,in, is a specific value in the original data set, is the formula for calculating the mean (mathematical expectation) of the original data set ： , The standard deviation of the original data set is calculated as: , is the number of samples in the dataset, It is an index variable, which quantifies qualitative data: it directly uses numerical mapping for pipe corrosion resistance grades (such as grades 1-5) and sealing scores (0-100 points), and calculates normalization parameters (such as minimum value, maximum value, mean value, standard deviation) for each data dimension separately to generate a normalized data set so that all eigenvalues ​​are uniformly mapped to the interval [-1, 1] or [0, 1].

[0080] Step S3.2: 18-dimensional multimodal spatiotemporal feature extraction, concentration mean, variance, gradient change rate for 12 consecutive hours, settlement accumulation within a 50-meter radius around the node, complaint density, concentration difference between adjacent nodes, pipe section service life degradation coefficient, pipe corrosion resistance grade, joint sealing score, real-time air pressure gradient, temperature influencing factor, wind speed diffusion coefficient, historical leakage frequency at the same location, response time for similar accidents, maintenance record coverage, node connectivity in the pipeline network, upstream and downstream pressure difference, and pipeline bending radius.

[0081] Step S3.3: Add spatiotemporal labels to each feature vector: spatial label: pipeline node ID, UTM coordinates (X, Y, Z), and region (such as administrative district, grid number); time label: time window start / end stamp (such as 2025-05-22 08:00:00~09:59:59). Organize the 18-dimensional features into a two-dimensional matrix according to the sample × feature dimension. Each sample corresponds to the feature vector of a pipeline node in a certain time window. When expanding to a three-dimensional matrix, the time step dimension can be added (such as 12 consecutive time windows of 12 hours, forming a tensor of 12 × number of nodes × 18 dimensions). The feature matrix is ​​sharded and stored in a distributed database according to the pipeline network area (such as administrative district, pressure zone).

[0082] In a preferred embodiment of the present invention, the above step S4 may include:

[0083] Step S4.1: Based on the spatial distribution characteristics of the historical leakage accident database, the leakage frequency of each pipeline network node in the past five years is counted, and two core monitoring points with leakage frequencies exceeding the preset threshold are selected as high-risk area benchmarks;

[0084] Step S4.2: Based on the high-risk area benchmark, extract the pipe type and service life degradation coefficient of the core monitoring point from the pipe network attribute database, and combine it with the GIS map to obtain the population density parameters within 500 meters around it;

[0085] Step S4.3: Generate a pipe risk weight based on the product of the pipe type's corrosion resistance grade and the degradation coefficient. This weight is used to amplify the sensor concentration values ​​in pipe sections with high corrosion propensity. Based on the linear relationship between population density and accumulated sedimentation, a population-sedimentation coupling weight is calculated. Nonlinear enhancement is performed on the sedimentation characteristics of high-density areas. Combined with the spatiotemporal overlap of historical complaint density and leak events, a complaint credibility weight is generated to enhance the contribution of complaint density characteristics in areas with high complaint frequencies.

[0086] Step S4.4: Apply the above weight factors to the sensor concentration, cumulative sedimentation and complaint density dimensions in the multimodal spatiotemporal feature matrix respectively, and dynamically modify the eigenvalues ​​of the core monitoring point and its adjacent nodes according to the formula: the corrected eigenvalue is equal to the original eigenvalue into dynamic weights.

[0087] In an embodiment of the present invention, core monitoring points are screened based on historical leakage frequencies to accurately locate high-risk areas, solving the problem of ambiguous identification of potential danger areas in traditional methods. Dynamic weighting factors are generated based on pipe corrosion resistance, service life degradation coefficient, and population density parameters, achieving differentiated enhancement of key characteristics such as sensor concentration, accumulated settlement, and complaint density. Concentration anomaly signals in highly corroded pipe sections are amplified by pipe risk weights, geological settlement risks in densely populated areas are highlighted by population-sedimentation coupling weights, and the value of human monitoring feedback in high-frequency complaint areas is enhanced with the help of complaint credibility weights. The characteristic values ​​of core monitoring points and their adjacent nodes are dynamically corrected, allowing the model to focus on the multimodal risk characteristics of historically high-incidence areas of leakage, effectively improving the feature expression capabilities in high-risk scenarios, avoiding interference with the model from data in low-risk areas, significantly enhancing the pertinence and sensitivity of leakage warnings, and providing more differentiated input data for subsequent spatiotemporal prediction models. This achieves a technical upgrade from global universal monitoring to regional precise warnings, and significantly improves the system's adaptability to complex pipe network environments.

[0088] In an embodiment of the present invention, the specific steps include:

[0089] Step S4.1: Count the accident records of each pipeline network node from the historical leakage accident database, count the number of leakages N at each node in the past 5 years, and calculate the leakage frequency , preset threshold Based on industry standards or historical data averages, filter out Greater than The nodes are arranged in descending order of leakage frequency, and the first two nodes are selected as core monitoring points. Through GIS spatial analysis, it is confirmed whether the geographical distribution of core monitoring points is clustered (for example, whether they are located in the same geological subsidence zone or old pipeline network area) and isolated abnormal points are excluded.

[0090] Step S4.2: Query the following parameters of the core monitoring points from the pipe network attribute library: Pipe type: such as cast iron pipe, steel pipe, PE pipe, etc., corresponding to the preset corrosion resistance level (such as cast iron pipe level 2, PE pipe level 4). Service life degradation coefficient: Based on the service life T of the pipe section and the design life T0, the formula is: ,calculate is the material degradation constant, where is the rate parameter, It is a time variable. Through GIS map service, a buffer zone with a radius of 500 meters is drawn with the core monitoring point as the center to extract the population density data in the area. (Unit: people / square kilometer). Data sources include census grids, real-time mobile signal density, etc.

[0091] Step S4.3: Pipe risk weight , =Corrosion resistance grade × degradation coefficient, weighted amplification of the sensor concentration value, the formula is: , (the higher the risk, the greater the concentration signal amplification factor), fitting population density through historical data and the cumulative amount of settlement The linear equation , calculate the slope and intercept , using the exponential function ,in, is the population-sediment coupling weight, is the coupling coefficient, Represents the parameters related to settlement, It is a population density parameter. When the population density is higher and the amount of sedimentation is greater, the weight value is greater. The number of coincidences between historical complaint events and leakage events in time (±2 hours) and space (±50 meters) within 50 meters around the core monitoring point is counted. , total number of complaints , then the overlap , weight generation: ,in, is the number of times historical complaint events and leakage events coincide in time and space, is the total number of complaints within 50 meters around the core monitoring point, It is the degree of space-time overlap.

[0092] Step S4.4: Directly perform weighted correction on the characteristic values ​​of sensor concentration, accumulated precipitation, and complaint density. Based on the topological connection relationship (such as upstream and downstream nodes of the pipeline) or spatial distance (such as nodes within 50 meters), apply partial weights according to the distance attenuation coefficient (such as the weight decreases by 10% for every 10-meter increase in distance). The sensor concentration dimension is: , the dimension of accumulated settlement: , complaint density dimension: , the corrected eigenvalues ​​replace the original values ​​of the corresponding nodes in the multimodal spatiotemporal feature matrix to form a dynamically corrected feature matrix.

[0093] In a preferred embodiment of the present invention, the above step S5 may include:

[0094] Step S5.1: The 12-hour corrected spatiotemporal feature matrix is ​​reorganized into a three-dimensional tensor by time step as the model input. A bidirectional LSTM layer is used to extract time series features to capture the long-term dependencies between concentration fluctuations and pressure gradient changes. A three-dimensional convolutional layer is used to mine the spatial correlation of the pipeline network topology.

[0095] Step S5.2: Perform a weighted fusion of the temporal features output by the LSTM and the spatial features extracted by the CNN using an attention mechanism to generate a leakage probability prediction value for each pipeline network node in the next 2 hours. Based on the 3D coordinates of the pipeline network nodes, a spatial interpolation algorithm is used to map the node-level prediction value to a 50 m × 50 m grid cell. The diffusion range is corrected by combining real-time wind speed and terrain elevation data to generate a leakage probability heat map with a time resolution of 15 minutes.

[0096] In this embodiment of the present invention, a model input structure that integrates time series and spatial topology information is constructed by reorganizing the spatiotemporal feature matrix corrected for 12 consecutive hours into a three-dimensional tensor. A bidirectional LSTM layer is used to deeply capture the long-term dependencies of dynamic data such as concentration fluctuations and pressure gradients. A three-dimensional convolutional layer is used to explore the spatial correlations between pipeline network nodes. This overcomes the limitations of single models in analyzing spatiotemporal features separately. An attention mechanism is used to adaptively weight the temporal features output by the LSTM and the spatial features extracted by the CNN, achieving an organic coupling of spatiotemporal information and significantly improving the accuracy of leak probability prediction. A spatial interpolation algorithm based on the three-dimensional coordinates of pipeline network nodes is used to map node-level predictions to 50-meter-by-50-meter grid cells. The diffusion range is corrected by combining real-time wind speed and terrain elevation data to generate a leak heat map with high spatiotemporal resolution. This achieves refined spatial distribution and dynamic deduction of leak risks, improving the spatial positioning accuracy of small leaks to the grid level and enabling a two-hour lead time for prediction. This provides a real-time, intuitive decision-making basis for emergency response and effectively addresses the spatial ambiguity and temporal lag problems of traditional early warning methods.

[0097] In an embodiment of the present invention, the specific steps include:

[0098] Step S5.1: Reorganize the corrected spatiotemporal feature matrix of 12 consecutive hours into a three-dimensional tensor according to the time step, and input the time step dimension of the three-dimensional tensor as a sequence into the bidirectional LSTM network. Each time step corresponds to a 15-minute feature vector (such as concentration mean, pressure gradient, etc.). The forward LSTM is used to capture the dependency of the past time series (such as the rising trend of concentration), and the backward LSTM is used to capture the dependency of the future time series (such as the impact of historical data on the current state). The time series feature vector containing time context information is output with a dimension of , the pipe network topology is mapped into a three-dimensional space grid (X, Y, Z) coordinates), the feature vector of each node is used as the attribute value of the grid point, and a three-dimensional convolution kernel is used as follows Perform convolution operations on the features of the node and its adjacent topological nodes to extract spatial local correlations (such as concentration differences between adjacent nodes and the impact of pipe network connectivity), and output a feature map containing spatial neighborhood information with a dimension of .

[0099] Step S5.2: Map the temporal features output by LSTM and the spatial features output by CNN to the same dimension through a fully connected layer , weighted fusion formula: , is the time series feature, The fused feature vector is input into the fully connected layer, and the leakage probability value of each node in the next 2 hours is output through the Sigmoid activation function. Based on the three-dimensional coordinates of the pipeline network node, , mapping the node prediction value to a 50m×50m grid cell, where is the total number of known points involved in interpolation, is the index of the known point, Is the point to be interpolated to The distance to a known point, It is a distance weight index. The probability value of each grid cell is the weighted average of all node values ​​in the cell, and the weight is inversely proportional to the distance from the node to the grid center. According to the real-time wind speed v (m / s) and wind direction, the leakage diffusion range is expanded along the dominant wind direction. The formula is: diffusion distance = v × prediction time × diffusion coefficient. Using DEM (digital elevation model) data, leakage diffusion in mountainous areas, river valleys and other terrain areas is blocked or accelerated. The leakage probability value of each grid cell is mapped to a color gradient (such as 0-30% is blue, 30%-70% is yellow, 70%-90% is orange, and >90% is red). The pipeline network topology layer is superimposed to generate a dynamic leakage probability heat map with a time resolution of 15 minutes and a spatial resolution of 50 meters × 50 meters.

[0100] In a preferred embodiment of the present invention, the above step S6 may include:

[0101] Step S6.1: Real-time analysis of the leakage probability value for each 50m x 50m grid cell in the thermal map. If the probability value reaches a first preset threshold of 30% or higher, the area is marked as a yellow warning area. Instructions for enhanced regional monitoring are issued, and the sensor sampling frequency in that area is dynamically increased to once per second. Mobile inspection equipment is dispatched for on-site verification.

[0102] Step S6.2: If the probability value reaches the second preset threshold value ≥ 70%, the area is marked as an orange warning zone, and a pipeline network pressure control instruction is obtained. Based on the topological relationship of the upstream pipe section of the leak point, the final pressure attenuation coefficient is calculated, and the pipeline network pressure is reduced to below the safety threshold through the remote control valve;

[0103] Step S6.3: If the probability value exceeds the third preset threshold value ≥90%, it is marked as a red warning area, and an emergency linkage instruction is received, which automatically triggers the valve closing and source isolation operation, closes all main control valves within 500 meters upstream and downstream of the leakage point, and sends an evacuation guidance signal to the emergency command system, and the associated electronic fence system activates the sound and light alarm devices within a radius of 1 km.

[0104] In an embodiment of the present invention, by establishing a multi-level threshold warning mechanism, accurate hierarchical control of leakage risks is achieved, leakage probability heat maps are analyzed in real time, and differentiated emergency strategies are implemented for different risk levels: for yellow warning areas, by increasing the sampling frequency and combining it with mobile inspections, timely capture and manual verification of early risks are achieved to avoid misjudgment; for orange warning areas, the pressure attenuation coefficient is dynamically calculated based on the pipeline network topology, and the pipeline network pressure is accurately regulated to reduce the risk of leakage spread; for red warning areas, multi-system linkage of valve shutdown, source interruption, emergency evacuation and sound and light alarms is automatically triggered to minimize the hazards of accidents. This hierarchical response mode effectively integrates monitoring, control and emergency response links, which not only improves the timeliness and accuracy of the warning, but also reduces the cost of over-response, providing a strong guarantee for scientific decision-making and efficient handling of gas leakage accidents.

[0105] In an embodiment of the present invention, the specific steps include:

[0106] Step S6.1: Continuously read the real-time probability value P of each 50m×50m grid cell in the leakage probability heat map, and update the analysis results according to the time step (15 minutes). When any grid cell is leaking for more than one time step (15 minutes), it is judged as a yellow warning. The sensor sampling frequency is increased: all fixed sensor nodes in the warning area are located, and instructions are sent through the NB-IoT protocol to increase the sampling frequency from the default 15 seconds / time to 1 second / time, and high-frequency concentration data is transmitted back in real time. Mobile inspection scheduling: Send a task instruction to the nearest mobile inspection equipment (such as a drone or inspection vehicle equipped with a gas detector), including the coordinates of the warning area and the priority level (yellow). After the mobile device arrives, it performs a carpet scan with a radius of 50 meters with the grid cell as the center, and obtains on-site concentration data through PID (photoionization detection) or laser spectroscopy technology, and transmits it back to the system for manual review.

[0107] Step S6.2: When the P of the grid unit is greater than or equal to 70% or the yellow warning area has not been downgraded within 1 hour and the risk range has expanded, it is upgraded to an orange warning. Based on the pipeline network topology model, the pipeline network node corresponding to the warning grid is located, the connection relationship of the upstream pipeline section is traced, and a tree diagram of the upstream pipeline section is generated. The key control valves (such as main pipeline valves and zoning valves) are identified. According to the diameter, length, material and current pressure value of the upstream pipeline section, the , calculate the minimum service pressure to be maintained after depressurization through hydraulic models (such as the Hazen-Williams formula) , the formula is: , through the step SCADA system, send instructions to the selected upstream valve, gradually reduce the pressure according to the attenuation coefficient (such as completing the pressure reduction within 5 minutes), monitor the pressure change curve in real time, and trigger secondary adjustment if the fluctuation exceeds ±5%.

[0108] Step S6.3: When the P of the grid unit is greater than or equal to 90% or the orange warning area has not been downgraded within 30 minutes and a sudden increase in concentration occurs (gradient change rate > 50% / 15 minutes), it is upgraded to a red warning. Based on the 3D topological model, all main control valves within 500 meters upstream and downstream of the leak point are determined (double calibration is performed using geographical distance and pipe length). A valve closing instruction is sent to the valve actuator via industrial Ethernet or wireless private network, requiring the valve to be closed within 90 seconds. The valve status (fully open / fully closed / faulty) is fed back to the system, and a report containing the leak location, risk, etc. is pushed to the emergency command system. The system can store structured data (such as JSON format) on the level and scope of impact, triggering the following operations: Electronic fence: The Beidou positioning system is used to demarcate a 1-kilometer area as an evacuation zone, and SMS warnings (including evacuation route links) are sent to mobile phone users in the area. Sound and light alarm: The smart fire hydrants, street lights and other IoT devices in the area are activated to send high-frequency sound and light signals (flashing frequency 2Hz, voice broadcast, emergency evacuation). Resource dispatch: Dispatch instructions are sent to fire, gas repair, medical and other departments simultaneously, along with the characteristics of the leaked material (such as methane concentration and diffusion rate) and the on-site 3D model.

[0109] like Figure 2 As shown, an embodiment of the present invention further provides a gas leak spatiotemporal correlation early warning system based on multimodal data fusion, including:

[0110] The acquisition module is used to collect real-time concentration data and a historical leakage accident database through the NB-IoT combustible gas sensor network deployed at key nodes in the gas pipeline network. Based on the real-time concentration data and the historical leakage accident database, the UTM projection coordinate system is used to align the geospatial data, establish a three-dimensional spatial mapping model of the pipeline network topology, and align the timestamps of all dynamic monitoring data streams using the NTP clock synchronization protocol to form a standardized data set with temporal and spatial correlation.

[0111] The calculation module is used to normalize the sensor concentration values, pipeline service life, real-time pressure gradient, cumulative sedimentation, and spatial density distribution characteristics of complaint events in the standardized data set, and extract a multimodal spatiotemporal feature matrix containing 18-dimensional feature vectors. Based on the multimodal spatiotemporal feature matrix and the spatial distribution characteristics of the historical leakage accident database, two core monitoring points with leakage frequencies above the threshold are selected, and their pipe type, service life degradation coefficient, and surrounding population density parameters are obtained. The sensor concentration, cumulative sedimentation, and complaint density dimensions of the multimodal spatiotemporal feature matrix are dynamically corrected using weighting factors. The corrected multimodal spatiotemporal feature matrix for 12 consecutive hours is input into the LSTM-CNN hybrid neural network model. Through time series feature extraction and spatial correlation analysis, a gas leakage probability prediction result for the next two hours is generated, and a leakage heat map with a spatial resolution of 50 meters × 50 meters and a temporal resolution of 15 minutes is output.

[0112] The processing module is used to trigger the regional monitoring enhancement command when the leakage probability value of each grid unit in the thermal map reaches the yellow warning threshold of 30%, start the pipeline pressure regulation when it reaches the orange threshold of 70%, and link the emergency command system to execute the automated disposal process of closing valves and cutting off sources and evacuating guidance when it exceeds the red threshold of 90%.

[0113] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A gas leak spatiotemporal correlation early warning method based on multimodal data fusion, characterized by: The method comprises: Step S1: Collect real-time concentration data and a historical leakage accident database through the NB-IoT combustible gas sensor network deployed at key nodes of the gas pipeline network; Step S2: Based on the real-time concentration data and the historical leakage accident database, the UTM projection coordinate system is used to align the geospatial data, and a three-dimensional spatial mapping model of the pipeline network topology is established. The NTP clock synchronization protocol is used to align the timestamps of all dynamic monitoring data streams to form a standardized data set with temporal and spatial correlation; Step S3: Normalize the sensor concentration values, pipe network service life, real-time pressure gradient, cumulative precipitation, and complaint event spatial density distribution characteristics in the standardized data set to extract a multimodal spatiotemporal feature matrix containing 18-dimensional feature vectors, including: Step S3.1: Normalize the sensor concentration values, pipe network service life, real-time pressure gradient, cumulative precipitation, and complaint event spatial density distribution characteristics in the standardized data set to obtain normalized data; Step S3.2: Extract the following 18-dimensional features from the normalized data: the mean, variance, and gradient change rate of the concentration for 12 consecutive hours, the accumulated settlement within a 50-meter radius around the node, the complaint density, the concentration difference between adjacent nodes, the service life degradation coefficient of the pipe section, the corrosion resistance grade of the pipe material, the sealing score of the joint, the real-time pressure gradient, the temperature influencing factor, the wind speed diffusion coefficient, the historical leakage frequency at the same location, the response time for similar accidents, the coverage of maintenance records, the connectivity of the node in the pipeline network, the upstream and downstream pressure difference, and the pipe bending radius; Step S3.3: Integrate the above features into a multidimensional matrix according to the spatiotemporal labels, and store the feature matrix in slices according to the pipe network area; Step S4: Based on the multimodal spatiotemporal feature matrix and the spatial distribution characteristics of the historical leakage accident database, two core monitoring points with leakage frequencies higher than the threshold are selected, and their pipe type, service life degradation coefficient, and surrounding population density parameters are obtained. The sensor concentration, accumulated sedimentation, and complaint density dimensions of the multimodal spatiotemporal feature matrix are dynamically corrected using weighting factors, including: Step S4.1: Based on the spatial distribution characteristics of the historical leakage accident database, the leakage frequency of each pipeline network node in the past five years is counted, and two core monitoring points with leakage frequencies exceeding the preset threshold are selected as high-risk area benchmarks; Step S4.2: Based on the high-risk area benchmark, extract the pipe type and service life degradation coefficient of the core monitoring point from the pipe network attribute database, and combine it with the GIS map to obtain the population density parameters within 500 meters around it; Step S4.3: Generate a pipe risk weight based on the product of the pipe type's corrosion resistance grade and the degradation coefficient. Calculate a population-sedimentation coupling weight based on the linear relationship between population density and accumulated settlement. Combined with the historical complaint density and the spatiotemporal overlap of leakage events, generate a complaint credibility weight. Step S4.4: Apply the pipe risk weight, population-sedimentation coupling weight, and complaint credibility weight to the sensor concentration, sedimentation accumulation, and complaint density dimensions in the multimodal spatiotemporal feature matrix, respectively, and dynamically modify the eigenvalues ​​of the core monitoring point and its adjacent nodes. Step S5: The corrected multimodal spatiotemporal feature matrix for 12 consecutive hours is input into the LSTM-CNN hybrid neural network model. Through time series feature extraction and spatial correlation analysis, a gas leak probability prediction result for the next two hours is generated. The leakage heat map with a spatial resolution of 50 meters × 50 meters and a temporal resolution of 15 minutes is output, including: Step S5.1: The 12-hour corrected spatiotemporal feature matrix is ​​reorganized into a three-dimensional tensor by time step as the model input. A bidirectional LSTM layer is used to extract time series features to capture the long-term dependencies between concentration fluctuations and pressure gradient changes. A three-dimensional convolutional layer is used to mine the spatial correlation of the pipeline network topology. Step S5.2: Perform a weighted fusion of the temporal features output by the LSTM and the spatial features extracted by the CNN using an attention mechanism to generate a leakage probability prediction value for each pipeline network node in the next 2 hours. Based on the 3D coordinates of the pipeline network nodes, a spatial interpolation algorithm is used to map the node-level prediction value to a 50 m × 50 m grid cell. The diffusion range is corrected by combining real-time wind speed and terrain elevation data to generate a leakage probability heat map with a time resolution of 15 minutes.

2. The gas leak spatiotemporal correlation early warning method based on multimodal data fusion according to claim 1 is characterized in that: The NB-IoT combustible gas sensor network deployed at key nodes of the gas pipeline network collects real-time concentration data and a historical leakage accident database, including: Step S1.1: Receive the gas concentration data stream from each sensor node in real time through the NB-IoT communication protocol, clean up the abnormal values, and obtain standardized time series data; Step S1.2: Extract the accident time, geographic coordinates, leakage intensity, and disposal records from the historical leakage accident database and store them by accident type.

3. The gas leak spatiotemporal correlation early warning method based on multimodal data fusion according to claim 2 is characterized in that: Based on real-time concentration data and a historical leak accident database, the UTM projection coordinate system is used to align geographic spatial data, establish a three-dimensional spatial mapping model of the pipeline network topology, and align the timestamps of all dynamic monitoring data streams using the NTP clock synchronization protocol to form a standardized data set with temporal and spatial correlation, including: Step S2.1: Convert the sensor node coordinates, historical leakage point coordinates, and pipe network topology data into a unified UTM projection coordinate system to eliminate distance calculation errors caused by the curvature of the earth in geographic coordinates. Construct a pipe network topology spatial model containing three-dimensional coordinates based on pipe segment connection relationships and node elevation data. Step S2.2: Based on the topological spatial model, the local clocks of all sensor nodes are synchronized at the millisecond level using the NTP protocol. A unified reference timestamp is added to the real-time concentration data stream and pipe network pressure monitoring data. Time information in historical leakage accident records is converted to a different time zone and compensated for time differences to ensure alignment with the real-time data stream. Step S2.3: Align the real-time data streams on the same time axis, use the network topology nodes as spatial units, perform sliding window statistics on the real-time concentration data and pressure gradient data in 15-minute time windows, and spatially superimpose the statistical results with the historical leakage events and sedimentation monitoring data in the corresponding time window to obtain a standardized data set with spatiotemporal labels.

4. The gas leak spatiotemporal correlation early warning method based on multimodal data fusion according to claim 3 is characterized in that: After step S5, the method further includes: Step S6: Based on the leakage probability value of each grid unit in the thermal map, when the yellow warning threshold of 30% is reached, the regional monitoring enhancement instruction is triggered; when the orange threshold of 70% is reached, the pipeline pressure regulation is started; when the red threshold of 90% is exceeded, the emergency command system is linked to execute the automated disposal process of closing valves and cutting off sources, and evacuation guidance.

5. A gas leak spatiotemporal correlation early warning system based on multimodal data fusion, the system implementing the method according to any one of claims 1 to 4, characterized in that: include: The acquisition module is used to collect real-time concentration data and a historical leakage accident database. Based on the real-time concentration data and the historical leakage accident database, the UTM projection coordinate system is used to align the geographic spatial data, establish a three-dimensional spatial mapping model of the pipeline network topology structure, and align the timestamps of all dynamic monitoring data streams through the NTP clock synchronization protocol to form a standardized data set with temporal and spatial correlation. The calculation module is used to normalize the sensor concentration values, pipeline service years, real-time pressure gradient, cumulative sedimentation, and spatial density distribution characteristics of complaint events in the standardized data set, and extract a multimodal spatiotemporal feature matrix containing 18-dimensional feature vectors; according to the multimodal spatiotemporal feature matrix, based on the spatial distribution characteristics of the historical leakage accident database, two core monitoring points with leakage frequency higher than the threshold are selected, and their pipe type, service year degradation coefficient and surrounding population density parameters are obtained. The sensor concentration, cumulative sedimentation and complaint density dimensions of the multimodal spatiotemporal feature matrix are dynamically corrected by weighting factors; the multimodal spatiotemporal feature matrix after correction for 12 consecutive hours is input into the LSTM-CNN hybrid neural network model, and the gas leakage probability prediction result for the next 2 hours is generated through time series feature extraction and spatial correlation analysis, and a leakage heat map with a spatial resolution of 50 meters × 50 meters and a time resolution of 15 minutes is output.

6. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 4 when executed by a processor.

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