Fire-fighting pipe fitting connector leakage real-time alarm system based on gas sensor

Abstract

The invention relates to the technical field of intelligent monitoring of fire-fighting equipment, in particular to a fire-fighting pipe fitting connector leakage real-time alarm system based on a gas sensor, which comprises the following steps of: acquiring concentration data of various gases by arranging a sensor array, comparing the concentration data with an environmental background feature library by utilizing a background noise suppression algorithm, and determining the leakage of the various gases; and after interference is filtered, an effective leakage characteristic signal is generated. The signal is analyzed through a multi-dimensional leakage evaluation model, and leakage confidence, intensity and preliminary positioning are obtained. And in combination with a fire-fighting pipeline topological structure, the system deduces a leakage influence range and estimates pressure loss, and finally triggers a grading alarm mechanism to generate a composite alarm instruction including grades, positions, influence areas and disposal suggestions. According to the system, false alarms caused by environmental interference can be reduced, the influence range of a leakage event is pre-judged in advance, and the alarm accuracy and the emergency disposal pertinence are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology for fire protection equipment, and in particular to a real-time alarm system for leaks at fire protection pipe fitting interfaces based on gas sensors. Background Technology

[0002] Real-time monitoring of leaks in fire protection piping systems primarily relies on pressure sensors or point-type gas detectors. Pressure sensors determine leaks by monitoring pressure drops within the pipeline; however, this method cannot distinguish between pressure changes caused by normal water use and minor leaks, exhibiting slow response and poor location accuracy. Point-type gas detectors typically deploy a single sensor near critical interfaces to monitor whether the concentration of a specific gas exceeds a preset static threshold. This type of technology directly compares the sensor's instantaneous reading with a fixed value.

[0003] Existing technical solutions have limitations. Readings from a single gas sensor are highly susceptible to natural fluctuations in the background gas values ​​of the monitored environment or interference from temporary, localized non-leakage factors, leading to frequent false alarms. More importantly, whether it's pressure monitoring or a simple gas concentration exceeding limit alarm, the output information is only a binary statement of alarm status. This information is one-dimensional, unable to make a preliminary spatial judgment of the leak point, and completely unable to assess the potential chain reactions and systemic functional degradation that a leak event might trigger under a specific pipeline network physical connection structure. Upon receiving an alarm, maintenance personnel find it difficult to quickly assess the severity level of the event and the potential impact boundaries.

[0004] There is a need for a technical method that can effectively eliminate environmental background interference and improve the accuracy of leak identification. At the same time, there is an urgent need for a technical means to quantitatively predict the development trend and system-level consequences of a leak after it has been confirmed, taking into account the inherent structure of the pipeline network. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and propose a real-time alarm system for leaks in fire-fighting pipe fitting interfaces based on gas sensors.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a real-time alarm system for leaks in fire-fighting pipe fitting interfaces based on gas sensors, comprising: The data acquisition module deploys multiple gas concentration acquisition terminals within a predetermined space range of the fire pipe fitting interface to form a sensor array. The sensor array periodically acquires gas characteristic data, which includes the current concentration values ​​of various specific gases and the acquisition timestamp. The background suppression module inputs the gas characteristic data into the environmental background feature library, filters out environmental background interference through a background noise suppression algorithm, and generates a normalized effective leakage characteristic signal. The leakage assessment module calls a multidimensional leakage assessment model to analyze and process the effective leakage characteristic signals. The multidimensional leakage assessment model outputs the leakage confidence score, leakage intensity level, and preliminary location coordinates of the leakage source. The impact simulation module, based on the leakage intensity level and the preliminary location coordinates of the leakage source, combined with the fire pipeline topology diagram, performs leakage impact range simulation calculations to generate potential impact areas and estimated pipeline pressure loss values. The alarm response module, based on the leakage confidence score, the potential impact area, and the estimated pipeline pressure loss, triggers a tiered alarm response mechanism to generate a composite alarm command that includes the alarm level, alarm location information, and emergency response suggestions.

[0007] As a further aspect of the present invention, the step of filtering out environmental background interference through a background noise suppression algorithm to generate a normalized effective leakage characteristic signal includes: From the environmental background feature library, retrieve the historical gas concentration baseline data, current environmental temperature and humidity data, and system operating condition data corresponding to the current collection time; For each specific gas in the gas characteristic data, the current concentration value collected is subtracted from the corresponding baseline concentration value in the historical gas concentration baseline data to obtain the original difference signal. Based on the current ambient temperature and humidity data, temperature drift compensation and humidity cross-interference compensation are performed on the original difference signal to obtain a preliminary compensation signal; Based on the system operating condition data, identify and filter out known characteristic interference signals generated by normal equipment start-up, shutdown, and maintenance operations; The preliminary compensation signal, after temperature drift compensation, humidity cross-interference compensation, and known characteristic interference filtering, is subjected to moving average filtering to generate a smooth concentration change trajectory for each specific gas. The concentration change trajectory constitutes the normalized effective leakage characteristic signal.

[0008] As a further aspect of the present invention, the step of calling the multidimensional leakage assessment model to analyze and process the effective leakage characteristic signal includes: The effective leakage characteristic signal is divided into continuous signal segments according to a time window; For each signal segment, the following evaluation steps are performed: extract the rising slope, peak size, and duration of change index of each specific gas concentration change trajectory in the signal segment; calculate the matching degree between the rising slope, peak size, and duration of change index and typical leakage patterns in the preset leakage feature fingerprint database, wherein the typical leakage patterns include pinhole continuous leakage, crack jet leakage, and interface loose diffusion leakage. The matching degree calculation result is input into the leakage probability calculation network, which outputs the probability value of the signal segment corresponding to the real leakage event, as the instantaneous leakage confidence at the corresponding time point; By combining the instantaneous leakage confidence of all signal segments within an evaluation period, the overall leakage confidence score is calculated using a time series aggregation algorithm. Based on the concentration peak size, concentration change rate and spatial correlation between data from multiple sensors in the sensor array in the effective leakage characteristic signal, the leakage intensity level is determined by a fuzzy inference system. Based on the temporal sequence of the concentration anomalies detected by each sensor in the sensor array and the concentration gradient distribution, the preliminary location coordinates of the leakage source are estimated using a time difference of arrival (TDOA) localization algorithm or a concentration gradient inversion algorithm.

[0009] As a further aspect of the present invention, the step of performing leakage impact range extrapolation calculations to generate potential impact areas and estimated pipeline pressure loss values ​​includes: The initial location coordinates of the leak source are mapped onto the digitized fire pipeline topology diagram to determine the specific pipe segment and interface location where the leak occurred. Centered on the leak point, the gas diffusion coefficient, ambient flow velocity, and diffusion direction parameters under the current leak scenario are obtained by querying the fluid diffusion model parameter table according to the leak intensity level. Based on the fire pipeline topology diagram, a real-time simulation based on computational fluid dynamics is initiated to simulate the diffusion process of leaked gas in the pipeline corridor space. Combined with the building ventilation conditions, the spatial range in which the gas concentration reaches the warning threshold within the preset warning time is calculated. The spatial range is marked as the potential impact area. Meanwhile, based on the leakage intensity level and the real-time pressure monitoring data of the pipeline where the leakage point is located, a pipeline fluid dynamics model is applied to calculate the estimated pressure loss of the pipeline caused by the leakage and predict the pressure drop trend.

[0010] As a further aspect of the present invention, the triggering graded alarm response mechanism generates a composite alarm command containing alarm level, alarm location information, and emergency response suggestions, including: A hierarchical alarm strategy matrix is ​​constructed, which defines the alarm level corresponding to different combinations of leakage confidence score intervals, leakage intensity levels, and pipeline pressure loss estimation value intervals. Based on the currently obtained leakage confidence score, leakage intensity level, and pipeline pressure loss estimate, the hierarchical alarm strategy matrix is ​​queried to determine the final alarm level, which includes at least four levels: attention, warning, alarm, and critical alarm. By integrating the preliminary location coordinates of the leak source, the range coordinates of the potential impact area, the pipe segment number and interface identifier where the leak point is located, the structured alarm location information is generated. Based on the determined alarm level and leakage intensity level, the corresponding operating steps, personnel evacuation suggestions, valve shut-off or leak plugging instructions are matched and extracted from the emergency plan knowledge base to form the emergency response suggestions. The alarm level, alarm location information, and emergency response suggestions are encapsulated into a standardized data packet to form the composite alarm command.

[0011] As a further aspect of the present invention, the step of matching and extracting corresponding operational steps, personnel evacuation suggestions, and valve shut-off or leak-stopping guidelines from the emergency response plan knowledge base to form the emergency response suggestions includes: Determine the level of emergency response plan that needs to be retrieved based on the alarm level; Based on the leakage intensity level and the size of the potential affected area, determine the scale of the emergency response required. Based on the functional attributes of the area where the preliminary location coordinates of the leak source are located, the distribution of the affected key equipment and personnel is determined; Using the alarm level, emergency action scale, and affected area functional attributes as joint query keys, a multi-condition search can be performed in the emergency plan knowledge base; The search results return one or more standard emergency response procedure templates and a targeted list of key points for handling the situation; Based on the current system operating data and the estimated pipeline pressure loss, the parameters in the standard emergency response procedure template are adjusted to generate specific and immediately executable emergency response recommendations.

[0012] As a further aspect of the present invention, the step of performing temperature drift compensation and humidity cross-interference compensation on the original difference signal based on the current ambient temperature and humidity data includes: A library of temperature-concentration response calibration curves for gas sensors is established, which stores the zero-point drift coefficient and sensitivity variation coefficient of each specific gas sensor at different temperatures. Based on the temperature value in the current ambient temperature and humidity data, query the calibration curve library to obtain the corresponding zero-point drift correction value and sensitivity correction factor; For the original difference signal, firstly, zero-point calibration is performed using the zero-point drift correction value, and then amplitude calibration is performed using the sensitivity correction factor to obtain the temperature-compensated signal. A humidity-cross-sensitivity matrix for gas sensors is established, which describes the proportion of cross-interference caused by changes in ambient humidity to the readings of different types of gas sensors. Based on the humidity value in the current ambient temperature and humidity data, the humidity-cross sensitivity matrix is ​​queried to calculate the false concentration increments of other gas readings caused by humidity changes. Subtracting the spurious concentration increment from the temperature-compensated signal yields the preliminary compensated signal after temperature drift compensation and humidity cross-interference compensation.

[0013] As a further aspect of the present invention, the construction steps of the multidimensional leakage assessment model include: Collect historical ship communication signal quality data, which includes signal reception strength information, signal-to-noise ratio information and corresponding signal quality level labels under different sea conditions and weather conditions. The collected historical ship communication signal quality data is preprocessed to extract signal quality characteristic parameters, including mean signal strength, signal strength variance, mean signal-to-noise ratio, and signal-to-noise ratio fluctuation. The preprocessed signal quality feature parameters are input into a neural network model for training. The neural network model includes an input layer, multiple hidden layers, and an output layer. The backpropagation algorithm is used to adjust the connection weights of the neural network model until the error between the signal quality assessment result output by the model and the signal quality level label is less than a preset threshold. The trained neural network model is saved as the multidimensional leakage assessment model, which is used to analyze and process the effective leakage feature signals acquired in real time.

[0014] As a further aspect of the present invention, the step of estimating the preliminary location coordinates of the leakage source based on the temporal sequence of concentration anomalies detected by each sensor in the sensor array and the concentration gradient distribution, using a time-of-arrival localization algorithm or a concentration gradient inversion algorithm, includes: All sensors that detected concentrations significantly exceeding the threshold within the same evaluation period are selected from the sensor array and considered as the effective response sensor set. Record the time point at which each sensor in the set of effective response sensors first detects a concentration anomaly as the arrival time; Calculate the time difference of arrival between any two valid response sensors; Based on the known spatial coordinate layout of the sensor array, a set of coordinate points that best fit all arrival time difference data is solved using the hyperbolic positioning equations or a grid search-based optimization algorithm, which serves as candidate locations for the leakage source. Simultaneously, the magnitude of the concentration peak detected by the effective response sensor set is analyzed, a spatial concentration distribution map is plotted, the direction of maximum change in the concentration gradient is found, and the intersection area of ​​its reverse extension lines is used to verify the candidate location of the leakage source. By combining the time difference location results and the concentration gradient location results, and through weighted averaging or setting a confidence region, the final preliminary location coordinates of the leak source are determined.

[0015] As a further aspect of the present invention, the step of initiating a real-time simulation based on computational fluid dynamics, based on the fire pipeline topology diagram, includes: The fire pipeline topology diagram is converted into a three-dimensional mesh model that can be recognized by computational fluid dynamics simulation software. The three-dimensional mesh model includes pipeline geometry, connection relationships, valve positions, and spatial obstacle information. Set simulation boundary conditions, including boundary conditions for leakage flow rate or leakage rate centered on the leak point and determined according to the leakage intensity level, and boundary conditions for inlet velocity and outlet pressure set according to the building ventilation system status. Select a turbulence model and mass transport equation suitable for gas diffusion, and configure the simulation time step and total duration; Start the transient simulation calculation and solve the flow field and concentration field at each time step; The simulation results are extracted in real time to show the distribution data of gas concentration at various points in space, and compared with the preset warning concentration thresholds at various levels to dynamically delineate the areas where the concentration exceeds the standard. When the simulation reaches the preset warning time, the three-dimensional spatial coordinate set of the area where the concentration exceeds the standard is extracted, projected onto a two-dimensional planar map and the outline is simplified to generate the potential impact area for alarm purposes.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: A sensor array consisting of multiple gas concentration acquisition terminals periodically collects data on various specific gases. The time-stamped, multi-dimensional feature data is dynamically compared with an environmental background feature database, and a background noise suppression algorithm is applied to filter out inherent fluctuations and accidental interference. This process transforms the reliance on single-point instantaneous absolute values ​​for judgment to multi-source spatiotemporal differential feature analysis, generating a normalized leak characteristic signal that excludes environmental noise and improving the accuracy of identifying real leak events.

[0017] By processing normalized signals using a multidimensional leakage assessment model to obtain quantified leakage confidence, intensity level, and preliminary spatial coordinates, these are coupled with the digital topology of the fire protection pipeline. Based on fluid dynamics principles, extrapolation calculations are performed to simulate the leakage diffusion path and influence boundaries within the pipeline network, generating a detailed list of potential impact areas and estimated pipeline pressure loss. This upgrades the system output from discrete point-based alarms to a comprehensive situational assessment that includes spatial impact range and predictions of system performance degradation.

[0018] A tiered alarm mechanism is triggered based on leakage confidence level, affected area, and pressure loss estimate. Different alarm levels are set according to a combination of multi-dimensional parameters, and a composite instruction is automatically generated, including the alarm level, precise location, impact prediction, and handling suggestions. This mechanism provides clear action guidance for alarm information, enabling maintenance personnel to execute differentiated priority handling procedures based on the expected severity and scope of the event. Attached Figure Description

[0019] Figure 1 This is a timing diagram of the real-time alarm system for fire-fighting pipe fitting interface leakage based on gas sensors as described in this invention. Figure 2 A flowchart illustrating the effects of the simulation module's operation; Figure 3 The kernel density distribution plot shows the correlation between the number of simulation grids and the effective simulation accuracy under different leakage intensities. Figure 4 A trend graph showing the correlation between leakage confidence level, pipeline pressure loss, and the number of abnormal sensors; Figure 5 The loss convergence curve for the training process of the multidimensional leakage assessment model. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0022] See Figure 1The data acquisition module deploys multiple gas concentration acquisition terminals within a predetermined space at the fire-fighting pipe fitting interface to form a sensor array. The sensor array periodically acquires gas characteristic data containing the current concentration values ​​and acquisition timestamps of various specific gases. The background suppression module inputs the gas characteristic data into an environmental background feature library and filters out environmental background interference through a background noise suppression algorithm to generate a normalized effective leakage characteristic signal. The leakage assessment module calls a multi-dimensional leakage assessment model to analyze and process the effective leakage characteristic signal, outputting a leakage confidence score, leakage intensity level, and preliminary location coordinates of the leakage source. The impact deduction module performs leakage impact range deduction calculations based on the leakage intensity level, the preliminary location coordinates of the leakage source, and the fire-fighting pipeline topology diagram, generating a potential impact area and pipeline pressure loss estimate. The alarm response module triggers a graded alarm response mechanism based on the leakage confidence score, potential impact area, and pipeline pressure loss estimate, generating a composite alarm command containing alarm level, alarm location information, and emergency response suggestions.

[0023] In one embodiment of the present invention, the background suppression module retrieves historical gas concentration baseline data, current ambient temperature and humidity data, and system operating condition data corresponding to the current acquisition time from the environmental background feature library. For each specific gas in the gas feature data, the current acquisition concentration value is subtracted from the corresponding baseline concentration value in the historical gas concentration baseline data to obtain the original difference signal. Based on the current ambient temperature and humidity data, the original difference signal is compensated for temperature drift and humidity cross-interference to obtain a preliminary compensation signal. Based on the system operating condition data, known characteristic interference signals generated by normal equipment start-up, shutdown, and maintenance operations are identified and filtered out. The preliminary compensation signal after temperature drift compensation, humidity cross-interference compensation, and known characteristic interference filtering is processed by moving average filtering to generate a smooth concentration change trajectory for each specific gas. The concentration change trajectory constitutes a normalized effective leakage characteristic signal. The temperature drift compensation and humidity cross-interference compensation process establishes a gas sensor temperature-concentration response calibration curve library. This library stores the zero-point drift coefficient and sensitivity variation coefficient for each specific gas sensor at different temperatures. Based on the temperature value in the current ambient temperature and humidity data, the library is consulted to obtain the corresponding zero-point drift correction value and sensitivity correction factor. The original difference signal is first zero-point calibrated using the zero-point drift correction value, and then amplitude calibrated using the sensitivity correction factor to obtain the temperature-compensated signal. A gas sensor humidity-cross-sensitivity matrix is ​​established, describing the proportion of cross-interference caused by changes in ambient humidity to the readings of different types of gas sensors. Based on the humidity value in the current ambient temperature and humidity data, the humidity-cross-sensitivity matrix is ​​consulted to calculate the spurious concentration increments caused by humidity changes to other gas readings. Subtracting the spurious concentration increments from the temperature-compensated signal yields the preliminary compensated signal after temperature drift compensation and humidity cross-interference compensation.

[0024] In practical implementation, the background suppression module begins operating with the periodic upload of gas characteristic data from the data acquisition module. This gas characteristic data includes the current concentration values ​​and acquisition timestamps of various specific gases. Based on the acquisition timestamps, the background suppression module retrieves the corresponding historical baseline gas concentration data, current ambient temperature and humidity data, and system operating condition data from the environmental background characteristic database. For each specific gas recorded in the gas characteristic data, the background suppression module calculates the difference between the current concentration value and the corresponding baseline concentration value from the historical baseline gas concentration data, thus obtaining the original difference signal for that gas. In practical implementation, the background suppression module performs temperature drift compensation and humidity cross-interference compensation on the original difference signal based on the current ambient temperature and humidity data. Temperature drift compensation relies on a pre-generated gas sensor temperature-concentration response calibration curve library. This library stores the zero-point drift coefficient and sensitivity variation coefficient for each specific gas sensor at different temperature points. The background suppression module reads the temperature value from the current ambient temperature and humidity data and queries the calibration curve library to obtain the zero-point drift correction value matching that temperature value. and sensitivity correction factor .

[0025] It is understandable that the original difference signal Zero-point calibration is achieved by subtracting the zero-point drift correction value. The amplitude calibration of the signal after zero-point calibration is achieved by multiplying it by a sensitivity correction factor. Achieve temperature-compensated signal From the formula Given. It can be understood that humidity cross-interference compensation is performed subsequently. The background suppression module calls a predefined humidity-cross-sensitivity matrix. The matrix quantifies the proportionality coefficient by which changes in ambient humidity affect the spurious increments of readings for other gas types in the readings of each gas sensor. Based on the humidity values ​​in the current ambient temperature and humidity data, the background suppression module calculates and generates a spurious concentration increment vector for all gas types in the current signal.

[0026] In some embodiments, from the temperature-compensated signal Subtracting the corresponding spurious concentration increments item by item, the background suppression module obtains a fully compensated preliminary compensation signal. The background suppression module then identifies and filters out interference signal segments with known characteristic patterns based on system operating condition data, including equipment start-up / shutdown logs and maintenance plans. In some embodiments, the background suppression module applies a fixed-length sliding time window for averaging filtering on the preliminary compensation signal. The window slides gradually over time, calculating and outputting the arithmetic mean of the data points within the window, thereby generating a smooth concentration change trajectory for each specific gas. This series of concentration change trajectories together constitutes the normalized effective leakage characteristic signal used by subsequent modules. Optionally, the window length of the sliding average filter is set according to the physical characteristics of gas diffusion and the system sampling rate. The historical gas concentration baseline data for the same period in the environmental background feature library is obtained through long-term statistical learning and dynamically updated and maintained according to daily, weekly, and seasonal cycles.

[0027] In one embodiment of the present invention, the leakage assessment module divides the effective leakage characteristic signal into continuous signal segments according to a time window. For each signal segment, it extracts the rising slope, peak size, and duration of change of each specific gas concentration change trajectory. The rising slope, peak size, and duration of change indicators are matched with typical leakage modes in a preset leakage characteristic fingerprint database. Typical leakage modes include pinhole continuous leakage, crack jet leakage, and interface loosening diffusion leakage. The matching degree calculation result is input to the leakage probability calculation network. The leakage probability calculation network outputs the probability value of the signal segment corresponding to the real leakage event as the instantaneous leakage confidence at the corresponding time point. The instantaneous leakage confidence of all signal segments within an assessment period is combined and the overall leakage confidence score is calculated by a time series aggregation algorithm. The leakage intensity level is determined by a fuzzy inference system based on the concentration peak size, concentration change rate, and spatial correlation between multiple sensor data in the sensor array in the effective leakage characteristic signal. Based on the temporal sequence of concentration anomalies detected by each sensor in the sensor array and the concentration gradient distribution, the initial location coordinates of the leakage source are estimated using a time difference of arrival localization algorithm or a concentration gradient inversion algorithm.

[0028] In practical implementation, the leak assessment module receives normalized valid leak feature signals generated by the background suppression module. The leak assessment module divides the valid leak feature signals into continuous and non-overlapping signal segments according to a fixed time window of preset length. For each segment, the leak assessment module performs feature extraction. This extraction calculates the rise slope, peak size, and duration of change index for each specific gas concentration change trajectory within the signal segment. The rise slope represents the rate of concentration increase per unit time, the peak size represents the extreme value of the concentration change, and the duration of change index is quantified by calculating the length of time the concentration value remains above a specific threshold. In practical implementation, the leak assessment module calculates the matching degree between the extracted rise slope, peak size, and duration of change index and typical leak patterns in a preset leak feature fingerprint database. Typical leak patterns include pinhole continuous leakage, crack jet leakage, and interface loosening diffusion leakage. Each typical leak pattern corresponds to a set of standardized feature parameter ranges.

[0029] It can be understood that the result of the matching degree calculation is input into a pre-trained leakage probability calculation network. This network is a neural network model that processes the input matching degree data and outputs a probability value between 0 and 1. This probability value represents the likelihood that the current signal segment corresponds to a real leakage event, and it is recorded as the instantaneous leakage confidence level at the corresponding time point. The leakage assessment module integrates the instantaneous leakage confidence levels corresponding to all signal segments within a complete assessment period and calculates an overall leakage confidence score using a time-series aggregation algorithm. This algorithm performs a weighted average of the instantaneous leakage confidence score sequence and considers the trend of confidence level changes over time. In some embodiments, the leakage assessment module simultaneously determines the leakage intensity level based on the concentration peak size, concentration change rate, and spatial correlation among data from multiple gas concentration acquisition terminals in the sensor array, using a fuzzy inference system. The fuzzy inference system uses the concentration peak size, concentration change rate, and spatial correlation as input variables, performs inference based on a preset fuzzy rule base and membership function, and outputs a discrete leakage intensity level. In some embodiments, the leak assessment module estimates the preliminary location coordinates of the leak source based on the temporal sequence of the gas concentration acquisition terminals in the sensor array detecting concentration anomalies and the concentration gradient distribution. When using the time difference of arrival (TDOA) location algorithm, the algorithm estimates the location of the leak source by solving a system of hyperbolic equations based on the time difference between the detection of anomalies by each sensor and the known spatial coordinates of the sensors.

[0030] Optionally, when using the concentration gradient inversion algorithm, the algorithm constructs a spatial concentration distribution model based on the concentration peak values ​​detected by each sensor, and infers the possible area of ​​the leak source by finding the direction of the maximum concentration gradient. (Leak confidence score) The calculation can be performed using the following formula:

[0031] in: This represents the total number of signal segments within an evaluation period. Representing the The instantaneous leakage confidence of a signal segment Representative bestowed upon the first The weighting coefficients of each signal segment, Represents the instantaneous leakage confidence sequence The linear trend term during the evaluation period, The weighting coefficients represent the trend terms.

[0032] In one embodiment of the present invention, see [reference] Figure 2 The impact simulation module maps the initial location coordinates of the leak source onto a digitized fire pipeline topology map to determine the specific pipe section and interface location where the leak occurred. Centered on the leak point, it queries the fluid diffusion model parameter table based on the leak intensity level to obtain the gas diffusion coefficient, environmental flow velocity, and diffusion direction parameters under the current leak scenario. Based on the fire pipeline topology map, it initiates a real-time simulation simulation based on computational fluid dynamics to simulate the diffusion process of leaked gas in the pipeline corridor space. Combined with the building ventilation conditions, it calculates the spatial range in which the gas concentration reaches the warning threshold within the preset warning time. This spatial range is marked as the potential impact area. At the same time, based on the leak intensity level and the real-time pressure monitoring data of the pipeline where the leak point is located, it applies the pipeline fluid dynamics model to calculate the estimated value of pipeline pressure loss caused by the leak and predict the pressure drop trend. Real-time simulation based on computational fluid dynamics (CFD) converts the fire pipeline topology diagram into a 3D mesh model recognizable by CFD simulation software. The 3D mesh model includes pipeline geometry, connection relationships, valve locations, and spatial obstacle information. Simulation boundary conditions are set, including leakage flow rate or leakage rate boundary conditions centered on the leak point and determined according to the leakage intensity level, as well as inlet velocity and outlet pressure boundary conditions set according to the building ventilation system status. A turbulence model and mass transport equation suitable for gas diffusion are selected, and the simulation time step and total duration are configured. Transient simulation calculation is initiated to solve the flow field and concentration field within each time step. The gas concentration distribution data at various points in space in the simulation results are extracted in real time and compared with the preset warning concentration thresholds at each level to dynamically delineate the concentration exceeding the standard area. When the simulation reaches the preset warning time, the 3D spatial coordinate set of the concentration exceeding the standard area is extracted, projected onto a 2D planar map, and the outline is simplified to generate the potential impact area for alarm purposes.

[0033] In practice, the impact simulation module receives the leakage intensity level and preliminary location coordinates of the leakage source from the leakage assessment module. It then maps these coordinates onto a pre-built digital fire pipeline topology map, which includes the spatial locations and topological relationships of all pipes, fittings, valves, and connections. Through coordinate mapping and matching, the impact simulation module determines the specific pipe segment number and interface identifier where the leak occurred. Centered on the leak point, the module queries a pre-set fluid diffusion model parameter table based on the received leakage intensity level. This table stores the gas diffusion coefficient, ambient flow velocity reference value, and dominant diffusion direction parameters corresponding to different leakage intensity levels. The impact simulation module obtains these key physical parameters for the current leakage scenario.

[0034] It is understandable that the impact simulation module initiates a real-time simulation based on computational fluid dynamics (CFD) on the fire protection piping topology diagram. The simulation first converts the fire protection piping topology diagram and its associated building structure information into a 3D mesh model recognizable by the CFD simulation software. This 3D mesh model precisely includes pipe geometry, connection relationships, valve locations, and information on obstacles within the corridor. It is also understandable that the impact simulation module sets the boundary conditions for the simulation. These boundary conditions include applying a source term at the leak point location in the 3D mesh model. The release flow rate or rate of the source term is obtained from the fluid diffusion model parameter table based on the leak intensity level. The boundary conditions also include inlet velocity boundary conditions and outlet pressure boundary conditions set according to the real-time status of the building's ventilation system.

[0035] In practical implementation, when the simulation calculation reaches the preset warning time, the impact simulation module extracts the three-dimensional spatial grid coordinate set corresponding to all areas with excessive concentrations at this moment. This three-dimensional coordinate set is projected onto a two-dimensional planar map and a contour simplification algorithm is used to generate one or more polygonal regions. These polygonal regions are marked as potential impact areas for subsequent alarms. Simultaneously, the impact simulation module calculates the estimated pressure loss of the pipeline based on the leakage intensity level and real-time pressure monitoring data of the pipeline where the leak point is located, using a pipeline fluid dynamics model. The pipeline fluid dynamics model treats the pipeline network as a lossy network, and the leak point as an additional flow outflow node. Optionally, the estimated pressure loss is calculated... An example of a formula that can be used is as follows:

[0036] in: Represents the equivalent pipe length upstream of the leak point. Represents the pipe diameter. This represents the leakage volumetric flow rate estimated from the leakage intensity level. Represents fluid density, This represents the function for calculating friction loss. Represents the flow velocity inside the pipe. The local resistance coefficient represents the leakage point. Optionally, the influence extrapolation module extrapolates the pressure decline trend curve over a future period based on the current pressure loss estimate and historical pressure change trends using time series forecasting methods.

[0037] See Figure 3 In the accuracy verification during the diffusion simulation phase, the correlation analysis between the number of simulation grids and the effective simulation accuracy relies on two-dimensional kernel density estimation and multi-level annotation techniques. Specifically, the horizontal axis represents the number of simulation grids (ten thousand), and the vertical axis represents the effective simulation accuracy (%). Different colored points represent different leakage intensity levels (Level 1 - Slight, Level 2 - Mild, Level 3 - Moderate, Level 4 - Severe). The gray contour lines in the figure are kernel density isolines, reflecting the spatial clustering of data points. They exhibit an elliptical distribution trend from the upper left to the lower right, indicating that the effective simulation accuracy decreases negatively with the increase in the number of simulation grids. In the Level 1 (slight) leakage scenario, when the number of simulation grids is in the range of 0 to 200,000, the effective simulation accuracy can be maintained at a relatively high level of 92% to 95%. However, in the Level 4 (severe) leakage scenario, when the number of simulation grids increases to the range of 500,000 to 700,000, the effective simulation accuracy drops to 80% to 83%. This trend reveals the weakening effect of increased computational complexity due to the increased number of grids on simulation accuracy, and this effect is more significant in high leakage intensity scenarios.

[0038] In one embodiment of the present invention, the alarm response module constructs a hierarchical alarm strategy matrix. The hierarchical alarm strategy matrix defines the alarm levels corresponding to different combinations of leakage confidence score intervals, leakage intensity levels, and pipeline pressure loss estimation value intervals. Based on the currently obtained leakage confidence score, leakage intensity level, and pipeline pressure loss estimation value, the hierarchical alarm strategy matrix is ​​queried to determine the final alarm level. The alarm levels include at least four levels: attention, warning, alarm, and severe alarm. The preliminary location coordinates of the leakage source, the range coordinates of the potential impact area, the pipe segment number where the leakage point is located, and the interface identifier are integrated to generate structured alarm location information. Based on the determined alarm level and leakage intensity level, the corresponding operation steps, personnel evacuation suggestions, valve shut-off or leak plugging guidelines are matched and extracted from the emergency plan knowledge base to form emergency response suggestions. The alarm level, alarm location information, and emergency response suggestions are encapsulated into a standardized data packet to form a composite alarm command. When matching and retrieving relevant operational steps, personnel evacuation suggestions, and valve shut-off or leak plugging guidelines from the emergency response plan knowledge base, the required emergency response plan level is determined based on the alarm level. The scale of emergency actions to be taken is determined based on the leakage intensity level and the size of the potential affected area. The distribution of key affected equipment and personnel is determined based on the functional attributes of the area where the preliminary location coordinates of the leak source are located. A multi-condition search is performed in the emergency response plan knowledge base using alarm level, emergency action scale, and affected area functional attributes as joint query keys. The search results return one or more standard emergency response process templates and a targeted list of key handling points. The parameters in the standard emergency response process templates are adjusted based on the current system operating condition data and pipeline pressure loss estimates to generate specific and immediately executable emergency handling suggestions.

[0039] In practical implementation, the alarm response module receives the leakage confidence score and leakage intensity level from the leakage assessment module, and the estimated pipeline pressure loss from the impact deduction module. The alarm response module incorporates a predefined hierarchical alarm strategy matrix to map the correspondence between input parameters and alarm levels. This hierarchical alarm strategy matrix is ​​a multidimensional lookup table that defines the unique alarm level corresponding to the combination of different numerical ranges of leakage confidence scores, different levels of leakage intensity, and different intervals of estimated pipeline pressure loss. See Table 1 for a simplified hierarchical alarm strategy matrix.

[0040] Table 1: Hierarchical Alarm Strategy Matrix ; It is understandable that the alarm response module queries the graded alarm strategy matrix based on the currently acquired leak confidence score, leak intensity level, and estimated pipeline pressure loss. It determines the final alarm level by matching the range of values, with at least four levels: alert, warning, alarm, and critical alarm. It is also understandable that the alarm response module integrates location and impact information from various modules to generate structured alarm location information. This integrated alarm location information includes the latitude and longitude or indoor coordinates of the initial leak source location, the set of vertex coordinates of the potential impact area, the pipe segment number where the leak point is located, and the specific interface identifier.

[0041] In implementation, the alarm response module uses alarm level, emergency action scale, and affected area functional attributes as joint query keys to perform multi-condition searches in the emergency plan knowledge base. The knowledge base stores standard emergency response process templates and targeted handling point lists for various scenarios. The search operation returns one or more standard emergency response process templates and handling point lists that best match the current scenario. The alarm response module adjusts and instantiates the parameters in the standard emergency response process templates based on current system operating data and estimated pipeline pressure loss. Finally, the alarm response module encapsulates the alarm level, structured alarm location information, and specific emergency handling suggestions into a standardized data packet, forming a composite alarm command. The composite alarm command is sent to the monitoring center, mobile terminals, and relevant emergency response equipment through the system's communication interface. Optionally, the alarm level... The process of determining the result can be represented by a decision function:

[0042] Where: function The query mapping logic represents the hierarchical alarm strategy matrix. This represents the confidence score for leakage. Represents the leakage intensity level. This represents the estimated pressure loss in the pipeline. Optionally, the alarm response module will record a complete event log while generating composite alarm commands. The event log includes all input parameters, intermediate decision data, and generated command content for post-event analysis and system auditing.

[0043] See Figure 4In real-time monitoring and analysis, the 1-hour continuous monitoring indicator trend chart fully presents the dynamic evolution of the fire-fighting pipe fitting interface leakage event. The blue curve represents the mean (normalized) leakage confidence level, which rises continuously from approximately 0.68 at 08:00, peaking at approximately 0.97 at 08:40 before gradually declining, reflecting the change in confidence level from the occurrence, development, and mitigation of the leakage event. The orange curve represents the mean (normalized) pipeline pressure loss, which begins to rise significantly after 08:30 and peaks at 08:40, demonstrating the changing pattern of pipeline pressure loss caused by the leakage as the event progresses. The red curve represents the number of abnormal sensors, increasing from an initial 0 to 10 at 08:40, and then gradually decreasing, visually demonstrating the range of affected sensors during the leakage propagation process. The background color zones correspond to graded alarm intervals: beige represents the warning-alarm interval, and light red represents the severe alarm interval. From the timeline perspective, the average leakage confidence score enters the warning-alarm interval after 08:15, and approaches or enters the severe alarm interval between 08:35 and 08:50. Combined with the peak changes in the number of abnormal sensors, the logic of "leakage confidence score interval [0.85, 0.95) and a large number of abnormal sensors correspond to alarm / severe alarm" in the hierarchical alarm strategy matrix can be verified.

[0044] In one embodiment of the present invention, the construction steps of the multidimensional leak assessment model include collecting historical gas leak feature data, which contains gas concentration change information under different environmental conditions and corresponding leak event labels. The collected historical gas leak feature data is preprocessed to extract leak feature parameters, including the mean slope of concentration rise, the variance of concentration peak, and the volatility of the change persistence index. The preprocessed leak feature parameters are input into a neural network model for training. The neural network model includes an input layer, multiple hidden layers, and an output layer. The backpropagation algorithm is used to adjust the connection weights of the neural network model until the error between the leak assessment result output by the model and the leak event label is less than a preset threshold. The trained neural network model is saved as a multidimensional leak assessment model for analyzing and processing the real-time acquired effective leak feature signals. Based on the temporal sequence of concentration anomalies detected by each sensor in the sensor array and the concentration gradient distribution, the preliminary location coordinates of the leak source are estimated using the time difference of arrival (TDOA) localization algorithm or the concentration gradient inversion algorithm. All sensors that detected concentrations significantly exceeding the threshold within the same evaluation period are selected as the effective response sensor set. The arrival time of each sensor in the effective response sensor set is recorded as the first time it detects a concentration anomaly. The arrival time difference between any two effective response sensors is calculated. Combining the known spatial coordinate layout of the sensor array, a set of coordinate points that best fits all arrival time difference data is solved using the hyperbolic localization equations or a grid search-based optimization algorithm as candidate locations for the leak source. Simultaneously, the magnitude of the concentration peak detected by the effective response sensor set is analyzed to draw a spatial concentration distribution map to find the direction of maximum change in the concentration gradient. The intersection of their backward extensions is used to verify the candidate locations of the leak source. The final preliminary location coordinates of the leak source are determined by combining the TDOA localization results and the concentration gradient localization results through a weighted average or by setting a confidence region.

[0045] In practical implementation, the construction steps of the multidimensional leak assessment model require the collection of historical gas leak characteristic data. This data originates from real leak events recorded during long-term system operation and artificially simulated leak experiments. This data includes gas concentration changes under different environmental conditions such as temperature, humidity, and ventilation, as well as a confirmed leak event label for each data sample. The leak event label indicates whether the data represents a real leak and its type. In practice, the collected historical gas leak characteristic data undergoes preprocessing. Preprocessing includes data cleaning to remove invalid or outlier segments, and time series alignment and normalization. Leakage characteristic parameters for model training are extracted from the preprocessed data. These extracted parameters include the mean slope of concentration increase, the variance of peak concentration, and the volatility of the persistence index. The mean slope of concentration increase reflects the average rate of concentration increase, the variance of peak concentration reflects the dispersion of peak value, and the volatility of the persistence index reflects the stability of the leak's persistence.

[0046] It is understood that the preprocessed and extracted leakage feature parameters are input into a neural network model for training. The neural network model includes an input layer to receive the feature parameter vector, multiple hidden layers to abstract features layer by layer, and an output layer to output the leakage assessment result. The training process uses the backpropagation algorithm, which calculates a loss function based on the difference between the model's output leakage assessment result and the leakage event labels carried in historical data. It iteratively adjusts the connection weights between neurons in each layer of the neural network model using gradient descent until the error between the model output and the true label is less than a preset threshold. In some embodiments, the structural parameters and connection weights of the trained neural network model that meets performance requirements are saved to form the final multidimensional leakage assessment model. This multidimensional leakage assessment model is deployed in the leakage assessment module to analyze and process the real-time acquired valid leakage feature signals and output a leakage confidence score. In some embodiments, the leak assessment module estimates the preliminary location coordinates of the leak source based on the temporal sequence of the concentration anomalies detected by each gas concentration acquisition terminal in the sensor array and the concentration gradient distribution. In practice, all gas concentration acquisition terminals that detected concentrations significantly exceeding a preset threshold within the same assessment period are first selected from the sensor array, and these gas concentration acquisition terminals are used to form an effective response sensor set.

[0047] In practice, the time point at which each gas concentration acquisition terminal in the effective response sensor set first detects a concentration anomaly is recorded, and this time point is taken as the signal arrival time of that gas concentration acquisition terminal. The arrival time difference between any two gas concentration acquisition terminals in the effective response sensor set is calculated. Combining the known, pre-calibrated spatial coordinate layout of the sensor array, the hyperbolic positioning equations or a grid search-based optimization algorithm are used to solve the problem. The hyperbolic positioning equations are constructed based on the arrival time difference and the speed of sound or gas diffusion rate. The goal is to find a set of spatial coordinate points such that the distance difference from this point to each gas concentration acquisition terminal best matches the distance difference calculated from the measured arrival time difference. This set of coordinate points is calculated as a candidate location for the leak source. Simultaneously, the magnitude of the concentration peak detected by each gas concentration acquisition terminal in the effective response sensor set is analyzed, and a spatial distribution map of the concentration peaks is plotted in a two-dimensional or three-dimensional spatial coordinate system. By analyzing the distribution map, the direction of maximum change in the concentration gradient is found. The intersection region of the backward extensions of the direction of maximum change in the concentration gradient is used to help verify the rationality of the aforementioned candidate locations for the leak source. The leak assessment module combines the candidate locations obtained from the time-difference localization algorithm with the verification area indicated by the concentration gradient inversion algorithm. Through weighted averaging or by setting a confidence region, the preliminary coordinates of the leak source are ultimately determined. Optionally, the weight adjustment process in the neural network model training can be expressed using the following formula:

[0048] in: Representative at the The set of all connection weight parameters of the neural network model at the next iteration. The learning rate is used to control the step size of each weight update. Represents the loss function Regarding weight parameters The gradient vector at the current value. Optionally, a grid search-based optimization algorithm traverses each grid point in a preset three-dimensional spatial grid, calculates the overall error between the theoretical arrival time difference and the measured arrival time difference, and selects the grid point coordinates that minimize the error function as the candidate location of the leakage source.

[0049] See Figure 5In the training process of the multidimensional leakage assessment model, the loss curves intuitively reflect the model's convergence performance and generalization ability. Specifically, the horizontal axis represents the training epoch, and the vertical axis represents the loss value. The blue curve represents the training loss, and the red curve represents the validation loss. In the early stage of training (Epoch 0-20), both curves show a rapid downward trend, with the training loss decreasing from approximately 0.76 to 0.20 and the validation loss decreasing from approximately 0.85 to 0.30, indicating that the model is rapidly learning the core patterns of leakage features and its fitting ability is continuously improving. In the middle stage (Epoch 20-40), the rate of decrease in the training loss tends to level off and gradually converges to around 0.04, while the validation loss fluctuates slightly but maintains an overall downward trend, eventually stabilizing at around 0.08. This indicates that the model has basically mastered the representation rules of effective leakage feature signals and no obvious overfitting phenomenon has occurred. In the later stages of training (Epoch 40-50), the difference between the training loss and the validation loss remained stable, and the validation loss did not show a significant rebound, proving that the multidimensional leakage assessment model has good generalization performance and can stably evaluate and process effective leakage feature signals in real-time scenarios.

[0050] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A gas sensor based real time alarm system for leak in fire fighting pipe joint interface characterized by, include: The data acquisition module deploys multiple gas concentration acquisition terminals within a predetermined space range of the fire-fighting pipe fitting interface to form a sensor array. The sensor array periodically acquires gas characteristic data, which includes the current concentration values ​​of various specific gases and the acquisition timestamp. The background suppression module inputs the gas characteristic data into the environmental background feature library, filters out environmental background interference through a background noise suppression algorithm, and generates a normalized effective leakage characteristic signal. The leakage assessment module calls a multidimensional leakage assessment model to analyze and process the effective leakage characteristic signals. The multidimensional leakage assessment model outputs the leakage confidence score, leakage intensity level, and preliminary location coordinates of the leakage source. The impact simulation module, based on the leakage intensity level and the preliminary location coordinates of the leakage source, combined with the fire pipeline topology diagram, performs leakage impact range simulation calculations to generate potential impact areas and estimated pipeline pressure loss values. The alarm response module, based on the leakage confidence score, the potential impact area, and the estimated pipeline pressure loss, triggers a tiered alarm response mechanism to generate a composite alarm command that includes the alarm level, alarm location information, and emergency response suggestions.

2. The gas sensor based fire service coupling interface leak real-time alert system of claim 1, wherein, The step of filtering out environmental background interference using a background noise suppression algorithm to generate a normalized effective leakage characteristic signal includes: From the environmental background feature library, retrieve the historical gas concentration baseline data, current environmental temperature and humidity data, and system operating condition data corresponding to the current collection time; For each specific gas in the gas characteristic data, the current concentration value collected is subtracted from the corresponding baseline concentration value in the historical gas concentration baseline data to obtain the original difference signal. Based on the current ambient temperature and humidity data, temperature drift compensation and humidity cross-interference compensation are performed on the original difference signal to obtain a preliminary compensation signal; Based on the system operating condition data, identify and filter out known characteristic interference signals generated by normal equipment start-up, shutdown, and maintenance operations; The preliminary compensation signal, after temperature drift compensation, humidity cross-interference compensation, and known characteristic interference filtering, is subjected to moving average filtering to generate a smooth concentration change trajectory for each specific gas. The concentration change trajectory constitutes the normalized effective leakage characteristic signal.

3. The gas sensor based fire service coupling interface leak real-time alert system of claim 1, wherein, The process of analyzing and processing the effective leakage characteristic signals by calling the multidimensional leakage assessment model includes: The effective leakage characteristic signal is divided into continuous signal segments according to a time window; For each signal segment, the following evaluation steps are performed: extract the rising slope, peak size, and duration of change index of each specific gas concentration change trajectory in the signal segment; calculate the matching degree between the rising slope, peak size, and duration of change index and typical leakage patterns in the preset leakage feature fingerprint database, wherein the typical leakage patterns include pinhole continuous leakage, crack jet leakage, and interface loose diffusion leakage. The matching degree calculation result is input into the leakage probability calculation network, which outputs the probability value of the signal segment corresponding to the real leakage event, as the instantaneous leakage confidence at the corresponding time point; By combining the instantaneous leakage confidence of all signal segments within an evaluation period, the overall leakage confidence score is calculated using a time series aggregation algorithm. Based on the concentration peak size, concentration change rate and spatial correlation between data from multiple sensors in the sensor array in the effective leakage characteristic signal, the leakage intensity level is determined by a fuzzy inference system. Based on the temporal sequence of the concentration anomalies detected by each sensor in the sensor array and the concentration gradient distribution, the preliminary location coordinates of the leakage source are estimated using a time difference of arrival (TDOA) localization algorithm or a concentration gradient inversion algorithm.

4. The real-time alarm system for leaking fire-fighting pipe fitting interfaces based on gas sensors according to claim 1, characterized in that, The calculation of the leakage impact range is performed to generate the potential impact area and estimated pipeline pressure loss, including: The initial location coordinates of the leak source are mapped onto the digitized fire pipeline topology diagram to determine the specific pipe segment and interface location where the leak occurred. Centered on the leak point, the gas diffusion coefficient, ambient flow velocity, and diffusion direction parameters under the current leak scenario are obtained by querying the fluid diffusion model parameter table according to the leak intensity level. Based on the fire pipeline topology diagram, a real-time simulation based on computational fluid dynamics is initiated to simulate the diffusion process of leaked gas in the pipeline corridor space. Combined with the building ventilation conditions, the spatial range in which the gas concentration reaches the warning threshold within the preset warning time is calculated. The spatial range is marked as the potential impact area. Meanwhile, based on the leakage intensity level and the real-time pressure monitoring data of the pipeline where the leakage point is located, a pipeline fluid dynamics model is applied to calculate the estimated pressure loss of the pipeline caused by the leakage and predict the pressure drop trend.

5. The real-time alarm system for fire-fighting pipe fitting interface leakage based on a gas sensor according to claim 1, characterized in that, The triggering hierarchical alarm response mechanism generates a composite alarm command that includes alarm level, alarm location information, and emergency response suggestions, including: A hierarchical alarm strategy matrix is ​​constructed, which defines the alarm level corresponding to different combinations of leakage confidence score intervals, leakage intensity levels, and pipeline pressure loss estimation value intervals. Based on the currently obtained leakage confidence score, leakage intensity level, and pipeline pressure loss estimate, the hierarchical alarm strategy matrix is ​​queried to determine the final alarm level, which includes at least four levels: attention, warning, alarm, and critical alarm. By integrating the preliminary location coordinates of the leak source, the range coordinates of the potential impact area, the pipe segment number and interface identifier where the leak point is located, the structured alarm location information is generated. Based on the determined alarm level and leakage intensity level, the corresponding operating steps, personnel evacuation suggestions, valve shut-off or leak plugging instructions are matched and extracted from the emergency plan knowledge base to form the emergency response suggestions. The alarm level, alarm location information, and emergency response suggestions are encapsulated into a standardized data packet to form the composite alarm command.

6. The real-time alarm system for fire-fighting pipe fitting interface leakage based on gas sensors according to claim 5, characterized in that, The process of matching and extracting relevant operational steps, personnel evacuation suggestions, and valve shut-off or leak-plugging guidelines from the emergency response plan knowledge base to form the emergency response suggestions includes: Determine the level of emergency response plan that needs to be retrieved based on the alarm level; Based on the leakage intensity level and the size of the potential affected area, determine the scale of the emergency response required. Based on the functional attributes of the area where the preliminary location coordinates of the leak source are located, the distribution of the affected key equipment and personnel is determined; Using the alarm level, emergency action scale, and affected area functional attributes as joint query keys, a multi-condition search can be performed in the emergency plan knowledge base; The search results return one or more standard emergency response procedure templates and a targeted list of key points for handling the situation; Based on the current system operating data and the estimated pipeline pressure loss, the parameters in the standard emergency response procedure template are adjusted to generate specific and immediately executable emergency response recommendations.

7. The real-time alarm system for fire-fighting pipe fitting interface leakage based on a gas sensor according to claim 2, characterized in that, The step of performing temperature drift compensation and humidity cross-interference compensation on the original difference signal based on the current ambient temperature and humidity data includes: A library of temperature-concentration response calibration curves for gas sensors is established, which stores the zero-point drift coefficient and sensitivity variation coefficient of each specific gas sensor at different temperatures. Based on the temperature value in the current ambient temperature and humidity data, query the calibration curve library to obtain the corresponding zero-point drift correction value and sensitivity correction factor; For the original difference signal, firstly, zero-point calibration is performed using the zero-point drift correction value, and then amplitude calibration is performed using the sensitivity correction factor to obtain the temperature-compensated signal. A humidity-cross-sensitivity matrix for gas sensors is established, which describes the proportion of cross-interference caused by changes in ambient humidity to the readings of different types of gas sensors. Based on the humidity value in the current ambient temperature and humidity data, the humidity-cross sensitivity matrix is ​​queried to calculate the false concentration increments of other gas readings caused by humidity changes. Subtracting the spurious concentration increment from the temperature-compensated signal yields the preliminary compensated signal after temperature drift compensation and humidity cross-interference compensation.

8. The method for automatically detecting the quality of ship communication signals according to claim 3, characterized in that, The construction steps of the multidimensional leakage assessment model include: Collect historical ship communication signal quality data, which includes signal reception strength information, signal-to-noise ratio information and corresponding signal quality level labels under different sea conditions and weather conditions. The collected historical ship communication signal quality data is preprocessed to extract signal quality characteristic parameters, including mean signal strength, signal strength variance, mean signal-to-noise ratio, and signal-to-noise ratio fluctuation. The preprocessed signal quality feature parameters are input into a neural network model for training. The neural network model includes an input layer, multiple hidden layers, and an output layer. The backpropagation algorithm is used to adjust the connection weights of the neural network model until the error between the signal quality assessment result output by the model and the signal quality level label is less than a preset threshold. The trained neural network model is saved as the multidimensional leakage assessment model, which is used to analyze and process the effective leakage feature signals acquired in real time.

9. The real-time alarm system for fire-fighting pipe fitting interface leakage based on gas sensors according to claim 3, characterized in that, Based on the temporal sequence of concentration anomalies detected by each sensor in the sensor array and the concentration gradient distribution, the preliminary location coordinates of the leakage source are estimated using a time-of-arrival localization algorithm or a concentration gradient inversion algorithm, including: All sensors that detected concentrations significantly exceeding the threshold within the same evaluation period are selected from the sensor array and considered as the effective response sensor set. Record the time point at which each sensor in the set of effective response sensors first detects a concentration anomaly as the arrival time; Calculate the time difference of arrival between any two valid response sensors; Based on the known spatial coordinate layout of the sensor array, a set of coordinate points that best fit all arrival time difference data is solved using hyperbolic positioning equations or grid search-based optimization algorithms, which serve as candidate locations for the leakage source. Simultaneously, the magnitude of the concentration peak detected by the effective response sensor set is analyzed, a spatial concentration distribution map is drawn, the direction of maximum change in the concentration gradient is found, and the intersection area of ​​its reverse extension lines is used to verify the candidate location of the leakage source. By combining the time difference location results and the concentration gradient location results, and through weighted averaging or setting a confidence region, the final preliminary location coordinates of the leak source are determined.

10. The real-time alarm system for fire-fighting pipe fitting interface leakage based on a gas sensor according to claim 4, characterized in that, Based on the aforementioned fire pipeline topology diagram, a real-time simulation based on computational fluid dynamics is initiated, including: The fire pipeline topology diagram is converted into a three-dimensional mesh model that can be recognized by computational fluid dynamics simulation software. The three-dimensional mesh model includes pipeline geometry, connection relationships, valve positions, and spatial obstacle information. Set simulation boundary conditions, including boundary conditions for leakage flow rate or leakage rate centered on the leak point and determined according to the leakage intensity level, and boundary conditions for inlet velocity and outlet pressure set according to the building ventilation system status. Select a turbulence model and mass transport equation suitable for gas diffusion, and configure the simulation time step and total duration; Start the transient simulation calculation and solve the flow field and concentration field at each time step; The simulation results are extracted in real time to show the distribution data of gas concentration at various points in space, and compared with the preset warning concentration thresholds at various levels to dynamically delineate the areas where the concentration exceeds the standard. When the simulation reaches the preset warning time, the three-dimensional spatial coordinate set of the area where the concentration exceeds the standard is extracted, projected onto a two-dimensional planar map and the outline is simplified to generate the potential impact area for alarm purposes.

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