Remote monitoring method and system for operation state of deluge alarm valve group

Abstract

The invention provides a remote monitoring method and system for the running state of a deluge alarm valve group. The method comprises the steps of determining space coordinates and a global diffusion direction vector of a target helium concentration abnormal diffusion region in a sealed cavity based on helium concentration distribution data, and constructing a target composite data frame based on phase characteristics and the helium concentration distribution data; determining a global leakage rate gradient of the target composite data frame in combination with the space coordinates and the global diffusion direction vector, extracting a multiple harmonic distortion amplification quantity when the target composite data frame continuously exceeds a preset reference curve for multiple times, and determining a leakage grade identifier according to the multiple harmonic distortion amplification quantity and the global leakage rate gradient; and generating a linkage instruction containing the leakage grade identifier, the space coordinate and the global diffusion direction vector, and triggering the operation state closed-loop control process of the deluge alarm valve group through the instruction. According to the technical scheme, the linkage instruction is generated after the leakage level is determined, closed-loop control of the deluge alarm valve group is triggered, and accurate leakage response is achieved.

Description

Technical Field

[0001] This application relates to the technical field of fire alarm, and particularly to a method and system for remotely monitoring the operating state of deluge alarm valve sets. Background Art

[0002] In an automatic sprinkler system, as a key component, the sealing performance of the deluge alarm valve set is directly related to the reliability and safety of the system. Due to the long-term high-pressure and high-humidity working environment of the deluge alarm valve set, changes in parameters such as the helium concentration distribution inside the sealing cavity, water pressure fluctuations, and the current phase characteristics of the fire pump station may indicate potential leakage risks. Therefore, real-time monitoring of these parameters and accurate judgment of leakage situations have become key technical requirements for ensuring the normal operation of fire protection facilities. Especially in large industrial facilities or high-rise buildings, leakage of the deluge alarm valve set may lead to serious consequences, so an efficient and accurate monitoring and early warning mechanism is needed.

[0003] Existing solutions collect relevant data in real time by arranging helium concentration sensors and water pressure sensors inside the sealing cavity and installing current phase monitoring devices at the fire pump station. Machine learning algorithms are used to analyze the collected helium concentration distribution, water pressure fluctuations, and current phase characteristics, build a data model, and judge whether there is a leakage risk through preset thresholds. This solution can achieve automated monitoring to a certain extent and reduce the need for manual intervention.

[0004] Although the existing solution realizes automated monitoring through multi-sensor data fusion and machine learning algorithms, it still has deficiencies. This solution has high requirements for the real-time processing of data. However, in practical applications, due to the large amount of data and high computational complexity, it is often difficult to achieve efficient real-time analysis, resulting in delays in leakage early warning. Secondly, the existing solution relies on preset static thresholds for judgment, and cannot dynamically adapt to complex and changeable working conditions, easily resulting in false alarms or missed alarms. This solution lacks the ability to accurately predict the leakage diffusion direction and rate, and is difficult to provide a comprehensive leakage level assessment and accurate linkage control instructions, limiting its practical application effect in complex automatic sprinkler systems. Summary of the Invention

[0005] An embodiment of this application provides a method and system for remotely monitoring the operating state of a deluge alarm valve set to solve the problem in the prior art that it is difficult to provide a comprehensive leakage level assessment and accurate linkage control instructions due to the lack of accurate prediction ability for the leakage diffusion direction and rate.

[0006] In a first aspect, an embodiment of this application provides a method for remotely monitoring the operating state of a deluge alarm valve set, including:

[0007] Obtain the helium concentration distribution data inside the sealing cavity of the deluge alarm valve set, the water pressure fluctuation parameters inside the deluge alarm valve set, and the current phase characteristics of the fire pump station. The fire pump station serves as the water supply power source and establishes a protocol-level linkage with the deluge alarm valve set;

[0008] Based on the helium concentration distribution data, determine the spatial coordinates and the global diffusion direction vector of the target helium concentration abnormal diffusion region inside the sealing cavity. Based on the phase characteristics of the water pressure fluctuation parameters and the helium concentration distribution data, construct a target composite data frame;

[0009] Combined with the spatial coordinates and the global diffusion direction vector, determine the global leakage rate gradient of the target composite data frame. When the global leakage rate gradient exceeds the preset reference curve continuously for multiple times, extract the multiple harmonic distortion increment in the current phase characteristics. According to the multiple harmonic distortion increment and the global leakage rate gradient, determine the leakage level identifier;

[0010] Generate a linkage command containing the leakage level identifier, the spatial coordinates, and the global diffusion direction vector, and trigger the closed-loop control process of the operating state of the deluge alarm valve set through the linkage command.

[0011] Optionally, the step of determining the spatial coordinates and the global diffusion direction vector of the target helium concentration abnormal diffusion region inside the sealing cavity based on the helium concentration distribution data, and constructing a target composite data frame based on the phase characteristics of the water pressure fluctuation parameters and the helium concentration distribution data includes:

[0012] Perform noise suppression processing on the helium concentration distribution data to generate denoised helium concentration distribution data. Grid the denoised helium concentration distribution data in three-dimensional space according to the geometric structure of the sealing cavity to generate uniformly distributed grid cells, calculate the helium concentration gradient values of each grid cell, and identify the initial helium concentration abnormal diffusion region;

[0013] Perform region growing processing on the initial helium concentration abnormal diffusion region. During the region growing processing, merge adjacent grid cells based on a preset gradient direction consistency criterion until the gradient value at the growth boundary is lower than the preset gradient threshold to obtain the target helium concentration abnormal diffusion region, and determine the spatial coordinates of the target helium concentration abnormal diffusion region;

[0014] Select multiple grid cells with larger gradient values in the target helium concentration abnormal diffusion region as multiple representative grid cells, calculate the diffusion direction vector of each representative grid cell, and perform normalized weighted average processing on all the diffusion direction vectors to generate the global diffusion direction vector of the target helium concentration abnormal diffusion region;

[0015] Perform Fourier transform on the water pressure fluctuation parameters, extract the phase characteristics within a specific frequency range that matches the resonance frequency of the sealed cavity structure from the Fourier transform results, perform sliding window smoothing processing and low-pass filtering operation on the phase characteristics within the specific frequency range to generate denoised phase characteristics, and use the denoised phase characteristics as the phase characteristics of the water pressure fluctuation parameters;

[0016] Calculate the water pressure phase fluctuation amplitude of each grid cell within the corresponding time window according to the phase characteristics of the water pressure fluctuation parameters;

[0017] Based on the helium concentration gradient values of each grid cell and the water pressure phase fluctuation amplitude, construct a three-dimensional coupling coefficient matrix, and perform a tensor product operation on the three-dimensional coupling coefficient matrix and the global diffusion direction vector to generate a target composite data frame.

[0018] Optionally, the step of based on the helium concentration gradient values of each grid cell and the water pressure phase fluctuation amplitude, construct a three-dimensional coupling coefficient matrix, and perform a tensor product operation on the three-dimensional coupling coefficient matrix and the global diffusion direction vector to generate a target composite data frame includes:

[0019] Perform normalization processing on the helium concentration gradient values of each grid cell respectively to generate a sequence of normalized helium concentration gradient values, and perform standardization processing on the water pressure phase fluctuation amplitude of each grid cell within the corresponding time window to generate a sequence of standardized water pressure phase fluctuation amplitudes;

[0020] Based on the sequence of helium concentration gradient values and the sequence of water pressure phase fluctuation amplitudes, calculate the gradient phase coupling coefficient of each grid cell, and the gradient phase coupling coefficient is obtained through the product operation of the helium concentration gradient value and the water pressure phase fluctuation amplitude;

[0021] Arrange the gradient phase coupling coefficients of all grid cells according to the spatial position to construct a three-dimensional coupling coefficient matrix, perform tensor expansion on the global diffusion direction vector to generate a diffusion direction tensor;

[0022] Combine the three-dimensional coupling coefficient matrix and the diffusion direction tensor to perform an element-by-element product operation to generate an initial composite data frame;

[0023] Perform spatial smoothing processing on the initial composite data frame to eliminate local data mutations and generate a target composite data frame.

[0024] Optionally, the step of combine the three-dimensional coupling coefficient matrix and the diffusion direction tensor to perform an element-by-element product operation to generate an initial composite data frame includes:

[0025] Perform dimensional verification on the three-dimensional coupling coefficient matrix to ensure that the spatial dimension of the three-dimensional coupling coefficient matrix is consistent with that of the diffusion direction tensor;

[0026] Perform weight correction on each element in the three-dimensional coupling coefficient matrix to adjust the weight value of the gradient phase coupling coefficient, generate a corrected three-dimensional coupling coefficient matrix, and perform normalization processing on the diffusion direction tensor to ensure that the modulus of the diffusion direction vector of each representative grid cell is a unit length, generating a normalized diffusion direction tensor;

[0027] Perform element-by-element multiplication operation on the corrected three-dimensional coupling coefficient matrix and the normalized diffusion direction tensor to generate an initial composite data frame.

[0028] Optionally, performing spatial smoothing processing on the initial composite data frame to eliminate local data mutations and generate a target composite data frame includes:

[0029] Perform local neighborhood analysis on the helium concentration gradient value of each grid cell in the initial composite data frame. During the local neighborhood analysis, calculate the difference in the helium concentration gradient value between each grid cell and its adjacent grid cells to generate a local data difference matrix;

[0030] Based on the local data difference matrix, identify the grid cells with helium concentration gradient value differences exceeding a preset difference threshold, mark them as local mutation grid cells, and perform interpolation correction on the helium concentration gradient values of the local mutation grid cells to generate corrected helium concentration gradient values;

[0031] Update the corrected helium concentration gradient values to the initial composite data frame to generate an intermediate composite data frame, perform smoothing processing on the intermediate composite data frame, and adjust the helium concentration gradient values of each grid cell based on preset spatial continuity constraints to generate a smoothed composite data frame;

[0032] Perform boundary consistency verification on the smoothed composite data frame, and correct the abnormal data points at the boundaries of each local mutation grid cell based on the data value gradient change of adjacent grid cells to generate a target composite data frame.

[0033] Optionally, combining the spatial coordinates and the global diffusion direction vector to determine the global leakage rate gradient of the target composite data frame. When the global leakage rate gradient continuously exceeds a preset reference curve multiple times, extract the multiple harmonic distortion amplification amounts in the current phase characteristics, and determine the leakage level identifier according to the multiple harmonic distortion amplification amounts and the global leakage rate gradient, including:

[0034] Based on the spatial coordinates, determine the global leakage rate gradient of each grid cell, perform time series accumulation on the leakage rate gradient of each grid cell to generate a leakage rate gradient time series, and based on the leakage rate gradient time series, in combination with the global diffusion direction vector, determine the global leakage rate gradient of the target composite data frame;

[0035] Judge whether the global leakage rate gradient exceeds a preset reference curve continuously for multiple times;

[0036] If so, determine an abnormal time window, where the abnormal time window is the time window corresponding to when the global leakage rate gradient exceeds the preset reference curve continuously for multiple times;

[0037] Within the abnormal time window, extract the multiple harmonic distortion amplification amounts in the current phase characteristics, perform time integration on the multiple harmonic distortion amplification amounts to generate a harmonic distortion accumulation amount;

[0038] Based on the ratio relationship between the harmonic distortion accumulation amount and the global leakage rate gradient, determine a leakage level identifier.

[0039] Optionally, the determining the global leakage rate gradient of the target composite data frame based on the leakage rate gradient time series and in combination with the global diffusion direction vector includes:

[0040] According to the helium concentration gradient values of each grid cell, calculate the change rate of the helium concentration gradient values between grid cells;

[0041] According to the change rate of the helium concentration gradient values, in combination with the global diffusion direction vector, the distance from the grid cell to the leakage source, and the estimated diffusion speed of helium in the target helium concentration abnormal diffusion region, calculate the weights of each grid cell;

[0042] In combination with historical data, optimize the weights of each grid cell to obtain the optimized weights of each grid cell, so as to construct a weighted matrix;

[0043] Combine the leakage rate gradient time series with the weighted matrix to obtain the global leakage rate gradient of the target composite data frame.

[0044] In a second aspect, an embodiment of the present application provides a remote monitoring system for the operating state of a deluge alarm valve group,

[0045] An acquisition module: acquire the helium concentration distribution data inside the sealing cavity of the deluge alarm valve group, the water pressure fluctuation parameters inside the deluge alarm valve group, and the current phase characteristics of the fire pump station. The fire pump station serves as a water supply power source and establishes a protocol-level linkage with the deluge alarm valve group;

[0046] Building module: Determine the spatial coordinates of the target helium concentration abnormal diffusion region inside the sealed cavity and the global diffusion direction vector based on the helium concentration distribution data, and construct a target composite data frame based on the phase characteristics of the water pressure fluctuation parameters and the helium concentration distribution data;

[0047] Extraction module: Combine the spatial coordinates and the global diffusion direction vector to determine the global leakage rate gradient of the target composite data frame. When the global leakage rate gradient exceeds the preset reference curve continuously for multiple times, extract the multiple harmonic distortion increment in the current phase characteristics, and determine the leakage level identifier according to the multiple harmonic distortion increment and the global leakage rate gradient;

[0048] Generation module: Generate a linkage instruction including the leakage level identifier, the spatial coordinates and the global diffusion direction vector, and trigger the closed-loop control process of the operating state of the deluge alarm valve set through the linkage instruction.

[0049] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a production intelligent monitoring method based on industrial Internet as described in any item of the first aspect.

[0050] In a fourth aspect, an embodiment of the present application provides a computing device and a computer storage medium storing a computer program, and when the computer program is executed by a computer, it implements a production intelligent monitoring method based on industrial Internet as described in any item of the first aspect.

[0051] In an embodiment of the present application, a method for remotely monitoring the operating state of a deluge alarm valve set is provided. The method includes: obtaining the helium concentration distribution data inside the sealed cavity of the deluge alarm valve set, the water pressure fluctuation parameters inside the deluge alarm valve set, and the current phase characteristics of the fire pump station. The fire pump station is used as a water supply power source and establishes a protocol-level linkage with the deluge alarm valve set; determining the spatial coordinates of the target helium concentration abnormal diffusion region inside the sealed cavity and the global diffusion direction vector based on the helium concentration distribution data, and constructing a target composite data frame based on the phase characteristics of the water pressure fluctuation parameters and the helium concentration distribution data; combining the spatial coordinates and the global diffusion direction vector to determine the global leakage rate gradient of the target composite data frame. When the global leakage rate gradient exceeds the preset reference curve continuously for multiple times, extract the multiple harmonic distortion increment in the current phase characteristics, and determine the leakage level identifier according to the multiple harmonic distortion increment and the global leakage rate gradient; generating a linkage instruction including the leakage level identifier, the spatial coordinates and the global diffusion direction vector, and triggering the closed-loop control process of the operating state of the deluge alarm valve set through the linkage instruction.

[0052] The method for remotely monitoring the operating status of a deluge alarm valve group provided by the embodiments of the present application, by combining helium concentration distribution data, water pressure fluctuation parameters, and the current phase characteristics of a fire pump station, can not only monitor potential leakage risks, but also accurately determine the specific location and diffusion direction of leakage based on this comprehensive data. Compared with the existing method of relying on static thresholds to judge leakage, this method improves the accuracy and real-time performance of detection. The embodiments of the present application use the multiple harmonic distortion increment and the global leakage rate gradient to determine the leakage level identifier, thereby realizing the dynamic assessment of the leakage situation. This enables the embodiments of the present application to better adapt to different working conditions and reduce false alarms and missed alarms. By constructing a target composite data frame and combining the global leakage rate gradient, the present application can not only accurately locate the leakage point and predict the direction and speed of leakage diffusion, but also generate a linkage instruction containing the leakage level identifier, spatial coordinates, and global diffusion direction vector based on this information, triggering the closed-loop control process of the operating status of the deluge alarm valve group. This method provides a more comprehensive leakage assessment and can execute corresponding control measures more accurately, effectively improving the response efficiency and reliability of the automatic sprinkler system. Further, the embodiments of the present application perform noise suppression and three-dimensional grid processing on the helium concentration distribution data, identify the abnormal diffusion region of helium concentration and calculate its spatial coordinates and diffusion direction vector; perform Fourier transform and filtering processing on the water pressure fluctuation parameters to extract phase characteristics, and combine the helium concentration gradient to construct a three-dimensional coupling coefficient matrix, improving the accuracy and real-time performance of the leakage monitoring of the deluge alarm valve group and meeting the requirements of the automatic sprinkler system under complex working conditions.

[0053] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0055] Figure 1 It is a flowchart of a method for remotely monitoring the operating status of a valve group provided by the embodiments of the present application;

[0056] Figure 2 It is a schematic structural diagram of a system for remotely monitoring the operating status of a valve group provided by the embodiments of the present application;

[0057] Figure 3 It is a schematic structural diagram of a computing device provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application.

[0059] In some processes described in the specification, claims and above-mentioned accompanying drawings of this application, a plurality of operations that appear in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 11, 12, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0060] The technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0061] Figure 1 The flowchart of a method for remotely monitoring the operating state of a valve group provided for the embodiments of this application is as Figure 1 shown, and the method includes:

[0062] S11. Obtain the helium concentration distribution data inside the sealing cavity of the deluge alarm valve group, the water pressure fluctuation parameters inside the deluge alarm valve group, and the current phase characteristics of the fire pump station. The fire pump station serves as the water supply power source and establishes a protocol-level linkage with the deluge alarm valve group.

[0063] Among them, the helium concentration distribution data is obtained by helium concentration sensors arranged in the sealing cavity, which is used to reflect the distribution of helium gas in the cavity and is a key indicator for leakage detection. The water pressure fluctuation parameters are collected by water pressure sensors, which are used to reflect the dynamic changes of the water pressure inside the deluge alarm valve group. Combining with the helium concentration data can assist in judging the leakage situation. The current phase characteristics are obtained by the current monitoring device of the fire pump station and are used to analyze the operating state of the pump station. The phase change can indirectly reflect the impact of leakage on the water supply system.

[0064] S12. Based on the helium concentration distribution data, determine the spatial coordinates and global diffusion direction vector of the target helium concentration abnormal diffusion area inside the sealing cavity, and construct a target composite data frame based on the phase characteristics of the water pressure fluctuation parameters and the helium concentration distribution data.

[0065] Among them, the target helium concentration abnormal diffusion region is the region where the helium concentration is higher than the normal value identified through the helium concentration distribution data, and this region may be the leakage source. The global diffusion direction vector reflects the diffusion direction of helium in the sealed cavity and is used to predict the leakage trend. The target composite data frame is a data structure generated after fusing the helium concentration distribution data with the phase characteristics of the water pressure fluctuation parameters and is used to comprehensively judge the leakage situation.

[0066] S13. Combine the spatial coordinates and the global diffusion direction vector to determine the global leakage rate gradient of the target composite data frame. When the global leakage rate gradient exceeds the preset reference curve continuously for multiple times, extract the multiple harmonic distortion increment in the current phase characteristics. Determine the leakage level identifier according to the multiple harmonic distortion increment and the global leakage rate gradient.

[0067] Among them, the global leakage rate gradient reflects the change trend of the leakage rate in space and time and is used to quantify the severity of the leakage. The multiple harmonic distortion increment is the abnormal change amount of the harmonic components in the current phase characteristics and is used to assist in judging the impact of the leakage on the water supply system. The leakage level identifier is the leakage severity level comprehensively determined according to the leakage rate gradient and the harmonic distortion increment. Exemplarily, the leakage level identifiers are low leakage level V1, medium leakage level V2, medium-high leakage level V3, high leakage level V4, and severe leakage level V5.

[0068] S14. Generate a linkage command including the leakage level identifier, spatial coordinates, and global diffusion direction vector, and trigger the closed-loop control process of the operating state of the deluge alarm valve group through the linkage command.

[0069] Among them, the linkage instruction includes control instructions for the leakage level, leakage source location, and diffusion direction, and is used to trigger the closed-loop control of the deluge alarm valve group. The association between the operating state of the deluge alarm valve group and the abnormal diffusion area of the target helium concentration is mainly reflected in the leakage detection and positioning capabilities of the system, especially during the monitoring and maintenance phases. Specifically, the following explanations are provided for leakage detection: In the automatic sprinkler fire extinguishing system, helium is used as a tracer gas to detect minute leaks in the sealed cavity. By monitoring the change in helium concentration, it is possible to detect whether there is a leakage situation. Due to its small molecule size, inactivity, and ease of detection, helium becomes an ideal leakage test medium. For the process of determining the leakage location, the following explanations are provided: Once an abnormal increase in helium concentration is detected, it indicates the presence of a leakage point (i.e., the leakage source). Based on the helium concentration distribution data, the spatial coordinates and diffusion direction vector of the abnormal diffusion area of the target helium concentration can be calculated. This helps to accurately locate the specific location of the leakage. For the impact on the operating state, the following explanations are provided: In the normal standby state of the deluge alarm valve group, if an abnormal diffusion area of the target helium concentration is detected, it may indicate a potential leakage risk in the system, and further inspection and handling are required to avoid affecting the reliability of the automatic sprinkler fire extinguishing system. In the activated or water flow state of the deluge alarm valve group, if an abnormal helium concentration is detected, in addition to paying attention to the fire response, it is also necessary to consider whether the problem is caused by an internal leakage in the automatic sprinkler fire extinguishing system, which may affect the fire extinguishing efficiency. In the fault state of the deluge alarm valve group, the abnormal helium concentration may be one of the causes of the fault, such as seal failure. In the embodiments of the present application, timely identification and repair of the abnormal diffusion area of the target helium concentration can help restore the normal function of the system. During the test state of the deluge alarm valve group, helium concentration monitoring can be used to verify the integrity of the automatic sprinkler fire extinguishing system, ensuring that all components are well-sealed and there is no leakage. The closed-loop control process automatically adjusts the operating state of the deluge alarm valve group according to the linkage instruction to cope with the leakage situation.

[0070] The following is a specific example:

[0071] In the automatic sprinkler fire extinguishing system of a certain high-rise building, an abnormal helium concentration was detected in the sealed cavity of the deluge alarm valve group. The remote monitoring system first obtained the distribution data through a helium concentration sensor, and combined with the water pressure fluctuation parameters and the current phase characteristics of the fire pump station, and identified that the leakage source was located in the northeast corner of the cavity. The spatial coordinates of the leakage area were determined through the region growing algorithm, and the global diffusion direction vector was calculated. Further, the remote monitoring system detected that the leakage rate gradient exceeded the reference curve three times in a row, and at the same time, harmonic distortion appeared in the current phase characteristics, and determined that the leakage level was "severe". The remote monitoring system generated a linkage instruction to trigger the closed-loop control process of the deluge alarm valve group, automatically closing the valve in the leakage area and starting the standby pump station to ensure the normal operation of the automatic sprinkler fire extinguishing system.

[0072] By executing S11 to S14, the solution of this embodiment of the present application realizes precise monitoring and efficient control of the leakage of the deluge valve group through multi-source data fusion and refined processing. The specific effects include: accurately positioning the leakage source, dynamically reflecting the leakage trend, avoiding false alarms and missed alarms, and quickly responding to leakage events through closed-loop control, improving the reliability and safety of the automatic sprinkler system, and meeting the leakage monitoring and control requirements under complex working conditions.

[0073] In a possible embodiment, in S12, based on the helium concentration distribution data, determine the spatial coordinates and the global diffusion direction vector of the target helium concentration abnormal diffusion region inside the sealed cavity, and construct a target composite data frame based on the phase characteristics of the water pressure fluctuation parameters and the helium concentration distribution data, including:

[0074] Step 121: Perform noise suppression processing on the helium concentration distribution data to generate denoised helium concentration distribution data. Grid the denoised helium concentration distribution data in three-dimensional space according to the geometric structure of the sealed cavity to generate uniformly distributed grid cells, calculate the helium concentration gradient values of each grid cell, and identify the initial helium concentration abnormal diffusion region.

[0075] Among them, the noise suppression processing refers to removing the noise interference in the helium concentration distribution data through a filtering algorithm to improve the data quality. The three-dimensional space gridding divides the sealed cavity into uniform three-dimensional grid cells, which is convenient for subsequent data analysis and calculation. The helium concentration gradient value reflects the change rate of the helium concentration in space and is used to identify the abnormal diffusion region. The initial helium concentration abnormal diffusion region is a possible leakage region initially identified through the gradient value. The initial target helium concentration abnormal diffusion region is an abnormal region in the helium concentration distribution that can be identified by analyzing the helium concentration gradient values of each grid cell. These regions usually show that their gradient values are higher than the surrounding regions. These regions may be leakage points or diffusion sources, and the diffusion source is the region around the leakage point. Calculating each grid cell means that the sealed cavity is divided into uniform three-dimensional grid cells, and the coordinates of each cell are (i, j, k), corresponding to the helium concentration C(i, j, k) (denoised data). Then, the central difference method is used to calculate the gradient vector and the amplitude of the gradient value of each grid cell in three-dimensional space, and then the boundary processing method is used to obtain them.

[0076] Step 122: Perform region growing processing on the initial helium concentration abnormal diffusion region. During the region growing processing, merge adjacent grid cells based on the preset gradient direction consistency criterion until the gradient value at the growth boundary is lower than the preset gradient threshold to obtain the target helium concentration abnormal diffusion region, and determine the spatial coordinates of the target helium concentration abnormal diffusion region.

[0077] Among them, the region growth process refers to gradually expanding the abnormal region by merging adjacent grid cells until a preset stop condition is met. The preset stop condition is that when the magnitude of the helium concentration gradient of adjacent grid cells at the growth boundary is lower than the preset gradient threshold, the growth stops. The gradient direction consistency criterion is used as the standard for judging whether adjacent grid cells belong to the same abnormal region. Adjacent grid cells in a three-dimensional grid refer to the cells that are in direct contact with the current target grid cell and share at least one face, forming a direct adjacent relationship in space. For the merging of adjacent grid cells, the following is a specific example: Suppose the initial abnormal region is a high-gradient cell A, and there is a cell B in its six-neighborhood. If the gradient magnitude of B is higher than the threshold and the cosine value of the angle between its gradient direction and the average direction of region A is ≥ 0.9 (highly consistent in direction), then B will be merged into the abnormal region where A is located. After merging, the average gradient direction of the region will be updated to the average of the gradient vectors of A and B, and the spatial boundary coordinates will also be extended to include the position of B. Subsequent iterations will continue to grow outward with B as the seed point.

[0078] The final leakage region determined after the region growth process for the target helium concentration abnormal diffusion region. Through the region growth process and the gradient direction consistency criterion, the target helium concentration abnormal diffusion region can be further accurately located, and its spatial coordinates and diffusion direction can be determined.

[0079] Step 123: Select multiple grid cells with relatively large gradient values as multiple representative grid cells within the target helium concentration abnormal diffusion region, calculate the diffusion direction vectors of each representative grid cell, and perform normalized weighted average processing on all the diffusion direction vectors to generate the global diffusion direction vector of the target helium concentration abnormal diffusion region.

[0080] Among them, the representative grid cell is a grid cell with a relatively large gradient value, which is used to reflect the main direction of leakage diffusion. The diffusion direction vector can reflect the diffusion direction of helium in the representative grid cell. The global diffusion direction vector is a comprehensive diffusion direction generated through normalized weighted average, which is used to predict the leakage trend. Normalized weighted average refers to a method of weighted average for multiple vectors, and its core steps include: vector normalization, weight assignment, and weighted average.

[0081] The following is a specific example: Assume that there are 3 representative grid cells in the target abnormal area, and their diffusion direction vectors and gradient values (as weights) are as follows: The original direction vector of cell A is (3, 0, 0), and the gradient value wA = 5; the original direction vector of cell B is (0, 4, 0), and the gradient value wB = 3; the original direction vector of cell C is (0, 0, 5), and the gradient value wC = 2. Vector normalization is to convert each vector into a unit vector. Therefore, the vector (3, 0, 0) corresponding to cell A can be converted into the unit vector (1, 0, 0), the vector (0, 4, 0) corresponding to cell B can be converted into the unit vector (0, 1, 0), and the vector (0, 0, 5) corresponding to cell C can be converted into the unit vector (0, 0, 1). Assign the total weight: w total = 5 + 3 + 2 = 10, and the normalized weights are: the weight of cell A is 5 / 10 = 0.5, the weight of cell B is 3 / 10 = 0.3, and the weight of cell C is 2 / 10 = 0.2. Perform weighted averaging to calculate the weighted sum: The combined vector = 0.5×(1, 0, 0) + 0.3×(0, 1, 0) + 0.2×(0,

[0082] 0, 1) = (0.5, 0.3, 0.2). Normalize the combined vector into a unit vector: The modulus length ≈ 0.616, and the global diffusion direction vector ≈ (0.812, 0.487, 0.325).

[0083] Step 124: Perform Fourier transform on the water pressure fluctuation parameters, extract the phase characteristics within a specific frequency range that matches the resonance frequency of the sealed cavity structure from the Fourier transform results, perform sliding window smoothing processing and low-pass filtering operations on the phase characteristics within the specific frequency range to generate the denoised phase characteristics, and use the denoised phase characteristics as the phase characteristics of the water pressure fluctuation parameters.

[0084] Among them, Fourier transform means converting the water pressure fluctuation parameters from the time domain to the frequency domain to facilitate the extraction of phase characteristics. The resonance frequency of the sealed cavity structure refers to the inherent vibration frequency of the sealed cavity (such as a closed structure like a pressure vessel, pipeline, etc.) under the action of external excitations (such as water pressure fluctuations, mechanical vibrations, etc.) due to its own physical characteristics (such as geometric shape, material stiffness, boundary constraints, etc.). When the external excitation frequency is consistent with the cavity resonance frequency, the cavity will undergo a resonance phenomenon, manifested as a significant increase in amplitude, which may lead to structural fatigue and even damage.

[0085] In the embodiments of the present application, frequency components consistent with the resonance frequency of the sealed cavity structure are extracted from the Fourier transform results, and these components are used to reflect the influence of leakage on water pressure. The sliding window smoothing process refers to reducing the fluctuations of the phase features through the sliding window averaging method. It should be understood that the sliding window averaging method is a data processing technique that reduces the influence of random errors or noise by calculating the average value of multiple data points in the phase features within a specific frequency range, thereby improving the stability and reliability of the denoised phase features. Its core idea is to utilize the statistical characteristics of the phase features within a specific frequency range to offset the abnormal fluctuations of individual phase features, thereby obtaining results closer to the true values.

[0086] Step 125: Calculate the water pressure phase fluctuation amplitude of each grid cell within the corresponding time window according to the phase features of the water pressure fluctuation parameters.

[0087] Among them, the water pressure phase fluctuation amplitude within the time window reflects the change amplitude of the water pressure fluctuation in terms of phase, and is used to quantify the influence of leakage on water pressure. The time window refers to dividing the continuous time series data into time periods of a fixed length, which is used for localized analysis and calculation of statistical features of dynamic changes.

[0088] In step 125, the time window is used to intercept the data segment of the water pressure fluctuation phase features within a certain time period, and then calculate the phase fluctuation amplitude within this time period to quantify the influence of leakage on water pressure.

[0089] The following is a specific example of the time window: Assume that the duration of the phase feature data of the water pressure fluctuation parameter is 10 seconds, and the sampling frequency is 100 Hz, that is, 100 data points per second, with a total of 1000 phase values, corresponding to the time series t = 0, 0.01, 0.02, …, 9.99 seconds. Select the following time window parameters: window length: 1 second, 1 second contains 100 data points, the sliding step size is 0.5 seconds, the window moves forward 0.5 seconds each time, and the overlap rate is 50%. The time window divides the first window: t = 0 to 1 second, containing the first 100 data points (index 0 to 99), the second window: t = 0.5 to 1.5 seconds, containing data point indices 50 to 149, the third window: t = 1 to 2 seconds, data point indices 100 to 199, and so on, until all 10 seconds of data are covered.

[0090] Step 126: Based on the helium concentration gradient values and the water pressure phase fluctuation amplitudes of each grid cell, construct a three-dimensional coupling coefficient matrix, and perform a tensor product operation on the three-dimensional coupling coefficient matrix and the global diffusion direction vector to generate a target composite data frame.

[0091] Among them, the three-dimensional coupling coefficient matrix reflects the coupling relationship between the helium concentration gradient value and the amplitude of the water pressure phase fluctuation. The target composite data frame is a comprehensive data structure generated by tensor product operation and is used for leakage monitoring and judgment.

[0092] The following is a specific example:

[0093] In the automatic sprinkler system of a certain chemical plant, an abnormal helium concentration is detected in the sealed cavity of the deluge alarm valve group. The remote monitoring system first performs noise suppression and three-dimensional grid processing on the helium concentration distribution data to identify the initial abnormal helium concentration diffusion region; determines the target abnormal helium concentration diffusion region and its spatial coordinates through the region growing algorithm; further, calculates the global diffusion direction vector. At the same time, the remote monitoring system performs Fourier transform and filtering processing on the water pressure fluctuation parameters, extracts the phase characteristics and calculates the amplitude of the water pressure phase fluctuation; finally, constructs a three-dimensional coupling coefficient matrix and performs a tensor product operation with the global diffusion direction vector to generate a target composite data frame for subsequent leakage monitoring and control.

[0094] By executing steps 121 to 126, the solution of this embodiment of the present application realizes the accurate analysis of the abnormal helium concentration diffusion region and the water pressure fluctuation characteristics through technologies such as noise suppression, region growing, data fusion, and tensor operation, generates a target composite data frame, provides reliable data support for leakage monitoring and control, improves the accuracy and real-time performance of the leakage monitoring of the deluge alarm valve group, and is applicable to the requirements of the automatic sprinkler system under complex working conditions.

[0095] In a possible embodiment, step 126, based on the helium concentration gradient value and the amplitude of the water pressure phase fluctuation of each grid unit, constructs a three-dimensional coupling coefficient matrix, and performs a tensor product operation on the three-dimensional coupling coefficient matrix and the global diffusion direction vector to generate a target composite data frame, including:

[0096] Step a1: Normalize the helium concentration gradient values of each grid unit respectively to generate a sequence of normalized helium concentration gradient values, and standardize the amplitude of the water pressure phase fluctuation of each grid unit within the corresponding time window to generate a sequence of standardized amplitudes of the water pressure phase fluctuation.

[0097] Among them, the normalization process scales the helium concentration gradient values to a unified range (such as [0, 1]) to eliminate the dimension difference and facilitate subsequent calculations. The standardization process converts the amplitude of the water pressure phase fluctuation into a distribution with a mean of 0 and a standard deviation of 1 to improve data comparability. The sequence of normalized helium concentration gradient values is a sequence formed by arranging the normalized helium concentration gradient values according to the grid units. The sequence of standardized amplitudes of the water pressure phase fluctuation is a sequence formed by arranging the standardized amplitudes of the water pressure phase fluctuation according to the grid units.

[0098] Step a2: Based on the helium concentration gradient value sequence and the water pressure phase fluctuation amplitude sequence, calculate the gradient phase coupling coefficient for each grid cell. The gradient phase coupling coefficient is obtained through the multiplication operation of the helium concentration gradient value and the water pressure phase fluctuation amplitude.

[0099] Among them, the gradient phase coupling coefficient reflects the coupling relationship between the helium concentration gradient value and the water pressure phase fluctuation amplitude, and is used to quantify the impact of leakage on the water pressure. Coupling is to couple the gradient and the phase.

[0100] Step a3: Arrange the gradient phase coupling coefficients of all grid cells according to their spatial positions to construct a three-dimensional coupling coefficient matrix, and perform tensor expansion on the global diffusion direction vector to generate a diffusion direction tensor.

[0101] Step a4: Combine the three-dimensional coupling coefficient matrix and the diffusion direction tensor to perform an element-by-element multiplication operation to generate an initial composite data frame.

[0102] Among them, the three-dimensional coupling coefficient matrix is a three-dimensional matrix formed by arranging the gradient phase coupling coefficients according to the spatial positions of the grid cells. The diffusion direction tensor is the preliminary fusion data generated through the element-by-element multiplication operation and is used for subsequent processing. The diffusion direction tensor is converted into a structure with the same dimension as the three-dimensional coupling coefficient matrix through tensor expansion.

[0103] Step a5: Perform spatial smoothing on the initial composite data frame to eliminate local data mutations and generate a target composite data frame.

[0104] Among them, spatial smoothing eliminates local mutations in the initial composite data frame through a filtering algorithm to improve data smoothness. The target composite data frame is the final fusion data after smoothing and is used for leakage monitoring and judgment.

[0105] The following is a specific example:

[0106] In the automatic sprinkler system of a large commercial complex, an abnormal helium concentration is detected in the sealing cavity of the deluge alarm valve group. The system first normalizes and standardizes the helium concentration gradient value and the water pressure phase fluctuation amplitude to generate corresponding sequences; further, calculates the gradient phase coupling coefficient for each grid cell and constructs a three-dimensional coupling coefficient matrix; at the same time, performs tensor expansion on the global diffusion direction vector to generate a diffusion direction tensor; generates an initial composite data frame through an element-by-element multiplication operation and performs spatial smoothing on it, and finally generates a target composite data frame for leakage monitoring and control.

[0107] By performing steps a1 to a5, the solution of the embodiment of the present application realizes the high-precision fusion of the helium concentration gradient value and the amplitude of the water pressure phase fluctuation through normalization, standardization, coupling coefficient calculation, tensor operation, and spatial smoothing processing, generates a target composite data frame, provides a reliable data basis for leakage monitoring, improves the accuracy and real-time performance of leakage judgment, and is applicable to the requirements of automatic sprinkler systems under complex working conditions.

[0108] In a possible embodiment, step a3, combining the three-dimensional coupling coefficient matrix with the diffusion direction tensor, performs an element-by-element multiplication operation to generate an initial composite data frame, including:

[0109] Step b1, perform a dimension check on the three-dimensional coupling coefficient matrix to ensure that the spatial dimensions of the three-dimensional coupling coefficient matrix and the diffusion direction tensor are consistent.

[0110] Among them, the dimension check examines whether the spatial dimension of the three-dimensional coupling coefficient matrix is consistent with the diffusion direction tensor to ensure data compatibility. Consistent spatial dimensions mean that the sizes (such as length, width, and height) of the matrix and the tensor in the three-dimensional space are exactly matched.

[0111] Step b2, correct the weight of each element in the three-dimensional coupling coefficient matrix to adjust the weight value of the gradient phase coupling coefficient, generate a corrected three-dimensional coupling coefficient matrix, and perform a normalization process on the diffusion direction tensor to ensure that the modulus length of the diffusion direction vector of each representative grid cell is a unit length, generating a normalized diffusion direction tensor.

[0112] Among them, the weight correction adjusts the weight value of the gradient phase coupling coefficient according to a preset rule to optimize the data fusion effect. The corrected three-dimensional coupling coefficient matrix is the gradient phase coupling coefficient matrix after weight correction. The normalized diffusion direction tensor is a diffusion direction tensor with a modulus length of unit length.

[0113] Step b3, perform an element-by-element multiplication operation on the corrected three-dimensional coupling coefficient matrix and the normalized diffusion direction tensor to generate an initial composite data frame.

[0114] Among them, the element-by-element multiplication operation means multiplying the elements at the corresponding positions in the matrix and the tensor to generate a new data structure. The initial composite data frame is the preliminary fusion data generated by the element-by-element multiplication operation and is used for subsequent processing.

[0115] The following is a specific example:

[0116] In the automatic sprinkler system of a chemical plant, an abnormal helium concentration was detected in the sealing cavity of the deluge alarm valve group. First, the system performs a dimensional check on the three-dimensional coupling coefficient matrix to ensure its spatial dimension is consistent with the diffusion direction tensor. Subsequently, each element in the three-dimensional coupling coefficient matrix is weighted and corrected to generate a corrected three-dimensional coupling coefficient matrix. At the same time, the diffusion direction tensor is normalized. Finally, the corrected three-dimensional coupling coefficient matrix and the normalized diffusion direction tensor are subjected to element-by-element multiplication to generate an initial composite data frame for leakage monitoring and control.

[0117] By performing steps b1 - b3, the solution of this embodiment of the present application realizes the high-precision fusion of the three-dimensional coupling coefficient matrix and the diffusion direction tensor through dimensional check, weight correction, normalization processing, and element-by-element multiplication, generates an initial composite data frame, provides a more reliable data basis for leakage monitoring, improves the accuracy and real-time performance of leakage judgment, and is applicable to the requirements of automatic sprinkler systems under complex working conditions.

[0118] In a possible embodiment, step a5, perform spatial smoothing on the initial composite data frame to eliminate local data mutations and generate a target composite data frame, including:

[0119] Step a51, perform local neighborhood analysis on the helium concentration gradient value of each grid cell in the initial composite data frame. During the local neighborhood analysis, calculate the difference in helium concentration gradient values between each grid cell and its adjacent grid cells to generate a local data difference matrix.

[0120] Among them, local neighborhood analysis refers to comparing and analyzing the helium concentration gradient values of each grid cell and its adjacent cells to identify local data differences. The local data difference matrix is a matrix that records the difference in helium concentration gradient values between each grid cell and its adjacent cells, and is used to identify local mutation regions.

[0121] Step a52, based on the local data difference matrix, identify the grid cells whose helium concentration gradient value difference exceeds a preset difference threshold, mark them as local mutation grid cells, and perform interpolation correction on the helium concentration gradient values of the local mutation grid cells to generate corrected helium concentration gradient values.

[0122] Among them, step a52 marks the grid cells whose helium concentration gradient value difference exceeds the preset difference threshold as local mutation grid cells. Local mutation grid cells are grid cells whose helium concentration gradient value difference exceeds the preset threshold, and may be data anomaly points. Interpolation correction refers to correcting the values of local mutation grid cells based on the helium concentration gradient values of adjacent grid cells to eliminate anomalies.

[0123] For example, for the local data difference matrix [0.3, 0.8, 1.5, 0.7, 0.4], the helium concentration gradient values of each cell are as follows: the helium concentration gradient value of cell 1 is 1.2, the helium concentration gradient value of cell 2 is 1.5, the helium concentration gradient value of cell 3 is 3.0, the helium concentration gradient value of cell 4 is 1.8, and the helium concentration gradient value of cell 5 is 1.3. There are 5 grid cells. Interpolate and correct the local mutation cell (such as cell 3), take the directly adjacent cells of the mutation cell, that is, cell 2 (gradient value 1.5) on the left of cell 3 and cell 4 (gradient value 1.8) on the right, and use the arithmetic mean method to calculate the correction value, and then correct the helium concentration gradient value of cell 3 to generate the corrected helium concentration gradient value.

[0124] Step a53: Update the corrected helium concentration gradient value to the initial composite data frame to generate an intermediate composite data frame, perform smoothing processing on the intermediate composite data frame, and adjust the helium concentration gradient value of each grid cell based on the preset spatial continuity constraint to generate a smoothed composite data frame.

[0125] Among them, the intermediate composite data frame is the data frame after updating the corrected helium concentration gradient value, which is used for further processing. The smoothing process eliminates local fluctuations in the data through a filtering algorithm to improve data smoothness. The spatial continuity constraint ensures that the changes in the helium concentration gradient values of adjacent grid cells conform to physical laws.

[0126] Step a54: Perform boundary consistency verification on the smoothed composite data frame, and correct the abnormal data points at the boundaries of each local mutation grid cell based on the data value gradient changes of adjacent grid cells to generate a target composite data frame.

[0127] Among them, the boundary consistency verification checks whether the data values at the boundaries of local mutation grid cells conform to the gradient change law of adjacent cells.

[0128] The following is a specific example:

[0129] In the automatic sprinkler system of a certain high-rise building, an abnormal helium concentration is detected in the sealed cavity of the deluge alarm valve group. The system first performs local neighborhood analysis on the initial composite data frame to generate a local data difference matrix; subsequently, identifies and corrects the helium concentration gradient values of local mutation grid cells to generate an intermediate composite data frame; further, performs smoothing processing on the intermediate composite data frame to generate a smoothed composite data frame; finally, performs boundary consistency verification on the smoothed composite data frame, corrects abnormal data points, and generates a target composite data frame for leakage monitoring and control.

[0130] By performing steps a51 to a54, the solution in the embodiments of the present application realizes high-precision correction and optimization of the initial composite data frame through local neighborhood analysis, interpolation correction, smoothing processing, and boundary consistency verification, generates the target composite data frame, improves the accuracy and reliability of the leakage monitoring data, and meets the requirements of the automatic sprinkler system under complex working conditions.

[0131] In a possible embodiment, S13: Combine the spatial coordinates and the global diffusion direction vector to determine the global leakage rate gradient of the target composite data frame. When the global leakage rate gradient exceeds the preset reference curve continuously for multiple times, extract the multiple harmonic distortion increase amounts in the current phase characteristics. Determine the leakage level identifier according to the multiple harmonic distortion increase amounts and the global leakage rate gradient, including:

[0132] Step c1: Based on the spatial coordinates, determine the global leakage rate gradient of each grid cell, perform time series accumulation on the leakage rate gradients of each grid cell to generate a leakage rate gradient time series, and based on the leakage rate gradient time series, combine the global diffusion direction vector to determine the global leakage rate gradient of the target composite data frame.

[0133] Among them, the global leakage rate gradient can reflect the change trend of the leakage rate in space and time, and is used to quantify the severity of the leakage. The leakage rate gradient time series refers to the sequence formed by arranging the leakage rate gradients of each grid cell in chronological order. The global leakage rate gradient of the target composite data frame is the final leakage rate gradient generated by integrating the time series and the global diffusion direction vector. The global leakage rate gradient refers to the trend of the helium concentration change over time in each unit after the interior of the sealed cavity is divided into multiple uniformly distributed grid cells. This trend reflects the leakage speed of helium (as a tracer gas) in a specific area. By analyzing these gradient values, the specific location of the leakage and its diffusion direction can be located. Specifically, the global leakage rate gradient is constructed based on the helium concentration gradient value and the water pressure phase fluctuation amplitude in the grid cell, reflecting the dynamic leakage situation in the local area.

[0134] Step c2: Determine whether the global leakage rate gradient exceeds the preset reference curve continuously for multiple times.

[0135] Among them, the preset reference curve is a leakage rate gradient threshold set according to historical data or experimental data, and is used to judge whether the leakage is abnormal.

[0136] Step c3: If so, determine the abnormal time window. The abnormal time window is the time window corresponding to when the global leakage rate gradient exceeds the preset reference curve continuously for multiple times.

[0137] Among them, the abnormal time window refers to the time period during which the global leakage rate gradient continuously exceeds the preset reference curve for multiple times, and is used to further analyze the leakage situation. Multiple times can refer to 3 times, 4 times, 10 times, etc., and the specific numerical value of multiple times is not specifically limited in the embodiments of the present application.

[0138] Step c4: Within the abnormal time window, extract the increment of multiple harmonic distortions in the current phase characteristics, and perform time integration on the increment of multiple harmonic distortions to generate the cumulative harmonic distortion.

[0139] Among them, the cumulative harmonic distortion refers to the total amount generated after performing time integration on the increment of multiple harmonic distortions, and is used to quantify the impact of leakage on the current phase.

[0140] Step c5: Determine the leakage level identifier based on the ratio relationship between the cumulative harmonic distortion and the global leakage rate gradient.

[0141] Among them, the leakage level identifier refers to the severity level of leakage determined according to the ratio relationship between the cumulative harmonic distortion and the global leakage rate gradient.

[0142] The following is a specific example:

[0143] In the automatic sprinkler system of a certain chemical plant, an abnormal helium concentration is detected in the sealing cavity of the deluge alarm valve group. The remote monitoring system first calculates the leakage rate gradient of each grid unit based on the spatial coordinates and generates a time series of the leakage rate gradient; further, in combination with the global diffusion direction vector, determines the global leakage rate gradient of the target composite data frame. Subsequently, when the remote monitoring system determines that the global leakage rate gradient continuously exceeds the preset reference curve three times, it determines the abnormal time window; within the abnormal time window, extracts the increment of multiple harmonic distortions in the current phase characteristics, and performs time integration on the increment of multiple harmonic distortions to generate the cumulative harmonic distortion; finally, based on the ratio relationship between the cumulative harmonic distortion and the global leakage rate gradient, determines that the leakage level is "severe" and generates the corresponding leakage level identifier.

[0144] By executing steps c1 to c5, the solution of the embodiment of the present application realizes precise monitoring and evaluation of the leakage situation through leakage rate gradient time series analysis, abnormal time window determination, cumulative harmonic distortion calculation, and leakage level determination, improves the accuracy and real-time performance of leakage judgment, and meets the requirements of the automatic sprinkler system under complex working conditions.

[0145] In a possible embodiment, step c1: Based on the time series of the leakage rate gradient, in combination with the global diffusion direction vector, determine the global leakage rate gradient of the target composite data frame, including:

[0146] Step d1: Calculate the change rate of the helium concentration gradient between grid cells based on the helium concentration gradient values of each grid cell.

[0147] Among them, the change rate of the helium concentration gradient reflects the change degree of the helium concentration gradient between adjacent grid cells and is used to quantify the dynamic characteristics of leakage diffusion.

[0148] Step d2: Calculate the weight of each grid cell based on the change rate of the helium concentration gradient, combined with the global diffusion direction vector, the distance from the grid cell to the leakage source, and the estimated diffusion velocity of helium in the target helium concentration abnormal diffusion region.

[0149] Among them, the grid cell weight reflects the importance of each grid cell in the leakage rate gradient calculation, comprehensively considering factors such as the change rate of the gradient value, diffusion direction, distance, and diffusion velocity.

[0150] Step d3: Optimize the weights of each grid cell in combination with historical data to obtain the optimized weights of each grid cell, so as to construct a weighted matrix.

[0151] Among them, the historical data is historical leakage data. Step d3 uses historical leakage data to correct the weights to improve the accuracy of weight calculation. The weighted matrix refers to the matrix formed by arranging the optimized weights according to the grid cell positions and is used for subsequent leakage rate gradient calculation. Grid cells closer to the leakage source may have higher weights because grid cells closer to the leakage source more directly reflect the immediate state of the leakage; while areas with faster diffusion velocities may also obtain higher weights because areas with faster diffusion velocities indicate that the leaked substance can spread faster.

[0152] Step d4: Combine the leakage rate gradient time series with the weighted matrix to obtain the global leakage rate gradient of the target composite data frame.

[0153] Among them, the global leakage rate gradient refers to the final leakage rate gradient generated by integrating the leakage rate gradient time series and the weighted matrix and is used to quantify the global trend of leakage.

[0154] The following is a specific example:

[0155] In the automatic sprinkler system of a chemical plant, an abnormal helium concentration is detected in the sealing cavity of the deluge alarm valve group. The remote monitoring system first calculates the change rate of the helium concentration gradient of each grid cell; subsequently, combines the global diffusion direction vector, the distance from the grid cell to the leakage source, and the estimated diffusion velocity of helium to calculate the weight of each grid cell; further, optimizes the weights using historical data to construct a weighted matrix; finally, combines the leakage rate gradient time series with the weighted matrix to generate the global leakage rate gradient of the target composite data frame for leakage monitoring and control.

[0156] By performing steps d1 to d4, the solution in the embodiments of the present application realizes high-precision calculation of the leakage rate gradient through helium concentration gradient value change rate calculation, weight optimization, and weighted matrix construction, generates the global leakage rate gradient of the target composite data frame, improves the accuracy and reliability of leakage monitoring, and meets the requirements of the automatic sprinkler system under complex working conditions.

[0157] Figure 2 The following is a schematic structural diagram of a remote monitoring system for the operating state of a valve group provided by the embodiments of the present application. As Figure 2 shown, the system includes:

[0158] An acquisition module 21, which acquires the helium concentration distribution data inside the sealing cavity of the deluge alarm valve group, the water pressure fluctuation parameters inside the deluge alarm valve group, and the current phase characteristics of the fire pump station. The fire pump station serves as the water supply power source and establishes a protocol-level linkage with the deluge alarm valve group.

[0159] A construction module 22, which determines the spatial coordinates and the global diffusion direction vector of the target helium concentration abnormal diffusion region inside the sealing cavity based on the helium concentration distribution data, and constructs a target composite data frame based on the phase characteristics of the water pressure fluctuation parameters and the helium concentration distribution data.

[0160] An extraction module 23, which determines the global leakage rate gradient of the target composite data frame in combination with the spatial coordinates and the global diffusion direction vector. When the global leakage rate gradient continuously exceeds the preset reference curve for multiple times, it extracts the multiple harmonic distortion increase amount in the current phase characteristics, and determines the leakage level identifier according to the multiple harmonic distortion increase amount and the global leakage rate gradient.

[0161] A generation module 24, which generates a linkage command including the leakage level identifier, the spatial coordinates, and the global diffusion direction vector, and triggers the closed-loop control process of the operating state of the deluge alarm valve group through the linkage command.

[0162] Figure 2 The described remote monitoring system for the operating state of a deluge alarm valve group can execute Figure 1 the remote monitoring method for the operating state of a deluge alarm valve group described in the embodiments shown. Its implementation principle and technical effects will not be elaborated. For the remote monitoring system for the operating state of a deluge alarm valve group in the above embodiments, the specific manners in which each module and unit perform operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0163] In a possible design, Figure 2 the remote monitoring system for the operating state of a deluge alarm valve group in the embodiments shown can be implemented as a computing device. As Figure 3 shown, the computing device may include a storage component 31 and a processing component 32.

[0164] The storage component 31 stores one or more computer instructions, and the one or more computer instructions are called and executed by the processing component 32.

[0165] The processing component 32 is configured to: obtain the helium concentration distribution data inside the sealed cavity of the deluge alarm valve set, the water pressure fluctuation parameters inside the deluge alarm valve set, and the current phase characteristics of the fire pump station. The fire pump station is used as a water supply power source and establishes a protocol-level linkage with the deluge alarm valve set; determine the spatial coordinates and the global diffusion direction vector of the target helium concentration abnormal diffusion region inside the sealed cavity based on the helium concentration distribution data, and construct a target composite data frame based on the phase characteristics of the water pressure fluctuation parameters and the helium concentration distribution data; combine the spatial coordinates and the global diffusion direction vector to determine the global leakage rate gradient of the target composite data frame. When the global leakage rate gradient exceeds a preset reference curve continuously for multiple times, extract the multiple harmonic distortion increase amount in the current phase characteristics, and determine the leakage level identifier according to the multiple harmonic distortion increase amount and the global leakage rate gradient; generate a linkage instruction including the leakage level identifier, the spatial coordinates, and the global diffusion direction vector, and trigger the closed-loop control process of the operating state of the deluge alarm valve set through the linkage instruction.

[0166] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is used to execute the above method.

[0167] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as random access memory (RAM), static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0168] Of course, the computing device may necessarily further include other components, such as an input / output interface, a display component, a communication component, etc.

[0169] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above-mentioned peripheral interface module can be an output device, an input device, etc.

[0170] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0171] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device can refer to a cloud server, and the above-mentioned processing component, storage component, etc. can be basic server resources leased or purchased from a cloud computing platform.

[0172] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 A method for remotely monitoring the operating state of a deluge alarm valve set shown in the embodiment.

[0173] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0174] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0175] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A remote monitoring method for the operating state of a deluge alarm valve set, characterized in that, Including: Obtain the helium concentration distribution data inside the sealed cavity of the deluge alarm valve set, the water pressure fluctuation parameters inside the deluge alarm valve set, and the current phase characteristics of the fire pump station. The fire pump station serves as a water supply power source and establishes a protocol-level linkage with the deluge alarm valve set; Based on the helium concentration distribution data, determine the spatial coordinates and global diffusion direction vector of the target helium concentration abnormal diffusion region inside the sealed cavity. Based on the phase characteristics of the water pressure fluctuation parameters and the helium concentration distribution data, construct a target composite data frame; Combined with the spatial coordinates and global diffusion direction vector, determine the global leakage rate gradient of the target composite data frame. When the global leakage rate gradient exceeds the preset reference curve continuously for multiple times, extract the multiple harmonic distortion amplification amounts in the current phase characteristics. According to the multiple harmonic distortion amplification amounts and the global leakage rate gradient, determine the leakage level identifier; Generate a linkage instruction including the leakage level identifier, the spatial coordinates, and the global diffusion direction vector, and trigger the closed-loop control process of the operation state of the deluge alarm valve set through the linkage instruction.

2. The method according to claim 1, wherein The method of determining the spatial coordinates and global diffusion direction vector of the target helium concentration abnormal diffusion region inside the sealed cavity based on the helium concentration distribution data, and constructing a target composite data frame based on the phase characteristics of the water pressure fluctuation parameters and the helium concentration distribution data includes: Perform noise suppression processing on the helium concentration distribution data to generate denoised helium concentration distribution data. Grid the denoised helium concentration distribution data in three-dimensional space according to the geometric structure of the sealed cavity to generate uniformly distributed grid cells, calculate the helium concentration gradient values of each grid cell, and identify the initial helium concentration abnormal diffusion region; Perform region growing processing on the initial helium concentration abnormal diffusion region. During the region growing processing, merge adjacent grid cells based on a preset gradient direction consistency criterion until the gradient value at the growth boundary is lower than the preset gradient threshold to obtain the target helium concentration abnormal diffusion region, and determine the spatial coordinates of the target helium concentration abnormal diffusion region; Select multiple grid cells with larger gradient values in the target helium concentration abnormal diffusion region as multiple representative grid cells, calculate the diffusion direction vector of each representative grid cell, and perform normalized weighted average processing on all the diffusion direction vectors to generate the global diffusion direction vector of the target helium concentration abnormal diffusion region; Perform Fourier transform on the water pressure fluctuation parameters, extract the phase characteristics in a specific frequency range that matches the resonance frequency of the sealed cavity structure from the Fourier transform results, perform sliding window smoothing processing and low-pass filtering operation on the phase characteristics in the specific frequency range to generate denoised phase characteristics, and use the denoised phase characteristics as the phase characteristics of the water pressure fluctuation parameters; According to the phase characteristics of the water pressure fluctuation parameters, calculate the water pressure phase fluctuation amplitudes of each grid cell in the corresponding time window. Construct a three-dimensional coupling coefficient matrix based on the helium concentration gradient values and the water pressure phase fluctuation amplitudes of each of the grid cells, and perform a tensor product operation on the three-dimensional coupling coefficient matrix and the global diffusion direction vector to generate a target composite data frame.

3. The method according to claim 2, characterized in that The constructing a three-dimensional coupling coefficient matrix based on the helium concentration gradient values and the water pressure phase fluctuation amplitudes of each of the grid cells, and performing a tensor product operation on the three-dimensional coupling coefficient matrix and the global diffusion direction vector to generate a target composite data frame includes: Perform normalization processing on the helium concentration gradient values of each of the grid cells respectively to generate a sequence of normalized helium concentration gradient values, and perform standardization processing on the water pressure phase fluctuation amplitudes of each of the grid cells within the corresponding time window to generate a sequence of standardized water pressure phase fluctuation amplitudes; Based on the sequence of helium concentration gradient values and the sequence of water pressure phase fluctuation amplitudes, calculate the gradient phase coupling coefficient of each grid cell, where the gradient phase coupling coefficient is obtained through the product operation of the helium concentration gradient value and the water pressure phase fluctuation amplitude; Arrange the gradient phase coupling coefficients of all grid cells according to the spatial positions to construct a three-dimensional coupling coefficient matrix, and perform tensor expansion on the global diffusion direction vector to generate a diffusion direction tensor; Combine the three-dimensional coupling coefficient matrix and the diffusion direction tensor to perform an element-by-element product operation to generate an initial composite data frame; Perform spatial smoothing processing on the initial composite data frame to eliminate local data mutations and generate a target composite data frame.

4. The method according to claim 3, characterized in that, The combining the three-dimensional coupling coefficient matrix and the diffusion direction tensor to perform an element-by-element product operation to generate an initial composite data frame includes: Perform dimension verification on the three-dimensional coupling coefficient matrix to ensure that the spatial dimensions of the three-dimensional coupling coefficient matrix and the diffusion direction tensor are consistent; Perform weight correction on each element in the three-dimensional coupling coefficient matrix to adjust the weight value of the gradient phase coupling coefficient to generate a corrected three-dimensional coupling coefficient matrix, and perform normalization processing on the diffusion direction tensor to ensure that the modulus length of the diffusion direction vector of each representative grid cell is a unit length to generate a normalized diffusion direction tensor; Perform an element-by-element product operation on the corrected three-dimensional coupling coefficient matrix and the normalized diffusion direction tensor to generate an initial composite data frame.

5. The method according to claim 3, characterized in that, The performing spatial smoothing processing on the initial composite data frame to eliminate local data mutations and generate a target composite data frame includes: Perform local neighborhood analysis on the helium concentration gradient values of each grid cell in the initial composite data frame. During the local neighborhood analysis process, calculate the difference in helium concentration gradient values between each grid cell and its adjacent grid cells to generate a local data difference matrix; Based on the local data difference matrix, identify the grid cells with helium concentration gradient value differences exceeding a preset difference threshold, mark them as local mutation grid cells, and perform interpolation correction on the helium concentration gradient values of the local mutation grid cells to generate corrected helium concentration gradient values; Update the corrected helium concentration gradient value to the initial composite data frame to generate an intermediate composite data frame, smooth the intermediate composite data frame, and adjust the helium concentration gradient value of each grid cell based on a preset spatial continuity constraint to generate a smoothed composite data frame; Perform a boundary consistency check on the smoothed composite data frame, and correct the abnormal data points at the boundaries of each locally mutated grid cell based on the data value gradient change of adjacent grid cells to generate a target composite data frame.

6. The method according to claim 1, characterized in that, Combined with the spatial coordinates and the global diffusion direction vector, determine the global leakage rate gradient of the target composite data frame. When the global leakage rate gradient continuously exceeds a preset reference curve multiple times, extract the multiple harmonic distortion amplification amounts in the current phase characteristics, and determine the leakage level identifier according to the multiple harmonic distortion amplification amounts and the global leakage rate gradient, including: Based on the spatial coordinates, determine the global leakage rate gradient of each grid cell, perform a time series accumulation on the leakage rate gradients of each grid cell to generate a leakage rate gradient time series, and based on the leakage rate gradient time series, combined with the global diffusion direction vector, determine the global leakage rate gradient of the target composite data frame; Judge whether the global leakage rate gradient continuously exceeds the preset reference curve multiple times; If so, determine an abnormal time window, where the abnormal time window is the time window corresponding to when the global leakage rate gradient continuously exceeds the preset reference curve multiple times; Within the abnormal time window, extract the multiple harmonic distortion amplification amounts in the current phase characteristics, and perform a time integration on the multiple harmonic distortion amplification amounts to generate a harmonic distortion accumulation amount; Determine the leakage level identifier based on the ratio relationship between the harmonic distortion accumulation amount and the global leakage rate gradient.

7. The method according to claim 6, characterized in that, The determining the global leakage rate gradient of the target composite data frame based on the leakage rate gradient time series, combined with the global diffusion direction vector, includes: According to the helium concentration gradient values of each grid cell, calculate the change rate of the helium concentration gradient values between grid cells; According to the change rate of the helium concentration gradient value, combined with the global diffusion direction vector, the distance from the grid cell to the leakage source, and the estimated diffusion speed of helium in the target helium concentration abnormal diffusion region, calculate the weights of each grid cell; Combined with historical data, optimize the weights of each grid cell to obtain the optimized weights of each grid cell to construct a weighted matrix; Combine the leakage rate gradient time series with the weighted matrix to obtain the global leakage rate gradient of the target composite data frame.

8. A remote monitoring system for the operating status of a deluge alarm valve set, characterized in that, Include: An acquisition module that acquires the helium concentration distribution data inside the sealing cavity of the deluge alarm valve set, the water pressure fluctuation parameters inside the deluge alarm valve set, and the current phase characteristics of the fire pump station. The fire pump station serves as a water supply power source and establishes a protocol-level linkage with the deluge alarm valve set; A construction module that determines the spatial coordinates and the global diffusion direction vector of the target helium concentration abnormal diffusion region inside the sealing cavity based on the helium concentration distribution data, and constructs a target composite data frame based on the phase characteristics of the water pressure fluctuation parameters and the helium concentration distribution data; An extraction module, in combination with spatial coordinates and a global diffusion direction vector, determines the global leakage rate gradient of the target composite data frame. When the global leakage rate gradient exceeds a preset reference curve continuously for multiple times, it extracts the increment of multiple harmonic distortions in the current phase characteristics, and determines the leakage level identifier according to the increment of multiple harmonic distortions and the global leakage rate gradient. A generation module generates a linkage instruction including the leakage level identifier, spatial coordinates, and the global diffusion direction vector, and triggers the closed-loop control process of the operating state of the deluge alarm valve group through the linkage instruction.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for remotely monitoring the operating state of a deluge alarm valve group according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that, A computer program is stored, and when the computer program is executed by a computer, it implements a method for remotely monitoring the operating state of a deluge alarm valve group according to any one of claims 1 to 7.

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