A combustible gas flammability measurement hysteresis compensation and prediction method and system for a LEL analyzer

Abstract

The application discloses a combustible gas combustibility measurement lag compensation and prediction method and system of an LEL analyzer, relates to the technical field of gas detection and analysis, and aims at the technical problem that the existing analyzer relies on fixed delay parameters and a simple linear extrapolation algorithm, resulting in large concentration prediction deviation, lagging and unreliable safety early warning. The method acquires fluid dynamics characteristics and sensor response characteristics, respectively extracts and generates gas density viscosity and sensor thermal inertia characteristic vectors; cross-fuses and reduces the dimensions of the two to generate a transmission lag time compensation model; inputs the current combustible gas concentration measurement value and the historical change rate into the model to obtain a lag compensation concentration value; then, combines a target area environment temperature and pressure correction factor to generate a real concentration prediction value through reverse propagation neural network mapping, and compares the lower explosive limit to output an early warning control instruction. The application accurately compensates for the measurement lag time, improves the real-time performance and anti-interference ability of concentration prediction in a complex dynamic environment.

Description

Technical Field

[0001] This invention relates to the field of gas detection and analysis technology, and in particular to a method and system for hysteresis compensation and prediction of combustibility measurement of LEL analyzers. Background Technology

[0002] LEL analyzers are key devices in the industrial safety field used for continuous monitoring of the lower explosive limit concentration of flammable gases in ambient air. These instruments typically employ catalytic combustion sensors or infrared optical sensors, using a suction pump to deliver gas samples from the target area to the detection chamber for analysis. In complex operating environments such as petrochemical plants, coal mines, and urban gas pipeline networks, LEL analyzers can assess the flammability of gas mixtures in real time and trigger early warning mechanisms when the concentration approaches the danger threshold, thereby preventing fires and explosions.

[0003] Existing LEL analyzers typically rely on a fixed time delay parameter to compensate for the gas's transfer from the sampling port to the sensor surface when measuring combustible gas concentration. The system obtains the average transfer time at a specific pipeline length and standard pump speed through calibration experiments, and then directly superimposes this as a static compensation factor onto the time axis of the measurement results. For concentration prediction, existing equipment often employs a simple linear extrapolation algorithm, calculating the rate of change based on the difference between the current concentration measurement and the previous measurement, and then estimating the concentration trend in the near future.

[0004] In practical industrial applications, the operating status of gas sampling pumps is affected by power fluctuations and mechanical wear. Simultaneously, the gas density and viscosity within the pipeline change with ambient temperature and pressure, resulting in a non-constant gas flow rate. Fixed time delay parameters cannot accurately reflect the true transmission lag. Simple linear extrapolation algorithms ignore the sensor's own thermal inertia hysteresis and the non-linear characteristics of gas concentration changes. When environmental conditions fluctuate drastically or the concentration rises sharply in the initial stages of a gas leak, this often leads to significant deviations in concentration predictions, failing to provide reliable safety warnings. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method and system for lag compensation and prediction of combustibility measurement in an LEL analyzer. It employs a dynamic compensation model constructed by integrating fluid dynamics characteristics and sensor thermal inertia characteristics, and combines environmental parameters for nonlinear mapping. This approach can accurately compensate for measurement lag time and improve the accuracy of true concentration prediction.

[0006] The above objectives can be achieved through the following approach:

[0007] A method for hysteresis compensation and prediction of combustible gas flammability measurement using an LEL analyzer includes: acquiring the operating current fluctuation parameters of the gas sampling pump and the acoustic impedance parameters of the pipeline inlet to obtain fluid dynamic characteristics and extracting features to generate a gas density and viscosity feature vector; extracting the initial temperature gradient parameters and response slope parameters of the catalytic combustion sensor to obtain sensor response characteristics and extracting features to generate a sensor thermal inertia feature vector; fusing the gas density and viscosity feature vector and the sensor thermal inertia feature vector to obtain multi-physics coupling characteristics and performing dimensionality reduction processing to generate a transmission hysteresis time compensation model; acquiring the current combustible gas concentration measurement value and historical concentration change rate to obtain basic concentration characteristics and inputting them into the transmission hysteresis time compensation model to generate a hysteresis compensation concentration value; acquiring the ambient temperature and pressure parameters of the target monitoring area to obtain an environmental correction factor and performing nonlinear mapping in combination with the hysteresis compensation concentration value to generate a true concentration prediction value; comparing the true concentration prediction value with the lower explosion limit threshold to obtain a safety status assessment result and generating instructions, outputting an early warning control instruction.

[0008] Optionally, the generation of the gas density and viscosity feature vector includes: acquiring the stator current signal and rotor speed signal of the gas sampling pump drive motor to obtain motor operating state parameters and performing frequency domain transformation to generate current spectrum distribution features; receiving the transmitted echo signal and phase difference signal of the ultrasonic sensor at the pipeline inlet to obtain acoustic propagation parameters and performing acoustic impedance calculation to generate acoustic impedance change rate features; splicing the current spectrum distribution features and acoustic impedance change rate features to obtain fluid dynamic features and performing principal component analysis to generate the gas density and viscosity feature vector.

[0009] Optionally, generating the sensor thermal inertia feature vector includes: reading the output voltage parameters of the Wheatstone bridge and the reference terminal voltage parameters of the catalytic combustion sensor in the initial stage of gas contact to obtain a differential voltage signal and performing first derivative calculation to generate a response slope parameter; obtaining the infrared radiation intensity parameters of the catalytic element surface and the ambient background radiation parameters to obtain the thermal radiation difference and performing temperature field inversion to generate an initial temperature gradient parameter; combining the response slope parameter and the initial temperature gradient parameter to obtain the sensor response characteristics and performing time series alignment to generate the sensor thermal inertia feature vector.

[0010] Optionally, the generation of the transmission lag time compensation model includes: cross-multiplying the elements in the gas density and viscosity feature vector with the elements in the sensor thermal inertia feature vector to obtain a cross-term matrix and performing normalization processing to generate multi-physics coupling features; extracting the eigenvalues ​​and eigenvectors of the multi-physics coupling features to obtain intrinsic orthogonal decomposition components and performing truncation processing to generate low-dimensional manifold features; mapping the low-dimensional manifold features to the time delay space to obtain delay time coefficients and performing polynomial fitting to generate the transmission lag time compensation model.

[0011] Optionally, generating the hysteresis-compensated concentration value includes: reading the digital signal output by the analog-to-digital converter module of the LEL analyzer and the range calibration coefficient to obtain the current combustible gas concentration measurement value and performing sliding window differential calculation to generate the historical concentration change rate; merging the current combustible gas concentration measurement value and the historical concentration change rate to obtain the basic concentration characteristics and performing matrix reconstruction to generate the concentration state matrix; inputting the concentration state matrix into the transmission hysteresis time compensation model to obtain the time compensation amount and combining it with the current combustible gas concentration measurement value to perform Taylor series expansion prediction to generate the hysteresis-compensated concentration value.

[0012] Optionally, generating the predicted true concentration value includes: receiving absolute temperature and atmospheric pressure values ​​sent by environmental monitoring nodes to obtain environmental state parameters and performing ideal gas law calculations to generate the gas molar volume change rate; converting the gas molar volume change rate and sensor temperature compensation coefficient to obtain an environmental correction factor and performing weight allocation to generate a corrected weight matrix; multiplying the corrected weight matrix with the hysteresis compensation concentration value to obtain a corrected concentration matrix and performing backpropagation neural network mapping to generate the predicted true concentration value.

[0013] Optionally, the safety status assessment result is obtained by comparing the actual concentration prediction value with the lower explosion limit threshold, and instructions are generated and output as early warning control instructions. This includes: calculating the difference between the actual concentration prediction value and the lower explosion limit threshold to obtain the concentration margin parameter and performing derivative of the change trend to generate an approximation rate feature; determining the fuzzy set to which the approximation rate feature and the concentration margin parameter belong to obtain the safety status assessment result and performing rule base matching to generate control action code; encapsulating the control action code and the target actuator address to obtain a data packet and performing protocol conversion to output the early warning control instructions.

[0014] Optionally, after outputting the early warning control command, the method further includes: collecting the action feedback signal of the target actuator and the valve opening parameter to obtain the execution status characteristics and performing closed-loop verification to generate an execution success flag; extracting the descent gradient parameter of the execution success flag and the predicted actual concentration value to obtain the control effect evaluation index and performing threshold comparison to generate a strategy adjustment signal; sending the strategy adjustment signal to the transmission lag time compensation model to obtain the model update command and performing parameter iteration to generate an updated transmission lag time compensation model.

[0015] Optionally, after generating the updated transmission lag time compensation model, the process further includes: extracting the polynomial coefficient parameters and system bias parameters of the updated transmission lag time compensation model to obtain model structure data and performing hash calculation to generate a model checksum; packaging the model structure data and model checksum to obtain a model update package and performing encryption processing to generate a secure update file; distributing the secure update file to the distributed LEL analyzer nodes to obtain a reception confirmation signal and perform version synchronization to generate a network-wide model consistency state.

[0016] This invention also provides a hysteresis compensation and prediction system for combustible gas flammability measurement of an LEL analyzer, comprising:

[0017] The fluid feature extraction module is used to obtain the operating current fluctuation parameters of the gas sampling pump and the acoustic impedance parameters of the pipeline inlet to obtain fluid dynamic features and perform feature extraction to generate gas density and viscosity feature vectors.

[0018] The thermal inertia extraction module is used to extract the initial temperature gradient parameters and response slope parameters of the catalytic combustion sensor to obtain the sensor response characteristics and perform feature extraction to generate the sensor thermal inertia feature vector.

[0019] The model generation module is used to fuse the gas density and viscosity feature vectors with the sensor thermal inertia feature vectors to obtain multi-physics coupling features and perform dimensionality reduction processing to generate a transmission lag time compensation model.

[0020] The compensation calculation module is used to collect the current combustible gas concentration measurement value and the historical concentration change rate to obtain the basic concentration characteristics and input them into the transmission lag time compensation model to generate the lag compensation concentration value.

[0021] The prediction mapping module is used to obtain the environmental temperature and pressure parameters of the target monitoring area, obtain the environmental correction factor, and combine it with the hysteresis compensation concentration value to perform nonlinear mapping to generate the true concentration prediction value.

[0022] The early warning control module is used to compare the actual concentration prediction value with the lower explosion limit threshold to obtain the safety status assessment result and generate instructions, and output early warning control instructions.

[0023] Compared with the prior art, the present invention has the following advantages:

[0024] This invention improves the accuracy of transmission lag time compensation during combustible gas concentration measurement by constructing a multiphysics coupling model by extracting fluid dynamics features and sensor thermal inertia features. It also enhances the real-time performance of predicting real combustible gas concentration under dynamic environments by combining historical concentration change rate and time compensation amount for Taylor series expansion prediction. At the same time, it uses environmental state parameters to generate a corrected weight matrix and performs neural network mapping to enhance the anti-interference ability of concentration measurement results under complex environmental conditions. Furthermore, it uses closed-loop verification and strategy adjustment signals to iterate the parameters of the transmission lag time compensation model, ensuring the consistency and long-term stability of the model in the distributed monitoring network. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0027] Figure 2 This is a schematic diagram of the transmission lag time compensation model curve of the present invention;

[0028] Figure 3 This is a schematic diagram of the Taylor expansion prediction for hysteresis compensation of combustible gas concentration according to the present invention.

[0029] Figure 4 This is a schematic diagram of environmental nonlinear mapping and lower explosion limit safety monitoring according to the present invention;

[0030] Figure 5 This is a schematic diagram of the three-dimensional surface of the security status fuzzy evaluation logic of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Reference Figure 1 As shown, a method for hysteresis compensation and prediction of combustibility measurement of combustible gas using an LEL analyzer is disclosed, the method comprising:

[0033] S1. Obtain the operating current fluctuation parameters of the gas sampling pump and the acoustic impedance parameters of the pipeline inlet to obtain fluid dynamic characteristics and perform feature extraction to generate a gas density and viscosity feature vector.

[0034] In one embodiment of the present invention, step S1 includes the following steps: acquiring the stator current signal and rotor speed signal of the gas sampling pump drive motor to obtain motor operating state parameters and performing frequency domain transformation to generate current spectrum distribution characteristics; receiving the transmitted echo signal and phase difference signal of the ultrasonic sensor at the pipeline inlet to obtain acoustic propagation parameters and performing acoustic impedance calculation to generate acoustic impedance change rate characteristics; splicing the current spectrum distribution characteristics and acoustic impedance change rate characteristics to obtain fluid dynamic characteristics and performing principal component analysis to generate gas density viscosity feature vectors.

[0035] Specifically, the operating status of the equipment is continuously monitored using current transformers and Hall effect sensors to acquire the stator current signal and rotor speed signal of the gas sampling pump drive motor. The stator current signal represents the current intensity flowing through the coils of the motor's stationary components, while the rotor speed signal reflects the rotation speed of the motor's rotating components. The stator current signal and rotor speed signal are combined to construct motor operating status parameters. These parameters comprehensively reflect the load and power output of the drive motor under specific operating conditions. To extract the periodic features hidden in the time series, the motor operating status parameters are processed to convert the time-varying signal into a frequency-varying distribution pattern, thereby generating a current spectrum distribution feature. This current spectrum distribution feature reveals the energy proportion of different frequency components in the total signal, and its calculation formula is as follows:

[0036] ,

[0037] in, Represents the characteristics of the current spectrum distribution. Represents the stator current signal. Represents the rotor speed signal. Represents the sampling time. This represents a dimensionless spectral scaling factor based on 300 fluid dynamics tests, used to adjust the amplitude.

[0038] At the pipeline inlet, the transmitted echo signal and phase difference signal from the ultrasonic sensor are received. The transmitted echo signal is the intensity of the sound wave as it propagates in the gas medium and is reflected back from the interface. The phase difference signal records the angular difference corresponding to the time delay between the transmitted and received waves. The transmitted echo signal and the phase difference signal are fused to obtain the acoustic propagation parameters. These parameters characterize the propagation characteristics of the sound wave in the current gas medium. Based on the acoustic propagation parameters, the acoustic impedance is calculated to generate the acoustic impedance change rate characteristic. This characteristic reflects the dynamic change in the degree of resistance of the gas medium to sound wave propagation. The specific formula is as follows:

[0039] ,

[0040] in, This represents the characteristic of the rate of change of acoustic impedance. Represents the transmitted echo signal. Represents the phase difference signal. This represents the reference time constant set based on 150 sets of ultrasonic calibration experiments, with the dimension of seconds. This represents a dimensionless acoustic correction factor, used to eliminate dimensional deviations caused by radians.

[0041] Subsequently, the current spectrum distribution characteristics and the acoustic impedance change rate characteristics are numerically concatenated to obtain the fluid dynamics characteristics. These fluid dynamics characteristics comprehensively encompass the mechanical dynamics and acoustic response information during gas flow. Dimensionality reduction processing is then performed on the fluid dynamics characteristics to extract the main changing directions and remove redundant information, ultimately generating a gas density and viscosity feature vector. This vector expresses the density and viscosity properties of the gas being measured in a simplified numerical form. The calculation logic is as follows:

[0042] ,

[0043] in, Represents the eigenvectors of gas density and viscosity. This represents the transpose of the dimensionless projection matrix calculated based on the principal component extraction algorithm. The normalized coefficient representing the reciprocal of the ampere is used. The normalization coefficient represents the unit of cubic meters per pascal second. Different physical quantities are converted into dimensionless values ​​for matrix operations using the normalization coefficient.

[0044] For example, setting the stator current signal The rotor speed signal is 5 amps. The frequency is 50 Hz, the sampling time t is 0.01 seconds, and the spectral scaling factor is... The value is 1. Substituting this value into the formula, the characteristics of the current spectrum distribution are calculated. for Ampere, set to transmit echo signal The phase difference signal is 100 Pascals. 0.5 radians, reference time constant For 2 seconds, acoustic correction factor The value is 1. Substituting this into the formula, the characteristic of the rate of change of acoustic impedance is calculated. Set the normalization coefficient to 100. The normalization coefficient is 0.2. 0.01, projection matrix transpose The gas density and viscosity eigenvector is calculated by substituting the elements 0.8 and 0.6 into the formula and finding that it is a vector containing the elements 0 and 0.

[0045] S2. Extract the initial temperature gradient parameters and response slope parameters of the catalytic combustion sensor to obtain the sensor response characteristics and perform feature extraction to generate the sensor thermal inertia feature vector;

[0046] In one embodiment of the present invention, step S2 includes the following steps: reading the output voltage parameters of the Wheatstone bridge of the catalytic combustion sensor and the reference terminal voltage parameters in the initial stage of gas contact to obtain a differential voltage signal and performing first derivative calculation to generate a response slope parameter; obtaining the infrared radiation intensity parameters of the catalytic element surface and the ambient background radiation parameters to obtain the thermal radiation difference and performing temperature field inversion to generate an initial temperature gradient parameter; combining the response slope parameter and the initial temperature gradient parameter to obtain the sensor response characteristics and performing time series alignment to generate a sensor thermal inertia feature vector.

[0047] Specifically, the output voltage parameters of the Wheatstone bridge and the reference voltage parameters of the catalytic combustion sensor are read during the initial stage of gas contact. The output voltage parameters of the Wheatstone bridge reflect the change in electrical signal caused by the resistance change of the measuring bridge arm due to the catalytic combustion of combustible gas. The reference voltage parameters represent the reference electrical signal of the compensation bridge arm that is not in contact with combustible gas. Subtracting the output voltage parameters of the Wheatstone bridge from the reference voltage parameters yields the differential voltage signal, which directly corresponds to the change in resistance caused by the concentration of combustible gas. The first derivative of the rate of change of the differential voltage signal with time is calculated to generate the response slope parameter. The response slope parameter characterizes the initial response rate of the sensor to changes in gas concentration, and its calculation formula is as follows:

[0048] ,

[0049] in, Represents the response slope parameter. This represents the output voltage parameters of the Wheatstone bridge. Represents the reference terminal voltage parameter. This represents the initial sampling time interval set based on 50 sensor calibration experiments.

[0050] Infrared radiation intensity parameters and ambient background radiation parameters are acquired from the surface of the catalytic element using an infrared thermometry module. The infrared radiation intensity parameters indicate the thermal radiation energy of the catalytic element during the reaction process, while the ambient background radiation parameters record the basic thermal radiation level of the surrounding environment. Subtracting the infrared radiation intensity parameters from the ambient background radiation parameters yields the thermal radiation difference, thus eliminating interference from ambient temperature fluctuations. Based on this thermal radiation difference, a temperature field inversion is performed to convert radiation energy into temperature distribution, generating initial temperature gradient parameters. These initial temperature gradient parameters reflect the spatial non-uniformity of the temperature distribution on the surface of the catalytic element, and their calculation relationship is as follows:

[0051] ,

[0052] in, Represents the initial temperature gradient parameters. Represents infrared radiation intensity parameters. Represents ambient background radiation parameters. Represents the feature size defined based on the physical structure of the sensor. This represents the conversion coefficient derived based on Stefan Boltzmann's law.

[0053] The response slope parameter is then numerically combined with the initial temperature gradient parameter to obtain the sensor response characteristics. These characteristics integrate the sensor's electrical response and thermal state. The sensor response characteristics are then aligned to a unified time axis using time series alignment processing, ultimately generating a sensor thermal inertia feature vector. This vector, presented as a multidimensional array, expresses the sensor's hysteresis characteristics during temperature changes. The calculation logic is as follows:

[0054] ,

[0055] in, Represents the sensor's thermal inertia eigenvector. This represents a dimensionless alignment matrix generated based on a time interpolation algorithm. The normalization coefficient represents a quantity measured in seconds per volt. The normalization coefficient represents the unit of measurement in meters per Kelvin. It is used to convert different physical quantities into dimensionless values ​​for matrix operations.

[0056] For example, setting the output voltage parameters of the Wheatstone bridge It is 2.5 volts, reference terminal voltage parameter The voltage is 1.0 volts, and the initial sampling time interval is... The time interval is 0.5 seconds. Substituting this into the formula, the response slope parameter is calculated to be 3.0 volts per second. The infrared radiation intensity parameter is then set. The ambient background radiation parameter is 300 watts per square meter. 100 watts per square meter, characteristic dimensions It is 0.01 meters, and the conversion factor is... The initial temperature gradient parameter is calculated by substituting 0.05 Kelvin per square meter into the formula. Set the normalization coefficient to 1000 Kelvin per meter. 0.1 volts per second, normalization factor Alignment matrix with a resolution of 0.001 meters per Kelvin Given a column vector containing elements 1 and 1, substituting these elements into the formula yields the final sensor thermal inertia feature vector. It is a vector containing the elements 1.3 and 1.3.

[0057] S3. The gas density and viscosity feature vector and the sensor thermal inertia feature vector are fused to obtain multi-physics coupling features and then dimensionality reduction is performed to generate a transmission lag time compensation model.

[0058] In one embodiment of the present invention, step S3 includes the following steps: cross-multiplying the elements in the gas density and viscosity feature vector with the elements in the sensor thermal inertia feature vector to obtain a cross-term matrix and performing normalization processing to generate multi-physics coupling features; extracting the eigenvalues ​​and eigenvectors of the multi-physics coupling features to obtain intrinsic orthogonal decomposition components and performing truncation processing to generate low-dimensional manifold features; mapping the low-dimensional manifold features to the time delay space to obtain delay time coefficients and performing polynomial fitting to generate a transmission lag time compensation model.

[0059] Specifically, refer to Figure 2 As shown, the system receives the gas density and viscosity feature vector and the sensor thermal inertia feature vector output from the previous step. The gas density and viscosity feature vector characterizes the hydrodynamic properties of the gas being measured, while the sensor thermal inertia feature vector reflects the hysteresis characteristics of the sensor during temperature changes. Cross-multiplication of the elements in the gas density and viscosity feature vector with those in the sensor thermal inertia feature vector yields a cross-term matrix. This matrix contains nonlinear interaction information between hydrodynamics and thermal response. The cross-term matrix is ​​then normalized to scale the values ​​to a uniform range, generating a multi-physics coupling feature. This multi-physics coupling feature eliminates numerical differences caused by different physical dimensions, ensuring that the coupling information is on the same scale. The calculation formula is as follows:

[0060] ,

[0061] in, Represents the characteristics of multiphysics coupling. Represents the eigenvectors of gas density and viscosity. The transpose matrix representing the sensor's thermal inertial eigenvectors. This represents the maximum scalar value calculated based on the absolute values ​​of the matrix elements.

[0062] Matrix decomposition is performed on the multiphysics coupling characteristics to extract eigenvalues ​​and eigenvectors. Eigenvalues ​​represent the magnitude of energy distribution in a specific direction, and eigenvectors indicate the specific spatial direction of these energy distributions. Combining the eigenvalues ​​and eigenvectors yields the intrinsic orthogonal decomposition components, which form an orthogonal basis describing the multiphysics coupling state. The intrinsic orthogonal decomposition components are truncated, retaining the main energy components and discarding minor fluctuations, to generate low-dimensional manifold features. These low-dimensional manifold features preserve the core dynamic evolution laws of the system with a more compact data structure. The computational relationship is as follows:

[0063] ,

[0064] in, Represents low-dimensional manifold characteristics. Represents eigenvalues, Represents the eigenvector. This represents the cutoff order set based on 300 wind tunnel calibration experiments.

[0065] Subsequently, the low-dimensional manifold features are mapped to a time delay space to obtain the time delay coefficients. The time delay space is a mathematical space used to describe the time required for gas to reach the sensor surface from the sampling inlet, and the time delay coefficients are the specific coordinate mapping values ​​within this space. Based on the time delay coefficients, a polynomial fitting is performed to construct the algebraic relationship between the input and output, ultimately generating a transmission lag time compensation model. The transmission lag time compensation model can dynamically calculate the time delay of gas transmission based on the current multiphysics state. The calculation logic is as follows:

[0066] ,

[0067] in, This represents the delay time output by the transmission lag time compensation model. The zero-order fitting coefficients represent quantities in seconds. The first-order fitting coefficients represent the units of seconds. The second-order fitting coefficients, representing quantities in seconds, are calculated and set based on calibration data from 500 different flow rates.

[0068] For example, a gas density viscosity eigenvector is defined. The transpose of the sensor's thermal inertial eigenvectors is a column vector containing elements 2 and 3. Given a row vector containing elements 1 and 4, substitute the elements into the formula to calculate the intersection term matrix, which is a matrix containing elements 2, 8, 3, and 12. Extract the scalar of the maximum value. The value is 12. The multiphysics coupling characteristics are calculated to obtain... Given a matrix containing the elements 0.1667, 0.6667, 0.25, and 1.0. Set the truncation order. If the value is 1, extract the feature value. The eigenvector is 1.1667. The value is 0.5547. Substituting this into the formula, we can calculate the characteristic of the low-dimensional manifold. The zero-order fitting coefficient is set to 0.647. The first-order fitting coefficient is 0.5. The second-order fitting coefficient is 1.2. The value is 0.1. Substitute this value into the formula to calculate the delay time output by the transmission lag time compensation model. It took 1.3182 seconds.

[0069] S4. Collect the current combustible gas concentration measurement value and the historical concentration change rate to obtain the basic concentration characteristics and input them into the transmission lag time compensation model to generate lag compensation concentration values.

[0070] In one embodiment of the present invention, step S4 includes the following steps: reading the digital signal output by the analog-to-digital conversion module of the LEL analyzer and the range calibration coefficient to obtain the current combustible gas concentration measurement value and performing sliding window differential calculation to generate the historical concentration change rate; merging the current combustible gas concentration measurement value and the historical concentration change rate to obtain the basic concentration characteristics and performing matrix reconstruction to generate the concentration state matrix; inputting the concentration state matrix into the transmission lag time compensation model to obtain the time compensation amount and combining it with the current combustible gas concentration measurement value to perform Taylor series expansion prediction to generate the lag compensation concentration value.

[0071] Specifically, the digital signal output from the analog-to-digital converter module of the LEL analyzer and the range calibration coefficient are read. The digital signal represents the discrete value of the sensor's analog voltage after digital processing. The range calibration coefficient is used to convert the discrete value into the actual physical concentration ratio. The digital signal is multiplied by the range calibration coefficient to obtain the current combustible gas concentration measurement value. A sliding window difference calculation is then performed on the current combustible gas concentration measurement value, calculating the difference between adjacent data within a data sequence of fixed time length, to generate the historical concentration change rate. The historical concentration change rate reflects the rate of increase or decrease of gas concentration in the recent period. The calculation formula is as follows:

[0072] ,

[0073] in, Represents the historical concentration change rate. The measured value of combustible gas concentration at the current moment. This represents the concentration record value from the previous moment, read from internal memory. This represents the sampling time interval set based on 50 dynamic response tests.

[0074] The baseline concentration features are obtained by combining the current combustible gas concentration measurement with the historical concentration change rate. These features include both the static absolute value and dynamic trend of the gas concentration. The baseline concentration features are then reconstructed into a multi-dimensional table matrix by arranging the one-dimensional data according to specific rules, generating a concentration state matrix. This concentration state matrix provides input features for subsequent models in a structured form, and the calculation relationships are as follows:

[0075] ,

[0076] in, Represents the concentration-state matrix. and This represents a dimensionless fundamental matrix defined based on data dimension alignment. The normalization coefficient, whose unit is the reciprocal of a percentage. The normalization coefficient represents the unit of measurement in seconds and percentages. It is used to eliminate the dimensional differences between different physical quantities.

[0077] Reference Figure 3 As shown, the concentration state matrix is ​​then input into the transmission lag time compensation model generated in the previous step to obtain the time compensation amount. The time compensation amount indicates the total time consumed for the gas to travel from the sampling point to the sensor and trigger a response. Combined with the current combustible gas concentration measurement value, a Taylor series expansion is performed to predict the future value using the current value and its rate of change. Finally, the lag-compensated concentration value is generated. The lag-compensated concentration value represents the true real-time concentration after eliminating the influence of transmission delay. The calculation logic is as follows:

[0078] ,

[0079] in, Represents the lag-compensated concentration value. This represents the time compensation amount, which is calculated and output by the transmission lag time compensation model based on the concentration state matrix and the lead time delay.

[0080] For example, set the current combustible gas concentration measurement value. The concentration recorded at the previous moment was 15%. 14% of the time interval The time is 0.5 seconds. Substituting these values ​​into the formula, the historical concentration change rate is calculated. Set the normalization factor to 2% per second. The normalization coefficient is the reciprocal of 0.1 percent. The base matrix is ​​calculated at 0.5 seconds per percentage point. The fundamental matrix is ​​a diagonal matrix containing elements 1 and 0. This is a diagonal matrix containing elements 0 and 1. Substituting these elements into the formula, the concentration-state matrix is ​​calculated. For a matrix containing diagonal elements 1.5 and 1.0, define the time compensation amount output by the transmission lag compensation model. The value is 1.2 seconds. Substituting this value into the formula, the lag-compensated concentration value is calculated, ultimately yielding the lag-compensated concentration value. It is 17.4 percent.

[0081] S5. Obtain the environmental temperature and pressure parameters of the target monitoring area to obtain the environmental correction factor, and combine it with the hysteresis compensation concentration value to perform nonlinear mapping to generate the true concentration prediction value.

[0082] In one embodiment of the present invention, step S5 includes the following steps: receiving absolute temperature and atmospheric pressure values ​​sent by environmental monitoring nodes to obtain environmental state parameters and performing ideal gas state equation calculations to generate gas molar volume change rate; converting the gas molar volume change rate and sensor temperature compensation coefficient to obtain environmental correction factors and performing weight allocation to generate a correction weight matrix; multiplying the correction weight matrix with the hysteresis compensation concentration value to obtain a corrected concentration matrix and performing backpropagation neural network mapping to generate a true concentration prediction value.

[0083] Specifically, refer to Figure 4 As shown, the system receives absolute temperature and atmospheric pressure values ​​from environmental monitoring nodes. The absolute temperature reflects the intensity of thermal motion of gas molecules within the target monitoring area, while the atmospheric pressure represents the pressure exerted by the gas on a unit area within that area. Combining the absolute temperature and atmospheric pressure values ​​yields environmental state parameters, which comprehensively describe the current thermodynamic environment of the gas. Based on these environmental state parameters, the ideal gas law describing the relationship between gas state variables is calculated, generating the gas molar volume change rate. The gas molar volume change rate characterizes the degree of deviation of the gas molar volume from the standard state under current environmental conditions. The calculation formula is as follows:

[0084] ,

[0085] in, Represents the rate of change of molar volume of gas. Represents the ideal gas constant. Represents the absolute temperature value. This represents the numerical value of atmospheric pressure. This represents the reference gas molar volume based on standard conditions.

[0086] The environmental correction factor is obtained by numerically converting the gas molar volume change rate with the sensor temperature compensation coefficient. The sensor temperature compensation coefficient is an inherent parameter used to compensate for the sensitivity drift of the sensor element caused by temperature changes. The environmental correction factor combines the dual correction effects of gas physical state changes and sensor hardware characteristics. The environmental correction factor is weighted according to the degree of influence of different environmental factors, generating a correction weight matrix. This correction weight matrix, in the form of a multi-dimensional array, provides basic weight information for subsequent concentration calibration. The calculation relationship is as follows:

[0087] ,

[0088] in, Represents the corrected weight matrix. This represents the weighting coefficients set based on 100 environmental simulation experiments. Represents the sensor temperature compensation coefficient. This represents the dimensionless identity matrix used for matrix dimension alignment.

[0089] The corrected weight matrix is ​​then multiplied by the hysteresis-compensated concentration value output from the previous step to obtain the corrected concentration matrix. This corrected concentration matrix contains concentration distribution information pre-calibrated for environmental factors. A backpropagation neural network mapping is then applied to the corrected concentration matrix, analyzing the complex mapping relationship between input and output using a multi-layer data feature extractor. Finally, a true concentration prediction value is generated. This true concentration prediction value represents the high-precision combustible gas concentration after eliminating environmental interference and completing nonlinear calibration. The calculation logic is as follows:

[0090] ,

[0091] in, This represents the predicted value of the actual concentration. This represents the non-linear activation function operation in a multi-layer data feature extractor. Representing the hysteresis compensation concentration value, the pre-training steps of this backpropagation neural network are as follows: Multiple sets of real combustible gas calibration samples from historical industrial sites and corresponding environmental temperature and pressure data are collected to construct an offline training set. The mean squared error (MSE) is used as the loss function, and the gradient descent algorithm is employed to calculate the prediction error relative to the network weight matrix. The gradient of the mapping bias term is used to continuously iterate and optimize the network parameters through error backpropagation until the loss function is lower than the set convergence threshold.

[0092] For example, an ideal gas constant is set. The absolute temperature is 8.314 joules per mole Kelvin. The atmospheric pressure is 300 Kelvin. The reference gas molar volume is 101325 Pascals. The value is 0.0224 cubic meters per mole. Substituting this into the formula, the molar volume change rate of the gas is calculated. The value is 1.0989. Set the weighting coefficient. The sensor temperature compensation coefficient is 0.9. It is 1.05, the identity matrix. Given a matrix containing diagonal elements 1 and 1, substitute the values ​​into the formula to calculate the corrected weight matrix. For a matrix containing diagonal elements 1.0384 and 1.0384, set the hysteresis compensation concentration value. For a column vector containing elements 17.4, perform a non-linear activation function operation. The simulation is set to a linear simplification where the input value is multiplied by 0.98. Substituting these values ​​into the formula, the predicted actual concentration is calculated, ultimately yielding the predicted actual concentration. It is 17.7067 percent.

[0093] S6. Compare the actual concentration prediction value with the lower explosion limit threshold to obtain the safety status assessment result and generate instructions, and output the early warning control instructions;

[0094] In one embodiment of the present invention, step S6 includes the following steps: calculating the difference between the predicted actual concentration value and the lower explosion limit threshold to obtain a concentration margin parameter and performing derivative of the change trend to generate an approximation rate feature; determining the fuzzy set to which the approximation rate feature and the concentration margin parameter belong to obtain a safety status assessment result and performing rule base matching to generate a control action code; encapsulating the control action code and the target actuator address to obtain a data packet and performing protocol conversion to output a warning control command.

[0095] Specifically, refer to Figure 5 As shown, the actual concentration prediction value output by the previous steps is obtained, and the preset lower explosion limit threshold is read from the system configuration. The actual concentration prediction value represents the current gas concentration after various compensations and corrections. The lower explosion limit threshold is the lowest concentration limit at which this type of flammable gas can explode. Subtracting the actual concentration prediction value from the lower explosion limit threshold yields the concentration margin parameter. This parameter directly reflects the safety margin between the current environment and the dangerous state. The derivative of the concentration margin parameter with respect to time is calculated to generate an approximation rate feature. The approximation rate feature indicates the rate at which the safety margin decreases, i.e., the speed at which danger approaches. The calculation formula is:

[0096] ,

[0097] in, Represents the approximation rate characteristic. This represents the lower explosive limit threshold. This represents the predicted value of the actual concentration. The sign represents time, and the negative sign is used to convert the decreasing margin trend into a positive approximation rate.

[0098] Based on preset fuzzy logic rules, the fuzzy sets to which the approximation rate feature and concentration margin parameter belong are determined. These fuzzy sets divide continuous numerical intervals into discrete state levels such as "safe," "alert," and "dangerous." The safety status assessment result is obtained by combining the fuzzy set affiliations of the two parameters. This result comprehensively characterizes the current environment's safety level. Based on the safety status assessment result, rule base matching is performed to generate control action codes. These control action codes are digital instruction identifiers used within the system to instruct specific actuators on what actions to take. The calculation relationship is as follows:

[0099] ,

[0100] in, Represents control action code, This represents a matching function based on a rule base established from 500 industrial safety contingency plans. Represents the fuzzy set evaluation function. This represents the concentration margin parameter.

[0101] The control action code and the target actuator address are then concatenated and encapsulated to obtain a data packet. The target actuator address specifies the specific hardware device to which the instruction needs to be sent, such as an exhaust fan or a shut-off valve. The data packet undergoes protocol conversion conforming to industrial communication standards, and finally, a warning control instruction is output. The warning control instruction is a standardized communication message that can be directly recognized and executed by the underlying hardware. The calculation logic is as follows:

[0102] ,

[0103] in, This represents an early warning and control instruction. This represents the protocol conversion function defined based on the Modbus protocol specification. This represents the data bit concatenation operation. This represents the address of the target implementing agency.

[0104] For example, setting a lower explosion limit threshold. The predicted actual concentration at the current moment is 100%. The concentration margin is 20%, and the predicted concentration at the previous time step (1 second interval) was 18%. Substitute these values ​​into the formula to calculate the concentration margin parameter at the current time step. The percentage, with the concentration margin parameter of the previous time step being 82%, is used to calculate the approximate rate characteristic. Set the fuzzy set evaluation function to 2% per second. exist For 80 and When the value is 2, the output status level is 2 (representing "alert"), and the rule base matching function... Output control action code when the input status level is 2. hexadecimal value Set the target execution address. hexadecimal value Substitute the data into the formula to perform data concatenation. Obtain the spliced ​​data Protocol conversion function Add a start bit and checksum to the spliced ​​data, and finally output the early warning control command. A hexadecimal sequence containing the complete message structure .

[0105] In one embodiment of the present invention, the method further includes the following steps: collecting the action feedback signal of the target actuator and the valve opening parameter to obtain the execution state characteristics and performing closed-loop verification to generate an execution success flag; extracting the descent gradient parameter of the execution success flag and the true concentration prediction value to obtain the control effect evaluation index and performing threshold comparison to generate a strategy adjustment signal; sending the strategy adjustment signal to the transmission lag time compensation model to obtain the model update instruction and performing parameter iteration to generate an updated transmission lag time compensation model.

[0106] Specifically, after outputting the early warning control command, the action feedback signal of the target actuator and the valve opening parameter are continuously collected. The action feedback signal represents the electrical response level of the actuator after receiving the command, and the valve opening parameter reflects the mechanical stroke ratio of the actual opening or closing of the physical valve. The action feedback signal and the valve opening parameter are weighted and combined to obtain the execution status characteristic, which comprehensively quantifies the actual action amplitude of the actuator. The execution status characteristic is used to perform closed-loop verification to determine whether the expected action target has been achieved, and an execution success flag is generated. The execution success flag confirms whether the control action has been effectively implemented in the form of a Boolean value, and the calculation formula is as follows:

[0107] ,

[0108] in, Represents the characteristics of the execution state. Represents action feedback signal, This represents the valve opening parameter. The conversion factor, in units of the reciprocal of volts, represents a quantity determined based on the electrical characteristics of the equipment. The conversion coefficient, representing a percentage reciprocal based on the mechanical travel setting, is used to determine the success flag via a step function, as follows:

[0109] ,

[0110] in, This indicates a successful execution flag. Represents the unit step function. This represents a dimensionless verification threshold set based on historical operation and maintenance data.

[0111] Extract the execution success flag and calculate the descent gradient parameter of the predicted true concentration output from the preceding steps as it decreases over time. The descent gradient parameter characterizes the rate at which the environmental hazard level is mitigated after the control action is implemented. Multiplying the execution success flag by the descent gradient parameter yields a control effectiveness evaluation index. This index objectively reflects the actual elimination effect of the current control strategy. A threshold comparison is performed between the control effectiveness evaluation index and a preset expected value to generate a strategy adjustment signal. This signal indicates the direction and magnitude of the current model parameters that need correction. The calculation relationship is as follows:

[0112] ,

[0113] in, Represents the evaluation index of control effectiveness. Represents the descent gradient parameters. The normalized coefficient, representing a percentage per second, is used to calculate the strategy adjustment signal using the following formula:

[0114] ,

[0115] in, This represents a strategy adjustment signal. This represents the dimensionless expected threshold set based on safety specifications.

[0116] Subsequently, a strategy adjustment signal is sent to the transmission lag time compensation model to obtain a model update instruction. The model update instruction converts the adjustment signal into a specific parameter modification step size. Based on the model update instruction, the fitting coefficients within the model are iterated to generate an updated transmission lag time compensation model. The updated transmission lag time compensation model can more accurately adapt to the dynamic changes of the current environment. The calculation logic is as follows:

[0117] ,

[0118] in, Represents the updated first-order fit coefficients. Represents the original first-order fitting coefficients. This represents the dimensionless learning rate set based on the gradient descent algorithm. The basic iteration step size is represented by the unit of seconds.

[0119] For example, setting motion feedback signals 5 volts, valve opening parameter 100% conversion factor The reciprocal of 0.1 volts, conversion factor The reciprocal of 0.005 percentage points is used to calculate the execution status characteristics using the formula. Set the verification threshold to 1.0. The step function is 0.8. Output execution success flag Set the descent gradient parameter to 1. 0.5 percent per second, normalization factor The control effect evaluation index is calculated by substituting 2 seconds per percentage point into the formula. Set the threshold to 1.0. The value is 1.5, which is used to calculate the strategy adjustment signal. , and thus Set the original first-order fitting coefficient to 0.5. The learning rate is 1.2 seconds. The basic iteration step size is 0.1. The time is 0.2 seconds. Substituting these values ​​into the formula, the updated first-order fitting coefficients are calculated. Finally, the updated first-order fitting coefficients were obtained. The time delay is 1.21 seconds, thus generating an updated transmission lag time compensation model.

[0120] In one embodiment of the present invention, the method further includes the following steps: extracting the polynomial coefficient parameters and system bias parameters of the updated transmission lag time compensation model to obtain model structure data and performing hash calculation to generate a model check code; packaging the model structure data and the model check code to obtain a model update package and performing encryption processing to generate a secure update file; distributing the secure update file to the distributed LEL analyzer node to obtain a reception confirmation signal and performing version synchronization to generate a network-wide model consistency state.

[0121] Specifically, polynomial coefficient parameters and system bias parameters are extracted from the updated transmission lag compensation model generated in the previous step to obtain model structure data, which is then hashed. The polynomial coefficient parameters represent the weight strength of the polynomial fitting curve in the model, and the system bias parameters represent the baseline offset. The polynomial coefficient parameters and bias parameters are concatenated bit by bit to obtain model structure data. This data completely contains the core logical information required by the model for concentration prediction. The model structure data is then hashed using a one-way hash algorithm to generate a unique data fingerprint, resulting in a model checksum. The model checksum is used to verify the integrity and tamper-proof status of the data during subsequent transmission. The calculation formula is as follows:

[0122] ,

[0123] ,

[0124] in, Represents the model structure data, Represents the polynomial coefficient parameters. Represents the bias parameter. This represents the data bit concatenation operation. Represents the model check code. This represents a one-way hash function based on information security standards.

[0125] The model structure data and model checksum are concatenated again to obtain the model update package. This package constitutes a complete communication unit containing payload data and verification tags. The model update package is then encrypted using a symmetric encryption algorithm to convert plaintext into ciphertext, generating a secure update file. This secure update file ensures the confidentiality of model parameters transmitted within the industrial network. The calculation relationship is as follows:

[0126] ,

[0127] ,

[0128] in, Representative model update package, This represents a security update file. This represents an encryption function based on the Advanced Encryption Standard (AES). This represents the system key set based on the system initialization configuration.

[0129] Subsequently, the security update file is distributed to each distributed LEL analyzer node via the industrial communication bus, and the system continuously listens for reception confirmation signals. Each distributed LEL analyzer node is an independently operating low-level detection device in the network. The reception confirmation signal indicates that a specific node has successfully received and decrypted the file. Based on the reception confirmation signal, each node is triggered to replace its local old model with the latest version for synchronization. This ultimately generates a network-wide model consistency state. A network-wide model consistency state signifies that all devices within the entire monitoring network are performing concentration prediction based on the same parameter standards. The calculation logic is as follows:

[0130] ,

[0131] in, This represents the consistency status of the entire network model; a value of 1 indicates consistency. Representing the The distributed LEL analyzer node with the sequence number returns a reception confirmation signal; a value of 1 indicates success. This represents the total number of nodes based on the network topology.

[0132] For example, setting polynomial coefficient parameters hexadecimal value Bias parameters hexadecimal value Substituting the data into the formula and performing data bit concatenation operations yields the model structure data. for Define a one-way hash function For input Output model check code for Substituting the data into the formula and combining it with the model structure data and model checksum, we obtain the model update package. for Set system key for Encryption function The encryption process is simulated using bitwise XOR operations, and the security update file is calculated by substituting the values ​​into the formula. for Set the total number of nodes. The received confirmation signal returned by the first distributed LEL analyzer node is number 3. The received confirmation signal returned by the second-order distributed LEL analyzer node is number 1. The received confirmation signal returned by the third distributed LEL analyzer node, which is 1. The value is 1. Substituting this value into the formula, the overall network model consistency state is calculated, and the overall network model consistency state is finally obtained. The value is 1.

[0133] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0134] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for hysteresis compensation and prediction of combustibility measurement of combustible gases using an LEL analyzer, characterized in that, include: The fluid dynamics characteristics are obtained by acquiring the operating current fluctuation parameters of the gas sampling pump and the acoustic impedance parameters of the pipeline inlet, and feature extraction is performed to generate a gas density and viscosity feature vector. The initial temperature gradient parameters and response slope parameters of the catalytic combustion sensor are extracted to obtain the sensor response characteristics, and feature extraction is performed to generate the sensor thermal inertia feature vector. By fusing the gas density and viscosity feature vectors with the sensor thermal inertia feature vectors, multi-physics coupling features are obtained and dimensionality reduction is performed to generate a transmission lag time compensation model. The basic concentration characteristics are obtained by collecting the current combustible gas concentration measurement value and the historical concentration change rate, and then input into the transmission lag time compensation model to generate lag compensation concentration value. The environmental temperature and pressure parameters of the target monitoring area are obtained to obtain the environmental correction factor, which is then combined with the hysteresis compensation concentration value to perform nonlinear mapping and generate the true concentration prediction value. The safety status assessment results are obtained by comparing the actual concentration prediction value with the lower explosion limit threshold, and instructions are generated and output as early warning control instructions.

2. The method for hysteresis compensation and prediction of combustibility measurement of combustible gas using an LEL analyzer according to claim 1, characterized in that, The process of generating the gas density and viscosity feature vector includes: acquiring the stator current signal and rotor speed signal of the gas sampling pump drive motor to obtain motor operating state parameters and performing frequency domain transformation to generate current spectrum distribution characteristics; receiving the transmitted echo signal and phase difference signal from the ultrasonic sensor at the pipeline inlet to obtain acoustic propagation parameters and performing acoustic impedance calculation to generate acoustic impedance change rate characteristics; and splicing the current spectrum distribution characteristics and acoustic impedance change rate characteristics to obtain fluid dynamic characteristics and performing principal component analysis to generate the gas density and viscosity feature vector.

3. The method for hysteresis compensation and prediction of combustibility measurement of combustible gas using an LEL analyzer according to claim 2, characterized in that, The generation of the sensor thermal inertia feature vector includes: reading the output voltage parameters of the Wheatstone bridge and the reference terminal voltage parameters of the catalytic combustion sensor in the initial stage of gas contact to obtain a differential voltage signal and performing first derivative calculation to generate a response slope parameter; obtaining the infrared radiation intensity parameters on the surface of the catalytic element and the ambient background radiation parameters to obtain the thermal radiation difference and performing temperature field inversion to generate an initial temperature gradient parameter; combining the response slope parameter and the initial temperature gradient parameter to obtain the sensor response characteristics and performing time series alignment to generate the sensor thermal inertia feature vector.

4. The method for hysteresis compensation and prediction of combustibility measurement of a gas analyzer according to claim 3, characterized in that, The generation of the transmission lag time compensation model includes: cross-multiplying the elements in the gas density and viscosity feature vector with the elements in the sensor thermal inertia feature vector to obtain a cross-term matrix and performing normalization processing to generate multi-physics coupling features; extracting the eigenvalues ​​and eigenvectors of the multi-physics coupling features to obtain intrinsic orthogonal decomposition components and performing truncation processing to generate low-dimensional manifold features; mapping the low-dimensional manifold features to the time delay space to obtain the delay time coefficients and performing polynomial fitting to generate the transmission lag time compensation model.

5. The method for hysteresis compensation and prediction of combustibility measurement of a gas analyzer according to claim 4, characterized in that, The process of generating the hysteresis-compensated concentration value includes: reading the digital signal output by the analog-to-digital converter module of the LEL analyzer and the range calibration coefficient to obtain the current combustible gas concentration measurement value and performing sliding window differential calculation to generate the historical concentration change rate; merging the current combustible gas concentration measurement value and the historical concentration change rate to obtain the basic concentration characteristics and performing matrix reconstruction to generate the concentration state matrix; inputting the concentration state matrix into the transmission hysteresis time compensation model to obtain the time compensation amount and combining it with the current combustible gas concentration measurement value to perform Taylor series expansion prediction to generate the hysteresis-compensated concentration value.

6. The method for hysteresis compensation and prediction of combustibility measurement of a gas analyzer according to claim 5, characterized in that, The process of generating the predicted true concentration includes: receiving absolute temperature and atmospheric pressure values ​​sent by environmental monitoring nodes to obtain environmental state parameters and performing ideal gas law calculations to generate the gas molar volume change rate; converting the gas molar volume change rate with the sensor temperature compensation coefficient to obtain an environmental correction factor and assigning weights to generate a correction weight matrix; multiplying the correction weight matrix with the hysteresis compensation concentration value to obtain a corrected concentration matrix and performing backpropagation neural network mapping to generate the predicted true concentration value.

7. The method for hysteresis compensation and prediction of combustibility measurement of a gas analyzer according to claim 6, characterized in that, The system compares the predicted actual concentration with the lower explosive limit threshold to obtain a safety status assessment result and generates instructions, outputting early warning control instructions. This includes: calculating the difference between the predicted actual concentration and the lower explosive limit threshold to obtain a concentration margin parameter and differentiating its changing trend to generate an approximation rate feature; determining the fuzzy set to which the approximation rate feature and the concentration margin parameter belong to obtain the safety status assessment result and performing rule base matching to generate control action code; encapsulating the control action code and the target actuator address to obtain a data packet and performing protocol conversion to output early warning control instructions.

8. The method for hysteresis compensation and prediction of combustibility measurement of a gas flammability analyzer according to claim 7, characterized in that, After outputting the early warning control command, the process also includes: collecting the action feedback signal of the target actuator and the valve opening parameter to obtain the execution status characteristics and performing closed-loop verification to generate an execution success flag; extracting the descent gradient parameter of the execution success flag and the actual concentration prediction value to obtain the control effect evaluation index and performing threshold comparison to generate a strategy adjustment signal; sending the strategy adjustment signal to the transmission lag time compensation model to obtain the model update command and performing parameter iteration to generate the updated transmission lag time compensation model.

9. A method for hysteresis compensation and prediction of combustibility measurement of a gas analyzer according to claim 8, characterized in that, After generating the updated transmission lag time compensation model, the process also includes: extracting the polynomial coefficient parameters and system bias parameters of the updated transmission lag time compensation model to obtain model structure data and performing hash calculation to generate a model check code; packaging the model structure data and model check code to obtain a model update package and performing encryption processing to generate a secure update file; distributing the secure update file to the distributed LEL analyzer nodes to obtain a reception confirmation signal and perform version synchronization to generate a network-wide model consistency state.

10. A hysteresis compensation and prediction system for combustible gas flammability measurement in an LEL analyzer, characterized in that, include: The fluid feature extraction module is used to obtain the operating current fluctuation parameters of the gas sampling pump and the acoustic impedance parameters of the pipeline inlet to obtain fluid dynamic features and perform feature extraction to generate gas density and viscosity feature vectors. The thermal inertia extraction module is used to extract the initial temperature gradient parameters and response slope parameters of the catalytic combustion sensor to obtain the sensor response features and perform feature extraction to generate the sensor thermal inertia feature vector. The model generation module is used to fuse the gas density and viscosity feature vectors with the sensor thermal inertia feature vectors to obtain multi-physics coupling features and perform dimensionality reduction processing to generate a transmission lag time compensation model. The compensation calculation module is used to collect the current combustible gas concentration measurement value and the historical concentration change rate to obtain the basic concentration characteristics and input them into the transmission lag time compensation model to generate the lag compensation concentration value. The prediction mapping module is used to obtain the environmental temperature and pressure parameters of the target monitoring area, obtain the environmental correction factor, and combine it with the hysteresis compensation concentration value to perform nonlinear mapping to generate the true concentration prediction value. The early warning control module is used to compare the actual concentration prediction value with the lower explosion limit threshold to obtain the safety status assessment result and generate instructions, and output early warning control instructions.

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