CN-121164540-B - Intelligent calibration method for underground coal mine methane sensor

CN121164540BCN 121164540 BCN121164540 BCN 121164540BCN-121164540-B

Abstract

The invention relates to the technical field of gas sensor calibration, in particular to an intelligent calibration method for a methane sensor in a coal mine, which comprises the steps of establishing a near-field session and obtaining a permeability tube metering certificate parameter and a calibration instruction set, obtaining a zero data set and a sealing score in a closed microcavity, generating a standard concentration track according to the metering certificate and collecting a standard segment data set, combining an information matrix with an H infinity norm and a target optimization temperature time section to form original collected data, carrying out robust deconvolution and a variable decibel leaf inference based on a first-order inertia dynamic model to obtain a robust point estimation parameter set, a posterior distribution and combination uncertainty, calculating a Wasserstein gravity center on a section adjacency graph and solving a norm consistency optimization release correction parameter set, and realizing traceable, steady and section consistency online correction and calibration trigger.

Inventors

YANG LIDONG
LI JIWEN
WANG ZHENGNIAN
WU LIZHONG
XU XUEPING
GUO MAN
LIAN YUANYUAN
LIU SHUGUO
GE QI
YAN SHUAI
SHI YUNDONG
Miao Fengliang
JU LI
LI WEIZHONG

Assignees

开滦（集团）有限责任公司

Dates

Publication Date: 20260508
Application Date: 20250916

Claims (6)

1. An intelligent calibration method for a methane sensor in a coal mine is characterized by comprising the following steps of: Establishing a near field communication session, and acquiring parameters of a permeate tube metering certificate and a calibration instruction set; Executing challenge response authentication when establishing a near field communication session, checking the digital signature and the validity period of the metering certificate parameters of the permeation tube, loading a calibration instruction set, determining energy and security constraint and an environmental baseline, and starting a calibration log record time stamp and a data index; generating a zero data set in the closed microcavity, performing acoustic pulse sealing diagnosis to obtain a sealing score, generating a standard concentration track according to the parameters of the osmotic tube metering certificate, collecting a standard segment data set, and adjusting a temperature time section according to the information matrix and the H infinite norm combined target to form original collected data; Calculating the permeation flux of the standard concentration track according to the permeation coefficient, the film thickness, the effective area and the pressure difference, obtaining the standard concentration track according to mass conservation in the closed volume, and writing the standard concentration track and the temperature time profile into a standard section data set; The temperature time section is iteratively updated by taking an information matrix as an information quantity index and taking an H infinity norm as a worst boundary index, so that the energy limit, the upper temperature limit and the temperature rise rate limit are met, and the updated temperature time section and the numerical value of the combined target are recorded as design evidence; performing robust deconvolution and variational Bayesian inference on the basis of the original acquired data under a first-order inertial dynamic model to obtain a robust point estimation parameter set and posterior distribution and form a combination uncertainty; Fusing posterior distribution of a plurality of sensors on a section adjacency graph, calculating Wasserstein gravity centers to obtain section reference distribution, combining a norm consistent optimization to obtain a correction parameter set, issuing a trigger for on-line correction and drift recalibration, and taking the section reference distribution as a priori of the next round of input design and parameter identification; The posterior distribution of the plurality of sensors is subjected to mean smoothing on the section adjacency graph according to node uncertainty weighting, then the Wasserstein gravity center is calculated to obtain section reference distribution, and the section reference distribution is used as the prior of the next round of input design and parameter identification.
2. The method of claim 1, wherein when the zero data set is generated in the closed microcavity, an approximately zero concentration environment is established by opening a zero micro valve and an adsorbent, and the original output of the sensor, the temperature in the cavity, the humidity in the cavity and the cavity pressure are synchronously acquired according to a uniform sampling clock to form the zero data set with time sequence labels.
3. The method of claim 1, wherein the acoustic pulse seal diagnostics injects acoustic pulses through the micro-actuator and collects echoes and cavity pressure transients, calculates seal scores based on echo decay constants and cavity pressure settling times, the seal scores being used for disturbance boundary settings and sample weight settings of the first-order inertial dynamic model.
4. The method of claim 1, wherein the baseline is derived from median statistics of the zero dataset over a stability window, the stability window being determined from a sliding variance threshold and a derivative threshold, the baseline being fixed for bias terms in the first-order inertial dynamic model.
5. The method of claim 1, wherein robust deconvolution is performed on a consistent sub-segment meeting temperature stability and seal scoring thresholds, robust point estimation of sensitivity and time constants is obtained by using a first-order inertial kernel, variance Bayesian inference is performed by using student t distribution observation noise and physical feasible region prior, and distribution near-end fusion is performed by using the robust point estimation as an anchor to generate posterior distribution and combination uncertainty.
6. The method of claim 1, wherein the correction parameter set is obtained by solving a norm consistent optimization on the segment adjacency graph, wherein the norm consistent optimization is iteratively implemented by using an alternate direction multiplier method, wherein the correction parameter set is written into a parameter version record and is finished to issue a digital signature, and the online correction output is used for calculating the Wasserstein distance between the sliding window drift index and the segment reference distribution, and when the threshold is reached, the re-execution of the calibration instruction set is triggered.

Description

Intelligent calibration method for underground coal mine methane sensor Technical Field The invention relates to the technical field of gas sensor calibration, in particular to an intelligent calibration method for a methane sensor under a coal mine. Background The underground methane concentration monitoring is directly related to decision-making timeliness and compliance traceability of ventilation regulation and control, operation shutdown rework and disaster early warning. The sensitivity and time constant drift can be caused by the fluctuation of the temperature and humidity of the field environment, the adsorption lag of the sensor and the pollution aging, and the roadway space heterogeneity and the wind current coupling also require the multi-point result to have section consistency. If the traceable field calibration capability and the group consistency release mechanism are lacking, threshold misjudgment, misinformation and missing report, accident duplication difficulty and maintenance cost increase can occur in the monitoring network. Disclosure of Invention Aiming at a plurality of problems existing in the prior art, the invention provides an intelligent calibration method for a methane sensor under a coal mine, which uses a standard concentration track driven by a metering certificate as stimulus, and combines an information matrix with an H infinite norm optimized temperature time section to obtain high information data; and optimizing and realizing section release by using the Wasserstein gravity center and a norm consistency, and finally obtaining traceable, steady and consistent online correction. An intelligent calibration method for a methane sensor in a coal mine comprises the following steps: Establishing a near field communication session, and acquiring parameters of a permeate tube metering certificate and a calibration instruction set; generating a zero data set in the closed microcavity, performing acoustic pulse sealing diagnosis to obtain a sealing score, generating a standard concentration track according to the parameters of the osmotic tube metering certificate, collecting a standard segment data set, and adjusting a temperature time section according to the information matrix and the H infinite norm combined target to form original collected data; performing robust deconvolution and variational Bayesian inference on the basis of the original acquired data under a first-order inertial dynamic model to obtain a robust point estimation parameter set and posterior distribution and form a combination uncertainty; The posterior distribution of a plurality of sensors is fused on a section adjacency graph, wasserstein gravity centers are calculated to obtain section reference distribution, a norm consistent optimization is combined to obtain a correction parameter set, the correction parameter set is issued for triggering on-line correction and drift recalibration, and the section reference distribution is used as the prior of the next round of input design and parameter identification. Compared with the prior art, the invention has the advantages that: The method comprises the steps of realizing measurement traceability and session level traceability of standard concentration tracks through near-field communication session and a permeate tube measurement certificate parameter link, realizing explicit modeling and sample self-adaptive weighting of leakage uncertainty through acoustic pulse seal diagnosis and seal score mapping disturbance boundary, realizing information gain maximization and worst boundary suppression in energy and safety constraint through information matrix and H infinite norm joint target optimization temperature time section, realizing robust point estimation parameter set and posterior distribution and generating combination uncertainty through robust deconvolution and variation Bayesian inference, realizing uniform correction parameter set release and outlier suppression of cross equipment through section adjacency graph mean smoothing and Wasserstein gravity center and one norm uniform optimization, and realizing parameter management closed loop and accident duplication through parameter version record and digital signature and drift recalibration trigger. Drawings FIG. 1 is a schematic flow chart of the method of the present invention. Detailed Description Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. As shown in fig. 1, the intelligent calibration method for the methane sensor under the coal mine comprises the following steps: Establishing a near field communication sess