A Blockchain-Based Method and System for Transmitting Coal Spontaneous Combustion Parameters in Goaf Areas

Abstract

This invention discloses a blockchain-based method and system for transmitting coal spontaneous combustion parameters in goaf areas, relating to the field of coal mine monitoring technology. The method involves collecting pipeline static parameters, operating condition parameters, and gas analysis data from various sampling points in the goaf area, performing time-series correlation analysis to obtain transmission delay status information, parsing the gas analysis data to obtain the analysis completion time and coal spontaneous combustion parameter data, and using the transmission delay status information and the analysis completion time to inversely deduce the physical sampling time of each sampling point. Multi-layer time-series anchoring processing is performed on the physical sampling time, analysis completion time, and coal spontaneous combustion parameter data of each sampling point to obtain time-series anchoring information. The time-series anchoring information and coal spontaneous combustion parameter data are encapsulated and submitted to the blockchain node in the goaf area for notarization, resulting in an on-chain time-series anchoring record. This reduces misjudgments of the abnormal order caused by differences in bundled tube transmission delays, improving the accuracy of coal spontaneous combustion trend analysis in goaf areas.

Description

Technical Field

[0001] This invention relates to the field of coal mine monitoring technology, specifically to a blockchain-based method and system for transmitting parameters of spontaneous combustion of coal in goaf areas. Background Technology

[0002] In coal mine goaf spontaneous combustion monitoring and early warning scenarios, continuous monitoring is required in areas prone to coal oxidation and heating, such as goaf areas, return air corners, enclosed areas, and areas behind the working face. This involves collecting coal spontaneous combustion parameters and transmitting them to the mine-end monitoring platform for trend analysis, risk warning, and accident tracing. Due to the enclosed space and complex environment of the goaf, it is difficult to directly deploy electrical sensors at some monitoring locations. Typically, gas samples from different sampling points in the goaf are extracted using a bundled tube method and sent to a gas analysis device, which then outputs the corresponding gas analysis data. To improve the reliability of coal spontaneous combustion parameters during transmission, storage, and sharing, existing technologies introduce a blockchain evidence storage mechanism to record the source, transmission nodes, and on-chain status of the coal spontaneous combustion parameter data, thereby achieving data tamper-proofing, traceability, and multi-entity sharing.

[0003] However, in scenarios combining goaf tube gas sampling with trusted blockchain transmission, coal spontaneous combustion parameter data is not immediately uploaded to the blockchain after being generated on-site in the goaf. Instead, it undergoes multiple stages, including the transport of gas samples through the tube system and the analysis output from the gas analysis device. Different sampling points correspond to different tube parameters, resulting in varying time offsets between the formation of the gas sample at the actual goaf state and the output of the gas analysis data. Existing blockchain-based evidence storage methods typically focus on the generation, reception, submission, and confirmation processes of data in the digital chain, using the time records in the digital processing stages as the time basis for the corresponding coal spontaneous combustion parameters. This makes it difficult to accurately reflect the actual time corresponding to the gas sample at the goaf sampling point, potentially leading to incorrect timing and location of coal spontaneous combustion anomalies in the goaf. For example, at deep sampling points in the goaf, due to longer tube systems or higher resistance, carbon monoxide concentration anomalies may occur earlier, but the gas analysis data may be output later. Conversely, at sampling points near sealed walls, due to shorter tube systems or lower resistance, slight fluctuations may result in earlier analysis and evidence storage. If the order of anomalies is determined directly based on the gas analysis data output time, edge-side processing time, or on-chain evidence storage time, it may lead to the erroneous assumption that the sampling point closest to the sealed wall experienced the anomaly first, thus misjudging the direction of coal spontaneous combustion, the location of the oxidation heating zone, and the area affected by air leakage. During accident debriefing or regulatory verification, the time basis may deviate from the actual physical evolution of the goaf, potentially causing misjudgments on critical matters such as the timeliness of response, the delay of alarms, and the accuracy of early warning assessments. In severe cases, this can affect the timing and location of safety decisions. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a blockchain-based method and system for transmitting coal spontaneous combustion parameters in goaf areas, which can effectively solve the problems mentioned in the background section.

[0005] To achieve the above objectives, the first aspect of the present invention is implemented through the following technical solution: a blockchain-based method for transmitting coal spontaneous combustion parameters in goaf areas, including arranging several sampling points in the goaf area, collecting pipeline static parameters, operating condition parameters and gas analysis data at each sampling point, performing time-series correlation analysis on the pipeline static parameters and operating condition parameters at each sampling point, and obtaining transmission delay status information for each sampling point.

[0006] The gas analysis data from each sampling point are parsed and processed to obtain the analysis completion time and coal spontaneous combustion parameter data for each sampling point. Based on the transmission delay status information of each sampling point and combined with the analysis completion time of each sampling point, the physical sampling time of each sampling point is deduced in reverse.

[0007] Multi-layer time-series anchoring processing was performed on the physical sampling time, analysis completion time, and coal spontaneous combustion parameter data of each sampling point to obtain the time-series anchoring information of each sampling point.

[0008] The time-series anchoring information and coal spontaneous combustion parameter data of each sampling point are encapsulated to obtain the evidence storage data package of each sampling point. The evidence storage data package of each sampling point is submitted to the blockchain node of the goaf area for evidence storage to obtain the on-chain time-series anchoring record of each sampling point.

[0009] Furthermore, the method for collecting pipeline static parameters, operating condition parameters, and gas analysis data at each sampling point is as follows: The static parameters of the pipeline at each sampling point include the total length of the bundle tube, the inner diameter of the bundle tube, the number of bends in the pipeline, the bend angle of the pipeline, the average elevation difference of the pipeline, the water filter model, and the dust filter model. The operating parameters of each sampling point include the current pipeline gas flow rate, the current water accumulation status of the water filter, and the current clogging level of the dust filter. The gas analysis data for each sampling point includes the cycle number of each sampling point, the unique identifier of the sampling point, the time when the sampling valve switching is completed, the analyzer output data frame, and the analyzer status record.

[0010] Furthermore, the method for performing time-series correlation analysis on the pipeline static parameters and operating condition parameters at each sampling point is as follows: Based on the total length of the tube bundle, the inner diameter of the tube bundle, and the current gas flow rate in the pipeline at each sampling point, the pipeline transport base duration at each sampling point is analyzed. Based on the number of pipe bends and the bend angle at each sampling point, the additional bend coefficient at each sampling point is obtained through analysis. Based on the average elevation difference of the pipeline at each sampling point, the additional elevation difference coefficient for each sampling point is obtained through analysis. Based on the pipeline basic duration, bending additional coefficient and drop additional coefficient of each sampling point, the pipeline delay duration of each sampling point is obtained; The sum of the water accumulation time value and the blockage time value at each sampling point is recorded as the resistance-added delay time at each sampling point. Extract the injection stabilization time parameter and the chromatographic analysis time parameter, and record the sum of the injection stabilization time parameter and the chromatographic analysis time parameter as the analysis processing time.

[0011] Furthermore, the method for obtaining the transmission delay state information of each sampling point is as follows: The total transmission delay time of the corresponding sampling point is obtained by summing the pipeline delay time, the resistance-added delay time, and the analysis and processing time of each sampling point. The total transmission delay time of each sampling point is then used to form the transmission delay status information of the corresponding sampling point.

[0012] Furthermore, the method for parsing and processing the gas analysis data at each sampling point is as follows: Extract the output timestamp from the analyzer output data frame at each sampling point and determine the output timestamp as the analysis completion time of the corresponding sampling point; Based on the analyzer output data frames from each sampling point, the coal spontaneous combustion parameter data for each sampling point are obtained through processing.

[0013] Furthermore, the method for obtaining the physical sampling time of each sampling point through reverse deduction is as follows: Read the corresponding total transmission delay duration from the transmission delay status information of each sampling point, and subtract the corresponding total transmission delay duration from the analysis completion time of each sampling point to obtain the physical sampling time of each sampling point.

[0014] Furthermore, the method for performing multi-layer time-series anchoring processing on the physical sampling time, analysis completion time, and coal spontaneous combustion parameter data of each sampling point is as follows: Extract the unique identifier and cycle number of the corresponding sampling point from the data frame output by the analyzer at each sampling point; Using the unique identifier and cycle number of the sampling point as an index, the physical sampling time, analysis completion time and coal spontaneous combustion parameter data of each sampling point are read. When the coal spontaneous combustion parameter data of each sampling point is received, the edge gateway reception time is generated. When the coal spontaneous combustion parameter data of each sampling point is encapsulated, the edge gateway packetization time is generated. The physical sampling time, analysis completion time, edge gateway reception time, and edge gateway packet encapsulation time of each sampling point are arranged according to the time hierarchy to form the time-series anchoring sequence of each sampling point.

[0015] Furthermore, the method for obtaining the temporal anchoring information of each sampling point is as follows: Time series analysis is performed on the time series anchored sequences of each sampling point to obtain the time series analysis results of the time series anchored sequences of the corresponding sampling points; The time-series analysis results of each sampling point are bound together with its unique identifier, cycle number, physical sampling time, analysis completion time, edge gateway reception time, edge gateway packetization time, and time-series anchoring sequence to obtain the time-series anchoring information of the corresponding sampling point. The time-series anchoring information is used to characterize the temporal correlation between coal spontaneous combustion parameter data from physical sampling in the goaf to edge gateway packetization.

[0016] Furthermore, the method for performing time-series analysis on the time-series anchored sequences of each sampling point is as follows: Extract the timing anchor sequence of each sampling point. If the physical sampling time of the timing anchor sequence of a certain sampling point is earlier than the analysis completion time, the analysis completion time is earlier than or equal to the edge gateway reception time, or the edge gateway reception time is earlier than or equal to the edge gateway packetization time, the timing analysis result of the timing anchor sequence of the corresponding sampling point is marked as timing valid. Otherwise, the timing analysis result of the timing anchor sequence of the corresponding sampling point is marked as timing abnormal.

[0017] A second aspect of the present invention provides a blockchain-based data transmission system for coal spontaneous combustion parameters in goaf areas, comprising: The time-series correlation analysis module is used to set up several sampling points in the goaf area, collect pipeline static parameters, operating condition parameters and gas analysis data of each sampling point, perform time-series correlation analysis on the pipeline static parameters and operating condition parameters of each sampling point, and obtain the transmission delay status information of each sampling point. The analysis and processing module is used to analyze and process the gas analysis data of each sampling point to obtain the analysis completion time and coal spontaneous combustion parameter data of each sampling point. Based on the transmission delay status information of each sampling point and combined with the analysis completion time of each sampling point, the physical sampling time of each sampling point is deduced in reverse. The time-series anchoring processing module is used to perform multi-layer time-series anchoring processing on the physical sampling time, analysis completion time and coal spontaneous combustion parameter data of each sampling point to obtain the time-series anchoring information of each sampling point; The encapsulation and processing module is used to encapsulate and process the time-series anchoring information and coal spontaneous combustion parameter data of each sampling point to obtain the evidence storage data package of each sampling point. The evidence storage data package of each sampling point is submitted to the blockchain node of the goaf area for evidence storage to obtain the on-chain time-series anchoring record of each sampling point.

[0018] The present invention has the following beneficial effects: This invention collects pipeline static parameters, operating condition parameters, and gas analysis data at each sampling point, and performs time-series correlation analysis on the pipeline static parameters and operating condition parameters, ensuring that the transport delay of gas samples in the bundled tube pipeline is no longer ignored. Compared to existing technologies that directly determine the corresponding time of coal spontaneous combustion parameter data based on gas analysis data output time, edge-side processing time, or on-chain evidence storage time, this invention can generate differentiated transmission delay status information based on the pipeline static parameters and operating condition parameters at each sampling point. This improves the accuracy of the characterization of gas sample transmission time at different sampling points, avoids uniformly processing time offsets formed under different bundled tube conditions, and improves the reliability of the time basis of coal spontaneous combustion parameter data in goaf areas.

[0019] This invention uses the transmission delay status information and analysis completion time of each sampling point to reverse-engineer the physical sampling time of each sampling point. This allows the coal spontaneous combustion parameter data in the analyzer's output data frame to correspond to the time when the gas sample enters the bundle tube at the sampling point. When judging the abnormal sequence of coal spontaneous combustion parameter data, it no longer relies solely on the analysis completion time, edge gateway reception time, edge gateway packetization time, or on-chain evidence storage results. Instead, it uses the physical sampling time as the time basis, reducing the risk of abnormal sequence reversal caused by data from deep sampling points being output later and data from near-end sampling points being output earlier. This improves the accuracy of judging the development direction of coal spontaneous combustion, the location of the oxidation heating zone, and the area affected by air leakage, and enhances the temporal reliability of coal spontaneous combustion trend analysis and risk warning.

[0020] Compared to existing blockchain evidence preservation methods that only emphasize data tamper-proofing and on-chain traceability, this invention enables on-chain records to simultaneously preserve the correlation between coal spontaneous combustion parameter data and physical sampling time, enhancing the support capability of on-chain time-series anchoring records for accident review, regulatory verification, and anomaly tracing. For time-series anchoring sequences marked as time-series anomalies in time-series analysis results, it can also exclude their participation in the ordering of coal spontaneous combustion parameter data anomalies, reducing the impact of errors in the anomaly time basis on subsequent safety decisions, and improving the interpretability and practical value of on-chain evidence preservation results. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0022] Figure 2 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0023] 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, and 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.

[0024] Please see Figure 1 As shown, the first aspect of the present invention provides a technical solution: a blockchain-based method for transmitting coal spontaneous combustion parameters in goaf areas, including arranging several sampling points in the goaf area, collecting pipeline static parameters, operating condition parameters and gas analysis data of each sampling point, performing time-series correlation analysis on the pipeline static parameters and operating condition parameters of each sampling point, and obtaining transmission delay status information of each sampling point.

[0025] Specifically, the method for collecting pipeline static parameters, operating condition parameters, and gas analysis data at each sampling point is as follows: The static parameters of the pipeline at each sampling point include the total length of the bundled tubes, the inner diameter of the bundled tubes, the number of bends in the pipeline, the bend angle, the average elevation difference of the pipeline, the water filter model, and the dust filter model. A sampling point refers to a location within the goaf used to extract gas samples. Each sampling point corresponds to a unique identifier and is connected to the gas analysis device via a bundled tube pipeline. The static parameters of the pipeline are used to characterize the fixed pipeline conditions for transporting gas samples from the sampling point to the gas analysis device.

[0026] The total length of the tube bundle characterizes the distance the gas sample travels from the sampling point to the gas analyzer, and is obtained through the tube bundle monitoring system's tube layout records. The inner diameter of the tube bundle characterizes the gas flow cross-section of the tube bundle, and is obtained through the tube bundle model parameters. The number of bends and the bend angles of the tube bundle characterize the impact of the tube bundle on the gas transport resistance, and are obtained through the tube construction records or tube inspection records. The average elevation difference of the tube bundle characterizes the height change between the sampling point and the gas analyzer, and is obtained through the sampling point location records and the gas analyzer location records. The water filter model and dust filter model characterize the basic resistance characteristics of the filter components in the tube bundle, and are obtained through the equipment configuration records of the tube bundle monitoring system.

[0027] The operating parameters for each sampling point include the current pipeline gas flow rate, the current water accumulation status of the water filter, and the current blockage level of the dust filter.

[0028] The current pipeline gas flow rate value is used to characterize the gas sample's transport flow rate in the bundled tube pipeline. It is obtained in real time through the flow detection unit of the bundled tube monitoring system. The operating condition parameters refer to the real-time parameters that change with the gas extraction state, filtration state, and pipeline resistance during the operation of the bundled tube monitoring system. They are used to characterize the current transport status of the gas sample in the bundled tube pipeline.

[0029] The current water accumulation status indicator of the water filter is used to characterize the degree to which water accumulation in the water filter hinders the gas transport process. It is determined by the water level detection value, which is collected by the water level sensor installed in the water filter. The bundle tube monitoring system compares the water level detection value with two preset water accumulation status judgment thresholds. When the water level detection value is lower than the preset first water accumulation threshold, the current water accumulation status indicator is determined to be no water accumulation; when the water level detection value is greater than or equal to the preset first water accumulation threshold and lower than the preset second water accumulation threshold, the current water accumulation status indicator is determined to be slight water accumulation; when the water level detection value is greater than or equal to the preset second water accumulation threshold, the current water accumulation status indicator is determined to be severe water accumulation.

[0030] The current clogging level indicator of the dust filter is used to characterize the degree to which dust deposits inside the filter obstruct the gas transport process. It is determined by the differential pressure detection value across the dust filter, which is collected by a differential pressure sensor installed in the dust filter. The detected differential pressure value is compared with three preset clogging level judgment thresholds. When the detected differential pressure value is lower than the preset first clogging threshold, the current clogging level indicator is determined to be unobstructed; when the detected differential pressure value is greater than or equal to the preset first clogging threshold but lower than the preset second clogging threshold, the current clogging level indicator is determined to be slightly clogged; when the detected differential pressure value is greater than or equal to the preset second clogging threshold but lower than the preset third clogging threshold, the current clogging level indicator is determined to be moderately clogged; when the detected differential pressure value is greater than or equal to the preset third clogging threshold, the current clogging level indicator is determined to be severely clogged. The introduction of operating condition parameters enables the delay model to have dynamic response capabilities, reflecting the actual impact of air flow fluctuations and the real-time status of filter components on transmission time. The current gas flow rate in the pipeline directly determines the gas velocity within the pipe, serving as the dynamic core for calculating the basic transmission time. The grading of water accumulation status in the water filter and the degree of clogging in the dust filter transforms continuous liquid level and differential pressure signals into discrete status indicators. This preserves the gradient characteristics of the operating conditions and facilitates subsequent matching of standardized additional delay times, solving the problem that relying solely on fixed parameters cannot detect pipeline deterioration.

[0031] By collecting real-time data on pipeline gas flow rate and the levels of water accumulation and dust clogging in the water filter, the system achieves quantitative perception of the dynamically changing resistance factors during gas sample transmission. This solves the problem that relying solely on static parameters cannot reflect the dynamic impact of real-time pipeline operating conditions on transmission delay, leading to deviations in time estimation.

[0032] The gas analysis data for each sampling point includes the cycle number of each sampling point, the unique identifier of the sampling point, the time when the sampling valve switching is completed, the analyzer output data frame, and the analyzer status record. Gas analysis data refers to the data set formed after the gas analysis device completes the component analysis of the gas samples at each sampling point.

[0033] The cycle number identifies a gas sampling cycle executed by the bundled tube monitoring system, generated by the system at the start of the cycle. The unique identifier for the sampling point identifies the sampling point in the goaf corresponding to the current gas sample, obtained from the sampling point configuration record of the bundled tube monitoring system. The sampling valve switching completion time characterizes the moment the bundled tube monitoring system switches to the sampling channel corresponding to the current sampling point, obtained from the sampling valve control record. The analyzer output data frame carries the data output by the gas analyzer after completing the analysis of the current gas sample components, obtained through the gas analyzer's communication interface. The analyzer output data frame includes an output timestamp, cycle number field, unique identifier for the sampling point field, carbon monoxide concentration field, oxygen concentration field, carbon dioxide concentration field, ethylene concentration field, acetylene concentration field, temperature data field, differential pressure data field, and data integrity flag field. The analyzer status record characterizes the operating status of the gas analyzer when outputting the analyzer output data frame, obtained through the gas analyzer's status interface. The analyzer status record includes normal analysis status, unstable sample injection status, cleaning status, fault status, and calibration status.

[0034] Specifically, the method for obtaining the transmission delay status information of each sampling point by performing time-series correlation analysis on the pipeline static parameters and operating condition parameters is as follows: Based on the total length of the bundled tubes, the inner diameter of the bundled tubes, and the current gas flow rate at each sampling point, the basic transport time for each sampling point is analyzed. The basic transport time refers to the basic transport time required for a gas sample to pass through the bundled tube pipeline at the current gas flow rate, without considering the influence of factors such as pipeline bends.

[0035] In this embodiment, the basic pipeline transportation time at each sampling point can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, This represents the basic pipeline transport duration at the i-th sampling point. This represents the inner diameter of the bundle tube at the i-th sampling point. This represents the total length of the bundle tube at the i-th sampling point. This represents the current pipeline gas flow rate value at the i-th sampling point, where i = 1, 2, 3, ..., n, i represents the sampling point number, and n represents the total number of sampling points.

[0036] Based on the number and angle of pipe bends at each sampling point, the additional bend coefficient for each sampling point is analyzed. The additional bend coefficient is a delay correction factor determined according to the number and angle of pipe bends, used to characterize the impact of bends in the bundled tube on the increase in gas delivery time.

[0037] In this embodiment, the bending coefficient at each sampling point can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, This represents the bending additional coefficient at the i-th sampling point. This represents the number of pipe bends at the i-th sampling point. To determine the bend angle of the pipeline The unit angle influence factor is obtained from the preset angle-resistance influence mapping relationship. This represents the pipe bend angle at the i-th sampling point. The preset bend angle-resistance influence mapping relationship defines the unit bend angle influence factor value corresponding to different angle ranges. The bend angle-resistance influence mapping relationship refers to the pre-established correspondence between the pipe bend angle and the unit bend angle influence factor, which is used as the basis for determining the value of the bend additional coefficient based on the pipe bend angle.

[0038] Based on the average elevation difference of the pipeline at each sampling point, the elevation difference additional coefficient for each sampling point is obtained. The elevation difference additional coefficient is a delay correction coefficient determined based on the average elevation difference of the pipeline, used to characterize the impact of the height change between the sampling point and the gas analysis device on the gas delivery time.

[0039] In this embodiment, the additional coefficient of elevation difference at each sampling point can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, This represents the additional coefficient for the drop at the i-th sampling point. This represents the average elevation difference of the pipeline at the i-th sampling point. This indicates the preset influence factor of unit elevation difference.

[0040] Based on the pipeline transport baseline time, bend additional factor, and drop additional factor for each sampling point, the pipeline transport delay time for each sampling point is obtained. The pipeline transport delay time refers to the time required for the gas sample to be transported in the bundled tube pipeline, as determined by the pipeline transport baseline time, bend additional factor, and drop additional factor.

[0041] In this embodiment, the pipeline delay time at each sampling point can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, This represents the pipeline delay time at the i-th sampling point. This represents the basic pipeline transport duration at the i-th sampling point. This represents the bending additional coefficient at the i-th sampling point. This represents the additional coefficient for the drop at the i-th sampling point.

[0042] Based on the current water accumulation status indicator and filter model at each sampling point, the additional time value for water accumulation at each sampling point is obtained. The additional time value for water accumulation is used to characterize the impact of water accumulation in the filter on the increase in gas delivery time.

[0043] The current water accumulation status identifier and filter model of each sampling point are matched with the water accumulation additional time value corresponding to the current water accumulation status identifier and filter model stored in the coal mine monitoring database. The water accumulation additional time value corresponding to the current water accumulation status identifier and filter model is counted and recorded as the water accumulation additional time value of each sampling point.

[0044] Based on the current clogging level and filter model of each sampling point, the clogging-related additional time value for each sampling point is obtained. The clogging-related additional time value is used to characterize the impact of filter clogging on the increase in gas delivery time.

[0045] The current clogging level indicator and filter model of each sampling point are matched with the clogging time value corresponding to the current clogging level indicator and filter model stored in the coal mine monitoring database. The clogging time value corresponding to the current clogging level indicator and filter model is counted and recorded as the clogging time value of each sampling point.

[0046] The sum of the additional time value of water accumulation and the additional time value of blockage at each sampling point is recorded as the resistance-induced additional delay time at each sampling point. The resistance-induced additional delay time is used to characterize the additional transmission delay caused by the combined effects of water accumulation in the water filter and blockage in the dust filter.

[0047] Extract the injection stabilization time parameter and the chromatographic analysis time parameter of the pre-set gas analyzer. The sum of these two parameters is recorded as the analysis processing time. The analysis processing time refers to the time required for the gas analyzer to complete both injection stabilization and chromatographic analysis, and is determined jointly by the injection stabilization time parameter and the chromatographic analysis time parameter.

[0048] The total transmission delay time for each sampling point is obtained by summing the pipeline delay time, resistance-induced delay time, and analysis processing time. This total transmission delay time is then used to construct the transmission delay status information for that sampling point. The transmission delay status information, derived from the analysis of pipeline static parameters and operating condition parameters at each sampling point, characterizes the time required for a gas sample to be transported from the sampling point to the gas analyzer and for analysis to be completed. By dynamically generating personalized delay profiles for each sampling point, replacing fixed empirical delay values, the problem of time synchronization caused by significant differences in pipelines and time-varying operating conditions at multiple sampling points in the goaf is solved, ensuring high confidence in subsequent back-calculation of physical sampling times.

[0049] The gas analysis data from each sampling point are parsed and processed to obtain the analysis completion time and coal spontaneous combustion parameter data for each sampling point. Based on the transmission delay status information of each sampling point and combined with the analysis completion time of each sampling point, the physical sampling time of each sampling point is deduced in reverse.

[0050] Specifically, the method for parsing and processing the gas analysis data from each sampling point to obtain the analysis completion time and coal spontaneous combustion parameters for each sampling point is as follows: Extract the output timestamp from the analyzer output data frame at each sampling point, and determine the output timestamp as the analysis completion time for the corresponding sampling point.

[0051] Based on the output data frames of the analyzer at each sampling point, the coal spontaneous combustion parameter data for each sampling point is obtained through analysis. The specific process is as follows: extract the carbon monoxide concentration field, oxygen concentration field, carbon dioxide concentration field, ethylene concentration field, acetylene concentration field, temperature data field, and pressure difference data field from the output data frames of the analyzer at each sampling point. The data corresponding to the carbon monoxide concentration field is determined as the carbon monoxide concentration of the corresponding sampling point, the data corresponding to the oxygen concentration field is determined as the oxygen concentration of the corresponding sampling point, the data corresponding to the carbon dioxide concentration field is determined as the carbon dioxide concentration of the corresponding sampling point, the data corresponding to the ethylene concentration field is determined as the ethylene concentration of the corresponding sampling point, the data corresponding to the acetylene concentration field is determined as the acetylene concentration of the corresponding sampling point, the data corresponding to the temperature data is determined as the temperature data of the corresponding sampling point, and the data corresponding to the pressure difference data field is determined as the pressure difference data of the corresponding sampling point. The carbon monoxide concentration, oxygen concentration, carbon dioxide concentration, ethylene concentration, acetylene concentration, temperature data, and pressure difference data of each sampling point are combined to form the coal spontaneous combustion parameter data for each sampling point.

[0052] Coal spontaneous combustion parameter data refers to a set of data used to characterize the risk status of coal spontaneous combustion at sampling points, including carbon monoxide concentration, oxygen concentration, carbon dioxide concentration, ethylene concentration, acetylene concentration, temperature data, and pressure difference data.

[0053] Specifically, the method for reverse-engineering the physical sampling time of each sampling point by combining the transmission delay status information of each sampling point with the analysis completion time of each sampling point is as follows: The total transmission delay time is read from the transmission delay status information of each sampling point. The analysis completion time of each sampling point is then subtracted from the corresponding total transmission delay time to obtain the physical sampling time of each sampling point. The physical sampling time characterizes the time it takes for the gas sample corresponding to the current analyzer output data frame to enter the bundle tube at the sampling point. This accurately reflects the real-time state of the goaf represented by the gas sample, ensuring that subsequent trend analysis and anomaly detection are strictly aligned with the actual time series on site, avoiding false alarms or missed alarms caused by delay fluctuations.

[0054] Multi-layer time-series anchoring processing was performed on the physical sampling time, analysis completion time, and coal spontaneous combustion parameter data of each sampling point to obtain the time-series anchoring information of each sampling point.

[0055] Specifically, the method for obtaining the time-series anchoring information for each sampling point by performing multi-layer time-series anchoring processing on the physical sampling time, analysis completion time, and coal spontaneous combustion parameter data of each sampling point is as follows: Using the unique identifier and cycle number of the sampling point as an index, the system reads the physical sampling time, analysis completion time, and coal spontaneous combustion parameter data for the corresponding sampling point. Upon completion of receiving the coal spontaneous combustion parameter data for the current sampling point, the edge gateway receives the data; upon completion of encapsulating the coal spontaneous combustion parameter data for the current sampling point, the edge gateway encapsulates the data. The edge gateway receive time is the time record generated when the edge gateway completes receiving the coal spontaneous combustion parameter data for the current sampling point. The edge gateway encapsulates the data when the edge gateway completes encapsulating the coal spontaneous combustion parameter data and timing anchoring information for the current sampling point.

[0056] The physical sampling time, analysis completion time, edge gateway reception time, and edge gateway packet encapsulation time of each sampling point are arranged according to the time hierarchy to form the time-series anchoring sequence of each sampling point.

[0057] When the physical sampling time of a sampling point is earlier than the analysis completion time, the analysis completion time is earlier than or equal to the edge gateway reception time, or the edge gateway reception time is earlier than or equal to the edge gateway packet encapsulation time, the timing analysis result of the timing anchor sequence of the corresponding sampling point is marked as timing valid; otherwise, the timing analysis result of the timing anchor sequence of the corresponding sampling point is marked as timing abnormal.

[0058] The time-series analysis results of each sampling point are bound together with its unique identifier, cycle number, physical sampling time, analysis completion time, edge gateway reception time, edge gateway packet encapsulation time, and time-series anchoring sequence to obtain the time-series anchoring information for the corresponding sampling point. This time-series anchoring information is used to characterize the temporal correlation between coal spontaneous combustion parameter data from physical sampling in the goaf to edge gateway packet encapsulation.

[0059] For time-series anchored sequences whose time-series analysis results are marked as time-series anomalies, the time-series anchoring information of the corresponding sampling points is marked as not participating in the order of anomalies in coal spontaneous combustion parameter data.

[0060] The time-series anchoring information and coal spontaneous combustion parameter data of each sampling point are encapsulated to obtain the evidence storage data package of each sampling point. The evidence storage data package of each sampling point is submitted to the blockchain node of the goaf area for evidence storage to obtain the on-chain time-series anchoring record of each sampling point.

[0061] The evidence storage data package refers to a data package formed by encapsulating the time-series anchoring information and coal spontaneous combustion parameter data of each sampling point. It is used to submit to the blockchain node in the goaf for evidence storage processing. The on-chain time-series anchoring record refers to the on-chain record formed by the blockchain node in the goaf after completing the evidence storage processing of the evidence storage data package of each sampling point. It is used to store the time-series anchoring information and coal spontaneous combustion parameter data of the corresponding sampling point, and to represent the on-chain correlation between the coal spontaneous combustion parameter data of the corresponding sampling point and the physical sampling time.

[0062] A second aspect of the present invention provides a blockchain-based data transmission system for coal spontaneous combustion parameters in goaf areas, comprising: The time-series correlation analysis module is used to set up several sampling points in the goaf area, collect pipeline static parameters, operating condition parameters and gas analysis data of each sampling point, perform time-series correlation analysis on the pipeline static parameters and operating condition parameters of each sampling point, and obtain the transmission delay status information of each sampling point.

[0063] The analysis and processing module is used to analyze and process the gas analysis data of each sampling point to obtain the analysis completion time and coal spontaneous combustion parameter data of each sampling point. Based on the transmission delay status information of each sampling point and combined with the analysis completion time of each sampling point, the physical sampling time of each sampling point is deduced in reverse.

[0064] The time-series anchoring processing module is used to perform multi-layer time-series anchoring processing on the physical sampling time, analysis completion time, and coal spontaneous combustion parameter data of each sampling point to obtain the time-series anchoring information of each sampling point.

[0065] The encapsulation and processing module is used to encapsulate and process the time-series anchoring information and coal spontaneous combustion parameter data of each sampling point to obtain the evidence storage data package of each sampling point. The evidence storage data package of each sampling point is submitted to the blockchain node of the goaf area for evidence storage to obtain the on-chain time-series anchoring record of each sampling point.

[0066] It should be noted that the blockchain-based method and system for transmitting coal spontaneous combustion parameters in goaf areas also includes a coal mine monitoring database, which stores the first water accumulation threshold, the second water accumulation threshold, the first blockage threshold, the second blockage threshold, the third blockage threshold, the angle-resistance influence mapping relationship, the unit elevation difference influence factor, the current water accumulation status identifier of the water filter and the water accumulation additional time value corresponding to the water filter model, and the current blockage degree identifier of the dust filter and the blockage additional time value corresponding to the dust filter model.

[0067] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0068] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A blockchain-based method for transmitting parameters of spontaneous combustion of coal in goaf areas, characterized in that, include: Several sampling points were set up in the goaf area to collect pipeline static parameters, operating condition parameters and gas analysis data at each sampling point. Time-series correlation analysis was performed on the pipeline static parameters and operating condition parameters at each sampling point to obtain the transmission delay status information of each sampling point. The gas analysis data of each sampling point are analyzed and processed to obtain the analysis completion time and coal spontaneous combustion parameter data of each sampling point. Based on the transmission delay status information of each sampling point and combined with the analysis completion time of each sampling point, the physical sampling time of each sampling point is deduced in reverse. Multi-layer time-series anchoring processing was performed on the physical sampling time, analysis completion time, and coal spontaneous combustion parameter data of each sampling point to obtain the time-series anchoring information of each sampling point; The time-series anchoring information and coal spontaneous combustion parameter data of each sampling point are encapsulated to obtain the evidence storage data package of each sampling point. The evidence storage data package of each sampling point is submitted to the blockchain node of the goaf area for evidence storage to obtain the on-chain time-series anchoring record of each sampling point.

2. The blockchain-based method for transmitting parameters of spontaneous combustion of coal in goaf areas according to claim 1, characterized in that, The method for collecting pipeline static parameters, operating condition parameters, and gas analysis data at each sampling point is as follows: The static parameters of the pipeline at each sampling point include the total length of the bundle tube, the inner diameter of the bundle tube, the number of bends in the pipeline, the bend angle of the pipeline, the average elevation difference of the pipeline, the water filter model, and the dust filter model. The operating parameters of each sampling point include the current pipeline gas flow rate, the current water accumulation status of the water filter, and the current clogging level of the dust filter. The gas analysis data for each sampling point includes the cycle number of each sampling point, the unique identifier of the sampling point, the time when the sampling valve switching is completed, the analyzer output data frame, and the analyzer status record.

3. The blockchain-based method for transmitting coal spontaneous combustion parameters in goaf areas according to claim 2, characterized in that, The method for performing time-series correlation analysis on the pipeline static parameters and operating condition parameters at each sampling point is as follows: Based on the total length of the tube bundle, the inner diameter of the tube bundle, and the current gas flow rate in the pipeline at each sampling point, the pipeline transport base duration at each sampling point is analyzed. Based on the number of pipe bends and the bend angle at each sampling point, the additional bend coefficient at each sampling point is obtained through analysis. Based on the average elevation difference of the pipeline at each sampling point, the additional elevation difference coefficient for each sampling point is obtained through analysis. Based on the pipeline basic duration, bending additional coefficient and drop additional coefficient of each sampling point, the pipeline delay duration of each sampling point is obtained; The sum of the water accumulation time value and the blockage time value at each sampling point is recorded as the resistance-added delay time at each sampling point. Extract the injection stabilization time parameter and the chromatographic analysis time parameter, and record the sum of the injection stabilization time parameter and the chromatographic analysis time parameter as the analysis processing time.

4. The blockchain-based method for transmitting parameters of spontaneous combustion of coal in goaf areas according to claim 3, characterized in that, The method for obtaining the transmission delay status information of each sampling point is as follows: The total transmission delay time of the corresponding sampling point is obtained by summing the pipeline delay time, the resistance-added delay time, and the analysis and processing time of each sampling point. The total transmission delay time of each sampling point is then used to form the transmission delay status information of the corresponding sampling point.

5. The blockchain-based method for transmitting parameters of spontaneous combustion of coal in goaf areas according to claim 3, characterized in that, The method for parsing and processing the gas analysis data from each sampling point is as follows: Extract the output timestamp from the analyzer output data frame at each sampling point and determine the output timestamp as the analysis completion time of the corresponding sampling point; Based on the analyzer output data frames from each sampling point, the coal spontaneous combustion parameter data for each sampling point are obtained through processing.

6. The blockchain-based method for transmitting coal spontaneous combustion parameters in goaf areas according to claim 1, characterized in that, The method for obtaining the physical sampling time of each sampling point through reverse deduction is as follows: Read the corresponding total transmission delay duration from the transmission delay status information of each sampling point, and subtract the corresponding total transmission delay duration from the analysis completion time of each sampling point to obtain the physical sampling time of each sampling point.

7. The blockchain-based method for transmitting parameters of spontaneous combustion of coal in goaf areas according to claim 1, characterized in that, The method for performing multi-layer time-series anchoring processing on the physical sampling time, analysis completion time, and coal spontaneous combustion parameter data of each sampling point is as follows: Extract the unique identifier and cycle number of the corresponding sampling point from the data frame output by the analyzer at each sampling point; Using the unique identifier and cycle number of the sampling point as an index, the physical sampling time, analysis completion time and coal spontaneous combustion parameter data of each sampling point are read. When the coal spontaneous combustion parameter data of each sampling point is received, the edge gateway reception time is generated. When the coal spontaneous combustion parameter data of each sampling point is encapsulated, the edge gateway packetization time is generated. The physical sampling time, analysis completion time, edge gateway reception time, and edge gateway packet encapsulation time of each sampling point are arranged according to the time hierarchy to form the time-series anchoring sequence of each sampling point.

8. The blockchain-based method for transmitting coal spontaneous combustion parameters in goaf areas according to claim 7, characterized in that, The method for obtaining the temporal anchoring information of each sampling point is as follows: Time series analysis is performed on the time series anchored sequences of each sampling point to obtain the time series analysis results of the time series anchored sequences of the corresponding sampling points; The timing analysis results of each sampling point are bound together with its unique identifier, cycle number, physical sampling time, analysis completion time, edge gateway reception time, edge gateway packet encapsulation time, and timing anchoring sequence to obtain the timing anchoring information of the corresponding sampling point. Temporal anchoring information is used to characterize the temporal correlation between coal spontaneous combustion parameter data and physical sampling in the goaf area to edge gateway packets.

9. The blockchain-based method for transmitting parameters of spontaneous combustion of coal in goaf areas according to claim 8, characterized in that, The method for performing time-series analysis on the time-series anchored sequences of each sampling point is as follows: Extract the timing anchor sequence of each sampling point. If the physical sampling time of the timing anchor sequence of a certain sampling point is earlier than the analysis completion time, the analysis completion time is earlier than or equal to the edge gateway reception time, or the edge gateway reception time is earlier than or equal to the edge gateway packetization time, the timing analysis result of the timing anchor sequence of the corresponding sampling point is marked as timing valid. Otherwise, the timing analysis result of the timing anchor sequence of the corresponding sampling point is marked as timing abnormal.

10. A blockchain-based data transmission system for coal spontaneous combustion parameters in goaf areas, characterized in that, The method for transmitting data on coal spontaneous combustion parameters in goaf areas based on blockchain as described in any one of claims 1-9 includes: The time-series correlation analysis module is used to set up several sampling points in the goaf area, collect pipeline static parameters, operating condition parameters and gas analysis data of each sampling point, perform time-series correlation analysis on the pipeline static parameters and operating condition parameters of each sampling point, and obtain the transmission delay status information of each sampling point. The analysis and processing module is used to analyze and process the gas analysis data of each sampling point to obtain the analysis completion time and coal spontaneous combustion parameter data of each sampling point. Based on the transmission delay status information of each sampling point and combined with the analysis completion time of each sampling point, the physical sampling time of each sampling point is deduced in reverse. The time-series anchoring processing module is used to perform multi-layer time-series anchoring processing on the physical sampling time, analysis completion time and coal spontaneous combustion parameter data of each sampling point to obtain the time-series anchoring information of each sampling point; The encapsulation and processing module is used to encapsulate and process the time-series anchoring information and coal spontaneous combustion parameter data of each sampling point to obtain the evidence storage data package of each sampling point. The evidence storage data package of each sampling point is submitted to the blockchain node of the goaf area for evidence storage to obtain the on-chain time-series anchoring record of each sampling point.

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