A method and terminal for detecting and classifying the components of explosive hazardous gases

Abstract

This invention relates to the field of gas composition analysis technology, specifically to a method and terminal for detecting and classifying the composition of explosive hazardous gases. The method includes the following steps: acquiring sensor node tags and sampling data; constructing a composition monitoring sequence; extracting combinations of differential channel tags and time segments; matching a combustible gas composition table with sampling locations; classifying configurational components; mapping channels and time ranges; and obtaining the explosion hazard status identification result. In this invention, a composition distribution chain is constructed by fusing sensor tags and sampling time; the temporal overlap relationship of component combinations is extracted by combining multi-channel data; corresponding segment identifiers are established to complete the identification of the gas's coordinated change structure; a hierarchical association is established through the mapping relationship between tags and a set composition table; structural hierarchy is divided by distinguishing between repeating and non-repeating components; and spatiotemporal continuity mapping is performed by combining distribution trends and channel coverage. This facilitates the location and judgment of the accumulation process of risky components and enhances the response and linkage capabilities to explosion triggers.

Description

Technical Field

[0001] This invention relates to the field of gas composition analysis technology, and in particular to a method and terminal for detecting and classifying the composition of explosive hazardous gases. Background Technology

[0002] The field of gas composition analysis technology involves the identification and determination of the types and concentrations of components in various gases. Core aspects include gas collection, physical or chemical parameter detection, signal conversion and data processing, as well as qualitative and quantitative analysis of components in gas mixtures. It is widely used in environmental monitoring, safety early warning, and industrial process control. Particularly in waste incineration, due to the complex composition of waste and variable combustion conditions, various explosive or toxic hazardous gas components, such as carbon monoxide, methane, hydrogen, and various combustible volatile organic gases, are continuously generated during high-temperature incineration and incomplete combustion stages. These components, their generation intensity, and spatial distribution exhibit significant dynamic changes, posing potential risks to the safe operation of the incineration system and the surrounding environment. Traditional methods and terminals for detecting and classifying explosive hazardous gas components typically involve collecting gas samples from the waste incineration area using a fixed sensor array. Gas components are identified using principles such as changes in thermal conductivity, ionization voltage response, or electrochemical reactions. The hazard level is then determined by comparing the output signal value or linear change relationship of a single sensor with a preset threshold.

[0003] Current technologies based on single-point sensors rely on independent detection results, making it difficult to reconstruct the synchronous changes and evolution trends of the same hazardous gas component across multiple sensing nodes. The detection mechanisms fail to effectively distinguish differences between different sensing channels and lack the ability to discern the overlapping distribution and temporal characteristics of multiple hazardous gas components during incineration. They also cannot characterize the superposition, migration, and diffusion characteristics between gas components. Furthermore, they exhibit poor adaptability under waste incineration conditions, and their information organization structure is insufficient to support a comprehensive analysis of the hazardous gas combination structure and its spatiotemporal correlation, easily leading to unclear hazard assessments and ambiguous risk zoning. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a method for detecting the composition and classifying the hazard of explosive gases.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for detecting and classifying the composition of explosive hazardous gases, comprising the following steps:

[0006] S1: Obtain target component label information of sensor nodes in the explosion source area, extract the gas type, corresponding label number and channel number of each detection data, establish the association between the detection data and the acquisition time, and connect the data according to the detection sequence to obtain the component monitoring time series set;

[0007] S2: Based on the component monitoring time series set, extract component labels from the differential channel within the same time period, pair and combine the first and last times to obtain a list of overlapping time period identifiers;

[0008] S3: Based on the overlapping time period identifier list, extract the gas type name, compare the gas with the set combustible gas composition table, connect the gas combination occurrence location and sampling time information to obtain the linkage configuration attribution matching table.

[0009] S4: Based on the linked configuration attribution matching table, extract the components that appear repeatedly in the configuration, connect the components with the sampling point positions of the corresponding configurations, and obtain the configuration order of the components of the non-repeating configurations to obtain the distribution set of the dangerous configuration components.

[0010] S5: Based on the distribution set of hazardous configuration components, check the location of each group of gases in the differentiated channels, read the corresponding occurrence time sequence, the corresponding combination channel range and time range, and obtain the explosion hazard status identification result.

[0011] As a further aspect of the present invention, the component monitoring time series set includes gas type, label number, channel number, sampling time, channel correspondence, and data time sequence; the overlapping group time period identifier list includes gas component combination, first occurrence time of combination, last occurrence time of combination, and time sequence information; the linkage configuration attribution matching table includes gas type name, set combustible gas component item, combination location number, and combination sampling time; the hazardous configuration component distribution set includes repeating components, sampling point location of repeating components, non-repeating components, configuration sequence correspondence, and component configuration location correspondence; and the explosion hazard status identification result includes gas combination channel location, combination occurrence time sequence, combination channel range, combination time range, and continuous label setting result.

[0012] As a further aspect of the present invention, the differentiated channel component label refers to the label identification that is collected by the differentiated sensor channel and corresponds to the differentiated gas component within the same time interval.

[0013] The location where the gas combination appears refers to the spatial location of the sampling point and the sensing node when the gas components and gas combination are detected during the monitoring process.

[0014] As a further aspect of the present invention, the non-repeating configurational component configuration order refers to the component order relationship formed by arranging gases that appear only once in the linked configuration according to the time or index order in the original configuration.

[0015] The combined channel range and time range refer to the set of sensor channel numbers covered by a gas combination during the detection process and the corresponding continuous time interval from the first to the last occurrence.

[0016] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0017] S101: Obtain the target component label information collected by the sensor nodes in the explosion source area, extract the corresponding gas type, label number and channel number according to the sampling time in the detection data, split the fields and match the corresponding relationships to obtain the gas component sampling parameter group;

[0018] S102: Based on the gas component sampling parameter group, filter data items with the same channel number field value, sort the data items in ascending order with the sampling time field as a reference, and align the data of the sequence under the same channel number according to the time sequence to obtain the time sequence group corresponding to the channel.

[0019] S103: Based on the time series group corresponding to the channel, extract the sampling time field in the sequence, splice the channel time series, and extend the result to the channel number to obtain the component monitoring time series set.

[0020] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0021] S201: Based on the sampling time of the component monitoring time series data set, extract data items that are in the same time interval but have different channel numbers, call the corresponding component label field, and compare the channel number with the label value according to the time index to obtain the data group corresponding to the multi-channel label;

[0022] S202: Based on the data group corresponding to the multi-channel labels, filter the component label content that appears repeatedly in the time series, count the number of times the label appears in the differentiated time interval, and retain the data segment position that appears at least twice to obtain the label repetition distribution sequence set;

[0023] S203: Based on the set of repeated label distribution sequences, extract the first and last occurrence times of each group of labels in the time series, pair and map the time fields with the corresponding labels, and expand them in chronological order to obtain a list of overlapping time period identifiers.

[0024] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0025] S301: Based on each group of data in the overlapping time period identifier list, extract the component label number within the corresponding time interval, call the mapping relationship between the label number and the gas name field, match the number value and replace the field content to obtain the gas name extraction sequence;

[0026] S302: Based on the gas name extraction sequence, retrieve the gas name field in the combustible gas composition table, compare the name field in the data, and obtain the corresponding sequence of the combustible gas.

[0027] S303: Based on the corresponding sequence of the combustible gas, extract the location number field and sampling time field within the corresponding time interval, pair the fields in order, expand the comparison relationship with the gas sequence as the index, and obtain the linkage configuration attribution matching table.

[0028] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0029] S401: Based on the gas label field corresponding to the configuration in the linkage configuration attribution matching table, the gas label value repeatability is retrieved, and after extracting the label items that appear more than once in the configuration, they are compared with the sampling point position field corresponding to the configuration to obtain the component label repeat position set.

[0030] S402: Based on the set of repeated positions of the component labels, extract the label items that do not appear in the gas label field of the set, extract the label content according to the configuration order index value corresponding to the original matching table, and pair the gas labels with the position fields according to the index order to obtain the configuration order label connection sequence.

[0031] S403: Based on the connection sequence of the component label repetition position set and the configuration order label, the label content and the corresponding configuration position are integrated and extracted, and the sampling positions of the repetition items and non-repetition items are connected according to the label arrangement order to obtain the distribution set of hazardous configuration components.

[0032] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0033] S501: Based on the distribution set of hazardous configuration components, extract the corresponding channel number and sampling time field for each group of gas components, retrieve the location of each component in the differentiated channel, and extract the associated time information to obtain the gas channel time correspondence set;

[0034] S502: Based on the gas channel time correspondence set, extract the channel number set and sampling time interval corresponding to each component, determine the field continuity, and obtain a continuous associated segment dataset;

[0035] S503: Based on the continuous associated fragment dataset, configure status labels for each group of data with associated features, and match the label fields with the channel locations of the gas components to obtain the explosion hazard status identification results.

[0036] A terminal for detecting and classifying the composition of explosive hazardous gases, comprising a memory and a processor, characterized in that the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for detecting and classifying the composition of explosive hazardous gases according to any one of claims 1 to 9.

[0037] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0038] In this invention, a component distribution chain is constructed by fusing sensor tags and sampling time. The temporal overlap relationship of component combinations is extracted by combining multi-channel data to establish corresponding segment identifiers, thereby completing the identification of the gas co-change structure. The attribution association is established by mapping the tags to a set component table, distinguishing between repeating and non-repeating components to divide the structural hierarchy. The spatiotemporal continuity is mapped by combining distribution trends and channel coverage, thereby promoting the location and judgment of the risk component accumulation process and enhancing the response linkage capability to explosion triggers. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of the steps of the present invention;

[0041] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0042] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0043] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0044] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0045] Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0046] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0047] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0048] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0049] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0050] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0051] Please see Figure 1 This invention provides a method for detecting and classifying the components of explosive hazardous gases, comprising the following steps:

[0052] S1: Obtain the target component label information recorded in the sensor nodes within the explosion source area, extract the gas type, corresponding label number and channel number of each detection data according to the sampling time, match the data with the same channel number to the acquisition time, and continuously connect the data in the order of detection to obtain the component monitoring time series set;

[0053] S2: Based on the sampling time of each data in the component monitoring time series set, extract the component labels from the differentiated channels within the same time period, identify the repeated label combinations, and pair the combinations with the corresponding first and last occurrence times in chronological order to obtain a list of overlapping time period identifiers;

[0054] S3: Based on each combination in the overlapping time period identifier list, extract the names of the included gas types, compare them with the corresponding items in the pre-set combustible gas composition table, associate the location number of the gas combination with the corresponding sampling time, and obtain the linkage configuration attribution matching table.

[0055] S4: Based on the repetition of gas labels in the linkage configuration attribution matching table, extract the components that appear repeatedly in the configuration, associate the components with the sampling point positions of the corresponding configurations, and then connect the non-repetitive components one by one according to the corresponding configuration order, so as to correspond the components with their respective configuration positions in sequence, and obtain the distribution set of hazardous configuration components.

[0056] S5: Based on the combination of each group of gases in the distribution of hazardous configuration components, check the position of each group of gases in the differentiated channels, read the corresponding occurrence time and arrange the time sequence, then continuously map the channel range and time range of each group of gases, set corresponding labels for combinations with continuous occurrence relationship, and obtain the explosion hazard status identification result.

[0057] The component monitoring time series set includes gas type, label number, channel number, sampling time, channel correspondence, and data time sequence. The overlapping group time period identifier list includes gas component combination, first occurrence time of combination, last occurrence time of combination, and time sequence information. The linkage configuration attribution matching table includes gas type name, set combustible gas component item, combination location number, and combination sampling time. The hazardous configuration component distribution set includes repeating components, sampling point location of repeating components, non-repeating components, configuration sequence correspondence, and component configuration location correspondence. The explosion hazard status identification results include gas combination channel location, combination occurrence time sequence, combination channel range, combination time range, and continuous label setting results.

[0058] Please see Figure 2 The specific steps of S1 are as follows:

[0059] S101: Obtain the target component label information collected by the sensor nodes in the explosion source area, extract the corresponding gas type, label number and channel number according to the sampling time in the detection data, split the fields and match the corresponding relationships to obtain the gas component sampling parameter group;

[0060] First, within the sensor node array distributed within the waste incineration explosion source area, the original data packets are intercepted and parsed. For the hexadecimal asynchronous communication frames reported by the sensors, the data payload area after the frame header is located to obtain the target component tag information. Based on the microsecond-level sampling timestamp carried in the detection data, a field stripping operation is performed to extract the corresponding gas type code, unique tag number, and physical hardware channel number. During this process, a field splitting operation is performed, dividing the composite string according to a predefined bit width. For example, the original field containing "GAS01-N7-CH2" is split into independent data items, and a matching correspondence between gas components and hardware channels is established. A specific example illustrates this: if at 14:05:01.200, the CH2 channel of node 03 captures a tag with a hexadecimal value of "0x01", and the mapping table determines that the corresponding gas type is methane, and the tag number is 7. By structurally reorganizing the resolved gas properties, time coordinates, and spatial location parameters, it is ensured that each independent sampling point has a complete property description. Then, all discrete sampling records are aggregated into a data set with a unified format to obtain the gas composition sampling parameter set.

[0061] S102: Based on the gas composition sampling parameter group, filter data items with the same channel number field value, sort the data items in ascending order with the sampling time field as a reference, and align the data of the sequence under the same channel number according to the time sequence to obtain the time sequence group corresponding to the channel.

[0062] First, all channel number fields are traversed, and data classification is performed. Data items with identical channel number field values ​​are filtered and grouped into the same logical cluster. Taking channel CH1 in an industrial site as an example, all concentration and state data generated by this channel during the monitoring period are extracted. Using the sampling time field as the absolute sorting benchmark, the data items within the cluster are sorted sequentially in ascending order to ensure that the data exhibits strict temporal linearity. For sequences under the same channel number, time alignment is performed. Considering the clock offset of different sensor hardware, a global synchronization step size parameter is introduced, set to 200 milliseconds. If channel CH1 is missing data at a certain standard scale point, the forward data retention action is invoked to fill in the missing value, i.e., the observation value of the previous moment is taken as the value of the current missing point, so that all monitoring channels have data records of equal length on the same time scale. For example, the sampling sequences of CH1 and CH5 are interpolated and padded at 200-millisecond intervals, so that every index position between 14:05:00 and 14:05:10 can achieve logical equivalence, resulting in the corresponding time sequence group for the channel.

[0063] S103: Based on the time series group corresponding to the channel, extract the sampling time field from the sequence, concatenate the channel time series, and extend the result to the channel number to obtain the component monitoring time series set;

[0064] First, all sampling time fields are extracted and concatenated to construct a continuous channel time series covering the entire monitoring period. Then, a result expansion process is performed, using this time axis as the basic framework and expanding vertically according to the physical channel number dimension to ensure each channel corresponds to a complete time index. During the actual filling process, the component detection values ​​at specific sampling points for each channel are filled into the corresponding matrix cells according to the time coordinate. For example, if the time series contains 50 sampling points, data vectors of length 50 are generated for channels CH1 to CH4, and component identifiers such as "methane" and "carbon monoxide" and their corresponding label status bits are precisely mapped to each index position in the vector. This operation integrates the originally independent channel data into a time matrix with multi-dimensional features, allowing the status of all monitoring points at any given time point to be retrieved synchronously, thus forming a panoramic data view reflecting the overall component evolution trend of the explosion source region, resulting in a component monitoring time series set.

[0065] Please see Figure 3 The specific steps of S2 are as follows:

[0066] S201: Based on the sampling time of the component monitoring time series data, extract data items that are in the same time interval but have different channel numbers, call the corresponding component label field, and compare the channel number with the label value according to the time index to obtain the data group corresponding to the multi-channel label;

[0067] First, the sampling time of the component monitoring time series data is retrieved, and a sliding time window of 500 milliseconds is set. A horizontal scan is performed within the time window to extract data items with inconsistent channel numbers within the same time interval. Then, the corresponding component label field is called, using the time index as a common key to compare the label values ​​detected by different physical channels at the same time. For example, at 14:10:05.000, the label value of channel CH1 is "TAG_01," while the label value of channel CH3 is "TAG_02." A comparison is performed to associate the label content of these two heterogeneous channels with this time point. In this way, the differences or commonalities in gas composition sensed by sensors at different spatial locations within the same time slice can be identified. This process iterates through the entire time series, structurally encapsulating each group of multi-channel label data with temporal overlap, recording the component response characteristics of different channels within the same time period, and obtaining the corresponding data groups for the multi-channel labels.

[0068] S202: Based on the multi-channel label corresponding data group, filter the component label content that appears repeatedly in the time series, count the number of times the label appears in the differentiated time interval, and retain the data segment position that appears at least twice to obtain the label repetition distribution sequence set;

[0069] First, a deep search of tag frequency is performed to filter recurring component tags in the time series. For each individual component tag, a statistical operation is performed to calculate the total number of times the tag appears in differentiated time intervals (i.e., non-continuous detection segments). A repetition judgment benchmark is introduced, set to 2 times, to filter out instantaneous anomalies caused by background noise interference from the sensor. A judgment operation is performed: if the tag "acetylene" is detected in the first minute and the third minute respectively, with a statistical count of 2, satisfying the judgment condition of at least two occurrences, the data segment location information of the tag is retained; if the tag "hydrogen" appears only once at a certain isolated time point, it is removed. By filtering the frequency of all channel data items, it is ensured that the retained data all have significant statistical correlations, eliminating sporadic measurement errors, and finally identifying the component distribution intervals with persistent risk characteristics, thus obtaining the tag repetition distribution sequence set.

[0070] S203: Based on the set of repeated label distribution sequences, extract the first and last occurrence times of each group of labels in the time series, pair and map the time fields with the corresponding labels, and expand them in chronological order to obtain a list of overlapping time period identifiers;

[0071] First, for each group of tags filtered by frequency, a time boundary extraction process is performed, extracting the first and last occurrence times of each tag in the time series. For example, the tag "TAG_CH4" is first triggered at 14:20:00 and last disappears at 14:25:00; these two time points are defined as the active boundary of this component. A pairing mapping process is then performed, associating these two time fields with the corresponding gas tag names, and arranging them linearly on the global time axis according to the chronological order of the time dimension. If multiple gas components exist (such as methane and ethane), they are sorted according to their respective first occurrence times, clearly presenting the coverage of each hazardous component on the time axis and their potential overlap relationships. This process transforms discrete, repetitively distributed data into a logical list with clearly defined time period identifiers, resulting in a list of overlapping group time period identifiers.

[0072] Please see Figure 4 The specific steps of S3 are as follows:

[0073] S301: Based on each group of data in the overlapping time period identifier list, extract the component label number within the corresponding time interval, call the mapping relationship between the label number and the gas name field, match the number value and replace the field content to obtain the gas name extraction sequence;

[0074] First, the time interval and corresponding component label number of each data group in the overlapping time period identifier list are retrieved. Then, the label number and gas name mapping table pre-stored in the storage unit is called. A matching operation is performed, retrieving the standard gas field content corresponding to each label number (e.g., "ID_101") in the mapping table, and then performing a field content replacement operation. For example, if the time period of a certain item in the list is 14:30:00 to 14:35:00, and its number is "ID_101", the corresponding name in the mapping table is "isobutane". The original number code is then directly updated to the text description "isobutane". This process iterates through all entries in the list, ensuring that all abstract numerical numbers are converted into gas name strings with physicochemical meaning, forming a time-series chain with gas names as the core, resulting in a gas name extraction sequence.

[0075] S302: Extract sequences based on gas names, retrieve the gas name field in the combustible gas composition table, compare the name field in the data, and obtain the corresponding sequence of the combustible gas.

[0076] First, a pre-defined combustible gas composition table is retrieved, which covers all known gas types with explosion risks in the current environment. A search and comparison process is then performed, comparing the name fields in the extracted sequences with the standard fields in the composition table one by one. The judgment logic is as follows: if the name in the sequence is "methane," and it exists in the combustible gas composition table, it is determined as a valid risk item and retained; if the name is "nitrogen" or "carbon dioxide," and it is not in the combustible gas composition table, it is determined as an interference item and filtered. This safety screening eliminates interference from ambient background gases in the identification of explosion hazards, ensuring that all subsequent calculations focus on actual combustible gas exposure events to obtain the corresponding combustible gas sequences.

[0077] S303: Based on the corresponding sequence of combustible gas, extract the location number field and sampling time field within the corresponding time interval, pair the fields in order, expand the comparison relationship with the gas sequence as the index, and obtain the linkage configuration attribution matching table;

[0078] First, the location number and sampling time fields associated with each gas during the initial sampling phase are traced. A pairing process is then performed, sequentially combining the location coordinates (e.g., "Monitoring Point 5") with the precise sampling timestamp to form a [location, time] tuple. Subsequently, using the gas sequence (e.g., "acetylene sequence") as the core index, a correlation is established, logically linking records of the same type of gas occurring at different geographical locations and time points. For example, if acetylene is detected at both location A and location B, the relevant information for both locations is uniformly linked under the acetylene index entry. Through this cross-anchoring of space and time, the diffusion trajectory and configuration distribution of hazardous gases within the monitoring area are outlined, resulting in a linked configuration attribution matching table.

[0079] Please see Figure 5 The specific steps of S4 are as follows:

[0080] S401: Based on the gas label field corresponding to the configuration in the linkage configuration attribution matching table, the repeatability of gas label values ​​is retrieved. After extracting the label items that appear more than once in the configuration, they are compared with the sampling point position field corresponding to the configuration to obtain the set of repeated positions of component labels.

[0081] First, each predefined linkage configuration in the linkage configuration attribution matching table is accessed, and the gas tag field corresponding to the configuration is extracted. A repeatability retrieval of the gas tag values ​​is then performed. A repeatability extraction criterion value is introduced, set to 1. For each tag item appearing within a configuration, the number of times it is covered by different monitoring locations is counted. Specifically, if the tag "TAG_07" is recorded at all three sampling points (location 1, location 2, and location 4) under a certain configuration, its occurrence count is 3. Since 3 is greater than 1, the extraction action is performed, spatially correlating the tag item and all its corresponding location fields. This step, by identifying the same components simultaneously sensed at multiple locations, locks down the common risk characteristics within the explosion source area, obtaining a set of component tag repeatability locations.

[0082] S402: Based on the set of repeated positions of component labels, extract the label items of gas label fields that do not appear in the set, extract the label content according to the configuration order index value corresponding to the original matching table, and pair the gas labels with the position fields according to the index order to obtain the configuration order label connection sequence.

[0083] First, isolated gas label entries not appearing in the repetition set are extracted from the linkage configuration attribution matching table. Based on the configuration order index value (e.g., 3rd position) of these labels in the original matching table, the corresponding label content is extracted. A pairing operation is performed, connecting the gas labels to the corresponding sampling point location fields one by one according to the index order, ensuring that each non-repetitive data item can find its corresponding location definition in the original spatial logical sequence. Through this ordered pairing, the complete component chain within the configuration is completed, so that the configuration not only contains common risk components but also local specific hazard signals, resulting in a configuration order label connection sequence.

[0084] S403: Based on the connection sequence of the component label repetition position set and the configuration order label, the label content and the corresponding configuration position are integrated and extracted. The sampling positions of the repetition items and non-repetition items are connected according to the label arrangement order to obtain the distribution set of hazardous configuration components.

[0085] First, the set of repeated positions of component labels and the sequence of configurational labels are integrated, and an integration and extraction process is performed on all involved label content and their corresponding spatial configurational positions. During processing, repeated and non-repeating label items are matched according to their order of arrangement on the time axis or logical axis. Specifically, the actions include merging multiple physical locations corresponding to repeated items according to the label order, and inserting the locations of non-repeating items into the corresponding sequence gaps, ensuring that all sampling locations can form a one-to-one logical mapping with specific gas components. This constructs a complete set that comprehensively reflects the relationship between gas components and spatial location distribution within hazardous configurations, resulting in a hazardous configuration component distribution set.

[0086] Please see Figure 6 The specific steps of S5 are as follows:

[0087] S501: Based on the distribution set of hazardous configuration components, extract the corresponding channel number and sampling time field for each gas component, retrieve the location of each component in the differentiated channel, and extract the associated time information to obtain the gas channel time correspondence set;

[0088] First, all associated channel numbers and sampling time fields are extracted. For each specific gas component (e.g., "propane-air mixture"), a location retrieval operation is performed in the original records of the differentiated channels to extract the associated high-precision timestamp information. This process involves cross-channel association searches: if a component appears at the first time point in channel 2 and is also detected at the second time point in channel 6, the channel numbers and corresponding time differences are recorded. Through this multi-dimensional backtracking retrieval, the response time correspondence of components across different hardware pathways is established, resulting in a gas channel time correspondence set.

[0089] S502: Based on the gas channel time correspondence set, extract the channel number set and sampling time interval corresponding to each component, determine the field continuity, and obtain a continuous associated segment dataset;

[0090] First, the channel number set and sampling time interval corresponding to each component are extracted, and the continuity of data correlation is determined. A time continuity determination coefficient is introduced, which is set to 1.5 times the sensor sampling period of 200 milliseconds, i.e., 300 milliseconds. The determination action is performed: the time difference between two adjacent sampling points is calculated. If the difference is less than or equal to 300 milliseconds, and the corresponding channel number belongs to the same predefined linkage monitoring zone, then the segment is determined to have field continuity. For example, if the time difference between adjacent sampling points is 200 milliseconds, since 200 is less than 300, the data stream is determined to be a continuously correlated signal. By traversing all data points, data items that meet the continuity condition are logically aggregated and marked as correlated segments, resulting in a continuously correlated segment dataset.

[0091] S503: Based on the continuous associated fragment dataset, configure status labels for each group of data with associated features, and associate the label fields with the channel locations of the gas components to obtain the explosion hazard status identification results;

[0092] First, based on the concentration change rate of the gas components within the segment (i.e., the concentration change divided by the time change), the duration (i.e., the end time minus the start time), and the number of channels involved, a pre-defined risk assessment logic is used to assign a corresponding safety status label to each data set. If the concentration change rate is greater than 0.5% per second and the duration is greater than 3 seconds, it is assigned the "high-risk explosion" label. Next, through field integration, the generated status labels are matched one-to-one with the channel location and time interval of the gas components, ensuring the accuracy of all data. In this way, the numerical sequence can be transformed into an identification result with clear safety warning significance, achieving effective location and classification of the explosion source's hazard level, and obtaining the explosion hazard status identification result.

[0093] A terminal for detecting and classifying the composition of explosive hazardous gases includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-mentioned steps of the method for detecting and classifying the composition of explosive hazardous gases.

[0094] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for detecting and classifying the hazard of an explosive gas mixture, characterized in that, Includes the following steps: S1: Obtain target component label information of sensor nodes in the explosion source area, extract the gas type, corresponding label number and channel number of each detection data, establish the association between the detection data and the acquisition time, and splice the data in the order of detection to obtain the component monitoring time series set; S2: Based on the component monitoring time series set, extract component labels from the differential channel within the same time period, pair and combine the first and last times to obtain a list of overlapping time period identifiers; S3: Based on the overlapping time period identifier list, extract the gas type name, compare the gas with the set combustible gas composition table, connect the gas combination occurrence location and sampling time information to obtain the linkage configuration attribution matching table. S4: Based on the linked configuration attribution matching table, extract the components that appear repeatedly in the configuration, connect the components with the sampling point positions of the corresponding configurations, and obtain the configuration order of the components of the non-repeating configurations to obtain the distribution set of the dangerous configuration components. S5: Based on the distribution set of hazardous configuration components, extract the occurrence location, sampling time and corresponding concentration data of each group of gas components in the differentiated channels, sort the sampling time of gas components in the channels, establish the time series correlation of gas components in multiple channels, and according to the time series, count the channel number set corresponding to the gas components, extract the first occurrence time and the last occurrence time, divide the combined channel range and the combined time range, calculate the concentration change rate and duration of gas components within the time range, and combine the number of channels covered and the time continuity to determine the degree of danger of gas components and obtain the explosion hazard status identification result; The specific steps of S4 are as follows: S401: Based on the gas label field corresponding to the configuration in the linkage configuration attribution matching table, the gas label value repeatability is retrieved, and after extracting the label items that appear more than once in the configuration, they are compared with the sampling point position field corresponding to the configuration to obtain the component label repeat position set. S402: Based on the set of repeated positions of the component labels, extract the label items that do not appear in the gas label field of the set, extract the label content according to the configuration order index value corresponding to the original matching table, and pair the gas labels with the position fields according to the index order to obtain the configuration order label connection sequence. S403: Based on the set of repeated positions of the component labels and the connection sequence of the configuration order labels, the label content and the corresponding configuration position are integrated and extracted, and the sampling positions of the repeated items and non-repeated items are connected according to the label arrangement order to obtain the distribution set of hazardous configuration components. The differentiated channel component label refers to the label identification that is collected by the differentiated sensor channel and corresponds to the differentiated gas component within the same time interval; The location where the gas combination appears refers to the spatial location of the sampling point and the sensing node when the gas components and gas combination are detected during the monitoring process; The non-repeating configurational component configuration order refers to the component order relationship formed by arranging gases that appear only once in the linked configuration according to their time or index order in the original configuration; The combined channel range and time range refer to the set of sensor channel numbers covered by a gas combination during the detection process and the corresponding continuous time interval from the first to the last occurrence.

2. The method of claim 1, wherein the method further comprises: The component monitoring time series set includes gas type, label number, channel number, sampling time, channel correspondence, and data time sequence. The overlapping group time period identifier list includes gas component combinations, the first occurrence time of the combination, the last occurrence time of the combination, and time sequence information. The linkage configuration attribution matching table includes gas type name, set combustible gas component item, combination location number, and combination sampling time. The hazardous configuration component distribution set includes repeating components, repeating component sampling point location, non-repeating components, configuration sequence correspondence, and component configuration location correspondence. The explosion hazard status identification result includes gas combination channel location, combination occurrence time sequence, combination channel range, combination time range, and continuous label setting result.

3. The method of claim 1, wherein the method further comprises: The specific steps of S1 are as follows: S101: Obtain the target component label information collected by the sensor nodes in the explosion source area, extract the corresponding gas type, label number and channel number according to the sampling time in the detection data, split the fields and match the corresponding relationships to obtain the gas component sampling parameter group; S102: Based on the gas component sampling parameter group, filter data items with the same channel number field value, sort the data items in ascending order with the sampling time field as a reference, and align the data of the sequence under the same channel number according to the time sequence to obtain the time sequence group corresponding to the channel. S103: Based on the time series group corresponding to the channel, extract the sampling time field in the sequence, splice the channel time series, and extend the result to the channel number to obtain the component monitoring time series set.

4. The method of claim 1, wherein the method further comprises: The specific steps of S2 are as follows: S201: Based on the sampling time of the component monitoring time series data set, extract data items that are in the same time interval but have different channel numbers, call the corresponding component label field, and compare the channel number with the label value according to the time index to obtain the data group corresponding to the multi-channel label; S202: Based on the data group corresponding to the multi-channel labels, filter the component label content that appears repeatedly in the time series, count the number of times the label appears in the differentiated time interval, and retain the data segment position that appears at least twice to obtain the label repetition distribution sequence set; S203: Based on the set of repeated label distribution sequences, extract the first and last occurrence times of each group of labels in the time series, pair and map the time fields with the corresponding labels, and expand them in chronological order to obtain a list of overlapping time period identifiers.

5. The method of claim 1, wherein the method further comprises: The specific steps for S3 are as follows: S301: Based on each group of data in the overlapping time period identifier list, extract the component label number in the corresponding time interval, call the mapping relationship between the label number and the gas name field, match the number value and replace the field content to obtain the gas name extraction sequence; S302: Based on the gas name extraction sequence, retrieve the gas name field in the combustible gas composition table, compare the name field in the data, and obtain the corresponding sequence of the combustible gas. S303: Based on the corresponding sequence of the combustible gas, extract the location number field and sampling time field within the corresponding time interval, pair the fields in order, expand the comparison relationship with the gas sequence as the index, and obtain the linkage configuration attribution matching table.

6. The explosive atmospheres gas composition detection and hazard classification method according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the distribution set of hazardous configuration components, extract the location of each gas component in the differentiated channel, the corresponding channel number, sampling time and concentration data, sort the sampling time of the gas components in the channel, establish the time series correlation of the gas components in multiple channels, and obtain the time concentration corresponding set of the gas channel. S502: Based on the gas channel time-concentration correspondence set, count the channel number set corresponding to each group of gas components, extract the first occurrence time and the last occurrence time, divide the combined channel range and the combined time range, and judge the data continuity according to the adjacent sampling time difference and the channel correlation relationship to obtain a continuous associated segment dataset. S503: Based on the continuous associated fragment dataset, calculate the concentration change rate and duration of each group of gas components within the combined time range, and combine the number of channels covered and the time continuity to determine the degree of danger of the gas components and obtain the corresponding status labels. Map the status labels with the channel location and time interval of the gas components to obtain the explosion hazard status identification result.

7. An explosive hazardous gas component detection and hazard classification terminal comprising a memory and a processor, characterized in that, The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for detecting and classifying the composition of explosive hazardous gases according to any one of claims 1 to 6.

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