Plant gas monitoring equipment periodic data management method, system, device and medium

Abstract

The application discloses a kind of plant gas monitoring equipment periodic data management method, system, equipment and medium, it is related to data processing technical field, comprising: obtaining monitored gas and to be monitored plant area, divide multiple monitoring areas, obtain regional information, set up gas monitoring equipment, obtain periodic concentration data;Obtain to be managed data, obtain concentration change rate, obtain static risk index;Obtain the i th monitoring area, and the monitoring area that is communicated with the i th monitoring area is regarded as effective influence area, according to the regional information of the i th monitoring area, concentration change rate, the regional information and concentration change rate of effective influence area obtain dynamic risk index;According to the static risk index and dynamic risk index of each monitoring area, obtain the gas risk index corresponding to to be monitored plant area, and according to the gas risk index corresponding to to be monitored plant area, obtain early warning strategy.The application has the advantages of accurate and effective early warning, accurate positioning risk and inhibiting invalid early warning.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method, system, equipment, and medium for periodic data management of gas monitoring equipment in a factory area. Background Technology

[0002] Currently, the gas monitoring systems widely deployed in industrial plants are mainly based on discrete sensor networks and use fixed threshold triggering mechanisms for alarm management, which is a common passive threshold alarm mechanism. This method has many shortcomings in the overall gas monitoring and management.

[0003] Specifically, existing methods calculate concentration data for each region in isolation, ignoring the physical connections between gas diffusion patterns in different regions. This makes it impossible to capture the cross-regional propagation trends of risks along equipment corridors or ventilation systems. Furthermore, static thresholds lack dynamic response capabilities to concentration change rates and regional characteristics (such as highly sensitive process areas and low-ventilation dead zones), leading to missed alarms in high-risk areas or false alarms in low-risk areas. Moreover, alarms only trigger the global emergency response plan, failing to match differentiated response measures based on the risk propagation path and evolution stage, resulting in resource misallocation (such as a plant-wide shutdown to address minor local leaks). More critically, existing technologies cannot integrate historical concentration trends with neighboring risk inputs, making it difficult to predict the diffusion direction and speed before gas concentrations reach dangerous thresholds, thus losing the window of opportunity for preventative intervention. Simultaneously, at the data processing level, the collected raw periodic data is processed only through simple arithmetic averaging or fixed weighting, without considering the different geographical connections between different regions or their varying sensitivities to the monitored gases. It also fails to introduce concentration change direction discrimination, and the entire plant's indicators are summed using the overall average, diluting high-risk signals and causing risk quantification to deviate from the true situation.

[0004] Ultimately, the aforementioned deficiencies have resulted in the factory's safety protection being in a "passive response" state for a long time, only dealing with issues after they exceed the limits, rather than accurately blocking the spread of risks in their early stages. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method, system, equipment, and medium for periodic data management of gas monitoring equipment in industrial plants.

[0006] A method for periodic data management of gas monitoring equipment in a factory area includes: acquiring the gas to be monitored and the factory area to be monitored; dividing the factory area to be monitored into multiple monitoring zones; acquiring the zone information of the monitoring zones; setting up gas monitoring equipment in each monitoring zone to collect the concentration data of the gas to be monitored; acquiring the periodic concentration data of the gas to be monitored collected in each monitoring zone within a continuous periodic time period based on the gas monitoring equipment; acquiring the periodic concentration data of each monitoring zone in the previous periodic time period at the current moment and using it as the data to be managed for each monitoring zone; and acquiring the periodic concentration data of each monitoring zone based on the periodic time period and the data to be managed for each monitoring zone. The concentration change rate of each monitoring area is calculated, and static risk indicators for each monitoring area are obtained based on the regional information and concentration change rate of each monitoring area. The i-th monitoring area is obtained, and the monitoring areas connected to the i-th monitoring area are taken as the effective influence area of ​​the i-th monitoring area. Dynamic risk indicators for the i-th monitoring area are obtained based on the regional information, concentration change rate, regional information and concentration change rate of the effective influence area of ​​the i-th monitoring area. Gas risk indicators for the monitored plant area are obtained based on the static and dynamic risk indicators of each monitoring area, and early warning strategies are obtained based on the gas risk indicators for the monitored plant area.

[0007] Optionally, obtaining the gas risk indicators corresponding to the monitored plant area based on the static and dynamic risk indicators of each monitoring area includes: obtaining the risk inclusion quantity based on the preset selection ratio of the monitored gas and the number of monitoring areas in the monitored plant area based on the selection ratio; selecting the monitoring areas in descending order of static risk indicators as the risk inclusion quantity and using them as inclusion areas; and obtaining the gas risk indicators corresponding to the monitored plant area based on the static and dynamic risk indicators of each inclusion area.

[0008] Optionally, the gas risk index corresponding to the monitored plant area can be obtained based on the static and dynamic risk indicators of each included area, and expressed as follows: ;in, For the gas risk indicators corresponding to the plant area to be monitored, The quantity affects the weight. To include the number of regions, For the j-th included region, This is the dynamic risk indicator for the j-th region.

[0009] Optionally, obtaining early warning strategies based on the gas risk indicators corresponding to the monitored plant area includes: presetting multiple continuous indicator ranges, each indicator range corresponding to a different early warning strategy, the early warning strategies including mild early warning, moderate early warning and severe early warning; placing the gas risk indicators corresponding to the monitored plant area into the corresponding indicator range, and obtaining the early warning strategy corresponding to that indicator range.

[0010] Optionally, the static risk indicators for each monitoring area can be obtained based on the regional information and concentration change rate of each monitoring area, and expressed as follows: ;in, Let i be the static risk indicator for the i-th monitoring area. Let be the real-time concentration of the gas being monitored in the i-th monitoring area. The risk concentration threshold of the monitored gas. Let i be the importance weight of the i-th monitoring area. Let be the rate of change of the concentration of the monitored gas in the i-th monitoring area. The proportion of the ventilation area in the i-th monitoring area. As a benchmark for ventilation area ratio, The hazard index of the gas being monitored.

[0011] Optionally, the dynamic risk index of the i-th monitoring area can be obtained based on the regional information of the i-th monitoring area, the concentration change rate, the regional information of the effective influence area of ​​the i-th monitoring area, and the concentration change rate, and is expressed as follows: ;in, For the i-th monitoring area, The number of effective impact areas for the i-th monitoring area. Let be the real-time concentration of the monitored gas in the k-th effective influence area of ​​the i-th monitoring area. Let be the real-time concentration of the gas being monitored in the i-th monitoring area. Let i be the importance weight of the i-th monitoring area. Let be the rate of change of the concentration of the monitored gas in the k-th effective influence area of ​​the i-th monitoring area.

[0012] A periodic data management system for plant gas monitoring equipment is also provided. The system includes: a data acquisition module for acquiring the monitored gas and the plant area to be monitored, dividing the plant area into multiple monitoring zones, acquiring the zone information of each monitoring zone, setting up gas monitoring equipment in each monitoring zone to collect the concentration data of the monitored gas, and acquiring the periodic concentration data of the monitored gas collected in each monitoring zone within a continuous periodic time period based on the gas monitoring equipment; and a first data processing module for acquiring the periodic concentration data of each monitoring zone in the previous periodic time period at the current moment and using it as the data to be managed for each monitoring zone, and acquiring the periodic concentration data of each monitoring zone based on the periodic time period and the data to be managed for each monitoring zone. The system comprises: a first monitoring area and a second data processing module; a third data processing module; a fourth data processing module; and a fifth data processing module. The first monitoring area is defined as the i-th monitoring area, and the monitoring areas connected to it are defined as the effective influence areas of the i-th monitoring area. The second data processing module obtains the i-th monitoring area's dynamic risk indicators based on the i-th monitoring area's regional information, concentration change rate, and the regional information and concentration change rate of the effective influence areas of the i-th monitoring area. The sixth data processing module obtains the gas risk indicators corresponding to the monitored plant area based on the static and dynamic risk indicators of each monitoring area, and obtains early warning strategies based on these gas risk indicators.

[0013] Optionally, the management and early warning module is also used to: select a preset proportion of the monitored gas, and obtain the risk inclusion quantity based on the selection proportion and the number of monitoring areas in the plant to be monitored; select monitoring areas in descending order of static risk indicators as the risk inclusion quantity and use them as inclusion areas; and obtain the gas risk indicators corresponding to the plant to be monitored based on the static and dynamic risk indicators of each inclusion area.

[0014] An electronic device is also provided, characterized in that it includes: a memory storing a computer program thereon; and a processor for executing the computer program in the memory to implement the above-mentioned periodic data management method for plant area gas monitoring equipment.

[0015] A non-transitory computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the aforementioned periodic data management method for plant gas monitoring equipment.

[0016] The beneficial effects of this invention are reflected in:

[0017] In the overall gas monitoring equipment cycle data management method of the plant, firstly, based on physical characteristics, zoning is combined with dynamically weighted static risk indicators to deeply integrate factors such as regional sensitivity, ventilation capacity, concentration change direction and rate. This allows high-risk areas to be identified before the concentration exceeds the standard, significantly reducing the false alarm rate, while suppressing false alarm interference from low-risk stable areas. Furthermore, a diffusion network is constructed through connectivity modeling, and dynamic risk indicators quantify the intensity of cross-regional risk input / output (such as the transmission pressure of a high-speed leak upstream of a pipeline to downstream). Combined with regional importance weights, this strengthens the perception of diffusion trends, realizing the gas propagation path and speed along the corridor. Early warnings are issued to seize the window of opportunity for preventive intervention. Furthermore, a key area focusing mechanism selects the core locations with the highest static risk and strengthens the combined signal of "high self-risk + strong diffusion pressure" through nonlinear aggregation (such as reaction zones with both rapid leakage and high pressure in the vicinity), avoiding the dilution of high-risk signals by the overall average. At the same time, regional quantity correction is included to further reflect the breadth of risk distribution and ensure that the indicators truly reflect the highest risk status of the entire plant. Furthermore, the gas risk indicator mapping early warning strategy matches the risk evolution stage and spatial distribution to avoid resource misallocation and form a proactive defense closed loop of "precise blocking - hierarchical prevention and control". Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0019] Figure 1 This is a schematic diagram illustrating the steps of the periodic data management method for plant gas monitoring equipment according to the present invention;

[0020] Figure 2 This is a schematic diagram of a portion of step S4 in the periodic data management method for plant gas monitoring equipment of the present invention;

[0021] Figure 3 This is a schematic diagram of another part of step S4 in the periodic data management method for plant gas monitoring equipment of the present invention;

[0022] Figure 4 This is a block diagram illustrating an electronic device according to an embodiment of the present invention.

[0023] Figure label:

[0024] 700 - Electronic device; 701 - Processor; 702 - Memory; 703 - Multimedia component; 704 - I / O interface; 705 - Communication component. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0027] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] like Figure 1 As shown, a method for periodic data management of gas monitoring equipment in a factory area is provided, including:

[0029] S1. Obtain the gas to be monitored and the plant area to be monitored, divide the plant area to be monitored into multiple monitoring areas, obtain the area information of the monitoring areas, set up gas monitoring equipment in each monitoring area to collect the concentration data of the gas to be monitored, and obtain the periodic concentration data of the gas to be monitored in each monitoring area within a continuous periodic time based on the gas monitoring equipment.

[0030] S2. Obtain the periodic concentration data of each monitoring area in the previous period of the current time and use it as the data to be managed for each monitoring area. Obtain the concentration change rate of each monitoring area based on the periodic time and the data to be managed for each monitoring area. Obtain the static risk index of each monitoring area based on the area information and concentration change rate of each monitoring area.

[0031] S3. Obtain the i-th monitoring area, and take the monitoring area connected to the i-th monitoring area as the effective influence area of ​​the i-th monitoring area. Based on the area information of the i-th monitoring area, the concentration change rate, the area information of the effective influence area of ​​the i-th monitoring area, and the concentration change rate, obtain the dynamic risk index of the i-th monitoring area.

[0032] S4. Obtain the gas risk indicators corresponding to the monitored plant area based on the static and dynamic risk indicators of each monitoring area, and obtain early warning strategies based on the gas risk indicators corresponding to the monitored plant area.

[0033] In this embodiment, it should be noted that in S1, a basic framework for plant area monitoring is established. First, the monitoring targets and scope are clearly defined, i.e., the specific types of gases to be monitored (such as methane, carbon monoxide, etc.) and the boundaries of the entire plant area are determined. Then, considering the logical rules governing the risk distribution and physical characteristics within the plant area, the entire plant area is divided into several monitoring zones. The division rules can focus on: the inherent sensitivity of different areas to the monitored gases (e.g., certain process units are more susceptible to serious consequences from leaks of specific gases due to material characteristics or reaction conditions), the criticality of the area itself in plant safety or production (e.g., control rooms, core reaction unit areas relative to ordinary warehouses or corridor areas), the actual connectivity between areas in terms of spatial location or equipment layout (especially areas relying on equipment corridors, pipeline corridors, or shared spaces to form potential gas diffusion paths), and significant differences in mechanical or natural ventilation capabilities among the areas (e.g., identifying dead zones with poor ventilation due to structural limitations). After completing this fine zoning, gas monitoring equipment is configured in each divided monitoring zone to continuously capture the presence of the target gas within that specific area. These gas monitoring devices operate continuously, periodically (e.g., every minute, every 5 minutes, or every half hour) collecting raw readings of the monitored gases within their respective monitoring areas, forming a concentration dataset organized in a time series (periodic concentration data), providing a data foundation for subsequent risk assessment.

[0034] Furthermore, assuming the target is to monitor the toxic gas hydrogen sulfide (H2S), in S1, the area can be partitioned according to user-defined rules: for example, the core reactor area for hydrogen sulfide raw material processing can be designated as a high-sensitivity, high-importance monitoring area with strong ventilation dependence; the long-distance enclosed process pipeline corridor connecting multiple workshops can be designated as one area; the material storage room located in a corner of the plant with extremely poor natural ventilation can be designated as another area; and the open main control room area, although it has a low risk of gas generation, has a correspondingly high sensitivity weight due to its high personnel density and extremely high importance; and so on. Hydrogen sulfide sensors are deployed in these different partitioned areas. All sensors collect instantaneous values ​​of hydrogen sulfide concentration in the air of their respective areas at fixed intervals (e.g., every 30 seconds) and record and store them sequentially, thereby generating a continuously updated, dedicated periodic concentration data stream for each area. This partitioned, multi-attribute, and time-series data acquisition model lays the data foundation for subsequent identification of static risks and understanding of dynamic diffusion.

[0035] In S2, for each defined monitoring area, its independent risk level, i.e., a static risk index, is calculated. First, based on the monitoring data and characteristics of each area, independently of other areas, a specific moment requiring assessment is selected. Then, all periodic concentration data collected by gas monitoring equipment within that area during the preceding complete period are retrieved. This recent historical data constitutes the area's manageable data. Based on these time-sorted concentration values, the rate of change of the monitored gas concentration in that area during this period is analyzed and calculated. This rate reflects the trend of increase or decrease in the gas concentration within that area. Subsequently, combined with the area's regional information (including its inherent sensitivity to the gas, regional importance weight, ventilation capacity, etc.), and considering the area's concentration change rate and unique regional attributes, a static risk index representing its own risk status is calculated. This index calculation deeply integrates regional characteristics: for example, it not only focuses on the current concentration level but also keenly perceives the direction of concentration change (increasing or decreasing) and the rate of change (rapid increases lead to higher static risk indices). Simultaneously, it incorporates the area's ventilation conditions (good ventilation mitigates risk) and its own importance as correction factors. Essentially, this indicator quantifies the level of risk based solely on the region's own state and short-term trends.

[0036] Furthermore, assume there are three monitoring areas in the plant: a core reaction zone with extremely high sensitivity to the target gas; a control room area with a large number of personnel but not a major leak source; and a poorly ventilated storage corner. For the core reaction zone: assuming its concentration shows a small but rapid upward trend in the most recent period, with a positive and fast rate of change, combined with its high sensitivity, high weighting, and limited ventilation capacity, these factors amplify its risk perception, significantly increasing its static risk index. Early warning systems will focus heavily on this area even if the concentration has not yet exceeded the standard. For the control room area: assuming its concentration is basically stable over a period (concentration change rate close to zero or slightly negative), although its concentration may be higher than some low-risk areas, the risk of leakage in this area is relatively low, its concentration trend is stable and there is no obvious upward signal. Combined with its good ventilation conditions, although its importance weight is high, the stable state and good ventilation will suppress the growth of its static risk index, reasonably mitigating its risk level. For poorly ventilated storage corners: assuming their concentration also shows a slow but continuous upward trend (positive rate of change, moderate rate), although this area is less important and its current concentration may not be high, its extremely poor ventilation conditions (severely insufficient ventilation area) will greatly amplify the risk brought by the slow upward trend, causing its static risk index to be calculated higher than that of well-ventilated areas with the same trend. Through the above calculations, the static risk index of each area is no longer a simple comparison based solely on the current single concentration value and a fixed threshold, as in existing methods. Instead, it deeply integrates the area's own concentration evolution trend (rate of change and direction), inherent risk sensitivity (sensitivity, weight), and environmental mitigation capacity (ventilation conditions), thus forming a more accurate and dynamic assessment of the independent risk status of the area. This lays a refined foundation for subsequent steps to screen key areas and comprehensively analyze the risks of the entire plant.

[0037] In S3, physical connectivity is analyzed to achieve dynamic risk assessment. First, for any specific monitoring area within the plant (e.g., area i), the system automatically identifies neighboring areas with direct physical connectivity (e.g., areas connected via equipment ducts, ventilation, or shared airflow paths) based on the plant's spatial topology model. These are defined as the effective influence areas of area i. Then, two sets of data are integrated: the risk status of area i itself (i.e., the static risk indicator elements calculated in S2, such as concentration change rate and regional characteristics), and the latest risk status of its effective influence areas (including the concentration change rate and regional attributes of these areas). By comparing the concentration value of this area with the concentration value of its effective influence areas, the directionality of risk input or spillover is identified (e.g., higher concentrations in neighboring areas indicate risk input). This is combined with the concentration change rate of the influence areas (rapid changes strengthen diffusion trends) and their own importance weights (high-weight areas need to be more sensitive to changes in neighboring areas) to finally generate a dynamic risk indicator for area i. This indicator essentially quantifies the cross-regional risk transmission pressure or potential diffusion potential currently faced by the area due to physical connectivity, reflecting the real-time dynamics of risk flow in the spatial network.

[0038] Furthermore, through S3, each monitoring area not only assesses its own status but also obtains a dynamic indicator reflecting its pivotal role (risk receiver or spread source) and potential propagation intensity within the entire spread network. This addresses the key deficiency of existing methods in failing to capture cross-regional correlations, providing data support for risk prediction and precise interception.

[0039] In S4, a plant-wide dynamic risk indicator is constructed and mapped to early warning strategies by focusing on key risk sources and transmission nodes. First, to avoid the distortion caused by existing overall averaging, a phased focusing mechanism is adopted. Based on a preset percentage parameter (e.g., 20%) for the monitored gas, the number of areas requiring key attention is calculated (e.g., selecting the top 20 high static risk areas from 100 areas). Then, the static risk indicators calculated in S2 are sorted from high to low, and a specific number of top-ranked areas are strictly selected as included areas. These included areas represent the core points within the plant area with the most prominent or sensitive risks. Next, for each selected included area, not only its own static risk level is examined, but also the dynamic risk indicator is incorporated. Through nonlinear enhancement logic processing of these paired data, combined signals with both high inherent risk and strong diffusion pressure are amplified, and the overall number of included areas is weighted and corrected. Finally, a gas risk indicator reflecting the highest risk state of the entire plant is output, whose value is dominated by the composite risk value of the most critical area, rather than the average of all areas, thus avoiding the dilution of high-risk signals in existing methods.

[0040] Furthermore, through S4, key risk sources in physical space (static indicators), networked transmission trends (dynamic indicators), and global early warning levels are dynamically linked: different threshold ranges are pre-set (e.g., 0-2 is mild, 2-4 is moderate, and >4 is severe). When the gas risk indicator falls into the corresponding range, a differentiated plan is triggered (e.g., a mild warning initiates regional inspections, a moderate warning closes related valves, and a severe warning evacuates people from the affected area).

[0041] In summary, the overall gas monitoring equipment cycle data management method for the entire plant firstly involves zoning based on physical characteristics, combined with dynamically weighted static risk indicators, and deeply integrating factors such as regional sensitivity, ventilation capacity, concentration change direction and rate. This allows high-risk areas to be identified before concentrations exceed limits, significantly reducing the false alarm rate while suppressing false alarms in low-risk, stable areas. Furthermore, by constructing a diffusion network through connectivity modeling, dynamic risk indicators quantify the intensity of cross-regional risk input / output (such as the transmission pressure from a high-speed leak upstream to downstream areas), and combining regional importance weights to enhance diffusion trend perception, thus realizing the gas propagation path along corridors. Early warning of path and velocity can seize the window of opportunity for preventive intervention. Furthermore, the key area focusing mechanism selects the core points with the highest static risk, and strengthens the combined signal of "high self-risk + strong diffusion pressure" through nonlinear aggregation (such as reaction zones with both rapid leakage and high pressure in the vicinity), avoiding the dilution of high-risk signals by the overall average, while incorporating regional quantity correction to further reflect the breadth of risk distribution and ensure that the indicators truly reflect the highest risk status of the entire plant. Furthermore, the gas risk indicator mapping early warning strategy matches the risk evolution stage and spatial distribution, avoids resource misallocation, and forms a proactive defense closed loop of "precise blocking - hierarchical prevention and control".

[0042] like Figure 2 As shown, in one embodiment, S4 involves obtaining the gas risk indicators corresponding to the monitored plant area based on the static and dynamic risk indicators of each monitoring area, including:

[0043] S41. Based on the preset selection ratio of the monitored gas, and based on the selection ratio and the number of monitoring areas in the plant area to be monitored, obtain the risk inclusion quantity;

[0044] S42. Select monitoring areas in descending order of static risk indicators as the risk inclusion areas and use them as the inclusion areas.

[0045] S43. Obtain the gas risk indicators corresponding to the plant area to be monitored based on the static and dynamic risk indicators of each included area.

[0046] In this embodiment, it should be noted that in S41, the size of the set of hotspot areas requiring focused investigation is dynamically defined. A preset proportional parameter, typically 0.1, is used based on the potential hazardous characteristics of the monitored gas (e.g., highly toxic gases require broader monitoring, while the scope can be appropriately narrowed for low-hazard gases). Subsequently, based on this preset proportional parameter and the total number of monitoring areas currently divided within the plant, the specific number of areas to be included in the "risk-included areas" set is calculated. This number is not a fixed value but is dynamically adjusted according to the zoning strategy and gas characteristics: for gases with a significantly increased risk or large plant areas, the scope of attention is automatically expanded; conversely, it is narrowed to ensure that the focus of analysis always matches the overall risk situation.

[0047] In step S42, based on the risk inclusion quantity calculated in S41, this step globally sorts the static risk indicators of all monitored areas in the plant (from high to low). The areas with the highest ranking and a quantity equal to the "risk inclusion quantity" are strictly selected and marked as "inclusion areas." This means that when assessing global risk, not all areas participate in the calculation equally; instead, priority is given to identifying the core locations with the most severe or sensitive conditions—for example, ventilation dead zones where concentrations are rapidly rising, or highly sensitive process areas. These "inclusion areas" represent the set of key areas within the plant with the highest independent risk level at the current moment, requiring the most vigilance, thus avoiding the dilution of the overall assessment results by interference signals from low-risk areas.

[0048] In S43, only the selected "included areas" are analyzed, focusing on the dual attributes of each area: its static risk intensity (reflecting the degree of independent risk) and its dynamic risk indicators (reflecting its pivotal pressure in the diffusion network—e.g., acting as a strong diffusion source or high-risk receiver). This is achieved by amplifying the signals of areas that simultaneously possess both high-risk inherent conditions and strong diffusion or input pressure (e.g., a core reaction zone with rapidly rising concentrations, facing rapid input of even higher concentrations from upstream neighbors). Furthermore, the overall number of included areas is weighted, with a higher number indicating a wider or denser risk distribution. Finally, from all included areas, the peak risk level represented by the area with the highest composite risk value (i.e., the most significant result after the aforementioned enhancement process) is identified and used as the core basis for the plant-wide gas risk indicator.

[0049] For example: Assuming the plant monitors combustible gas hydrogen (H2) at a preset ratio of 15%, and there are 20 areas in the plant, S41 calculates the number of risks to be included as 3 (20 * 15% = 3). S42 ranks the static risk indicators of all 20 areas: warehouse corners (high weight, poor ventilation, slow concentration increase) are ranked first, followed by pipeline junctions (stable concentration but high importance), and third is the unloading area (slight concentration fluctuations). The top 3 are selected as the areas to be included. In S43, the static risk of the warehouse corner is high (poor condition), but its dynamic indicators show no diffusion or input from the surrounding area (isolated risk); the static risk of the pipeline junction is medium (relatively stable), but its dynamic indicators show that it is rapidly absorbing risk input from two high-concentration areas upstream (strong input pressure); the static risk of the unloading area is low (small fluctuations), and its dynamic indicators show weak risk output. Nonlinear processing significantly amplifies the signal at the "pipeline junction" (because it combines moderate static risk with strong dynamic input pressure). Considering that the included area is 3 (quantity weighted), the final gas risk index of the entire plant is mainly determined by the peak composite risk at the pipeline junction.

[0050] In one implementation, the gas risk index corresponding to the monitored plant area obtained in step S43 based on the static and dynamic risk indicators of each included area is expressed as follows:

[0051] ;in,

[0052] For the gas risk indicators corresponding to the plant area to be monitored, The quantity affects the weight. To include the number of regions, For the j-th included region, This is the dynamic risk indicator for the j-th region.

[0053] In this embodiment, it should be noted that throughout the entire expression, A composite risk amplification mechanism has been implemented. Specifically, it solves the problem of isolated alarms, strongly correlates static risk (its own leakage intensity) and dynamic risk (diffusion pressure), and captures the combined signal of "high self-risk + strong diffusion trend"; it avoids the dilution of average values, calculates composite values ​​independently for each region, and does not superimpose interference from low-risk regions. Strengthen static risk (even if) (The baseline value is also 1). To suppress the linear growth of dynamic risk indicators and solve the problem of misjudging the trend of dynamic risk indicators, existing weighting methods linearly amplify the noise of high dynamic risk indicators, while logarithmic compression focuses more on the region corresponding to dynamic risk indicators within a certain range.

[0054] Furthermore, This approach reflects the principle that peak risk dominates the overall situation, effectively preventing the dilution of risk signals. The risk across the entire plant is dominated by the most severe single point (rather than the average across all regions), ensuring that high-risk sources are not masked. A severe warning only requires one key area to reach a threshold to trigger, avoiding response delays. Therefore, given that the existing overall average value would be lowered by a large amount of safe zone data (e.g., only one out of 10 regions is high-risk), this implementation uses the max function to force a response to the highest risk point.

[0055] Furthermore, It achieves risk distribution breadth correction; specifically, N is determined in advance only by the gas hazard and the total number of areas, quantifying the risk distribution density. When N is large (such as highly toxic gas, large plant area), the emergency scope needs to be expanded. When N is small (such as low-risk gas, small plant area), the focus is on local treatment, avoiding the resource misallocation of "shutting down the whole plant to deal with a single high-risk point". At the same time, when N is large, the gas risk index is increased to improve the response level (such as whole plant inspection).

[0056] For example: Suppose the total number of monitoring areas is 10, and the preset ratio is 20% (the gas hazard is highly toxic). It is 0.05. The value is 2 (calculated using 10 * 0.2); included in region 1. , Included in region 2. , Substitute them into the expression for calculation. .

[0057] In summary, although the static risk indicators for inclusion in Zone 2 are higher than those for inclusion in Zone 1 ( However, the strong diffusion pressure in Region 1 (dynamic risk indicator) is taken into account. The amplification and enhancement of the composite value is greater, therefore the actual risk of including it in region 1 is higher than that of including it in region 2. The plant-wide gas risk indicators are dominated by Zone 1, enabling precise resource allocation. This means that the early warning strategy includes prioritizing the blocking of diffusion paths in Zone 1 (such as closing pipeline valves).

[0058] like Figure 3 As shown, in one embodiment, the early warning strategy obtained in S4 based on the gas risk indicators corresponding to the monitored plant area includes:

[0059] S44. Preset multiple continuous indicator ranges, each indicator range corresponding to a different early warning strategy, the early warning strategy including mild early warning, moderate early warning and severe early warning;

[0060] S45. Identify the gas risk indicators of the plant area to be monitored and place them within the corresponding indicator range, and obtain the early warning strategy corresponding to that indicator range.

[0061] In this implementation, it should be noted that a tiered early warning threshold system is constructed in S44. Specifically, the continuous risk space is first discretized, that is, multiple non-overlapping continuous index ranges are preset (e.g., [0, 2), [2, 4), [4, +∞)), each range corresponding to a specific early warning level (mild / moderate / severe). This addresses the shortcomings of the "global emergency plan" by forcibly mapping continuous risk values ​​to discrete action levels, avoiding the crude response of a complete plant shutdown simply because a risk exceeds a threshold. The range boundary values ​​are preset based on historical accident data and gas characteristics (e.g., the lower limit of a severe early warning corresponds to a risk level that may trigger a chain reaction of accidents). This solves the problem of resource mismatch: different levels trigger differentiated plans to ensure that the response intensity accurately matches the actual severity of the risk (e.g., a local leak does not require a complete plant shutdown).

[0062] In S45, risk status and response strategies are dynamically matched, and the plant-wide gas risk indicators calculated in S43 are obtained in real time. Preset ranges are scanned and their corresponding intervals are locked. Then, corresponding strategies are automatically triggered. For a mild warning, when the risk value falls below the lower limit, a non-intrusive response is automatically initiated (e.g., increased manual inspections of high-risk areas and checks of ventilation equipment). For a moderate warning, when the risk value crosses a critical threshold, localized measures are activated (e.g., automatically closing related process valves and starting exhaust ventilation in designated areas). For a severe warning, when the risk value exceeds the highest threshold, targeted blocking is executed (e.g., evacuating relevant personnel along the diffusion path and isolating upstream leak source equipment). During strategy execution, information on the risk-dominant area is simultaneously retrieved (i.e., the area with the highest composite risk value in S43). For example, in a severe warning, not only is an alarm triggered, but the core diffusion path (e.g., ventilation ducts or equipment corridors) is located based on the dynamic risk indicators in S3, prioritizing blocking of this path rather than a complete plant shutdown.

[0063] In one implementation, the static risk index of each monitoring area obtained in S2 based on the regional information and concentration change rate of each monitoring area is expressed as follows:

[0064] ;in,

[0065] Let i be the static risk indicator for the i-th monitoring area. Let be the real-time concentration of the gas being monitored in the i-th monitoring area. The risk concentration threshold of the monitored gas. Let i be the importance weight of the i-th monitoring area. Let be the rate of change of the concentration of the monitored gas in the i-th monitoring area. The proportion of the ventilation area in the i-th monitoring area. As a benchmark for ventilation area ratio, The hazard index of the gas being monitored.

[0066] In this embodiment, it should be noted that throughout the entire expression, As a concentration baseline ratio, the real-time concentration With preset risk threshold Using the ratio as a benchmark, continuous risk quantification assessment avoids a single threshold directly determining the result and overcomes the shortcomings of fixed thresholds. The baseline risk value is still maintained at 80%; at the same time, the risk of underreporting is eliminated, and a warning may be triggered in highly sensitive areas (such as reaction vessels) when the concentration reaches the 60% threshold.

[0067] Furthermore, This is a dynamic trend amplification item. Among them, For gas hazard index, the inherent property constant of the gas (such as...) Hydrogen sulfide Methane); highly toxic gases maintain a basic risk even if the concentration remains unchanged (e.g., (Risk value increases by 50%) The symbol for the direction of change. ( This indicates an increase in concentration, leading to increased risk. ( This indicates a decrease in concentration, leading to a reduction in risk. Short-term concentration fluctuations that cause a decrease automatically offset false alarms (such as after the ventilation system is activated). ); It is a nonlinear rate of change response. To monitor the importance weight of the area (control room) Larger warehouse ), It exhibits exponential decay, with a strong response to low-speed changes and a weak response to high-speed changes.

[0068] Furthermore, As a ventilation inhibitor, The actual ventilation ratio ( (representing the proportion of the theoretical maximum ventilation area) This represents the baseline for the proportion of ventilation area (i.e., the proportion of the maximum effective ventilation area, typically taken as 0.5). It ensures that risks in low-ventilation areas are not suppressed, such as warehouse corners. → 100% risk value retention; simultaneously achieving risk suppression in well-ventilated areas and open areas. → The risk value is automatically reduced by 50%.

[0069] Application examples include early warning of leaks in highly sensitive reaction zones, where the scenario parameter is a hydrogen sulfide treatment zone. , , (Poor ventilation) (Concentration threshold of 45%) =0.6 (rapid upward trend). Substituting this into the expression, the static risk indicator is calculated. In summary, when the concentration reaches only 45% of the threshold, the risk value reaches 1.125. Combined with dynamic risk indicators, this greatly increases the probability of triggering an early warning and intensifying automatic ventilation. However, existing methods, due to concentrations not reaching the threshold, offer virtually no response and fail to initiate local control measures. This approach evolves rigid, static thresholds into a dynamic risk assessment engine that integrates real-time status, evolutionary trends, regional characteristics, and environmental impacts, reversing the predicament of passive response.

[0070] In one implementation, the dynamic risk index of the i-th monitoring area obtained in S3 based on the regional information of the i-th monitoring area, the concentration change rate, the regional information of the effective influence area of ​​the i-th monitoring area, and the concentration change rate is expressed as follows:

[0071] ;in,

[0072] For the i-th monitoring area, The number of effective impact areas for the i-th monitoring area. Let be the real-time concentration of the monitored gas in the k-th effective influence area of ​​the i-th monitoring area. Let be the real-time concentration of the gas being monitored in the i-th monitoring area. Let i be the importance weight of the i-th monitoring area. Let be the rate of change of the concentration of the monitored gas in the k-th effective influence area of ​​the i-th monitoring area.

[0073] In this embodiment, it should be noted that, Risk direction determination, when the concentration in adjacent areas When, output 1 (risk input), when When the risk is detected, the output is -1 (risk output). Inter-regional correlations are established through concentration gradients to quantify the direction of risk transmission and accurately identify whether the region is a risk receiver or a source of spread. This enables the capture of cross-regional propagation trends, especially when the equipment corridor is upstream. Larger than this region When the risk is entered, the output is 1 (risk is being entered), for this region. Greater than neighboring cells Output -1 (This area is spreading).

[0074] Furthermore, This is a quantification of risk intensity. Among them, It represents the absolute value of the rate of change of concentration in the neighborhood (reflecting the speed of change). This represents the importance weight of the region (highly sensitive areas have higher weights). Specifically, when... When the value is less than 0.3, the quantifiable risk intensity increases rapidly, indicating a sensitive response to slow changes. When the value is greater than 1, the growth of the risk intensity quantification item slows down rapidly, indicating a slow response to rapid changes. A dynamic adjustment mechanism has been implemented, such as in the control room. (Highly sensitive) warehouse (Low sensitivity) Solves the problem of ignoring regional characteristics, same Under high value The regional dynamic risk indicators are higher.

[0075] Furthermore, Achieve multi-source accumulation, accumulating the contribution values ​​of all effectively affected areas, including high-risk adjacent areas ( This will amplify the dynamic risk indicators, and the safety adjacent areas ( This will keep dynamic risk indicators unchanged or reduce them, while also contributing to the capture of transmission trends.

[0076] A periodic data management system for plant gas monitoring equipment is also provided, the system including:

[0077] The data acquisition module is used to acquire the gas to be monitored and the plant area to be monitored, divide the plant area to be monitored into multiple monitoring areas, acquire the area information of the monitoring areas, set up gas monitoring equipment in each monitoring area to collect the concentration data of the gas to be monitored, and acquire the periodic concentration data of the gas to be monitored in each monitoring area within a continuous periodic time period based on the gas monitoring equipment.

[0078] The first data processing module is used to obtain the periodic concentration data of each monitoring area in the previous period of the current time and use it as the data to be managed for each monitoring area. It also obtains the concentration change rate of each monitoring area based on the periodic time and the data to be managed for each monitoring area, and obtains the static risk indicators of each monitoring area based on the area information and concentration change rate of each monitoring area.

[0079] The second data processing module is used to acquire the i-th monitoring area, and to take the monitoring area connected to the i-th monitoring area as the effective influence area of ​​the i-th monitoring area, and to acquire the dynamic risk index of the i-th monitoring area based on the area information of the i-th monitoring area, the concentration change rate, the area information of the effective influence area of ​​the i-th monitoring area, and the concentration change rate.

[0080] The management and early warning module is used to obtain the gas risk indicators corresponding to the monitored plant area based on the static and dynamic risk indicators of each monitoring area, and to obtain early warning strategies based on the gas risk indicators corresponding to the monitored plant area.

[0081] In one implementation, the management and early warning module is further configured to: obtain the risk inclusion quantity based on a preset selection ratio of the monitored gas and the number of monitoring areas in the plant area to be monitored based on the selection ratio and the number of monitoring areas in the plant area to be monitored; select the monitoring areas as the risk inclusion quantity in descending order of static risk indicators and use them as inclusion areas; and obtain the gas risk indicators corresponding to the plant area to be monitored based on the static risk indicators and dynamic risk indicators of each inclusion area.

[0082] In this embodiment, it should be noted that the specific operation method of the above-mentioned plant area gas monitoring equipment periodic data management system has been described in detail in the embodiment of the relevant plant area gas monitoring equipment periodic data management method, and will not be elaborated here.

[0083] Figure 4 This is a block diagram of an electronic device for a periodic data management method for gas monitoring equipment in a factory area, according to an exemplary embodiment. Figure 4 As shown, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an I / O interface 704 (input / output interface), and a communication component 705.

[0084] The processor 701 controls the overall operation of the electronic device 700 to complete all or part of the steps in the aforementioned method for periodic data management of the plant gas monitoring equipment. The memory 702 stores various types of data to support the operation of the electronic device 700. This data may include, for example, instructions for any application or method operating on the electronic device 700, and application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 702 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 702 or transmitted via communication component 705. The audio component also includes at least one speaker for outputting audio signals. I / O interface 704 provides an interface between processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0085] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described periodic data management method for plant gas monitoring equipment.

[0086] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described periodic data management method for plant gas monitoring equipment. For example, the computer-readable storage medium may be the memory 702 including program instructions, which may be executed by the processor 701 of the electronic device 700 to complete the above-described periodic data management method for plant gas monitoring equipment.

[0087] In another exemplary embodiment, a computer program product is also provided, which includes a computer program executable by a programmable device, the computer program having a code portion for performing the above-described plant gas monitoring equipment periodic data management method when executed by the programmable device.

[0088] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0089] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0090] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A plant gas monitoring device periodic data management method characterized by, The method comprises the following steps: acquiring a monitored gas and a factory area to be monitored, dividing the factory area to be monitored into a plurality of monitoring areas, acquiring area information of the monitoring areas to be monitored, setting a gas monitoring device for collecting concentration data of the monitored gas in each monitoring area, and acquiring periodic concentration data of the monitored gas collected by the gas monitoring device in each monitoring area within a continuous periodic time period; acquiring periodic concentration data of each monitoring area within a periodic time period at a previous moment of time as to-be-managed data, acquiring a concentration change rate according to the periodic time period and the to-be-managed data of each monitoring area, and acquiring a static risk index according to the area information and the concentration change rate of each monitoring area; wherein the static risk indicator is represented as: ; wherein, is a static risk indicator of the i-th monitoring area, is a real-time concentration of the monitored gas in the i-th monitoring area, is a risk concentration threshold of the monitored gas, is an importance weight of the i-th monitoring area, is a concentration change rate of the monitored gas in the i-th monitoring area, is a ventilation area ratio of the i-th monitoring area, is a ventilation area ratio reference, is a danger index of the monitored gas; acquiring an ith monitoring area, taking a monitoring area in communication with the ith monitoring area as an effective influence area of the ith monitoring area, and acquiring a dynamic risk index according to the area information, the concentration change rate of the ith monitoring area, the area information and the concentration change rate of the effective influence area; wherein the dynamic risk indicator of the ith monitoring area is represented as: ; wherein, is the dynamic risk indicator of the ith monitoring area, is the number of effective influence areas of the ith monitoring area, is the real-time concentration of the monitored gas in the kth effective influence area of the ith monitoring area, is the concentration change rate of the monitored gas in the kth effective influence area of the ith monitoring area; acquiring a risk count number according to a preset selection ratio of the monitored gas and according to the selection ratio and the number of monitoring areas in the factory area to be monitored, selecting monitoring areas with the number of risk count numbers in a descending order of the static risk index as count-in areas, acquiring a gas risk index corresponding to the factory area to be monitored according to the static risk index and the dynamic risk index of each count-in area, and acquiring an early warning strategy according to the gas risk index corresponding to the factory area to be monitored; Wherein, the gas risk index corresponding to the to-be-monitored plant area is represented as: ; wherein, is the gas risk index corresponding to the to-be-monitored plant area, is the quantity influence weight, is the quantity of the region, is the static risk index of the jth region, is the dynamic risk index of the jth region.

2. The plant gas monitoring device periodic data management method according to claim 1, characterized by, the early warning strategy is acquired according to the gas risk index corresponding to the factory area to be monitored, which comprises the following steps: a plurality of continuous index ranges are preset, each index range corresponds to a different early warning strategy, and the early warning strategy comprises a mild early warning, a moderate early warning and a severe early warning; the gas risk index corresponding to the factory area to be monitored is put into a corresponding index range, and the early warning strategy corresponding to the index range is acquired.

3. A plant gas monitoring device periodic data management system characterized by, The system is used to realize the factory area gas monitoring device periodic data management method in claim 1 or claim 2, and the system comprises: a data acquisition module for acquiring a monitored gas and a factory area to be monitored, dividing the factory area to be monitored into a plurality of monitoring areas, acquiring area information of the monitoring areas to be monitored, setting a gas monitoring device for collecting concentration data of the monitored gas in each monitoring area, and acquiring periodic concentration data of the monitored gas collected by the gas monitoring device in each monitoring area within a continuous periodic time period; a first data processing module for acquiring periodic concentration data of each monitoring area within a periodic time period at a previous moment of time as to-be-managed data of each monitoring area, acquiring a concentration change rate of each monitoring area according to the periodic time period and the to-be-managed data of each monitoring area, and acquiring a static risk index of each monitoring area according to the area information and the concentration change rate of each monitoring area; The second data processing module is configured to acquire the ith monitoring area, take the monitoring areas in communication with the ith monitoring area as an effective influence area of the ith monitoring area, and acquire a dynamic risk index of the ith monitoring area according to the area information and the concentration change rate of the ith monitoring area, the area information and the concentration change rate of the effective influence area of the ith monitoring area. The management warning module is configured to acquire a gas risk index corresponding to the to-be-monitored plant area according to the static risk index and the dynamic risk index of each monitoring area, and acquire a warning strategy according to the gas risk index corresponding to the to-be-monitored plant area.

4. An electronic device, comprising: The method comprises the following steps: a memory having a computer program stored thereon; a processor configured to execute the computer program in the memory to implement the plant gas monitoring device periodic data management method in claim 1 or claim 2.

5. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the plant gas monitoring device periodic data management method in claim 1 or claim 2.

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