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

Abstract

The invention discloses a plant gas monitoring equipment periodic data management method, system, equipment and medium, and relates to the technical field of data processing, and the method comprises the following steps: obtaining monitored gas and a to-be-monitored plant, dividing into a plurality of monitoring areas, obtaining area information, setting gas monitoring equipment, and obtaining periodic concentration data; acquiring to-be-managed data, acquiring a concentration change rate, and acquiring a static risk index; obtaining the ith monitoring area, taking the monitoring area communicated with the ith monitoring area as an effective influence area, and obtaining a dynamic risk index according to the area information and the concentration change rate of the ith monitoring area and the area information and the concentration change rate of the effective influence area; and obtaining a gas risk index corresponding to the to-be-monitored plant according to the static risk index and the dynamic risk index of each monitoring area, and obtaining an early warning strategy according to the gas risk index corresponding to the to-be-monitored plant. The method has the advantages of accurate and effective early warning, accurate risk positioning and ineffective early warning suppression.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a plant gas monitoring equipment periodic data management method, system, device and medium. BACKGROUND

[0002] The current widely deployed gas monitoring system in industrial plants is mainly based on discrete sensor network, which adopts fixed threshold trigger mechanism for alarm management, that is, the passive threshold alarm mechanism is commonly used. This method has many defects in the whole gas monitoring management.

[0003] Specifically, the existing method calculates the concentration data of each region in isolation, ignores the physical correlation of gas diffusion between regions, and cannot capture the risk transmission trend along the equipment corridor or ventilation system. The static threshold lacks dynamic response capability to concentration change rate and regional characteristics (such as high sensitivity process area and low ventilation dead angle), resulting in false negatives in high-risk areas or false positives in low-risk areas. Further, after the alarm, only global emergency plans are triggered, and differentiated disposal measures cannot be matched according to the risk transmission path and evolution stage, resulting in resource mismatch (such as full-plant shutdown to deal with local minor leakage). More importantly, the existing technology cannot integrate historical concentration trends and neighborhood risk inputs, making it difficult to predict the diffusion direction and speed before the gas concentration reaches the danger threshold, missing the preventive intervention window period. At the data processing level, the collected raw periodic data is only processed by simple arithmetic mean or fixed weighting, without considering the different geographical relationships and different sensitivities of different regions to the monitored gas, and without introducing concentration change direction discrimination. The global mean sum of the whole plant index is used, the high-risk signal is diluted, and the risk quantification deviates from the true situation.

[0004] Finally, the above defects together cause the plant safety protection to be in a "passive response" state for a long time, and only after the standard is exceeded, the disposal is made, but the precise blocking cannot be realized in the early stage of risk diffusion. SUMMARY

[0005] In view of the defects in the prior art, the present application provides a plant gas monitoring equipment periodic data management method, system, device and medium.

[0006] The application discloses a kind of plant gas monitoring equipment periodic data management method, comprising: obtaining the gas to be monitored and the plant to be monitored, and the plant to be monitored is divided into multiple monitoring areas, and the area information of the monitoring area to be monitored is obtained, and in each monitoring area, gas monitoring equipment for collecting the concentration data of the monitored gas is arranged, and the periodic concentration data of the monitored gas collected by each monitoring area in the continuous period time period is obtained based on the gas monitoring equipment;The periodic concentration data of each monitoring area in the previous period time period at the current time is obtained as the to-be-managed data of each monitoring area, and the concentration change rate of each monitoring area is obtained according to the period time period and the to-be-managed data of each monitoring area, and the static risk index of each monitoring area is obtained according to the area information and the concentration change rate of each monitoring area;The i th monitoring area is obtained, and the monitoring area communicated with the i th monitoring area is regarded as the effective influence area of the i th monitoring area, and the dynamic risk index of the i th monitoring area is obtained according to the area information, the concentration change rate of the i th monitoring area, the area information and the concentration change rate of the effective influence area of the i th monitoring area;The gas risk index corresponding to the plant to be monitored is obtained according to the static risk index and the dynamic risk index of each monitoring area, and the early warning strategy is obtained according to the gas risk index corresponding to the plant to be monitored.

[0007] Optionally, the gas risk index corresponding to the plant to be monitored is obtained according to the static risk index and the dynamic risk index of each monitoring area, comprising: according to the monitored gas, a selected proportion is preset, and the risk count number is obtained according to the selected proportion and the number of monitoring areas in the plant to be monitored;The monitoring area with the number of risk count number is selected in turn as the count area according to the static risk index from high to low order;The gas risk index corresponding to the plant to be monitored is obtained according to the static risk index and the dynamic risk index of each count area.

[0008] Optionally, the gas risk index corresponding to the plant to be monitored is obtained according to the static risk index and the dynamic risk index of each count area, which is expressed as: ; wherein, the gas risk index corresponding to the plant to be monitored, the number of influence weight, the number of count area, the static risk index of the j th count area, the dynamic risk index of the j th count area.

[0009] Optionally, the early warning strategy is obtained according to the gas risk index corresponding to the plant to be monitored, comprising: a plurality of continuous index ranges are preset, each index range corresponds to different early warning strategy, and the early warning strategy includes mild early warning, moderate early warning and severe early warning;The gas risk index corresponding to the plant to be monitored falls into the corresponding index range, and the early warning strategy corresponding to the index range is obtained.

[0010] Optionally, the static risk index of each monitoring area is obtained according to the area information and the concentration change rate of each monitoring area, and is expressed as: wherein, is the static risk index of the ith monitoring area, is the real-time concentration of the monitored gas in the ith monitoring area, is the risk concentration threshold of the monitored gas, is the importance weight of the ith monitoring area, is the concentration change rate of the monitored gas in the ith monitoring area, is the ventilation area ratio of the ith monitoring area, is the ventilation area ratio benchmark, is the danger index of the monitored gas.

[0011] Optionally, the dynamic risk index of the ith monitoring area is obtained 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 of the ith monitoring area, and is expressed as: wherein, is the dynamic risk index of the ith monitoring area, is the number of the effective influence area 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 real-time concentration of the monitored gas in the ith monitoring area, is the importance weight of the ith monitoring area,

[0012] ​​​Also provided is a plant gas monitoring device cycle data management system, the system comprising: a data acquisition module configured to acquire a monitored gas and a plant to be monitored, divide the plant to be monitored into a plurality of monitoring areas, acquire area information of the monitoring areas to be monitored, set a gas monitoring device for collecting concentration data of the monitored gas in each monitoring area, and acquire cycle concentration data of the monitored gas collected by the gas monitoring device in each monitoring area in a continuous cycle time period; a first data processing module configured to acquire cycle concentration data of each monitoring area in a previous cycle time period at a current time as to-be-managed data of each monitoring area, acquire a concentration change rate of each monitoring area according to the cycle time period and the to-be-managed data of each monitoring area, and acquire a static risk index of each monitoring area according to the area information and the concentration change rate of each monitoring area; a second data processing module configured to acquire an i-th monitoring area, set monitoring areas in communication with the i-th monitoring area as effective influence areas of the i-th monitoring area, and acquire a dynamic risk index of the i-th monitoring area according to the area information and the concentration change rate of the i-th monitoring area and the area information and the concentration change rate of the effective influence areas of the i-th monitoring area; and a management and early warning module configured to acquire a gas risk index corresponding to the plant to be monitored 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 plant to be monitored.

[0013] Optionally, the management and early warning module is further configured to: acquire a risk count number according to a preset selection ratio of the monitored gas and the number of monitoring areas in the plant to be monitored according to the selection ratio; select monitoring areas with the number of the risk count number in order from high to low according to the static risk index and set the monitoring areas as count-in areas; and acquire the gas risk index corresponding to the plant to be monitored according to the static risk index and the dynamic risk index of each count-in area.

[0014] Also provided is an electronic device, characterized in that comprising: a memory having a computer program stored thereon; and a processor configured to execute the computer program in the memory to implement the plant gas monitoring device cycle data management method.

[0015] Also provided is a non-transitory computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the plant gas monitoring device cycle data management method.

[0016] The beneficial effects of the present application are embodied in: In the whole plant gas monitoring equipment cycle data management method, first, based on the partition of physical characteristics, combined with the dynamic weighting static risk index, the regional sensitivity, ventilation capacity, concentration change direction and rate and other elements are deeply integrated, so that the high risk area is identified when the concentration is not over standard, the false negative rate is significantly reduced, and the false alarm interference of the low risk stable area is inhibited; further, the diffusion network is constructed by connectivity modeling, the dynamic risk index quantifies the cross-regional risk input / output intensity (such as the conduction pressure of the downstream of the pipeline upstream high-speed leakage), and the diffusion trend perception is strengthened combined with the regional importance weight, the early warning of gas along the corridor propagation path and speed is realized, and the preventive intervention window period is occupied; further, the key area focusing mechanism selects the core point with the highest static risk, and the combination signal of "high self risk + strong diffusion pressure" (such as the reaction zone with fast leakage and high pressure in the neighborhood) is strengthened through nonlinear aggregation, the dilution of the global mean to the high risk signal is avoided, and the risk distribution width is further reflected by the regional number correction, so that the index can truly reflect the highest risk state of the whole plant; further, the gas risk index mapping early warning strategy matches the risk evolution stage and spatial distribution, avoids resource mismatch, and forms the active defense closed loop of "precise blocking - hierarchical prevention and control". BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed 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, each element or part is not necessarily drawn according to the actual proportion.

[0018] Figure 1 The step schematic diagram of the plant gas monitoring equipment cycle data management method of the present application; Figure 2 The step schematic diagram of part of S4 in the plant gas monitoring equipment cycle data management method of the present application; Figure 3 Another part of the step schematic diagram of S4 in the plant gas monitoring equipment cycle data management method of the present application; Figure 4 The block diagram of an electronic device according to an embodiment of the present application is shown.

[0019] Reference signs: 700 - electronic device, 701 - processor, 702 - memory, 703 - multimedia component, 704 - I / O interface, 705 - communication component. DETAILED DESCRIPTION

[0020] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0022] It should be noted that: similar reference numbers and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0023] As shown in Figure 1 A plant gas monitoring equipment periodic data management method is provided, comprising: S1, obtaining a monitored gas and a to-be-monitored plant, dividing the to-be-monitored plant into a plurality of monitoring areas, obtaining area information of the to-be-monitored areas, setting a gas monitoring equipment for collecting the concentration data of the monitored gas in each monitoring area, and obtaining the periodic concentration data of the monitored gas collected by the gas monitoring equipment in each monitoring area within a continuous periodic time period; S2, obtaining the periodic concentration data of each monitoring area within a previous periodic time period at the current time as the to-be-managed data of each monitoring area, obtaining the concentration change rate of each monitoring area according to the periodic time period and the to-be-managed data of each monitoring area, and obtaining the static risk index of each monitoring area according to the area information and the concentration change rate of each monitoring area; S3, obtaining an i-th monitoring area, taking the monitoring areas in communication with the i-th monitoring area as the effective influence areas of the i-th monitoring area, and obtaining the dynamic risk index of the i-th monitoring area according to the area information, the concentration change rate of the i-th monitoring area, the area information and the concentration change rate of the effective influence areas of the i-th monitoring area; S4, obtaining the corresponding gas risk index of the to-be-monitored plant according to the static risk index and the dynamic risk index of each monitoring area, and obtaining the early warning strategy according to the corresponding gas risk index of the to-be-monitored plant.

[0024] In this embodiment, it is noted that in S1, a basic framework of plant monitoring is established. First, the monitoring target and scope are determined, i.e., the specific gas species to be monitored (such as methane, carbon monoxide, etc.) and the boundary of the entire plant are determined. Then, the entire plant is divided into several monitoring regions according to logical rules considering the risk distribution and physical characteristics within the plant. The division rules can focus on: the inherent sensitivity of different regions to the monitored gas (for example, certain process units are more likely to cause serious consequences due to material characteristics or reaction conditions for a specific gas leakage), the criticality level of the region itself in the plant safety or production (such as control room, core reaction device area relative to ordinary warehouse or corridor area), the actual connection relationship between regions in spatial position or equipment arrangement (especially the regions that rely on equipment corridors, pipeline corridors or shared spaces to form potential gas diffusion paths), and the significant difference in mechanical ventilation or natural ventilation capacity of each region (such as identifying those dead corners that are limited by structure and cause poor ventilation). After completing this fine zoning, gas monitoring devices are configured for each divided monitoring region, with the purpose of continuously capturing the presence of target gases within the specific region. These gas monitoring devices will continuously run, periodically (e.g., every minute, every 5 minutes, or every half hour) collecting raw readings of the monitored gas in the respective monitoring region, forming a time series organized concentration data set (periodic concentration data) to provide a data basis for subsequent risk assessment.

[0025] Further, assuming that the target is to monitor the toxic gas hydrogen sulfide (H2S). In S1, zoning can be performed according to self-set rules: for example, the core reaction kettle area for processing hydrogen sulfide raw materials is divided into a high-sensitivity, high-importance, and ventilation-dependent monitoring region; a long-distance closed process pipeline corridor connecting multiple workshops is divided into a region; a material storage room located in the corner of the plant with extremely poor natural ventilation conditions is divided into a region; and an open main control room area is divided into another region, although its gas generation risk is low, but due to the high concentration of personnel and high importance, its sensitivity weight is also relatively high; and so on. In these divided regions, hydrogen sulfide sensors are deployed respectively. All sensors collect the instantaneous value of hydrogen sulfide concentration in the air of the region at a fixed period (such as every 30 seconds) and record and store it in time sequence, thereby generating a continuous and updated periodic concentration data stream for each region. This regional, multi-attribute, and time-series data acquisition mode lays a data foundation for subsequent identification of static risks and understanding of dynamic diffusion.

[0026] In S2, for each divided monitoring area, its independent risk level, i.e. static risk indicator, is calculated. First, for each area's own monitoring data and characteristics, a current moment to be evaluated is selected independently of other areas, and all periodic concentration data collected by the gas monitoring device in the area within a complete period of time before this moment is extracted back, which part of recent history data constitutes the area's to-be-managed data. Based on these time-ordered concentration values, the concentration change rate of the monitored gas in the area within this period is calculated and analyzed, which reflects the trend of the area's own gas concentration increase or decrease speed. Then, combined with the area information of the area (which includes the inherent sensitivity of the area to the gas, the importance weight of the area, the ventilation capacity, etc.), the static risk indicator representing the risk status of the area is calculated based on the concentration change rate of the area and its unique area attributes. The calculation of this indicator deeply integrates the area characteristics: for example, not only paying attention to the current concentration value, but also acutely sensing the concentration change direction (rising or falling) and change rate (fast rising leading to the increase of static risk indicator), and taking the ventilation status of the area (good ventilation will alleviate the risk) and its importance as correction factors into account. Essentially, the indicator quantifies the risk level based only on the area's own state and short-term change trend.

[0027] Further, assume that there are three monitoring areas in the plant: one is the core reaction area with extremely high sensitivity to the target gas; one is the control room area with a large number of personnel gathering but not the main leakage source; and one is the storage corner with extremely poor ventilation conditions. For the core reaction area: assume that its concentration shows a small and rapid upward trend in the last period, and the concentration change rate is positive and the rate is fast. Combined with its high sensitivity, high weight, and limited ventilation capacity, these factors amplify its risk perception, and its static risk index will be significantly increased, and the early warning will pay close attention to this area even if the concentration has not exceeded the standard. For the control room area: assume that its concentration is basically stable in a period (the concentration change rate is close to zero or slightly negative), although its concentration value may be higher than that of some low-risk areas, but because the risk of leakage in this area itself 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, but the stable state and good ventilation will inhibit the growth of its static risk index, so that its risk level is reasonably inhibited. For the storage corner with poor ventilation: assume that its concentration also shows a slow but continuous upward trend (concentration change rate is positive, rate is medium), although the importance of this area is low and the current concentration may not be high, but its extremely poor ventilation conditions (ventilation area ratio is seriously insufficient) will greatly amplify the risk brought by the slow upward trend, resulting in its static risk index being calculated to be higher than that of the area with good ventilation under the same trend. Through the above calculation, the static risk index of each area is no longer a simple comparison based on the current single concentration value and fixed threshold in the existing method, but deeply integrates the concentration evolution trend (change rate and direction) of the area itself, the inherent risk sensitivity (sensitivity, weight), and the environmental mitigation ability (ventilation condition), thereby forming a more accurate and dynamic assessment of the independent risk state of the area. This lays a fine foundation for the subsequent steps of screening key areas and comprehensive analysis of the whole plant risk.

[0028] In S3, the dynamic risk assessment is analyzed based on the physical connectivity. First, for any specific monitoring area (such as the i-th area) in the plant, based on the spatial topology model of the plant, the adjacent areas that have direct physical connectivity with it (such as areas connected by equipment pipelines, ventilation or shared airflow paths) are automatically identified, which are defined as the effective influence areas of the i-th area. Then, the risk state of the i-th area itself (i.e. the static risk indicators related elements calculated in S2, such as concentration change rate and area characteristics) and the latest risk state of its effective influence areas (including the concentration change rate and area attributes of these areas) are integrated. By comparing the concentration value of the area with the concentration value difference of its effective influence areas, the directionality of risk input or overflow is identified (for example, higher concentration in the adjacent area indicates risk input), and combined with the concentration change rate of the influence area (fast change will strengthen the diffusion trend) and the importance weight of itself (high weight area needs more sensitive response to the change of the adjacent area), the dynamic risk indicator of the i-th area is finally generated. This indicator essentially quantifies the cross-area risk transmission pressure or potential diffusion potential that the area currently faces due to physical connectivity, reflecting the real-time dynamics of risk flow in the spatial network.

[0029] Further, through S3, each monitoring area not only assesses its own state, but also obtains a dynamic indicator reflecting its pivotal role (risk receiver or diffusion source) and potential transmission strength in the entire diffusion network. This solves the key defect of existing methods that cannot capture cross-area correlation, providing data support for predicting risk and precise interception.

[0030] In S4, by focusing on key risk sources and transmission nodes, a full-plant-level dynamic risk indicator is constructed and a warning strategy is mapped. First, to avoid the distortion of existing global average processing, a phased focusing mechanism is adopted, and a proportion parameter (such as 20%) is preset according to the monitored gas to calculate the number of areas that need to be focused on (for example, the top 20 high static risk areas are selected from 100 areas). Then, according to the static risk indicators calculated in S2, the areas are ranked from high to low, and a specific number of areas with high static risk are strictly selected as the included areas, which represent the core points with the most prominent or sensitive risk in the current plant. Then, for each selected included area, not only its own static risk level is considered, but also the dynamic risk indicator is included. By processing these paired data through nonlinear reinforcement logic, the combination signal of high self-risk and strong diffusion pressure is highlighted, and the number of included areas is weighted and corrected. Finally, a gas risk indicator reflecting the highest risk state of the whole plant is output, whose value is dominated by the composite risk value of the most critical area, rather than the average value of all areas, avoiding the problem of dilution of high-risk signals in existing methods.

[0031] Further, by S4, the key risk sources on the physical space (static indicators), the networked conduction trend (dynamic indicators), and the global early warning level are dynamically associated: different threshold ranges are preset (such as 0-2 for mild, 2-4 for moderate, and >4 for severe), and the gas risk indicators falling into the corresponding range trigger differentiated plans (such as mild early warning starting regional inspection, moderate early warning closing associated valves, and severe early warning evacuating personnel in the affected area).

[0032] In summary, in the whole plant gas monitoring equipment periodic data management method, first, based on the zoning of physical characteristics, combined with dynamically weighted static risk indicators, the regional sensitivity, ventilation capacity, concentration change direction and rate, and other elements are deeply integrated, so that high-risk areas are identified even when the concentration is not over standard, significantly reducing the false negative rate, while suppressing false positives in low-risk stable areas; further, by connectivity modeling to build a diffusion network, dynamic risk indicators quantify the cross-regional risk input / output intensity (such as high-speed leakage upstream of the pipeline affecting the conduction pressure downstream), combined with the importance weight of the region to strengthen the diffusion trend perception, early warning of gas along the corridor propagation path and speed, and preemptive preventive intervention window; further, the key area focusing mechanism selects the core point with the highest static risk, through nonlinear aggregation to strengthen the combined signal of "high self-risk + strong diffusion pressure" (such as a reaction zone with fast leakage and high pressure in the neighborhood), avoiding dilution of high-risk signals by global mean, while counting the number of regions to further reflect the risk distribution breadth, ensuring that the indicators truly reflect the highest risk status of the whole plant; further, the gas risk indicator mapping early warning strategy matches the risk evolution stage and spatial distribution, avoiding resource mismatch, forming a "precise blocking - hierarchical prevention and control" active defense closed loop.

[0033] As shown in Figure 2 In one embodiment, the S4 includes: S41, according to the selected proportion of the monitored gas, and according to the selected proportion and the number of monitoring areas in the to-be-monitored plant, the risk count number is obtained; S42, the monitoring areas with the number of risk count number are selected in order from high to low according to the static risk indicators and are taken as the count areas; S43, according to the static risk indicators and the dynamic risk indicators of each count area, the gas risk indicators corresponding to the to-be-monitored plant are obtained.

[0034] In this embodiment, it is necessary to explain that in S41, the size of the hotspot region set that needs to be focused on is dynamically framed. According to the potential hazard characteristics of the monitored gas (for example, highly toxic gas needs more extensive monitoring, and low-hazard gas can be appropriately reduced in scope), a proportion parameter is preset, generally 0.1. Then, according to the preset proportion parameter and the total number of monitoring regions currently divided in the plant area, the specific number of the "risk included region" set that needs to be included is calculated. This number is not a fixed value, but will be dynamically adjusted with the partition strategy and gas characteristics: for highly risky gas or large plant area, the scope of attention will be automatically expanded; otherwise, it will be reduced, ensuring that the analysis focus always matches the overall risk situation.

[0035] In S42, based on the risk included number calculated in S41, the static risk indicators of all monitoring regions in the plant area are globally sorted (from high to low). The regions ranked at the top, the number of which is equal to the "risk included number", are strictly selected and marked as "included regions". This means that not all regions are equally involved in the calculation when evaluating the global risk, but the core points with the most serious or most sensitive conditions are preferentially locked in - for example, ventilation dead angles with rapidly rising concentrations, high-sensitivity process areas, etc. These "included regions" represent the highest independent risk level and the most critical region set that needs to be vigilant at the current moment in the plant area, thereby avoiding the dilution of the overall evaluation results by the interference signals of low-risk regions.

[0036] In S43, only for the selected "included regions", the dual attributes of each region are analyzed: its own static risk intensity (reflecting the independent risk degree) and its dynamic risk indicator (reflecting its hub pressure in the diffusion network - for example, as a strong diffusion source or high-risk receiver). By amplifying the signals of regions that have both high-risk self-state and strong diffusion or input pressure (for example, a core reaction zone with rapidly rising concentration itself, while facing higher concentration rapid input from the upstream neighborhood). At the same time, the overall number of included regions will be considered, and the increase in the number indicates that the risk distribution is more extensive or more intensive. Finally, from all the included regions, the peak risk level represented by the region with the highest composite risk value (i.e., the most significant result after the above enhancement) is found, which is used as the core basis for the gas risk indicator of the whole plant.

[0037] For example, assume that the plant monitors the flammable gas hydrogen (H2) with a preset ratio of 15%, and there are 20 areas in the plant. S41 calculates the risk count as 3 (20*15%=3). S42 sorts the static risk indicators of all 20 areas: the warehouse corner (high weight and poor ventilation, slow concentration rise) ranks first, followed by the pipeline intersection (stable concentration but high importance), and the unloading area (slight concentration fluctuation) ranks third. The top three are selected as the count areas. In S43, the warehouse corner has a high static risk (poor condition itself), but its dynamic indicator shows that there is no diffusion or input around it (isolated risk); the pipeline intersection has a medium static risk (relatively stable itself), but its dynamic indicator shows that it is rapidly absorbing the risk input from the two high-concentration areas upstream (strong input pressure); and the unloading area has a low static risk (small fluctuation itself), and its dynamic indicator shows that its risk output is weak. Through nonlinear processing, the signal of the "pipeline intersection" will be significantly amplified (because it has both medium static risk and strong dynamic input pressure), and considering that the count areas are 3 (quantity weighting), the final gas risk indicator of the whole plant is mainly determined by the peak composite risk of the pipeline intersection.

[0038] In one embodiment, the gas risk indicator of the to-be-monitored plant area is obtained according to the static risk indicator and the dynamic risk indicator of each count area in S43, which is represented as: ; wherein, is the gas risk indicator of the to-be-monitored plant area, is the quantity influence weight, is the number of count areas, is the static risk indicator of the jth count area, is the dynamic risk indicator of the jth count area.

[0039] In this embodiment, it should be noted that in the entire expression, the composite risk amplification mechanism is realized. Specifically, the isolated alarm problem is solved, the strong correlation between static risk (self-leakage intensity) and dynamic risk (diffusion pressure) is captured, and the combination signal of "high self-risk + strong diffusion trend" is captured; the average value dilution is avoided, and the composite value is calculated independently for each area without adding the interference of low-risk areas. Among them, the static risk is strengthened (even if the baseline value is 1); is used to suppress the linear growth of the dynamic risk indicator, and solves the trend misjudgment of the dynamic risk indicator. The existing weighting linearly amplifies the noise of the high dynamic risk indicator, while the logarithmic compression pays more attention to the area corresponding to the dynamic risk indicator within a certain degree.

[0040] Further, The peak risk dominates the global, effectively preventing risk signal dilution, and the plant risk is dominated by the most serious single point (rather than the average of each region), ensuring that high-risk sources are not hidden. A severe warning can be triggered by only one key region reaching the threshold, avoiding response delays. Therefore, in the case of existing global mean being pulled down by a large amount of safe zone data (such as only 1 high-risk point in 10 regions), the max in the embodiment forces the highest risk point to respond.

[0041] Further, The risk distribution breadth correction is achieved; specifically, N is determined only by the gas hazard and the total number of regions in advance, and the risk distribution density is quantified. When N is large (such as highly toxic gas, large plant), the emergency range needs to be expanded, and when N is small (such as low-risk gas, small plant), focus on local disposal to avoid "full plant shutdown to respond to a single high-risk point" resource mismatch, while N is large, increase the gas risk index, so as to improve the response level (such as full plant inspection).

[0042] For example: assuming the total number of monitoring regions is 10, the preset ratio is 20% (gas hazard is highly toxic), 0.05, 2 (calculated by 10*0.2); region 1 is counted, , Region 2 is counted, , Substitute into the expression to calculate, .

[0043] In summary, although the static risk index of region 2 is higher than that of region 1 when region 2 is counted ( ), the strong diffusion pressure of region 1 (dynamic risk index ) amplifies the composite value more, so the actual risk of region 1 is higher than that of region 2 when region 1 is counted ( ). The plant gas risk index is dominated by region 1, achieving precise resource allocation, that is, the warning strategy includes preferentially blocking the diffusion path of region 1 (such as closing the pipeline valve).

[0044] As shown in Figure 3 , in one embodiment, S4 obtains a warning strategy according to the gas risk index corresponding to the to-be-monitored plant area, which includes: S44, preset a plurality of continuous index ranges, each index range corresponds to a different warning strategy, and the warning strategy includes mild warning, moderate warning and severe warning; S45, the gas risk index corresponding to the to-be-monitored plant area falls into the corresponding index range, and the warning strategy corresponding to the index range is obtained.

[0045] In this embodiment, it is necessary to explain that in S44, a hierarchical early warning threshold system is constructed. Specifically, the risk continuous space is discretized first, that is, a plurality of non-overlapping continuous index ranges (such as [0, 2), [2, 4), [4, +∞)) are preset, and each range corresponds to a specific early warning level (mild / moderate / severe). Thus, the defect of “global emergency plan” is solved, the continuous risk value is forced to be mapped to the discrete action level, and the traditional over-standard and full-plant shutdown extensive response is avoided. The range boundary value is based on historical accident data and gas characteristics (such as the lower limit value of severe early warning corresponding to the risk level that may cause a chain accident). The resource mismatch is solved: different levels trigger differentiated plans to ensure that the response intensity accurately matches the actual severity of the risk (such as local leakage without full-plant shutdown).

[0046] In S45, the risk state and response strategy are dynamically matched, the full-plant gas risk index calculated in S43 is acquired in real time, the preset range is scanned and the interval to which it belongs is locked. Then the corresponding strategy is automatically triggered. When the risk value falls within the lower limit range, the mild early warning is automatically started (such as strengthening manual inspection in high-risk areas, checking ventilation equipment); when the risk value crosses the critical threshold, the moderate early warning is activated (such as automatically closing the associated process valve and starting the specified area exhaust); when the risk value breaks through the highest threshold, the severe early warning is executed (such as evacuating related personnel on the diffusion path and isolating the upstream leakage source equipment). When the strategy is executed, the risk dominant area information (that is, the area with the highest composite risk value in S43) is synchronously called, for example, when the severe early warning occurs, the core diffusion path (such as a ventilation pipe or a device corridor) is located based on the dynamic risk index of S3, and the path is blocked preferentially rather than the full-plant shutdown.

[0047] In one embodiment, the static risk index of each monitoring area is obtained according to the area information and the concentration change rate of each monitoring area in S2, which is represented as: , , ; wherein, is the static risk index of the ith monitoring area, is the real-time concentration of the monitored gas in the ith monitoring area, is the risk concentration threshold of the monitored gas, is the importance weight of the ith monitoring area, is the concentration change rate of the monitored gas in the ith monitoring area, is the ventilation area ratio of the ith monitoring area, is the ventilation area ratio reference, is the danger index of the monitored gas.

[0048] In this embodiment, it should be noted that the entire expression is The real-time concentration is divided by the preset risk threshold value as a reference, and the continuous risk is quantitatively evaluated, avoiding the direct determination of the result by a single threshold value, and overcoming the defects of a fixed threshold value, while still retaining 80% of the reference risk value when ; At the same time, the false alarm hazard is eliminated, and the high-sensitivity area (such as a reaction kettle) may start an early warning when the concentration reaches 60% of the threshold value.

[0049] Further, is a dynamic trend amplification term. Among them, is the gas risk index, which is a constant inherent property of the gas (such as hydrogen sulfide methane); even if the concentration of a toxic gas does not change, the basic risk is still maintained (such as when the risk value is increased by 50%); is a change direction symbol, ( ) represents an increase in concentration, resulting in an increase in risk, ( ) represents a decrease in concentration, resulting in a decrease in risk. When the short-term concentration fluctuates and decreases, false alarms are automatically offset (such as after the ventilation system is started ); is a non-linear change rate response, is the importance weight of the monitoring area (the control room is greater than the warehouse ), is an exponential decay, with a strong response to low-speed changes and a weak response to high-speed changes.

[0050] Further, is a ventilation suppression factor, is the actual ventilation ratio ( represents the theoretical maximum ventilation area ratio), and represents the ventilation area ratio reference (i.e., the maximum effective ventilation area ratio, which is generally taken as 0.5). This achieves risk suppression in low-ventilation areas, with the risk value being retained at 100% in the corners of the warehouse → ; At the same time, it achieves risk suppression in well-ventilated areas, with the risk value being automatically released by 50% in open areas → .

[0051] Application examples, such as early warning of a leak in a high-sensitivity reaction area, have a scene parameter of a hydrogen sulfide treatment area, , , (poor ventilation), (concentration 45% threshold), =0.6 (rapid upward trend). Substitute into the expression to calculate the static risk index In summary, when the concentration is only 45% of the threshold, the risk value reaches 1.125. When combined with dynamic risk indicators, this highly likely triggers an early warning and automatically increases ventilation. However, existing methods, because the concentration does not reach the threshold, have little response and are unable to initiate local treatment measures. Evolving from rigid static thresholds into a dynamic risk assessment engine that integrates real-time status, evolutionary trends, regional characteristics, and environmental impacts, can overcome the passive response dilemma.

[0052] In one embodiment, in S3, the dynamic risk index of the i-th monitoring area is obtained based on the regional information, concentration change rate of the i-th monitoring area, and the regional information and concentration change rate of the effective impact area of ​​the i-th monitoring area, and is expressed as: ;in, is the dynamic risk index of the i-th monitoring area, is the number of effective influence areas of the i-th monitoring area, is the real-time concentration of the monitored gas in the kth effective impact area of ​​the i-th monitoring area, is the real-time concentration of the monitored gas in the i-th monitoring area, is the importance weight of the i-th monitoring area, is the concentration change rate of the monitored gas in the kth effective influence area of ​​the i-th monitoring area.

[0053] In this embodiment, it should be noted that Risk direction judgment, when the concentration of adjacent areas When , the output is 1 (risk input), when When the concentration gradient is established, the output is -1 (risk output). Through the concentration gradient, the correlation between regions is established, the direction of risk transmission is quantified, and the local area is accurately identified as the risk receiver or the diffusion source. The cross-regional transmission trend is captured. When the equipment corridor upstream Larger than this area When , output 1 (risk is entering), this area Larger than neighboring areas , output -1 (this area is spreading).

[0054] Further, is the risk intensity quantification item. is the absolute value of the neighborhood concentration change rate (reflecting the speed of change), is the importance weight of the region (highly sensitive areas have higher weights). When the risk intensity quantification item is less than 0.3, it increases rapidly, indicating a sharp response to slow changes. When >1, the risk intensity quantification item grows rapidly slows down, representing a high-speed change response slowly. A dynamic adjustment mechanism is realized, such as a control room (high sensitivity), warehouse (low sensitivity), solves the problem of ignoring regional characteristics, same value, high The regional dynamic risk index is higher.

[0055] Further, Realize multi-source accumulation, accumulate the contribution value of all effective influence areas, high-risk adjacent areas ( ) will make the dynamic risk index superimposed and enlarged, and safe adjacent areas ( ) will make the dynamic risk index unchanged or reduced, while contributing to capturing the spread trend.

[0056] Also provided is a plant gas monitoring equipment periodic data management system, the system comprising: A data acquisition module is used to acquire the monitored gas and the to-be-monitored plant area, divide the to-be-monitored plant area into multiple monitoring areas, acquire the area information of the to-be-monitored area, set a gas monitoring equipment for collecting the concentration data of the monitored gas in each monitoring area, and acquire the periodic concentration data of the monitored gas collected by the gas monitoring equipment in each monitoring area within a continuous periodic time period; A first data processing module is used to acquire the periodic concentration data of each monitoring area within a previous periodic time period at the current time as the to-be-managed data of each monitoring area, acquire the concentration change rate of each monitoring area according to the periodic time period and the to-be-managed data of each monitoring area, and acquire the static risk index of each monitoring area according to the area information and the concentration change rate of each monitoring area. A second data processing module is used to acquire the ith monitoring area, and the monitoring areas in communication with the ith monitoring area are regarded as the effective influence areas of the ith monitoring area, and the dynamic risk index of the ith monitoring area is acquired 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 areas of the ith monitoring area. A management and early warning module is used to acquire the 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.

[0057] In one embodiment, the management early warning module is further configured to: obtain a risk count number according to a preset selection ratio of the monitored gas, and according to the selection ratio and a number of monitoring areas in the to-be-monitored plant area; select the monitoring areas with the number of risk count number in order of high to low of the static risk indexes, and take the selected monitoring areas as count-in areas; and obtain the gas risk index corresponding to the to-be-monitored plant area according to the static risk index and the dynamic risk index of each count-in area.

[0058] In the embodiment, it should be noted that, as to the plant gas monitoring equipment periodic data management system, the specific manner of performing operations has been described in detail in the embodiments of the plant gas monitoring equipment periodic data management method, and will not be described in detail here.

[0059] Figure 4 is a block diagram of an electronic device for a plant gas monitoring equipment periodic data management method according to an example embodiment. As shown in Figure 4 The electronic device 700 can include one or more of a processor 701, a memory 702, a multimedia component 703, an I / O interface 704 (input / output interface), and a communication component 705.

[0060] The processor 701 is configured to control overall operations of the electronic device 700 to complete all or part of the steps of the factory gas monitoring device periodic data management method described above. The memory 702 is configured to store various types of data to support operations of the electronic device 700, which can 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, pictures, audio, video, and the like. The memory 702 can be implemented by 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 memory, flash memory, magnetic disk or optical disk. The multimedia component 703 can include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is configured to output and / or input audio signals. For example, the audio component can include a microphone configured to receive external audio signals. The received audio signals can be further stored in the memory 702 or transmitted through the communication component 705. The audio component also includes at least one speaker configured to output audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, which can be a keyboard, a mouse, a button, and the like. The buttons can be virtual buttons or physical buttons. The communication component 705 is configured to perform 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, and the like, or a combination of one or more of them, is not limited herein. Therefore, the corresponding communication component 705 can include a Wi-Fi module, a Bluetooth module, an NFC module, and the like.

[0061] In an exemplary embodiment, the electronic device 700 can 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, micro-controllers, microprocessors, or other electronic elements for executing the above-mentioned plant gas monitoring device periodic data management method.

[0062] 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-mentioned plant gas monitoring device periodic data management method. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, which can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned plant gas monitoring device periodic data management method.

[0063] In another exemplary embodiment, a computer program product is also provided, which contains a computer program capable of being executed by a programmable device, and the computer program has code portions for executing the above-mentioned plant gas monitoring device periodic data management method when executed by the programmable device.

[0064] The preferred embodiments of the present disclosure are described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details in the above-described embodiments. Within the technical concept scope of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.

[0065] In addition, it should be noted that each specific technical feature described in the above-described specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, various possible combinations are not described again in the present disclosure.

[0066] In addition, any combination of various different embodiments of the present disclosure can also be made, as long as it does not deviate from the idea of the present disclosure, and it should also be considered as the disclosed content of the present disclosure.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some or all of the technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the description of the present application.

Claims

1. A method for managing periodic data of gas monitoring equipment in a factory area, characterized in that: include: Obtaining the monitored gas and the plant area to be monitored, dividing the plant area to be monitored into multiple monitoring areas, obtaining regional information of the areas to be monitored, and setting up gas monitoring equipment in each monitoring area for collecting concentration data of the monitored gas, and obtaining periodic concentration data of the monitored gas collected in each monitoring area within a continuous periodic time period based on the gas monitoring equipment; Obtain the periodic concentration data of each monitoring area in the previous periodic time period at the current moment and use it as the data to be managed, obtain the concentration change rate based on the periodic time period and the data to be managed of each monitoring area, and obtain the static risk index based on the regional information and concentration change rate of each monitoring area; 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, and obtain the dynamic risk index based on the regional information and concentration change rate of the i-th monitoring area, the regional information and concentration change rate of the effective influence area; The gas risk indicators corresponding to the plant area to be monitored are obtained based on the static risk indicators and dynamic risk indicators of each monitoring area, and the early warning strategy is obtained based on the gas risk indicators corresponding to the plant area to be monitored.

2. The method for managing periodic data of plant gas monitoring equipment according to claim 1, characterized in that: The method of obtaining the 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 includes: The selected ratio is preset according to the monitored gas, and the risk inclusion quantity is obtained based on the selected ratio and the number of monitoring areas in the plant to be monitored; Select monitoring areas with the same number of risk inclusions as the static risk indicators in descending order and use them as inclusion areas; The gas risk indicators corresponding to the plant area to be monitored are obtained based on the static risk indicators and dynamic risk indicators of each included area.

3. The method for managing periodic data of plant gas monitoring equipment according to claim 2, characterized in that: The gas risk index corresponding to the plant area to be monitored is obtained based on the static risk index and dynamic risk index of each included area as follows: ;in, is the gas risk index corresponding to the plant area to be monitored, is the quantity impact weight, is the number of regions included, is the static risk index of the jth included area, is the dynamic risk indicator of the jth included region.

4. The method for managing periodic data of factory gas monitoring equipment according to claim 1, characterized in that: The early warning strategy obtained according to the gas risk indicators corresponding to the monitored plant area includes: Preset multiple continuous indicator ranges, each indicator range corresponds to a different warning strategy, the warning strategy includes mild warning, moderate warning and severe warning; The gas risk indicators corresponding to the plant area to be monitored fall into the corresponding indicator range, and the early warning strategy corresponding to the indicator range is obtained.

5. The method for managing periodic data of factory gas monitoring equipment according to claim 1, characterized in that: The static risk index of each monitoring area obtained according to the regional information and concentration change rate of each monitoring area is expressed as: , , ;in, is the static risk index of the i-th monitoring area, is the real-time concentration of the monitored gas in the i-th monitoring area, is the risk concentration threshold of the monitored gas, is the importance weight of the i-th monitoring area, is the concentration change rate of the monitored gas in the i-th monitoring area, is the ventilation area ratio of the i-th monitoring area, is the ventilation area ratio benchmark, It is the danger index of the monitored gas.

6. The method for managing periodic data of factory gas monitoring equipment according to claim 1, characterized in that: The dynamic risk index of the ith monitoring area is obtained according to the regional information, concentration change rate of the ith monitoring area, regional information and concentration change rate of the effective impact area of ​​the ith monitoring area as follows: ;in, is the dynamic risk index of the i-th monitoring area, is the number of effective influence areas of the i-th monitoring area, is the real-time concentration of the monitored gas in the kth effective impact area of ​​the i-th monitoring area, is the real-time concentration of the monitored gas in the i-th monitoring area, is the importance weight of the i-th monitoring area, is the concentration change rate of the monitored gas in the kth effective influence area of ​​the i-th monitoring area.

7. A plant gas monitoring equipment periodic data management system, characterized in that: The system comprises: A data acquisition module is used to acquire the monitored gas and the plant area to be monitored, divide the plant area to be monitored into multiple monitoring areas, obtain regional information of the areas to be monitored, and set up gas monitoring equipment for collecting concentration data of the monitored gas in each monitoring area. The gas monitoring equipment then acquires periodic concentration data of the monitored gas collected in each monitoring area within a continuous periodic time period. A first data processing module is used to obtain the periodic concentration data of each monitoring area in the previous periodic time period at the current moment and use it as the data to be managed for each monitoring area, and obtain the concentration change rate of each monitoring area based on the periodic time period and the data to be managed for each monitoring area, and obtain the static risk index of each monitoring area based on the regional information and concentration change rate of each monitoring area; The second data processing module is used to obtain the i-th monitoring area, and use the monitoring area connected to the i-th monitoring area as the effective influence area of ​​the i-th monitoring area, and obtain the dynamic risk index of the i-th monitoring area based on the regional information and concentration change rate of the i-th monitoring area, and the regional information and concentration change rate of the effective influence area of ​​the i-th monitoring area; The management and early warning module is used to obtain the gas risk indicators corresponding to the monitored plant area based on the static risk indicators 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.

8. The plant gas monitoring equipment periodic data management system according to claim 7, characterized in that: The management warning module is also used to: The selected ratio is preset according to the monitored gas, and the risk inclusion quantity is obtained based on the selected ratio and the number of monitoring areas in the plant to be monitored; Select monitoring areas with the same number of risk inclusions as the static risk indicators in descending order and use them as inclusion areas; The gas risk indicators corresponding to the plant area to be monitored are obtained based on the static risk indicators and dynamic risk indicators of each included area.

9. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the plant gas monitoring equipment periodic data management method as described in any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for managing periodic data of plant gas monitoring equipment as described in any one of claims 1 to 6 is implemented.

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