Ammonia gas leakage monitoring and early warning method and system

Abstract

The invention relates to the technical field of ammonia gas monitoring, in particular to an ammonia gas leakage monitoring and early warning method and system. Comprising the following steps: establishing a plurality of monitoring sub-regions according to equipment parameters, and setting monitoring parameters in each monitoring sub-region; according to a preset feedback time node, obtaining a monitoring data packet of each monitoring sub-region, and according to all the monitoring data packets, generating a risk evaluation value of each monitoring sub-region; judging whether an early warning instruction is generated or not according to all the risk evaluation values; the ammonia gas storage area is accurately monitored by establishing a plurality of monitoring sub-areas, auxiliary judgment is carried out by establishing a multi-stage monitoring diagnosis system and collecting characteristic monitoring index parameters of risk sub-areas with abnormal states, and meanwhile, ammonia gas concentration parameters in the risk sub-areas are corrected according to a preset correction model, so that the ammonia gas storage area is accurately monitored. Therefore, the diagnosis precision of each risk sub-region is improved, the potential ammonia gas leakage risk is early warned in time, the maintenance efficiency is improved, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of ammonia gas monitoring, and particularly to a method and system for monitoring and warning ammonia gas leakage. Background Art

[0002] Urea hydrolysis technology has become one of the ideal alternatives to liquid ammonia due to its relatively low risk, especially in large coal-fired power plants, and its application can greatly reduce the existence of major hazard sources. However, since ammonia gas is a colorless gas with a strong pungent odor and has obvious harm to human health, an effective ammonia gas leakage monitoring system needs to be established.

[0003] Currently, ammonia gas sensors and other devices are usually used to monitor ammonia gas in ammonia gas storage equipment. However, this method has a high cost, and it can only give an alarm when ammonia gas leaks to a certain extent, and it cannot give an alarm in time for potential ammonia gas leakage risks, thus affecting the stable operation of the power plant. Summary of the Invention

[0004] The purpose of the present application is: to solve the above technical problems, the present application provides a method and system for monitoring and warning ammonia gas leakage, aiming to improve the warning efficiency for ammonia gas leakage risks and ensure the safe operation of the power plant.

[0005] In some embodiments of the present application, the ammonia gas storage area is accurately monitored by establishing multiple monitoring sub-areas, and by periodically obtaining the ammonia gas concentration data in each monitoring sub-area, the ammonia gas leakage risk is warned in time, and the leakage point is quickly located to avoid safety accidents caused by ammonia gas leakage.

[0006] In some embodiments of the present application, a multi-level monitoring and diagnosis system is established. By collecting the characteristic monitoring index parameters of the risk sub-areas with abnormal states for auxiliary judgment, and at the same time, according to the preset correction model, the ammonia gas concentration parameters in the risk sub-areas are corrected, so as to improve the diagnosis accuracy for each risk sub-area, warn the potential ammonia gas leakage risk in time, ensure the stable operation of the power plant, improve the maintenance efficiency, and reduce the maintenance cost.

[0007] In some embodiments of the present application, a method for monitoring and warning ammonia gas leakage is provided, including:

[0008] Establishing multiple monitoring sub-areas according to equipment parameters and setting monitoring parameters in each monitoring sub-area;

[0009] Obtaining the monitoring data packets of each monitoring sub-area according to the preset feedback time nodes, and generating a risk evaluation value for each monitoring sub-area according to all the monitoring data packets;

[0010] Judging whether to generate a warning instruction according to all the risk evaluation values;

[0011] Among them, when establishing multiple monitoring sub-regions, it includes:

[0012] Establish a sequence A of monitoring sub-regions, A = (a 1 , a 2 …a i …a n ), where a i is the i-th monitoring sub-region, and n is the number of monitoring sub-regions.

[0013] In some embodiments of the present application, according to the obtained monitoring data packets of each monitoring sub-region, it includes:

[0014] Set a i as the target monitoring sub-region in sequence according to the sequence A of monitoring sub-regions;

[0015] Set multiple primary monitoring points and multiple secondary monitoring points in the target monitoring sub-region;

[0016] Collect the ammonia concentration values of each primary monitoring point according to the preset feedback time node;

[0017] Generate a monitoring data packet of the target monitoring sub-region according to all the ammonia concentration values;

[0018] Generate the monitoring data packets of each monitoring sub-region in sequence.

[0019] In some embodiments of the present application, generating the risk evaluation values of each monitoring sub-region includes:

[0020] Obtain the monitoring data packet of the target monitoring sub-region at the current feedback time node;

[0021] Generate a sequence B of ammonia concentration values, B = (b 1 , b 2， b i …b m) , where b i is the ammonia concentration value of the i-th primary monitoring point in the target monitoring sub-region at the current feedback time node; m is the number of primary monitoring points in the target monitoring sub-region;

[0022] Generate the risk evaluation value f of the target monitoring sub-region at the current monitoring time node;

[0023]

[0024] Among them, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; b' is the safety value of the ammonia concentration in the target monitoring sub-region; Δb is the average value of all the data in the sequence B of ammonia concentration values;

[0025] Generate the risk evaluation values of each monitoring sub-region at the current feedback time node in sequence;

[0026] Establish a risk evaluation value sequence F at the current feedback time node, F = (f 1 , f 2 … f i … f n ), where f i is the risk evaluation value of the i-th monitoring sub-region at the current feedback time node.

[0027] In some embodiments of the present application, determining whether to generate a warning instruction includes:

[0028] Preset a first risk evaluation value threshold F1 and a second risk evaluation value threshold F2, and F1 < F2;

[0029] Obtain the risk evaluation value sequence F at the current feedback time node;

[0030] If f i < F1, no warning instruction is generated for the i-th monitoring sub-region at the current feedback time node;

[0031] If F1 < f i < F2, the i-th monitoring sub-region is a risk sub-region at the current feedback time node;

[0032] If f i > F2, a secondary warning instruction is generated for the i-th monitoring sub-region at the current feedback time node;

[0033] Establish a risk sub-region sequence A1 at the current feedback time node according to all risk sub-regions, A1 = (a 11 , a 12 … a 1i … a 1n1 ); where a 1i is the i-th risk sub-region at the current feedback time node; n1 is the number of risk sub-regions at the current feedback time node;

[0034] Generate the abnormal evaluation values of each risk sub-region;

[0035] Judge whether to generate a warning instruction for each risk sub-region according to all abnormal evaluation values.

[0036] In some embodiments of the present application, generating the abnormal evaluation values of each risk sub-region includes:

[0037] Set a 1i as the target risk sub-region in sequence according to the risk sub-region sequence A1;

[0038] Obtain the feedback data of each secondary monitoring point of the target risk sub-region;

[0039] Generate an abnormal evaluation value k for the target risk sub-region based on all feedback data;

[0040] Generate the abnormal evaluation values for each risk sub-region in sequence;

[0041] Establish an abnormal evaluation value sequence K at the current feedback time node, K = (k 1 , k 2 … k i … k n1 ), where k i is the abnormal evaluation value of the i-th risk sub-region at the current feedback time node;

[0042] Preset a first abnormal evaluation value threshold K1 and a second abnormal evaluation value threshold K2;

[0043] If k i < K1, no warning instruction is generated for the i-th risk sub-region at the current feedback time node;

[0044] If K1 < k i < K2, a first-level warning instruction is generated for the i-th risk sub-region at the current feedback time node;

[0045] If k i > K2, a second-level warning instruction is generated for the i-th risk sub-region at the current feedback time node.

[0046] In some embodiments of the present application, generating the abnormal evaluation value k of the target risk sub-region includes:

[0047]

[0048] Among them, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; θ is the number of characteristic monitoring indicators; μ i is the influence factor of the i-th characteristic monitoring indicator; g i is the reference value of the i-th characteristic monitoring indicator in the target risk sub-region at the current feedback time node; g' i is the standard reference value of the i-th characteristic monitoring indicator; Y(i) is a selection coefficient; if (g i - g' i ) > 0, Y(i) = 1; if (g i - g' i ) < 0, Y(i) = 0; U is the corrected risk value of the target risk sub-region.

[0049] In some embodiments of the present application, generating the corrected risk value U of the target risk sub-region includes:

[0050] Generate multiple associated sub-regions of the target risk sub-region according to a preset association model;

[0051] Generate a correction instruction according to the monitoring data packets of each associated sub-region;

[0052] Generate a corrected risk value U of the target risk sub-region according to the correction instruction;

[0053]

[0054] Among them, e5 is a preset fifth weight coefficient; e6 is a preset sixth weight coefficient; Q5 is a preset fifth fixed coefficient; Q6 is a preset sixth fixed coefficient; m1 is the number of first-level monitoring points in the target risk sub-region; b 1i is the ammonia concentration value at the current feedback time node of the i-th first-level monitoring point in the target risk sub-region; r is a correction coefficient generated based on the correction instruction; b 1 ' is the safety value of the ammonia concentration within the target risk sub-region; Δb 1 is the average value of the ammonia concentration values of all first-level monitoring points within the target risk sub-region.

[0055] In some embodiments of the present application, an ammonia leakage monitoring and early warning system is provided, including:

[0056] A central control unit for multiple monitoring sub-regions according to device parameters;

[0057] A monitoring unit includes multiple concentration sub-modules and multiple monitoring sub-modules. The concentration sub-module is used to collect the ammonia concentration values in each monitoring sub-region; the monitoring sub-module is used to collect the characteristic monitoring index parameters in each monitoring sub-region;

[0058] The central control unit includes:

[0059] A first processing module for establishing a monitoring sub-region sequence A, A = (a 1 , a 2 …a i …a n ), where a i is the i-th monitoring sub-region and n is the number of monitoring sub-regions;

[0060] A second processing module for obtaining the monitoring data packets of each monitoring sub-region according to the preset feedback time node;

[0061] A third processing module for generating a risk evaluation value of each monitoring sub-region according to all the monitoring data packets;

[0062] An early warning module for judging whether to generate an early warning instruction according to all the risk evaluation values;

[0063] The second processing module is further used for:

[0064] Set \(a\) successively according to the sequence \(A\) of the monitoring sub - regions i as the target monitoring sub - region;

[0065] Set a plurality of first - level monitoring points and a plurality of second - level monitoring points in the target monitoring sub - region;

[0066] Collect the ammonia concentration values of each first - level monitoring point according to the preset feedback time node;

[0067] Generate a monitoring data packet of the target monitoring sub - region according to all the ammonia concentration values;

[0068] Generate the monitoring data packets of each monitoring sub - region in sequence.

[0069] In some embodiments of the present application, the third processing module is further configured to:

[0070] Obtain the monitoring data packet of the target monitoring sub - region at the current feedback time node;

[0071] Generate a sequence \(B\) of ammonia concentration values, \(B=(b\) 1 , \(b\) 2， \(b\) i … \(b\) m) , where \(b\) i is the ammonia concentration value of the \(i\) - th first - level monitoring point in the target monitoring sub - region at the current feedback time node; \(m\) is the number of first - level monitoring points in the target monitoring sub - region;

[0072] Generate a risk evaluation value \(f\) of the target monitoring sub - region at the current monitoring time node;

[0073]

[0074] where \(e1\) is a preset first weight coefficient; \(e2\) is a preset second weight coefficient; \(Q1\) is a preset first fixed coefficient; \(Q2\) is a preset second fixed coefficient; \(b'\) is the safety value of the ammonia concentration in the target monitoring sub - region; \(\Delta b\) is the average value of all the data in the sequence \(B\) of ammonia concentration values;

[0075] Generate the risk evaluation values of each monitoring sub - region at the current feedback time node in sequence;

[0076] Establish a sequence \(F\) of risk evaluation values at the current feedback time node, \(F=(f\) 1 , \(f\) 2 … \(f\) i … \(f\) n ), where \(f\) i is the risk evaluation value of the \(i\) - th monitoring sub - region at the current feedback time node.

[0077] In some embodiments of the present application, the warning module is further configured to:

[0078] Preset a first risk evaluation value threshold F1 and a second risk evaluation value threshold F2, and F1 < F2;

[0079] Obtain a risk evaluation value sequence F at the current feedback time node;

[0080] If f i < F1, the i-th monitoring sub-region does not generate a warning instruction at the current feedback time node;

[0081] If F1 < f i < F2, the i-th monitoring sub-region is a risk sub-region at the current feedback time node;

[0082] If f i > F2, the i-th monitoring sub-region generates a secondary warning instruction at the current feedback time node;

[0083] Establish a risk sub-region sequence A1 at the current feedback time node according to all risk sub-regions, A1 = (a 11 , a 12 … a 1i … a 1n1 ); where a 1i is the i-th risk sub-region at the current feedback time node; n1 is the number of risk sub-regions at the current feedback time node;

[0084] Generate the abnormal evaluation value of each risk sub-region;

[0085] Judge whether each risk sub-region generates a warning instruction according to all abnormal evaluation values.

[0086] Compared with the prior art, the ammonia leakage monitoring and warning method and system in the embodiments of the present application have the beneficial effects that:

[0087] By establishing multiple monitoring sub-regions to accurately monitor the ammonia storage area, and by periodically obtaining the ammonia concentration data in each monitoring sub-region, the ammonia leakage risk is warned in time, and the leakage point is quickly located to avoid safety accidents caused by ammonia leakage.

[0088] A multi-level monitoring and diagnosis system is established. By collecting the characteristic monitoring index parameters of the risk sub-regions in abnormal states for auxiliary judgment, and at the same time, the ammonia concentration parameters in the risk sub-regions are corrected according to the preset correction model, the diagnosis accuracy of each risk sub-region is improved, the potential ammonia leakage risk is warned in time, the stable operation of the power plant is ensured, the maintenance efficiency is improved, and the maintenance cost is reduced. Description of the Drawings

[0089] Figure 1 is a schematic flowchart of an ammonia leakage monitoring and warning method in a preferred embodiment of the embodiments of the present application. Detailed implementation manners

[0090] The following further describes in detail the specific implementation manners of the present application in conjunction with the accompanying drawings and embodiments. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0091] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0092] The terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "plurality" is two or more.

[0093] In the description of the present application, it should be noted that unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0094] As Figure 1 shown, a method for monitoring and warning ammonia leakage in a preferred embodiment of an embodiment of the present application includes:

[0095] S101: Establish a plurality of monitoring sub-regions according to equipment parameters, and set monitoring parameters within each monitoring sub-region;

[0096] S102: Obtain monitoring data packets of each monitoring sub-region according to a preset feedback time node, and generate a risk evaluation value for each monitoring sub-region according to all the monitoring data packets;

[0097] S103: Determine whether to generate a warning instruction according to all the risk evaluation values;

[0098] Among them, when establishing a plurality of monitoring sub-regions, it includes:

[0099] Establish a sequence A of monitoring sub-regions, A = (a1 , a 2 … a i … a n ), where a i is the i-th monitored sub-region, and n is the number of monitored sub-regions.

[0100] Specifically, multiple monitored sub-regions are set according to the operating parameters and historical leakage parameters of the equipment, so as to achieve comprehensive monitoring of the ammonia storage equipment. Locate the leakage point in time and improve the maintenance efficiency.

[0101] Specifically, according to the monitoring data packets obtained from each monitored sub-region, including:

[0102] According to the sequence A of monitored sub-regions, a i is set as the target monitored sub-region;

[0103] Multiple primary monitoring points and multiple secondary monitoring points are set in the target monitored sub-region;

[0104] Collect the ammonia concentration values of each primary monitoring point according to the preset feedback time node;

[0105] Generate the monitoring data packet of the target monitored sub-region according to all the ammonia concentration values;

[0106] Generate the monitoring data packets of each monitored sub-region in sequence.

[0107] Specifically, according to the equipment parameters in the target sub-region, the equipment points prone to leakage are set as primary monitoring points, and a concentration sub-module (ammonia sensor) is set on each primary monitoring point to collect the real-time ammonia concentration value, so as to give an early warning of the ammonia leakage problem in time and ensure the safe operation of the power plant.

[0108] Specifically, according to the historical leakage parameters, multiple characteristic monitoring indicators are extracted. Such as vibration parameters, temperature, infrared images and other parameters, and multiple secondary monitoring points are set according to the characteristic monitoring indicators, and the real-time parameters of each characteristic monitoring indicator are obtained by collecting the feedback data of each secondary monitoring point.

[0109] Specifically, when there is an ammonia leakage point, the equipment at the leakage point will have abnormal vibrations, temperature drops, abnormal infrared images, abnormal noises, etc. Corresponding characteristic monitoring indicators are extracted according to each abnormal situation.

[0110] In the preferred embodiment of this application, the risk evaluation values of each monitored sub-region are generated, including:

[0111] Obtain the monitoring data packet of the target monitored sub-region at the current feedback time node;

[0112] Generate the sequence B of ammonia concentration values, B = (b1 , b 2， b i …b m) , where b i is the ammonia concentration value of the i-th primary monitoring point in the target monitoring sub-region at the current feedback time node; m is the number of primary monitoring points in the target monitoring sub-region;

[0113] Generate the risk evaluation value f of the target monitoring sub-region at the current monitoring time node;

[0114]

[0115] where, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; b' is the safety value of the ammonia concentration in the target monitoring sub-region; Δb is the average value of all data in the ammonia concentration value sequence B;

[0116] Generate the risk evaluation values of each monitoring sub-region at the current feedback time node in sequence;

[0117] Establish the risk evaluation value sequence F at the current feedback time node, F=(f 1 , f 2 …f i …f n ), where f i is the risk evaluation value of the i-th monitoring sub-region at the current feedback time node.

[0118] Specifically, normalize all parameters in the model through the preset first fixed coefficient and second fixed coefficient, so that each parameter is in the same value range.

[0119] Specifically, the larger the risk evaluation value, the greater the possibility of ammonia leakage in the current target monitoring sub-region.

[0120] Specifically, judging whether to generate a warning instruction includes:

[0121] Preset the first risk evaluation value threshold F1 and the second risk evaluation value threshold F2, and F1 < F2;

[0122] Obtain the risk evaluation value sequence F at the current feedback time node;

[0123] If f i < F1, the i-th monitoring sub-region does not generate a warning instruction at the current feedback time node;

[0124] If F1 < f i < F2, the i-th monitoring sub-region is a risk sub-region at the current feedback time node;

[0125] If f i > F2, the i-th monitored sub-region generates a secondary warning instruction at the current feedback time node;

[0126] Based on all risk sub-regions, establish a risk sub-region sequence A1 at the current feedback time node, A1 = (a 11 , a 12 …a 1i …a 1n1 ); where a 1i is the i-th risk sub-region at the current feedback time node; n1 is the number of risk sub-regions at the current feedback time node;

[0127] Generate the abnormal evaluation value of each risk sub-region;

[0128] Based on all abnormal evaluation values, judge whether each risk sub-region generates a warning instruction.

[0129] Specifically, the first risk evaluation value threshold and the second risk evaluation value threshold can be set according to historical parameters.

[0130] Specifically, if the risk evaluation value of a single monitored sub-region is greater than the preset first risk evaluation value threshold, it indicates that there is an abnormal risk in the current monitored sub-region. It is necessary to collect the real-time parameters of each characteristic monitoring index for auxiliary diagnosis, improve the diagnosis accuracy of each monitored sub-region, avoid increasing the maintenance cost due to misjudgment, and reduce the maintenance efficiency.

[0131] Specifically, the secondary warning instruction means that there is a serious ammonia leakage risk in the current monitored sub-region, and it is necessary to immediately carry out maintenance to eliminate the risk, avoid serious accidents caused by ammonia leakage, and ensure the stable operation of the power plant.

[0132] It can be understood that in the above embodiments, by establishing multiple monitored sub-regions to accurately monitor the ammonia storage area, and by periodically obtaining the ammonia concentration data in each monitored sub-region, timely warning of ammonia leakage risk is carried out, and at the same time, the leakage point is quickly located to avoid safety accidents caused by ammonia leakage.

[0133] In the preferred embodiment of the present application, generating the abnormal evaluation value of each risk sub-region includes:

[0134] Set a 1i as the target risk sub-region in sequence according to the risk sub-region sequence A1;

[0135] Obtain the feedback data of each secondary monitoring point in the target risk sub-region;

[0136] Generate the abnormal evaluation value k of the target risk sub-region according to all feedback data;

[0137] Generate the abnormal evaluation values of each risk sub-region in sequence;

[0138] Establish an abnormal evaluation value sequence K at the current feedback time node, K = (k 1 , k 2 …k i …k n1 ), where k i is the abnormal evaluation value of the i-th risk sub-region at the current feedback time node;

[0139] Preset a first abnormal evaluation value threshold K1 and a second abnormal evaluation value threshold K2;

[0140] If k i < K1, no warning instruction is generated for the i-th risk sub-region at the current feedback time node;

[0141] If K1 < k i < K2, a first-level warning instruction is generated for the i-th risk sub-region at the current feedback time node;

[0142] If k i > K2, a second-level warning instruction is generated for the i-th risk sub-region at the current feedback time node.

[0143] Specifically, the first abnormal evaluation value threshold and the second evaluation value threshold can be set according to historical parameters.

[0144] Specifically, the first-level warning instruction means that there may be ammonia leakage in the current risk sub-region, and the risk sub-region needs to be inspected within a specified time limit to eliminate potential failure risks and ensure the safe operation of the power plant.

[0145] Specifically, generating the abnormal evaluation value k of the target risk sub-region includes:

[0146]

[0147] Among them, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; θ is the number of characteristic monitoring indicators; μ i is the influence factor of the i-th characteristic monitoring indicator; g i is the reference value of the i-th characteristic monitoring indicator in the target risk sub-region at the current feedback time node; g' i is the standard reference value of the i-th characteristic monitoring indicator; Y(i) is a selection coefficient; if (g i - g' i ) > 0, Y(i) = 1; if (g i - g' i ) < 0, Y(i) = 0; U is the corrected risk value of the target risk sub-region.

[0148] Specifically, by presetting a third fixed coefficient and a fourth fixed coefficient, all parameters in the model are normalized so that each parameter is within the same value range.

[0149] Specifically, the larger the abnormal evaluation value is, the greater the possibility of ammonia leakage in the current risk sub-region, and it is necessary to promptly repair potential risk points to eliminate failure risks.

[0150] Specifically, generating a corrected risk value U for the target risk sub-region includes:

[0151] Generating multiple associated sub-regions for the target risk sub-region according to a preset association model;

[0152] Generating a correction instruction according to the monitoring data packets of each associated sub-region;

[0153] Generating a corrected risk value U for the target risk sub-region according to the correction instruction;

[0154]

[0155] Among them, e5 is a preset fifth weight coefficient; e6 is a preset sixth weight coefficient; Q5 is a preset fifth fixed coefficient; Q6 is a preset sixth fixed coefficient; m1 is the number of primary monitoring points in the target risk sub-region; b 1i is the ammonia concentration value at the current feedback time node of the i-th primary monitoring point in the target risk sub-region; r is a correction coefficient generated based on the correction instruction; b 1 ' is the safety value of the ammonia concentration in the target risk sub-region; Δb 1 is the average value of the ammonia concentration values of all primary monitoring points in the target risk sub-region.

[0156] Specifically, by presetting a fifth fixed coefficient and a sixth fixed coefficient, all parameters in the model are normalized so that each parameter is within the same value range.

[0157] Specifically, an association model is established based on the position parameters of each monitoring sub-region and the air flow parameters inside the plant area.

[0158] Specifically, an associated sub-region means that the leaked ammonia in the current monitoring sub-region will flow to the target risk sub-region, resulting in abnormal ammonia concentration in the target risk sub-region. By collecting the ammonia concentration values in each associated sub-region, a corresponding correction coefficient is generated to correct the ammonia concentration safety value in the target risk sub-region, thereby improving the diagnostic accuracy of ammonia leakage risk in the target risk monitoring sub-region.

[0159] It can be understood that in the above embodiments, a multi-level monitoring and diagnosis system is established. By collecting the characteristic monitoring index parameters of the risk sub-regions with abnormal states, auxiliary judgment is carried out. At the same time, the ammonia concentration parameters in the risk sub-regions are corrected according to the preset correction model, so as to improve the diagnosis accuracy of each risk sub-region, timely give early warnings of potential ammonia leakage risks, ensure the stable operation of the power plant, improve the maintenance efficiency, and reduce the maintenance cost.

[0160] Based on another preferred embodiment of an ammonia leakage monitoring and early warning method in any of the above preferred embodiments, in this preferred embodiment, an ammonia leakage monitoring and early warning system is provided, including:

[0161] A central control unit for multiple monitoring sub-regions according to equipment parameters;

[0162] A monitoring unit includes multiple concentration sub-modules and multiple monitoring sub-modules. The concentration sub-modules are used to collect the ammonia concentration values in each monitoring sub-region; the monitoring sub-modules are used to collect the characteristic monitoring index parameters in each monitoring sub-region;

[0163] Specifically, the concentration sub-module is preferably an ammonia sensor, which is arranged at each first-level monitoring point, and the monitoring sub-module is arranged at each second-level monitoring point. The monitoring sub-module is preferably various sensors, such as temperature sensors, sound sensors, vibration sensors, and infrared image acquisition devices, and corresponding devices are selected according to the data types required to be collected at each second-level monitoring point.

[0164] The central control unit includes:

[0165] A first processing module for establishing a monitoring sub-region sequence A, A=(a 1 , a 2 …a i …a n ), where a i is the i-th monitoring sub-region and n is the number of monitoring sub-regions;

[0166] A second processing module for obtaining the monitoring data packets of each monitoring sub-region according to the preset feedback time node;

[0167] A third processing module for generating the risk evaluation values of each monitoring sub-region according to all the monitoring data packets;

[0168] An early warning module for judging whether to generate an early warning instruction according to all the risk evaluation values;

[0169] The second processing module is further used for:

[0170] Successively setting a i as the target monitoring sub-region according to the monitoring sub-region sequence A;

[0171] Set multiple first-level monitoring points and multiple second-level monitoring points within the target monitoring sub-region;

[0172] Collect the ammonia concentration values of each first-level monitoring point according to the preset feedback time node;

[0173] Generate a monitoring data packet for the target monitoring sub-region based on all the ammonia concentration values;

[0174] Generate the monitoring data packets for each monitoring sub-region in sequence.

[0175] In a preferred embodiment of the present application, the third processing module is further configured to:

[0176] Obtain the monitoring data packet of the target monitoring sub-region at the current feedback time node;

[0177] Generate an ammonia concentration value sequence B, B = (b 1 , b 2， b i …b m) , where b i is the ammonia concentration value of the i-th first-level monitoring point in the target monitoring sub-region at the current feedback time node; m is the number of first-level monitoring points in the target monitoring sub-region;

[0178] Generate a risk evaluation value f for the target monitoring sub-region at the current monitoring time node;

[0179]

[0180] where, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; b' is the safety value of the ammonia concentration within the target monitoring sub-region; Δb is the average value of all the data in the ammonia concentration value sequence B;

[0181] Generate the risk evaluation values for each monitoring sub-region at the current feedback time node in sequence;

[0182] Establish a risk evaluation value sequence F at the current feedback time node, F = (f 1 , f 2 …f i …f n ), where f i is the risk evaluation value of the i-th monitoring sub-region at the current feedback time node.

[0183] In a preferred embodiment of the present application, the warning module is further configured to:

[0184] Preset a first risk evaluation value threshold F1 and a second risk evaluation value threshold F2, and F1 < F2;

[0185] Obtain the risk evaluation value sequence F of the current feedback time node;

[0186] If f i <F1, the i-th monitoring sub-region does not generate a warning instruction at the current feedback time node;

[0187] If F1 < f i <F2, the i-th monitoring sub-region is a risk sub-region at the current feedback time node;

[0188] If f i >F2, the i-th monitoring sub-region generates a secondary warning instruction at the current feedback time node;

[0189] Establish the risk sub-region sequence A1 of the current feedback time node according to all risk sub-regions, A1 = (a 11 , a 12 …a 1i …a 1n1 ); where, a 1i is the i-th risk sub-region at the current feedback time node; n1 is the number of risk sub-regions at the current feedback time node;

[0190] Generate the abnormal evaluation value of each risk sub-region;

[0191] Judge whether each risk sub-region generates a warning instruction according to all abnormal evaluation values.

[0192] According to the first concept of the present application, by establishing multiple monitoring sub-regions, the ammonia storage area is accurately monitored, and by periodically obtaining the ammonia concentration data in each monitoring sub-region, the ammonia leakage risk is warned in time, and at the same time, the leakage point is quickly located to avoid safety accidents caused by ammonia leakage.

[0193] According to the second concept of the present application, a multi-level monitoring and diagnosis system is established. By collecting the characteristic monitoring index parameters of the risk sub-regions with abnormal states, auxiliary judgment is carried out. At the same time, according to the preset correction model, the ammonia concentration parameters in the risk sub-regions are corrected to improve the diagnosis accuracy of each risk sub-region, warn the potential ammonia leakage risk in time, ensure the stable operation of the power plant, improve the maintenance efficiency, and reduce the maintenance cost.

[0194] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and replacements can be made, and these improvements and replacements should also be regarded as the protection scope of the present application.

Claims

1. A method for monitoring and early warning of ammonia leakage, characterized in that: include: Establish multiple monitoring sub-areas according to equipment parameters, and set monitoring parameters in each monitoring sub-area; Obtain monitoring data packets for each monitoring sub-area according to preset feedback time nodes, and generate risk assessment values ​​for each monitoring sub-area based on all monitoring data packets; Determine whether to generate an early warning instruction based on all risk assessment values; Among them, when multiple monitoring sub-areas are established, they include: Establish a monitoring sub-area sequence A, A = (a1, a2…a i …a n ), where a i is the ith monitoring sub-area, and n is the number of monitoring sub-areas.

2. The ammonia leakage monitoring and early warning method according to claim 1, characterized in that: Obtain monitoring data packets for each monitoring sub-area, including: According to the monitoring sub-area sequence A, set a i Monitor sub-areas for the target; Set up multiple primary monitoring points and multiple secondary monitoring points in the target monitoring sub-area; Collect the ammonia concentration value of each primary monitoring point according to the preset feedback time node; Generate a monitoring data packet for the target monitoring sub-area according to all ammonia concentration values; Generate monitoring data packets for each monitoring sub-area in turn.

3. The ammonia leakage monitoring and early warning method according to claim 2, characterized in that: Generate risk assessment values ​​for each monitoring sub-area, including: Obtain the monitoring data packet of the target monitoring sub-area at the current feedback time node; Generate ammonia concentration value series B, B = (b1, b 2， b i …b m) , where b i is the ammonia concentration value of the i-th primary monitoring point in the target monitoring sub-area at the current feedback time node; m is the number of primary monitoring points in the target monitoring sub-area; Generate a risk assessment value f of the target monitoring sub-area at the current monitoring time node; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; b' is the safe value of ammonia concentration in the target monitoring sub-area; Δb is the average value of all data in the ammonia concentration value series B; Generate the risk assessment value of each monitoring sub-area at the current feedback time node in sequence; Establish the risk evaluation value sequence F of the current feedback time node, F = (f1, f2…f i …f n ), where f i is the risk assessment value of the i-th monitoring sub-area at the current feedback time node.

4. The ammonia leakage monitoring and early warning method according to claim 3, characterized in that: Determine whether to generate an early warning instruction, including: A first risk assessment value threshold F1 and a second risk assessment value threshold F2 are preset, and F1<F2; Get the risk assessment value sequence F of the current feedback time node; If f i <F1, the i-th monitoring sub-area does not generate a warning instruction at the current feedback time node; If F1<f i <F2, the i-th monitoring sub-area is a risk sub-area at the current feedback time node; If f i >F2, the i-th monitoring sub-area generates a secondary warning instruction at the current feedback time node; According to all risk sub-areas, a risk sub-area sequence A1 of the current feedback time node is established, A1=(a 11 , a 12 …a 1i …a 1n1 ), where a 1i is the ith risk sub-region at the current feedback time node; n1 is the number of risk sub-regions at the current feedback time node; Generate anomaly evaluation values ​​for each risk sub-area; Determine whether to generate a warning instruction for each risk sub-area based on all abnormal evaluation values.

5. The ammonia leakage monitoring and early warning method according to claim 4, characterized in that: Generate abnormal evaluation values ​​for each risk sub-area, including: According to the risk sub-area sequence A1, set a 1i is the target risk sub-area; Obtain feedback data from each secondary monitoring point in the target risk sub-area; Generate an abnormal evaluation value k of the target risk sub-area based on all feedback data; Generate abnormal evaluation values ​​for each risk sub-area in turn; Establish the abnormal evaluation value sequence K of the current feedback time node, K = (k1, k2...k i …k n1 ), where k i is the abnormal evaluation value of the i-th risk sub-area at the current feedback time node; Preset a first abnormal evaluation value threshold K1 and a second abnormal evaluation value threshold K2; If k i <K1, the i-th risk sub-area does not generate a warning instruction at the current feedback time node; If K1<k i <K2, the i-th risk sub-area generates a first-level warning instruction at the current feedback time node; If k i >K2, the i-th risk sub-area generates a secondary warning instruction at the current feedback time node.

6. The ammonia leakage monitoring and early warning method according to claim 5, characterized in that: Generate the abnormal evaluation value k of the target risk sub-area, including: Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; θ is the number of characteristic monitoring indicators; μ i is the influencing factor of the i-th characteristic monitoring indicator; g i is the reference value of the i-th characteristic monitoring indicator in the target risk sub-area at the current feedback time node; g' i is the standard reference value of the i-th characteristic monitoring index; Y(i) is the selection coefficient; if (g i -g' i )>0, Y(i)=1; if (g i -g' i )<0, Y(i)=0; U is the modified risk value of the target risk sub-area.

7. The ammonia leakage monitoring and early warning method according to claim 6, characterized in that: Generate the modified risk value U of the target risk sub-area, including: Generate multiple associated sub-regions of the target risk sub-region according to a preset association model; generating correction instructions according to the monitoring data packets of each associated sub-area; Generate a modified risk value U of the target risk sub-area according to the modification instruction; Among them, e5 is the preset fifth weight coefficient; e6 is the preset sixth weight coefficient; Q5 is the preset fifth fixed coefficient; Q6 is the preset sixth fixed coefficient; m1 is the number of primary monitoring points in the target risk sub-area; b 1i is the ammonia concentration value of the current feedback time node of the i-th first-level monitoring point in the target risk sub-area; r is the correction coefficient generated based on the correction instruction; b1' is the safety value of the ammonia concentration in the target risk sub-area; Δb1 is the average ammonia concentration value of all first-level monitoring points in the target risk sub-area.

8. An ammonia gas leakage monitoring and early warning system, using the ammonia gas leakage monitoring and early warning system according to any one of claims 1 to 7, characterized in that: include: The central control unit is used to monitor multiple sub-areas according to equipment parameters; A monitoring unit, comprising a plurality of concentration submodules and a plurality of monitoring submodules, wherein the concentration submodule is used to collect ammonia concentration values ​​in each monitoring subarea; The monitoring submodule is used to collect characteristic monitoring index parameters in each monitoring subarea; The central control unit comprises: The first processing module is used to establish a monitoring sub-area sequence A, A = (a1, a2...a i …a n ), where a i is the ith monitoring sub-area, and n is the number of monitoring sub-areas; The second processing module is used to obtain monitoring data packets of each monitoring sub-area according to a preset feedback time node; The third processing module is used to generate a risk assessment value for each monitoring sub-area according to all monitoring data packets; An early warning module is used to determine whether to generate an early warning instruction based on all risk assessment values; The second processing module is also used for: According to the monitoring sub-area sequence A, set a i Monitor sub-areas for the target; Set up multiple primary monitoring points and multiple secondary monitoring points in the target monitoring sub-area; Collect the ammonia concentration value of each primary monitoring point according to the preset feedback time node; Generate a monitoring data packet for the target monitoring sub-area according to all ammonia concentration values; Generate monitoring data packets for each monitoring sub-area in turn.

9. The ammonia leakage monitoring and early warning system according to claim 8, characterized in that: The third processing module is also used for: Obtain the monitoring data packet of the target monitoring sub-area at the current feedback time node; Generate ammonia concentration value series B, B = (b1, b 2， b i …b m) , where b i is the ammonia concentration value of the i-th primary monitoring point in the target monitoring sub-area at the current feedback time node; m is the number of primary monitoring points in the target monitoring sub-area; Generate a risk assessment value f of the target monitoring sub-area at the current monitoring time node; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; b' is the safe value of ammonia concentration in the target monitoring sub-area; Δb is the average value of all data in the ammonia concentration value series B; Generate the risk assessment value of each monitoring sub-area at the current feedback time node in sequence; Establish the risk evaluation value sequence F of the current feedback time node, F = (f1, f2…f i …f n ), where f i is the risk assessment value of the i-th monitoring sub-area at the current feedback time node.

10. The ammonia leakage monitoring and early warning system according to claim 9, characterized in that: The early warning module is also used for: A first risk assessment value threshold F1 and a second risk assessment value threshold F2 are preset, and F1<F2; Get the risk assessment value sequence F of the current feedback time node; If f i <F1, the i-th monitoring sub-area does not generate a warning instruction at the current feedback time node; If F1<f i <F2, the i-th monitoring sub-area is a risk sub-area at the current feedback time node; If f i >F2, the i-th monitoring sub-area generates a secondary warning instruction at the current feedback time node; According to all risk sub-areas, a risk sub-area sequence A1 of the current feedback time node is established, A1=(a 11 , a 12 …a 1i …a 1n1 ), where a 1i is the ith risk sub-region at the current feedback time node; n1 is the number of risk sub-regions at the current feedback time node; Generate anomaly evaluation values ​​for each risk sub-area; Determine whether to generate a warning instruction for each risk sub-area based on all abnormal evaluation values.

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