Combustor performance monitoring system

Abstract

The invention relates to the technical field of combustor monitoring, in particular to a combustor performance monitoring system. Comprising a central control unit used for establishing a performance evaluation model and generating a plurality of evaluation indexes; the monitoring unit comprises a plurality of monitoring sub-modules, and the monitoring sub-modules are used for collecting operation parameters of the combustor and generating a feedback data packet; and the working condition unit is used for establishing a plurality of working condition categories according to the historical parameters and setting the correction amount of each working condition category. The performance of the combustor is comprehensively monitored through the performance evaluation model and multiple evaluation indexes established by the central control unit, and meanwhile, the performance evaluation model is corrected in real time by establishing multiple working condition parameters, so that the performance evaluation value is more accurate, and interference of working condition factors is eliminated. And early warning is carried out on performance reduction and fault risks of the combustor in time.

Description

Technical Field

[0001] The present application relates to the technical field of burner monitoring, and in particular to a burner performance monitoring system. Background Art

[0002] The burner performance monitoring system is the core equipment of the furnace safety monitoring system. It can monitor the relevant operating parameters of the burner, and continuously perform logical judgments and calculations according to the safety conditions specified in the explosion-proof regulations. Through the corresponding interlocking devices, the burner can complete necessary operations according to the established procedures to avoid the accumulation of explosive air-fuel mixture in the furnace and flue. When conditions that endanger the safety of the burner occur, all fuel entering the burner will be quickly cut off to prevent the occurrence of furnace explosion accidents.

[0003] However, the current burner performance monitoring system often only analyzes one or two monitoring indicators. The detection results of the real-time operating status of the burner are not ideal, the error is large, and it is unable to provide timely warning of burner performance degradation and failure risks. Summary of the invention

[0004] The purpose of this application is: to solve the above-mentioned technical problems, this application provides a burner performance monitoring system, which aims to improve the monitoring efficiency and diagnostic accuracy of burner performance, timely warn of abnormal operating conditions of the burner, and ensure the stability of burner operation.

[0005] In some embodiments of the present application, the performance of the burner is comprehensively monitored through the performance evaluation model and multiple evaluation indicators established by the central control unit. At the same time, by establishing multiple operating parameters, the performance evaluation model is corrected in real time, thereby making the performance evaluation value more accurate and eliminating the interference of operating conditions.

[0006] In some embodiments of the present application, a first-level anchor value is set for each evaluation indicator based on the correction amount under different operating conditions, and the real-time performance of the burner is comprehensively evaluated based on all the first-level anchor values ​​and the real-time reference value and optimal reference value of each evaluation indicator, thereby improving the monitoring accuracy of the burner performance and providing timely warning of burner performance degradation and failure risks.

[0007] In some embodiments of the present application, a burner performance monitoring system is provided, comprising:

[0008] The central control unit is used to establish a performance evaluation model and generate multiple evaluation indicators;

[0009] A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are used to collect operating parameters of the burner and generate a feedback data packet;

[0010] The working condition unit is used to establish multiple working condition categories based on historical parameters and set the correction amount for each working condition category.

[0011] In some embodiments of the present application, the central control unit includes:

[0012] A first control module, used to set working parameters of the monitoring unit;

[0013] A first processing module is used to obtain a feedback data packet according to a preset feedback time node, and extract reference values ​​of various evaluation indicators according to the feedback data packet;

[0014] A first correction module is used to extract the operating condition parameters in the feedback data packet and set the correction coefficient of the performance evaluation model according to the operating condition parameters;

[0015] A second processing module generates a performance evaluation value of the burner according to the revised performance evaluation model and reference values ​​of various evaluation indicators;

[0016] The first warning module determines whether to generate a warning instruction according to the performance evaluation value of the burner.

[0017] In some embodiments of the present application, the operating condition unit includes:

[0018] The first operating condition module is used to generate multiple operating condition indicators and sequentially generate multiple value intervals of a single operating condition indicator according to historical operating parameters;

[0019] The second working condition module selects a single value interval of each working condition indicator in turn to combine, generates a single working condition category, selects in turn in a loop, and establishes a working condition category sequence A, A = (a1, a2...am), where m is the number of working condition categories and ai is the i-th working condition category;

[0020] The third operating condition module is used to set the correction amount for each operating condition category.

[0021] In some embodiments of the present application, the first processing module is further used to:

[0022] Establish an evaluation index sequence B, B = (b1, b2...bn), where n is the number of evaluation indicators and bi is the i-th evaluation indicator;

[0023] Get the feedback data packet of the current feedback time node;

[0024] According to the evaluation index sequence B, the i-th evaluation index is set as the target evaluation index;

[0025] Obtain all collected values ​​of the target evaluation indicators in the feedback data packet and remove abnormal data;

[0026] Generate a reference value of the target evaluation index at the current feedback time node according to the average value of the remaining collected values ​​in the elimination result;

[0027] Generate the evaluation index reference value sequence C of the current feedback time, C = (c1, c2...cn), where ci is the reference value of the i-th evaluation index at the current feedback time node.

[0028] In some embodiments of the present application, the first correction module is further used to:

[0029] Extract current operating condition parameters according to the feedback data packet of the current feedback time node;

[0030] Generate similarity evaluation values ​​between the working condition at the current feedback time node and each working condition category according to the current working condition parameters and the preset similarity evaluation model;

[0031] Establish a similarity evaluation value sequence F, F = (f1, f2...fm), where fi is the similarity evaluation value between the working condition at the current feedback time node and the i-th working condition category;

[0032] Preset a first similarity evaluation value threshold F1;

[0033] If fi ≥ F1 exists, the working condition of the current feedback time node is set as the i-th working condition category, and the first-level anchor value of each evaluation indicator of the current feedback time node is set according to the correction amount of the i-th working condition category;

[0034] If all the data in the similarity evaluation value series are less than F1, select the working condition categories corresponding to the two largest similarity evaluation values ​​in the similarity evaluation value series F, and set the primary anchor value of each evaluation indicator at the current feedback time node according to the correction amount of the selected working condition category.

[0035] Establish a first-level anchor value sequence D, D = (d1, d2...dn), where di is the first-level anchor value of the i-th evaluation indicator at the current feedback time node.

[0036] In some embodiments of the present application, the second processing module is further used to:

[0037] Set the optimal reference value of each evaluation index and establish the optimal reference value sequence H, H = (h1, h2...hn), where hi is the optimal reference value of the i-th evaluation index;

[0038] Generate the performance evaluation value k of the current feedback time node;

[0039]

[0040] Among them, Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; αi is the influencing factor of the i-th evaluation indicator; μi is the fluctuation compensation coefficient of the i-th evaluation indicator at the current feedback time node.

[0041] In some embodiments of the present application, the first control module is further used to:

[0042] Get the performance evaluation value k1 of the previous feedback time node;

[0043] According to the performance evaluation value k1, set the number of collection times r of all evaluation indicators at the current feedback time node;

[0044] Presetting a first performance evaluation value interval (K1, K2), a second performance evaluation value interval (K2, K3), and a third performance evaluation value interval (K3, K4);

[0045] If the performance evaluation value k1 is within the preset first performance evaluation value interval, the acquisition times r is set to the preset first acquisition times r1, that is, r=r1;

[0046] If the performance evaluation value k1 is within the preset second performance evaluation value interval, the acquisition times r is set to the preset second acquisition times r2, that is, r=r2;

[0047] If the performance evaluation value k1 is within the preset third performance evaluation value interval, the acquisition number r is set to the preset third acquisition number r3, that is, r=r3; and r1>r2>r3.

[0048] In some embodiments of the present application, when setting the fluctuation compensation coefficient μi, it includes:

[0049] Select target evaluation indicators;

[0050] Obtain all collected values ​​of the target evaluation indicators in the feedback data packet;

[0051] Establish the collection value series J of the target evaluation index, J = (j1, j2…j r ), where j i is the i-th collected value, r is the number of collections at the current feedback time node;

[0052] Eliminate abnormal data;

[0053] Generate a first-level collection value sequence J1 according to the elimination result, J1 = (j'1, j'2...j' r4 ), where j' i is the i-th level collection value, (r-r4) is the rejection amount;

[0054] Generate the fluctuation evaluation value t of the target evaluation index according to the first-level collection value series J1;

[0055]

[0056] Among them, e4 is the preset fourth weight coefficient, e5 is the preset fifth weight coefficient, Q4 is the preset fourth fixed coefficient; Q5 is the preset fifth fixed coefficient; Δj is the average value of all data in the first-level collection value series J1;

[0057] Set the fluctuation compensation coefficient μ of the target evaluation index according to the fluctuation evaluation value t;

[0058] Generate the fluctuation compensation coefficient of each evaluation indicator at the current feedback time node in turn.

[0059] In some embodiments of the present application, when setting the fluctuation compensation coefficient μ of the target evaluation index, it includes:

[0060] Presetting a first fluctuation evaluation value interval (T1, T2), a second fluctuation evaluation value interval (T2, T3) and a third fluctuation evaluation value interval (T3, T4);

[0061] If the fluctuation evaluation value t is within the preset first fluctuation evaluation value interval, the fluctuation compensation coefficient μ is set to the preset first fluctuation compensation coefficient μ' 1， That is, μ = μ'1;

[0062] If the fluctuation evaluation value t is within the preset second fluctuation evaluation value interval, the fluctuation compensation coefficient μ is set to the preset second fluctuation compensation coefficient μ' 2， That is, μ = μ'2;

[0063] If the fluctuation evaluation value t is within the preset third fluctuation evaluation value interval, the fluctuation compensation coefficient μ is set to the preset third fluctuation compensation coefficient μ' 3， That is, μ = μ' 3； And μ'3<μ'2<μ'1=1.

[0064] Compared with the prior art, the burner performance monitoring system of the embodiment of the present application has the following beneficial effects:

[0065] The performance evaluation model and multiple evaluation indicators established by the central control unit can comprehensively monitor the performance of the burner. At the same time, by establishing multiple operating parameters, the performance evaluation model can be corrected in real time, making the performance evaluation value more accurate and eliminating the interference of operating conditions.

[0066] Based on the correction amount under different working conditions, the first-level anchor value of each evaluation indicator is set, and the real-time performance of the burner is comprehensively evaluated according to all the first-level anchor values ​​and the real-time reference value and optimal reference value of each evaluation indicator, so as to improve the monitoring accuracy of the burner performance and provide timely warning of burner performance degradation and failure risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a structural schematic diagram of a burner performance monitoring system in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0068] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0069] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply 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 understood as a limitation on the present application.

[0070] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0071] In the description of this 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 can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0072] like Figure 1 As shown, a burner performance monitoring system according to a preferred embodiment of the present application includes:

[0073] The central control unit is used to establish a performance evaluation model and generate multiple evaluation indicators;

[0074] A monitoring unit, including a plurality of monitoring submodules, wherein the monitoring submodules are used to collect operating parameters of the burner and generate a feedback data packet;

[0075] The working condition unit is used to establish multiple working condition categories based on historical parameters and set the correction amount for each working condition category.

[0076] Specifically, the central control unit includes:

[0077] A first control module, used to set working parameters of the monitoring unit;

[0078] A first processing module is used to obtain a feedback data packet according to a preset feedback time node, and extract reference values ​​of various evaluation indicators according to the feedback data packet;

[0079] A first correction module is used to extract the operating condition parameters in the feedback data packet and set the correction coefficient of the performance evaluation model according to the operating condition parameters;

[0080] A second processing module generates a performance evaluation value of the burner according to the revised performance evaluation model and reference values ​​of various evaluation indicators;

[0081] The first warning module determines whether to generate a warning instruction according to the performance evaluation value of the burner.

[0082] Specifically, the evaluation indicators include but are not limited to: burner efficiency, burner power, burner failure frequency, burner adjustment range and various operating parameters of the burner.

[0083] Specifically, the operating unit includes:

[0084] The first operating condition module is used to generate multiple operating condition indicators and sequentially generate multiple value intervals of a single operating condition indicator according to historical operating parameters;

[0085] The second working condition module selects a single value interval of each working condition indicator in turn to combine, generates a single working condition category, selects in turn in a loop, and establishes a working condition category sequence A, A = (a1, a2...am), where m is the number of working condition categories and ai is the i-th working condition category;

[0086] The third operating condition module is used to set the correction amount for each operating condition category.

[0087] Specifically, the operating condition indicators include multiple parameters such as fuel flow, coal powder quality, and gas pressure. By gradient differentiation of each parameter and random combination, multiple operating condition categories are generated.

[0088] Specifically, the correction amount based on different operating condition categories refers to the fluctuation of the standard reference value of each evaluation index caused by the influence of the operating condition parameters. By setting the correction amount, the disturbance of the operating condition change on the performance evaluation is eliminated, thereby avoiding misjudgment of the burner performance due to the operating condition change.

[0089] In a preferred embodiment of the present application, the first processing module is also used for:

[0090] Establish an evaluation index sequence B, B = (b1, b2...bn), where n is the number of evaluation indicators and bi is the i-th evaluation indicator;

[0091] Get the feedback data packet of the current feedback time node;

[0092] According to the evaluation index sequence B, the i-th evaluation index is set as the target evaluation index;

[0093] Obtain all collected values ​​of the target evaluation indicators in the feedback data packet and remove abnormal data;

[0094] Generate a reference value of the target evaluation index at the current feedback time node according to the average value of the remaining collected values ​​in the elimination result;

[0095] Generate the evaluation index reference value sequence C of the current feedback time, C = (c1, c2...cn), where ci is the reference value of the i-th evaluation index at the current feedback time node.

[0096] Specifically, when collecting data at each feedback time node, it is necessary to collect multiple operating values ​​for each evaluation indicator, eliminate abnormal data, and then generate reference values ​​for each evaluation indicator at a single feedback time node based on the average value of the eliminated data, thereby improving data collection accuracy and providing data support for subsequent burner performance evaluation.

[0097] Specifically, the first correction module is also used to:

[0098] Extract current operating condition parameters according to the feedback data packet of the current feedback time node;

[0099] Generate similarity evaluation values ​​between the working condition at the current feedback time node and each working condition category according to the current working condition parameters and the preset similarity evaluation model;

[0100] Establish a similarity evaluation value sequence F, F = (f1, f2...fm), where fi is the similarity evaluation value between the working condition at the current feedback time node and the i-th working condition category;

[0101] Preset a first similarity evaluation value threshold F1;

[0102] If fi ≥ F1 exists, the working condition of the current feedback time node is set as the i-th working condition category, and the first-level anchor value of each evaluation indicator of the current feedback time node is set according to the correction amount of the i-th working condition category;

[0103] If all the data in the similarity evaluation value series are less than F1, select the working condition categories corresponding to the two largest similarity evaluation values ​​in the similarity evaluation value series F, and set the primary anchor value of each evaluation indicator at the current feedback time node according to the correction amount of the selected working condition category.

[0104] Establish a first-level anchor value sequence D, D = (d1, d2...dn), where di is the first-level anchor value of the i-th evaluation indicator at the current feedback time node.

[0105] Specifically, the current real-time working condition is determined according to the similarity evaluation model. If there is a situation where the similarity evaluation value is greater than the threshold, the correction value of the corresponding working condition category can be directly called. If there is no situation where the similarity evaluation value is greater than the threshold, the two working condition categories with the highest similarity evaluation values ​​are selected, and the correction values ​​of the two working condition categories are weighted according to the ratio of the corresponding similarity evaluation values ​​to obtain the final correction value.

[0106] Specifically, the initial standard reference value of each evaluation index is set according to the historical operating parameters, and the primary anchor value of each evaluation index is set according to the real-time correction amount and the initial standard reference value, eliminating the disturbance of operating condition fluctuations on performance evaluation.

[0107] In a preferred embodiment of the present application, the second processing module is also used for:

[0108] Set the optimal reference value of each evaluation index and establish the optimal reference value sequence H, H = (h1, h2...hn), where hi is the optimal reference value of the i-th evaluation index;

[0109] Generate the performance evaluation value k of the current feedback time node;

[0110]

[0111] Among them, Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; αi is the influencing factor of the i-th evaluation indicator; μi is the fluctuation compensation coefficient of the i-th evaluation indicator at the current feedback time node.

[0112] Specifically, all data are normalized by presetting a first fixed coefficient, a second fixed coefficient, and a third fixed coefficient so that each parameter is within the same value range.

[0113] Specifically, the lower the performance evaluation value, the greater the possibility that the current burner has performance degradation and failure risks. When the performance evaluation value is lower than the preset threshold, a maintenance instruction needs to be generated to promptly eliminate the failure risks in the burner and ensure the stable operation of the burner.

[0114] It can be understood that in the above embodiment, based on the correction amount under different working conditions, the first-level anchor value of each evaluation indicator is set, and the real-time performance of the burner is comprehensively evaluated according to all the first-level anchor values ​​and the real-time reference value and the optimal reference value of each evaluation indicator, so as to improve the monitoring accuracy of the burner performance and provide timely warning of burner performance degradation and failure risks.

[0115] In a preferred embodiment of the present application, the first control module is also used for:

[0116] Get the performance evaluation value k1 of the previous feedback time node;

[0117] According to the performance evaluation value k1, set the number of collection times r of all evaluation indicators at the current feedback time node;

[0118] Presetting a first performance evaluation value interval (K1, K2), a second performance evaluation value interval (K2, K3), and a third performance evaluation value interval (K3, K4);

[0119] If the performance evaluation value k1 is within the preset first performance evaluation value interval, the acquisition times r is set to the preset first acquisition times r1, that is, r=r1;

[0120] If the performance evaluation value k1 is within the preset second performance evaluation value interval, the acquisition times r is set to the preset second acquisition times r2, that is, r=r2;

[0121] If the performance evaluation value k1 is within the preset third performance evaluation value interval, the acquisition number r is set to the preset third acquisition number r3, that is, r=r3; and r1>r2>r3.

[0122] Specifically, by dynamically adjusting the number of collection times r of the evaluation index, accidental results caused by single sampling can be avoided, while ensuring the data collection accuracy of each evaluation index and reducing the monitoring cost of the burner.

[0123] In a preferred embodiment of the present application, when setting the fluctuation compensation coefficient μi, it includes:

[0124] Select target evaluation indicators;

[0125] Obtain all collected values ​​of the target evaluation indicators in the feedback data packet;

[0126] Establish the collection value series J of the target evaluation index, J = (j1, j2…jr ), where j i is the i-th collected value, r is the number of collections at the current feedback time node;

[0127] Eliminate abnormal data;

[0128] Generate a first-level collection value sequence J1 according to the elimination result, J1 = (j'1, j'2...j' r4 ), where j' i is the i-th level collection value, (r-r4) is the rejection amount;

[0129] Generate the fluctuation evaluation value t of the target evaluation index according to the first-level collection value series J1;

[0130]

[0131] Among them, e4 is the preset fourth weight coefficient, e5 is the preset fifth weight coefficient, Q4 is the preset fourth fixed coefficient; Q5 is the preset fifth fixed coefficient; Δj is the average value of all data in the first-level collection value series J1;

[0132] Set the fluctuation compensation coefficient μ of the target evaluation index according to the fluctuation evaluation value t;

[0133] Generate the fluctuation compensation coefficient of each evaluation indicator at the current feedback time node in turn.

[0134] Specifically, each parameter is normalized by setting the fourth fixed coefficient and the fifth fixed coefficient, so that each parameter is within the same value range.

[0135] Specifically, the larger the fluctuation evaluation value is, the more unstable the current evaluation index is, and the burner is also at risk of unstable operation.

[0136] Specifically, when setting the fluctuation compensation coefficient μ of the target evaluation indicator, it includes:

[0137] Presetting a first fluctuation evaluation value interval (T1, T2), a second fluctuation evaluation value interval (T2, T3) and a third fluctuation evaluation value interval (T3, T4);

[0138] If the fluctuation evaluation value t is within the preset first fluctuation evaluation value interval, the fluctuation compensation coefficient μ is set to the preset first fluctuation compensation coefficient μ' 1， That is, μ = μ'1;

[0139] If the fluctuation evaluation value t is within the preset second fluctuation evaluation value interval, the fluctuation compensation coefficient μ is set to the preset second fluctuation compensation coefficient μ' 2， That is, μ = μ'2;

[0140] If the fluctuation evaluation value t is within the preset third fluctuation evaluation value interval, the fluctuation compensation coefficient μ is set to the preset third fluctuation compensation coefficient μ' 3， That is, μ = μ' 3； And μ'3<μ'2<μ'1=1.

[0141] Specifically, the fluctuation compensation coefficient of each evaluation index is dynamically adjusted according to the real-time fluctuation evaluation value, thereby improving the diagnostic accuracy of the burner's real-time performance diagnosis, timely repairing the burner's operating fluctuations, and eliminating the risk of failure.

[0142] According to the first concept of the present application, the performance of the burner is comprehensively monitored through the performance evaluation model and multiple evaluation indicators established by the central control unit. At the same time, by establishing multiple operating parameters, the performance evaluation model is corrected in real time, thereby making the performance evaluation value more accurate and eliminating the interference of operating conditions.

[0143] According to the second concept of the present application, based on the correction amount under different working conditions, the primary anchor value of each evaluation index is set, and the real-time performance of the burner is comprehensively evaluated according to all the primary anchor values ​​and the real-time reference value and the optimal reference value of each evaluation index, so as to improve the monitoring accuracy of the burner performance and timely warn the burner performance degradation and failure risk. The above is only the preferred implementation mode of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and replacements can be made without departing from the technical principles of the present application, and these improvements and replacements should also be regarded as the protection scope of the present application.

Claims

1. A burner performance monitoring system, characterized in that: include: The central control unit is used to establish a performance evaluation model and generate multiple evaluation indicators; A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are used to collect operating parameters of the burner and generate a feedback data packet; The working condition unit is used to establish multiple working condition categories based on historical parameters and set the correction amount for each working condition category.

2. The burner performance monitoring system according to claim 1, characterized in that: The central control unit comprises: A first control module, used to set working parameters of the monitoring unit; A first processing module is used to obtain a feedback data packet according to a preset feedback time node, and extract reference values ​​of various evaluation indicators according to the feedback data packet; A first correction module is used to extract the operating condition parameters in the feedback data packet and set the correction coefficient of the performance evaluation model according to the operating condition parameters; A second processing module generates a performance evaluation value of the burner according to the revised performance evaluation model and reference values ​​of various evaluation indicators; The first warning module determines whether to generate a warning instruction according to the performance evaluation value of the burner.

3. The burner performance monitoring system according to claim 2, characterized in that: The operating unit comprises: The first operating condition module is used to generate multiple operating condition indicators and sequentially generate multiple value intervals of a single operating condition indicator according to historical operating parameters; The second working condition module selects a single value interval of each working condition indicator in turn to combine, generates a single working condition category, selects in turn in a loop, and establishes a working condition category sequence A, A = (a1, a2...am), where m is the number of working condition categories and ai is the i-th working condition category; The third operating condition module is used to set the correction amount for each operating condition category.

4. The burner performance monitoring system according to claim 3, characterized in that: The first processing module is also used for: Establish an evaluation index sequence B, B = (b1, b2...bn), where n is the number of evaluation indicators and bi is the i-th evaluation indicator; Get the feedback data packet of the current feedback time node; According to the evaluation index sequence B, the i-th evaluation index is set as the target evaluation index; Obtain all collected values ​​of the target evaluation indicators in the feedback data packet and remove abnormal data; Generate a reference value of the target evaluation index at the current feedback time node according to the average value of the remaining collected values ​​in the elimination result; Generate the evaluation index reference value sequence C of the current feedback time, C = (c1, c2...cn), where ci is the reference value of the i-th evaluation index at the current feedback time node.

5. The burner performance monitoring system according to claim 4, characterized in that: The first correction module is also used for: Extract current operating condition parameters according to the feedback data packet of the current feedback time node; Generate similarity evaluation values ​​between the working condition at the current feedback time node and each working condition category according to the current working condition parameters and the preset similarity evaluation model; Establish a similarity evaluation value sequence F, F = (f1, f2...fm), where fi is the similarity evaluation value between the working condition at the current feedback time node and the i-th working condition category; Preset a first similarity evaluation value threshold F1; If fi ≥ F1 exists, the working condition of the current feedback time node is set as the i-th working condition category, and the first-level anchor value of each evaluation indicator of the current feedback time node is set according to the correction amount of the i-th working condition category; If all the data in the similarity evaluation value series are less than F1, select the working condition categories corresponding to the two largest similarity evaluation values ​​in the similarity evaluation value series F, and set the primary anchor value of each evaluation indicator at the current feedback time node according to the correction amount of the selected working condition category. Establish a first-level anchor value sequence D, D = (d1, d2...dn), where di is the first-level anchor value of the i-th evaluation indicator at the current feedback time node.

6. The burner performance monitoring system according to claim 5, characterized in that: The second processing module is also used for: Set the optimal reference value of each evaluation index and establish the optimal reference value sequence H, H = (h1, h2...hn), where hi is the optimal reference value of the i-th evaluation index; Generate the performance evaluation value k of the current feedback time node; Among them, Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; αi is the influencing factor of the i-th evaluation indicator; μi is the fluctuation compensation coefficient of the i-th evaluation indicator at the current feedback time node.

7. The burner performance monitoring system according to claim 6, characterized in that: The first control module is also used for: Get the performance evaluation value k1 of the previous feedback time node; According to the performance evaluation value k1, set the number of collection times r of all evaluation indicators at the current feedback time node; Presetting a first performance evaluation value interval (K1, K2), a second performance evaluation value interval (K2, K3), and a third performance evaluation value interval (K3, K4); If the performance evaluation value k1 is within the preset first performance evaluation value interval, the acquisition times r is set to the preset first acquisition times r1, that is, r=r1; If the performance evaluation value k1 is within the preset second performance evaluation value interval, the acquisition times r is set to the preset second acquisition times r2, that is, r=r2; If the performance evaluation value k1 is within the preset third performance evaluation value interval, the acquisition number r is set to the preset third acquisition number r3, that is, r=r3; and r1>r2>r3.

8. The burner performance monitoring system according to claim 7, characterized in that: When setting the fluctuation compensation coefficient μi, it includes: Select target evaluation indicators; Obtain all collected values ​​of the target evaluation indicators in the feedback data packet; Establish the collection value series J of the target evaluation index, J = (j1, j2…j r ), where j i is the i-th collected value, r is the number of collections at the current feedback time node; Eliminate abnormal data; Generate a first-level collection value sequence J1 according to the elimination result, J1 = (j'1, j'2...j' r4 ), where j' i is the i-th level collection value, (r-r4) is the rejection amount; Generate the fluctuation evaluation value t of the target evaluation index according to the first-level collection value series J1; Among them, e4 is the preset fourth weight coefficient, e5 is the preset fifth weight coefficient, Q4 is the preset fourth fixed coefficient; Q5 is the preset fifth fixed coefficient; Δj is the average value of all data in the first-level collection value series J1; Set the fluctuation compensation coefficient μ of the target evaluation index according to the fluctuation evaluation value t; Generate the fluctuation compensation coefficient of each evaluation indicator at the current feedback time node in turn.

9. The burner performance monitoring system according to claim 8, characterized in that: When setting the fluctuation compensation coefficient μ of the target evaluation index, it includes: Presetting a first fluctuation evaluation value interval (T1, T2), a second fluctuation evaluation value interval (T2, T3) and a third fluctuation evaluation value interval (T3, T4); If the fluctuation evaluation value t is within the preset first fluctuation evaluation value interval, the fluctuation compensation coefficient μ is set to the preset first fluctuation compensation coefficient μ' 1， That is, μ = μ'1; If the fluctuation evaluation value t is within the preset second fluctuation evaluation value interval, the fluctuation compensation coefficient μ is set to the preset second fluctuation compensation coefficient μ' 2， That is, μ = μ'2; If the fluctuation evaluation value t is within the preset third fluctuation evaluation value interval, the fluctuation compensation coefficient μ is set to the preset third fluctuation compensation coefficient μ' 3， That is, μ = μ' 3； And μ'3<μ'2<μ'1=1.

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