An apparatus and method for online monitoring of boiler combustion status based on spectral analysis

Abstract

The present invention discloses a device and method for online monitoring of boiler combustion status based on spectral analysis, which relates to the technical field of boiler combustion monitoring. It includes a processing module for obtaining the original temperature and emissivity of the flame according to the flame spectral signal; an acquisition module for acquiring all factors that may affect the flame temperature, and screening out the factors that affect the flame temperature with a causal relationship as boiler combustion information according to the factors that may affect the flame temperature and the flame temperature; a correction module for performing the first multi-dimensional correction on the original temperature according to the fixed information to obtain the first-corrected temperature, and performing the second multi-dimensional correction on the first-corrected temperature according to the fluctuation information to obtain the second-corrected temperature, and correcting the original emissivity based on the second-corrected temperature. This application performs the first multi-dimensional correction on the original temperature according to the fixed information, and performs the second multi-dimensional correction on the first-corrected temperature according to the fluctuation information, improving the accuracy of boiler combustion status monitoring.

Description

Technical Field

[0001] The present application relates to the technical field of boiler combustion monitoring, and more specifically, to an apparatus and method for online monitoring of boiler combustion status based on spectral analysis. Background Art

[0002] An automatic system that takes monitoring and control measures to protect the boiler furnace from explosion (external explosion and internal explosion) when the combustion in the boiler furnace goes out. The FSSS includes a burner control system (BCS) and a furnace safety system (FSS).

[0003] However, the FSSS mainly monitors the presence or absence of flames and cannot monitor the combustion status of the flames, resulting in low monitoring accuracy.

[0004] Therefore, how to improve the accuracy of flame monitoring is a technical problem to be solved at present. Summary of the Invention

[0005] The present invention provides an apparatus for online monitoring of boiler combustion status based on spectral analysis to solve the technical problem of low accuracy of flame monitoring in the prior art. Applied to an optical fiber spectral analyzer, the apparatus includes:

[0006] A processing module, configured to obtain a flame spectral signal and obtain the original temperature and emissivity of the flame according to the flame spectral signal;

[0007] An acquisition module, configured to obtain all factors that may affect the flame temperature, and screen out the factors that affect the flame temperature and have a causal relationship as boiler combustion information according to the factors that may affect the flame temperature and the flame temperature;

[0008] A correction module, configured to screen out fixed information and fluctuation information according to the boiler combustion information, perform a first multi-dimensional correction on the original temperature according to the fixed information to obtain a first-corrected temperature, perform a second multi-dimensional correction on the first-corrected temperature according to the fluctuation information to obtain a second-corrected temperature, and correct the original emissivity based on the second-corrected temperature.

[0009] In some embodiments of the present application, the acquisition module is specifically configured to:

[0010] Establish a cause state space according to all factors that may affect the flame temperature, and establish an effect state space according to the flame temperature;

[0011] Obtain a non-linear dependence degree according to the cause state space and the effect state space, and determine the factors that affect the flame temperature and have a causal relationship according to the non-linear dependence degree.

[0012] In some embodiments of the present application, the acquisition module is further specifically configured to:

[0013] If the non - linear dependence exceeds the first threshold, the factor that may affect the flame temperature has a causal relationship with the flame temperature.

[0014] In some embodiments of the present application, the correction module is further configured to:

[0015] Determine the weight of the fixed information based on the non - linear dependence, and determine the correction order of the fixed information for the original temperature according to the weight of the fixed information.

[0016] In some embodiments of the present application, the correction module is specifically configured to:

[0017] Perform a first multi - dimensional correction on the original temperature according to the fixed information, including:

[0018] Correct the original temperature based on the fixed information in sequence according to the correction order;

[0019] After each correction, determine the compensation value according to the correction value, and perform the next correction based on the compensation value and the correction value.

[0020] In some embodiments of the present application, the correction module is specifically configured to:

[0021] Determine the weight of the fluctuation information according to the fluctuation information, and determine the correction order of the fluctuation information for the once - corrected temperature according to the weight of the fluctuation information.

[0022] In some embodiments of the present application, the correction module is specifically configured to:

[0023] Perform a second multi - dimensional correction on the once - corrected temperature according to the fluctuation information, including:

[0024] Correct the once - corrected temperature based on the fluctuation information in sequence according to the correction order;

[0025] After each correction, determine the compensation value according to the correction value, and perform the next correction based on the compensation value and the correction value.

[0026] In some embodiments of the present application, the correction module is specifically configured to:

[0027] Correct the original emissivity based on the twice - corrected temperature, including:

[0028] Determine the correction coefficient of the original emissivity based on the twice - corrected temperature, and correct the original emissivity according to the correction coefficient of the original emissivity.

[0029] Correspondingly, the present application also provides a method for online monitoring of boiler combustion status based on spectral analysis, which is applied to an optical fiber spectral analyzer, and the method includes:

[0030] Obtain the flame spectral signal, and obtain the original temperature and emissivity of the flame based on the flame spectral signal;

[0031] Obtain all factors that may affect the flame temperature, and based on the factors that may affect the flame temperature and the flame temperature, screen out the factors that affect the flame temperature with a causal relationship as the boiler combustion information;

[0032] Screen out the fixed information and the fluctuating information according to the boiler combustion information, perform the first multi-dimensional correction on the original temperature according to the fixed information to obtain the temperature after the first correction, perform the second multi-dimensional correction on the temperature after the first correction according to the fluctuating information to obtain the temperature after the second correction, and correct the original emissivity based on the temperature after the second correction.

[0033] By applying the above technical solutions, a processing module is used to obtain the flame spectral signal and obtain the original temperature and emissivity of the flame based on the flame spectral signal; an acquisition module is used to obtain all factors that may affect the flame temperature, and based on the factors that may affect the flame temperature and the flame temperature, screen out the factors that affect the flame temperature with a causal relationship as the boiler combustion information; a correction module is used to screen out the fixed information and the fluctuating information according to the boiler combustion information, perform the first multi-dimensional correction on the original temperature according to the fixed information to obtain the temperature after the first correction, perform the second multi-dimensional correction on the temperature after the first correction according to the fluctuating information to obtain the temperature after the second correction, and correct the original emissivity based on the temperature after the second correction. This application performs the first multi-dimensional correction on the original temperature according to the fixed information and the second multi-dimensional correction on the temperature after the first correction according to the fluctuating information, improving the accuracy of monitoring the boiler combustion state (temperature and emissivity). Brief Description of the Drawings

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0035] Figure 1 Shows a schematic structural diagram of a device for online monitoring of boiler combustion state based on spectral analysis proposed in an embodiment of the present invention;

[0036] Figure 2 Shows a schematic flowchart of a method for online monitoring of boiler combustion state based on spectral analysis proposed in an embodiment of the present invention. Detailed Embodiments

[0037] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0038] The embodiment of the present application provides a device for online monitoring of boiler combustion state based on spectral analysis, which is applied to an optical fiber spectral analyzer. The principle of the optical fiber spectral analyzer is that the radiant energy of the flame is converged by the lens in the probe and then projected onto the optical fiber. The radiant signal is transmitted to the CCD optical fiber spectrometer through the optical fiber, and the obtained flame spectral signal is input into a computer for processing.

[0039] As shown in Figure 1 the figure, the device includes:

[0040] A processing module, configured to obtain a flame spectral signal and obtain the original temperature and emissivity of the flame according to the flame spectral signal.

[0041] In this embodiment, the obtained temperature and emissivity here are the most original data used to describe the boiler combustion state. There are many interferences in the original data and it needs to be corrected.

[0042] An acquisition module, configured to acquire all factors that may affect the flame temperature, and screen out the factors that affect the flame temperature with a causal relationship as boiler combustion information according to the factors that may affect the flame temperature and the flame temperature.

[0043] In this embodiment, all factors that may affect the flame temperature include but are not limited to boiler conditions, the number of viewing holes, the alkali metal content in coal, boiler load, the number of burner layers, pulverized coal information, and the amount of high-temperature ash. Screen the causal relationship of the above factors, and use the factors with causal relationship after screening as boiler combustion information.

[0044] In order to improve in some embodiments of the present application, the acquisition module is specifically configured to: establish a cause state space according to all factors that may affect the flame temperature, and establish an effect state space according to the flame temperature; obtain the non-linear dependence according to the cause state space and the effect state space, and determine the factors that affect the flame temperature with a causal relationship according to the non-linear dependence.

[0045] In this embodiment, all factors that may affect the flame temperature here are historical data, and the flame temperature is also historical data. There is a time sequence between the two, that is, the time to obtain the factors is before and the time of the flame temperature is after.

[0046] In some embodiments of the present application, the acquisition module is further specifically configured to: if the non-linear dependence exceeds a first threshold, then the factor that may affect the flame temperature has a causal relationship with the flame temperature.

[0047] Establish state spaces for all factors that may affect the flame temperature and the flame temperature, and perform all possible combinations pairwise. Note that it is a pairwise combination of the factors that may affect the flame temperature and the flame temperature. Set the cause state space as X and the effect state space as Y, and calculate the average Euclidean distance between Xn and its k nearest neighbor points. The formula is as follows:

[0048]

[0049] For the sample point xn in X, xrn,1, xrn,2,... xrn,k represent the k nearest neighbor points of xn in X.

[0050] For the sample point yn in Y, ysn,1, ysn,2... ysn,k represent the k nearest neighbor points of yn in Y. Map them to the X space and calculate the average Euclidean distance between xn and its k nearest neighbor points xsn,1, xsn,2,... xsn,k. The formula is as follows:

[0051]

[0052] Judge the causal relationship between the two according to the mapping relationship of the state space. The non-linear dependence formula is as follows:

[0053]

[0054] 0 < S X→Y ≤ 1, S X→Y approaches 0 (less than a preset first threshold), the state spaces X and Y are independent of each other, that is, the factor that may affect the flame temperature and the flame temperature are independent of each other, and there is no causal relationship between the two. S X→Y is significantly greater than 0, there is a causal relationship from X to Y, that is, the factor that may affect the flame temperature and the flame temperature have a causal relationship. S X→Y The greater the value, the stronger the causality.

[0055] The correction module is used to screen out fixed information and fluctuating information according to the boiler combustion information, perform the first multi-dimensional correction on the original temperature according to the fixed information to obtain the temperature after the first correction, perform the second multi-dimensional correction on the temperature after the first correction according to the fluctuating information to obtain the temperature after the second correction, and correct the original emissivity based on the temperature after the second correction.

[0056] In this embodiment, the fixed information refers to information that does not change and has strong fixity, such as the number of sight glass holes and the number of burners. The fluctuating information refers to information with strong volatility that changes with some factors, such as the boiler load and pulverized coal information. The pulverized coal information includes properties such as coal particle size, concentration, and temperature. The original temperature is first corrected multi-dimensionally according to the fixed information, and the corrected temperature is used as the temperature after the first correction. The temperature after the first correction is secondarily corrected multi-dimensionally according to the fluctuating information. Since there is a Planck's law between temperature and emissivity, there are certain rules for the change of flame radiation with wavelength and temperature. The original emissivity is corrected according to the corrected temperature after the second correction.

[0057] In some embodiments of the present application, in order to improve the accuracy of the correction, the correction module is further configured to: determine the weight of the fixed information based on the non-linear dependence degree, and determine the correction order of the fixed information on the original temperature according to the weight of the fixed information.

[0058] In this embodiment, the non-linear dependence degree can describe the causal strength between the fixed information and the flame temperature. Based on this, the weight of the fixed information is determined. The greater the weight, the earlier the correction order. This is to avoid correcting the temperature by multiple factors simultaneously, which may lead to a larger error.

[0059] In some embodiments of the present application, the correction module is specifically configured to: perform the first multi-dimensional correction on the original temperature according to the fixed information, including: correcting the original temperature based on the fixed information in sequence according to the correction order; determining the compensation value according to the correction value after each correction, and performing the next correction based on the compensation value and the correction value.

[0060] In this embodiment, for example, the fixed information includes the number of sight glass holes, the number of burners, and the distance from the flame detector probe to the flame. The weights of the three are determined according to the non-linear dependence degree, and the weights of the number of sight glass holes, the number of burners, and the distance from the flame detector probe to the flame gradually decrease. That is, first correct the original temperature according to the number of sight glass holes, set the sum of the corrected value and the compensation value as the first quantity, then correct the first quantity according to the number of burners, set the sum of the corrected value and the compensation value as the second quantity, and then correct according to the distance from the flame detector probe to the flame, and set the sum of the corrected value and the compensation value as the third quantity.

[0061] Specifically:

[0062] Set the number of sight glass holes as A, and preset the number of sight glass hole array A0(A1, A2, A3, A4), where A1, A2, A3, A4 are all preset values, and A1 < A2 < A3 < A4;

[0063] Set the initial temperature as H, and preset the initial temperature correction coefficient array F0(F1, F2, F3, F4), where F1, F2, F3, F4 are all preset values, and 0.8 < F1 < F2 < F3 < F4 < 1.2;

[0064] Determine a correction coefficient according to the relationship between the number of sight holes for observing fire and each preset number of sight holes for observing fire, and correct the initial temperature;

[0065] If A < A1, determine the first preset correction coefficient F1 as the correction coefficient, and the corrected initial temperature is H * F1;

[0066] If A1 ≤ A < A2, determine the second preset correction coefficient F2 as the correction coefficient, and the corrected initial temperature is H * F2;

[0067] If A2 ≤ A < A3, determine the third preset correction coefficient F3 as the correction coefficient, and the corrected initial temperature is H * F3;

[0068] If A3 ≤ A < A4, determine the fourth preset correction coefficient F4 as the correction coefficient, and the corrected initial temperature is H * F4.

[0069] Obtain a compensation value from the compensation value table according to the magnitude of H * F0, and H * F0 + the compensation value is the first quantity.

[0070] Set the number of burner layers as B, and the preset burner layer array B0(B1, B2, B3, B4), where B1, B2, B3, B4 are all preset values, and B1 < B2 < B3 < B4;

[0071] Set the first quantity as H1, and the preset correction coefficient array G0(G1, G2, G3, G4), where G1, G2, G3, G4 are all preset values, and 0.8 < G1 < G2 < G3 < G4 < 1.2;

[0072] Determine a correction coefficient according to the relationship between the number of burner layers and each preset number of burner layers, and correct the first quantity;

[0073] If B < B1, determine the first preset correction coefficient G1 as the correction coefficient, and the corrected temperature is H1 * G1;

[0074] If B1 ≤ B < B2, determine the second preset correction coefficient G2 as the correction coefficient, and the corrected temperature is H1 * G2;

[0075] If B2 ≤ B < B3, determine the third preset correction coefficient G3 as the correction coefficient, and the corrected temperature is H1 * G3;

[0076] If B3 ≤ B < B4, determine the fourth preset correction coefficient G4 as the correction coefficient, and the corrected temperature is H1 * G4.

[0077] Find the compensation value from the compensation table according to H1 * G0, and H1 * G0 + the compensation value is the second quantity;

[0078] Set the distance from the flame detector probe to the flame as C, and preset an array of distances from the flame detector probe to the flame C0 (C1, C2, C3, C4), where C1, C2, C3, and C4 are all preset values, and C1 < C2 < C3 < C4;

[0079] Set the second quantity as H2, and preset an array of correction factors J0 (J1, J2, J3, J4), where J1, J2, J3, and J4 are all preset values, and 0.8 < J1 < J2 < J3 < J4 < 1.2;

[0080] Determine the correction factor according to the relationship between the distance from the flame detector probe to the flame and each preset distance from the flame detector probe to the flame, and correct the second quantity;

[0081] If C < C1, determine the first preset correction factor J1 as the correction factor, and the corrected temperature is J1 * H2;

[0082] If C1 ≤ C < C2, determine the second preset correction factor J2 as the correction factor, and the corrected temperature is J2 * H2;

[0083] If C2 ≤ C < C3, determine the third preset correction factor J3 as the correction factor, and the corrected temperature is J3 * H2;

[0084] If C3 ≤ C < C4, determine the fourth preset correction factor J4 as the correction factor, and the corrected temperature is J4 * H2.

[0085] Find the compensation value in the compensation table according to H2 * J0, and H2 * J0 + the compensation value is the third quantity, that is, the initial temperature after the first correction.

[0086] In order to further improve the accuracy of the correction, in some embodiments of the present application, the correction module is specifically configured to: determine the weight of the fluctuation information according to the fluctuation information, and determine the correction order of the fluctuation information on the temperature after the first correction according to the weight of the fluctuation information.

[0087] In this embodiment, determine the weight of the fluctuation information according to the magnitude of the fluctuation information itself, because the fluctuation information is constantly changing.

[0088] In some embodiments of the present application, the correction module is specifically configured to: perform a second multi-correction on the temperature after the first correction according to the fluctuation information, including: correcting the temperature after the first correction based on the fluctuation information in sequence according to the correction order; determining the compensation value according to the correction value after each correction, and performing the next correction based on the compensation value and the correction value.

[0089] In this embodiment, the specific process of correcting the temperature after the first correction according to the fluctuation information is the same as the process of correcting the initial temperature according to the fixed information, and will not be elaborated here.

[0090] In some embodiments of the present application, the correction module is specifically configured to: correct the original emissivity based on the temperature after the second correction, including: determining the correction coefficient of the original emissivity based on the temperature after the second correction, and correcting the original emissivity according to the correction coefficient of the original emissivity.

[0091] In this embodiment, the above content is specifically:

[0092] Set the temperature after the second correction as D, and preset the temperature array D0 (D1, D2, D3, D4) after the second correction, where D1, D2, D3, D4 are all preset values, and D1 < D2 < D3 < D4;

[0093] Set the original emissivity as P, and preset the correction coefficient array Q0 (Q1, Q2, Q3, Q4) of the original emissivity, where Q1, Q2, Q3, Q4 are all preset values, and 0.8 < Q1 < Q2 < Q3 < Q4 < 1.2;

[0094] Determine the correction coefficient of the original emissivity according to the relationship between the temperature after the second correction and each preset temperature after the second correction, and correct the original emissivity;

[0095] If D < D1, determine the first preset correction coefficient Q1 of the original emissivity as the correction coefficient of the original emissivity, and the corrected emissivity is P * Q1;

[0096] If D1 ≤ D < D2, determine the second preset correction coefficient Q2 of the original emissivity as the correction coefficient of the original emissivity, and the corrected emissivity is P * Q2;

[0097] If D2 ≤ D < D3, determine the third preset correction coefficient Q3 of the original emissivity as the correction coefficient of the original emissivity, and the corrected emissivity is P * Q3;

[0098] If D3 ≤ D < D4, determine the fourth preset correction coefficient Q4 of the original emissivity as the correction coefficient of the original emissivity, and the corrected emissivity is P * Q4.

[0099] By applying the above technical solution, a processing module is configured to obtain a flame spectral signal and derive the original temperature and emissivity of the flame based on the flame spectral signal; an acquisition module is configured to obtain all factors that may affect the flame temperature, and screen out the factors that affect the flame temperature and have a causal relationship as boiler combustion information based on the factors that may affect the flame temperature and the flame temperature; a correction module is configured to screen out fixed information and fluctuation information based on the boiler combustion information, perform a first multi-dimensional correction on the original temperature according to the fixed information to obtain the first-corrected temperature, perform a second multi-dimensional correction on the first-corrected temperature according to the fluctuation information to obtain the second-corrected temperature, and correct the original emissivity based on the second-corrected temperature. In this application, the original temperature is first multi-dimensionally corrected according to the fixed information, and the first-corrected temperature is second multi-dimensionally corrected according to the fluctuation information, which improves the accuracy of monitoring the boiler combustion state (temperature and emissivity).

[0100] Those skilled in the art can understand that the modules in the device in the implementation scenario can be distributed in the device in the implementation scenario according to the description of the implementation scenario, or can be correspondingly changed and located in one or more devices different from this implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0101] Correspondingly, the present application also provides a method for online monitoring of boiler combustion state based on spectral analysis, which is applied to an optical fiber spectral analyzer, as Figure 2 shown, the method includes:

[0102] Obtain a flame spectral signal, and derive the original temperature and emissivity of the flame based on the flame spectral signal;

[0103] Obtain all factors that may affect the flame temperature, and screen out the factors that affect the flame temperature and have a causal relationship as boiler combustion information based on the factors that may affect the flame temperature and the flame temperature;

[0104] Screen out fixed information and fluctuation information based on the boiler combustion information, perform a first multi-dimensional correction on the original temperature according to the fixed information to obtain the first-corrected temperature, perform a second multi-dimensional correction on the first-corrected temperature according to the fluctuation information to obtain the second-corrected temperature, and correct the original emissivity based on the second-corrected temperature.

[0105] In some embodiments of the present application, obtaining a flame spectral signal and deriving the original temperature and emissivity of the flame based on the flame spectral signal includes:

[0106] Establish a cause state space according to all factors that may affect the flame temperature, and establish an effect state space according to the flame temperature;

[0107] Obtain the non-linear dependence according to the cause state space and the effect state space, and determine the factors that affect the flame temperature and have a causal relationship according to the non-linear dependence.

[0108] In some embodiments of the present application, obtaining a flame spectral signal and obtaining the original temperature and emissivity of the flame based on the flame spectral signal includes:

[0109] If the non - linear dependence exceeds a first threshold, the factor that may affect the flame temperature has a causal relationship with the flame temperature.

[0110] In some embodiments of the present application, the method further includes:

[0111] Determining the weight of the fixed information based on the non - linear dependence, and determining the correction order of the fixed information for the original temperature according to the weight of the fixed information.

[0112] In some embodiments of the present application, the method further includes:

[0113] Performing a first multi - dimensional correction on the original temperature according to the fixed information, including:

[0114] Correcting the original temperature based on the fixed information in sequence according to the correction order;

[0115] After each correction, determining a compensation value according to the correction value, and performing the next correction based on the compensation value and the correction value.

[0116] In some embodiments of the present application, the method further includes:

[0117] Determining the weight of the fluctuation information according to the fluctuation information, and determining the correction order of the fluctuation information for the once - corrected temperature according to the weight of the fluctuation information.

[0118] In some embodiments of the present application, the method further includes:

[0119] Performing a second multi - dimensional correction on the once - corrected temperature according to the fluctuation information, including:

[0120] Correcting the once - corrected temperature based on the fluctuation information in sequence according to the correction order;

[0121] After each correction, determining a compensation value according to the correction value, and performing the next correction based on the compensation value and the correction value.

[0122] In some embodiments of the present application, the method further includes:

[0123] Correcting the original emissivity based on the twice - corrected temperature, including:

[0124] Determining a correction coefficient of the original emissivity based on the twice - corrected temperature, and correcting the original emissivity according to the correction coefficient of the original emissivity.

[0125] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by hardware or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present invention.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. An apparatus for online monitoring of boiler combustion status based on spectral analysis, which is applied to an optical fiber spectral analyzer, and is characterized in that, The device includes: A processing module, configured to obtain a flame spectral signal and obtain the original temperature and emissivity of the flame according to the flame spectral signal; An acquisition module, configured to obtain all factors that may affect the flame temperature, and screen out the factors that affect the flame temperature with a causal relationship as boiler combustion information according to the factors that may affect the flame temperature and the flame temperature; A correction module, configured to screen out fixed information and fluctuation information according to the boiler combustion information, perform a first multi-dimensional correction on the original temperature according to the fixed information to obtain the first-corrected temperature, perform a second multi-dimensional correction on the first-corrected temperature according to the fluctuation information to obtain the second-corrected temperature, and correct the original emissivity based on the second-corrected temperature.

2. The device according to claim 1, characterized in that The acquisition module is specifically configured to: Establish a cause state space according to all factors that may affect the flame temperature, and establish an effect state space according to the flame temperature; Obtain the non-linear dependence degree according to the cause state space and the effect state space, and determine the factors that affect the flame temperature with a causal relationship according to the non-linear dependence degree.

3. The device according to claim 2, characterized in that, The acquisition module is also specifically configured to: If the non-linear dependence degree exceeds a first threshold, then the factor that may affect the flame temperature has a causal relationship with the flame temperature.

4. The device according to claim 3, characterized in that The correction module is also configured to: Determine the weight of the fixed information based on the non-linear dependence degree, and determine the correction order of the fixed information for the original temperature according to the weight of the fixed information.

5. The device according to claim 4, characterized in that, The correction module is specifically configured to: Perform a first multi-dimensional correction on the original temperature according to the fixed information, including: Successively correct the original temperature based on the fixed information according to the correction order; Determine a compensation value according to the correction value after each correction, and perform the next correction based on the compensation value and the correction value.

6. The device according to claim 1, characterized in that, The correction module is specifically configured to: Determine the weight of the fluctuation information according to the fluctuation information, and determine the correction order of the fluctuation information for the first-corrected temperature according to the weight of the fluctuation information.

7. The device according to claim 6, characterized in that, The correction module is specifically configured to: Perform a second multi-dimensional correction on the first-corrected temperature according to the fluctuation information, including: Successively correct the first-corrected temperature based on the fluctuation information according to the correction order; Determine a compensation value according to the correction value after each correction, and perform the next correction based on the compensation value and the correction value.

8. The device according to claim 1, characterized in that, The correction module is specifically configured to: Correct the original emissivity based on the second-corrected temperature, including: Determine a correction coefficient of the original emissivity based on the second-corrected temperature, and correct the original emissivity according to the correction coefficient of the original emissivity.

9. A method for online monitoring of boiler combustion status based on spectral analysis, which is applied to an optical fiber spectral analyzer, is characterized in that, The method includes: Obtain a flame spectral signal, and obtain the original temperature and emissivity of the flame according to the flame spectral signal; Obtain all factors that may affect the flame temperature, and screen out the factors that affect the flame temperature with a causal relationship as boiler combustion information according to the factors that may affect the flame temperature and the flame temperature; Screen out fixed information and fluctuation information according to the boiler combustion information, perform a first multi-dimensional correction on the original temperature according to the fixed information to obtain the first-corrected temperature, perform a second multi-dimensional correction on the first-corrected temperature according to the fluctuation information to obtain the second-corrected temperature, and correct the original emissivity based on the second-corrected temperature.

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