Real-time monitoring and automatic blocking system for spark of hot blast stove

Abstract

The invention relates to the technical field of hot-blast stove combustion safety, in particular to a hot-blast stove spark real-time monitoring and automatic blocking system, which comprises a spark monitoring module configured to be capable of collecting spark state information and extracting spark state characteristics from the state information; the combustion monitoring module is configured to be capable of obtaining fuel combustion state data and fuel information; the safety control module is configured to be capable of obtaining data from the spark monitoring module and the combustion monitoring module, reversely deducing combustion state abnormity based on the spark state characteristics, and establishing a mapping relation between the spark state characteristics and the combustion abnormity; and the self-blocking control module is configured to be capable of realizing graded blocking by utilizing a graded blocking strategy according to the mapping relation between the spark state characteristics and the combustion abnormity, so that the system safety is improved.

Description

Technical Field

[0001] This invention relates to the field of hot blast stove combustion safety technology, and in particular to a real-time monitoring and automatic blocking system for hot blast stove sparks. Background Technology

[0002] Combustion safety monitoring in hot blast stoves is a core aspect of industrial safety production, primarily used to prevent accidents such as runaway combustion, explosions, backfires, flameouts, and fires. Spark monitoring is a crucial part of combustion safety monitoring because sparks appearing in high-temperature gases directly reflect the combustion state and serve as key indicators for assessing combustion efficiency, stability, and safety. In the grain drying industry, spark monitoring and blocking within the hot air ducts of hot blast stoves play a vital role in preventing sparks from igniting the grain.

[0003] Existing technologies for monitoring sparks mainly include: tracking the temperature field distribution of sparks using infrared thermal imagers; acquiring the trajectory of Martian motion using high-speed cameras; and monitoring spark concentration by scattering particles using lasers.

[0004] After obtaining various spark state parameters, how to determine whether combustion abnormalities have occurred based on these parameters and fuel characteristics, and how to take measures to ensure combustion safety; as well as how to effectively block the fire to prevent it, are urgent problems to be solved. Summary of the Invention

[0005] This invention establishes a mapping relationship between spark state characteristics and abnormal combustion state by real-time monitoring of the spark state, thereby determining whether the combustion state of the hot blast stove is abnormal and taking corresponding measures to ensure combustion safety.

[0006] The technical solution proposed in this invention is: a real-time monitoring and automatic blocking system for hot blast stove sparks, comprising: A spark monitoring module is configured to collect spark status information and extract spark status features from the status information. A combustion monitoring module, configured to acquire fuel combustion status data and fuel information; A safety control module is configured to acquire data from the spark monitoring module and the combustion monitoring module, and to infer combustion state anomalies based on spark state characteristics, thereby establishing a mapping relationship between spark state characteristics and combustion anomalies. The self-blocking control module is configured to implement graded blocking based on the mapping relationship between spark state characteristics and combustion anomalies, thereby improving system safety.

[0007] Preferably, the spark monitoring module is configured to collect spark state information and extract spark state features from the state information, including: Spark temperature information is obtained through infrared imaging equipment and temperature sensors; Capture the trajectory information of sparks using a high-speed camera; Spark concentration information is obtained online using a laser scattering particulate counter. Information on spark carbon content was obtained using Raman spectroscopy. Spark surface temperature features are extracted from spark temperature information, spark velocity features are extracted from spark trajectory information, and spark number concentration features are extracted from spark concentration information.

[0008] Preferably, the step of inferring combustion state anomalies based on spark state characteristics and establishing a mapping relationship between spark state characteristics and combustion anomalies includes: Performing a quantitative diagnosis of combustion anomalies includes the following steps: Establish a combustion anomaly index : ; in, ( t () indicates the surface temperature of Mars. Indicates the reference value for combustion temperature. ( t () indicates the speed of Mars's motion; Indicates the design flue gas velocity; ( t () indicates spark concentration; Indicates the spark concentration threshold; This represents the 50% cumulative particle size of the spark, i.e., the median particle size. Indicates the particle size threshold; ( t () indicates the carbon content of the spark; if If so, then combustion is considered normal; if If so, it is determined that the combustion is abnormal.

[0009] Preferably, after determining the combustion abnormality, combustion abnormality type identification is performed to establish a mapping relationship between spark state characteristics and combustion abnormalities, including: Constructing a combined sequence of Martian feature changes: ; if and If so, the combustion anomaly type is determined to be oxygen-deficient combustion; Indicates the time interval for data collection; Constructing the second combination sequence of Martian feature changes: ; if and If so, the combustion anomaly type is determined to be local overheating; Constructing the third sequence of combined changes in Martian features: ; if and If so, the combustion anomaly is determined to be due to improper air distribution; Constructing the fourth combination sequence of Martian feature changes: ; if and If so, the combustion anomaly type is determined to be a fuel anomaly.

[0010] Preferably, the step of inferring combustion state anomalies based on spark state characteristics and establishing a mapping relationship between spark state characteristics and combustion anomalies further includes: The phase space of Martian surface temperature is reconstructed using Takens' embedding theorem: ];in, Indicates the embedding dimension. 'Indicates a delay time; ;in, Indicates the data collection period; Constructing a recursion graph and performing recursive quantitative analysis includes: The state vector in phase space The comparisons are performed to generate a binary matrix, i.e., a recursive graph; The spark state characteristics within the data acquisition period are obtained, a time series of spark state characteristics is constructed, and the Hurst exponent is used to analyze them. Describe the long-range correlation of time series of spark state characteristics.

[0011] Preferably, the state vector in the phase space The comparisons generate a binary matrix, i.e., a recursion graph, which includes: Calculate the recursion rate This is used to quantify the probability of repeating spark temperature states and reflect combustion stability. Among them, the similarity threshold ; Represents the Heaviside jump function; computational determinism This is to indicate whether periodic oscillations exist in the phase space; ;in, Represents any element value in the recursive graph. This represents the length of the diagonal in the recursive graph. The length of the diagonal in the recursive graph is... The number of line segments; The spark state characteristics within the data acquisition period are obtained, a time series of spark state characteristics is constructed, and the Hurst exponent is used to analyze them. Long-range correlations describing the time series of spark state characteristics include: ;in, The range of the time series representing the characteristics of spark state; Indicates standard deviation; Indicates the length of the time window; ;in, Indicates the amount of time window adjustment; Establishing a nonlinear mapping of spark states using a hybrid kernel support vector machine (SVR) includes: Add a non-linear term to the original combustion anomaly index, namely: ; The kernel function for SVR is: ;in, Indicates the Gaussian kernel weights. Indicates the Gaussian kernel bandwidth. Indicates the order of a polynomial; Indicates SVR input variables; Through a self-attention mechanism, the weights are adaptively adjusted, that is: , =1, 2, 3, 4, 5; where, Indicates the sharpening factor; This indicates the sensitivity of the output to the input parameters.

[0012] Preferably, the step of using a graded blocking strategy based on the mapping relationship between spark state characteristics and combustion anomalies to achieve graded blocking and improve system safety includes: A combustion risk level assessment is conducted, with the comprehensive combustion risk index set as follows: ;in, Indicates safe flow rate. Represents the step function; This indicates the risk value for slagging and the risk value for unburned volatile matter; ;in, This indicates the ratio of SiO2 content to CaO content; Indicates the sphericity of the spark. >0.85; ;in, Indicates particle size is smaller than The concentration of the spark; Classify combustion risks: when At that time, the overall combustion risk is Level 1; a warning signal is issued; the blocking measure is to adjust the air-fuel ratio. when At that time, the overall combustion risk was level two; the blocking measures were cyclone interception and water mist interception. when At that time, the overall combustion risk was level three; the containment measures were to stop combustion, implement emergency inerting, and detonation containment.

[0013] Preferably, the step of inferring combustion state anomalies based on spark state characteristics and establishing a mapping relationship between spark state characteristics and combustion anomalies further includes: A fuel characteristic adaptation layer is introduced into the combustion anomaly index to accommodate the characteristics of powdered fuels, including: Obtain fuel information, which includes: the sieve aperture size through which 90% of the powdered fuel particles pass, i.e., the 90% cumulative particle size of the fuel particles; the volatile matter content of the fuel sensing ash-free machine; the ash softening temperature and the moisture content of the received basis; Constructing fuel characteristic vectors ;in, This indicates the 90% cumulative particle size of the fuel particles. Indicates the reference particle size of the fuel; This indicates that the fuel is free of ash-based volatiles. Indicates the ash softening temperature. Indicates the moisture content of the fuel received; Constructing the fuel-spark transfer function: ; Based on the corrected parameters , Calculate the combustion anomaly index to obtain the combustion anomaly index of the fused fuel characteristics. ; Adding a fuel phase to the existing phase space creates a new phase space: ;in, Indicates the time constant of fuel characteristics; Add a fuel similarity metric to the SVR kernel function to obtain the corrected kernel function: ;in, Indicates Manhattan distance; when switching fuel, i.e. ; It automatically reduces the weight of historical data, thereby reducing the impact of fuel characteristics on the combustion anomaly index.

[0014] Preferably, the step of using a graded blocking strategy based on the mapping relationship between spark state characteristics and combustion anomalies to achieve graded blocking and improve system safety further includes: Adjusting blocking measures according to fuel characteristics to improve blocking effectiveness includes: Adjust the water mist concentration based on the ash-free volatile matter content of the fuel; the adjusted water mist concentration ;in, The baseline water mist concentration; Adjust the inert gas concentration according to the fuel particle size; the adjusted inert gas concentration is: .

[0015] The present invention also provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the hot blast stove spark real-time monitoring and automatic blocking system.

[0016] The beneficial effects of this invention are: 1. This invention, based on the monitored spark state characteristic data, reverse-engineers combustion state anomalies and establishes a mapping relationship between spark state parameters and combustion anomalies. Specifically, it constructs a combustion anomaly index (essentially the degree of combustion entropy increase anomaly) to reflect the degree to which the hot blast stove combustion system deviates from the ideal combustion state.

[0017] 2. In order to adapt to the nonlinear characteristics of the combustion process, this invention constructs a phase space and uses an SVR model with mixed kernel functions based on the phase space. By capturing local abrupt changes in the state through Gaussian kernels and fitting the global change trend of the state through polynomials, the combustion anomaly index adapts to the nonlinear combustion state, more accurately reflects the relationship between the combustion state and the spark state, improves the identification accuracy of combustion anomalies, reduces the probability of false alarms, and provides accurate data for subsequent automatic shutdown, thereby improving the combustion safety of the hot blast stove. Attached Figure Description

[0018] Figure 1 This is a block diagram of the real-time monitoring and automatic blocking system for hot blast stove sparks of the present invention. Detailed Implementation

[0019] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious modifications will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0020] It is understood that the term "a" should be understood as "at least one" or "one or more," that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0021] Example 1: refer to Figure 1 The technical solution provided by this invention is: a real-time monitoring and automatic blocking system for hot blast stove sparks, including: a spark monitoring module, a combustion monitoring module, a safety control module, and a self-blocking control module; The spark monitoring module is configured to collect spark status information and extract spark status features from the status information. The combustion monitoring module is configured to acquire fuel combustion status data and fuel information; The safety control module is configured to acquire data from the spark monitoring module and the combustion monitoring module, and infer combustion anomalies based on spark state characteristics, thereby establishing a mapping relationship between spark state characteristics and combustion anomalies. The self-blocking control module is configured to implement graded blocking based on the mapping relationship between spark state characteristics and combustion anomalies, thereby improving system safety.

[0022] The spark monitoring module's functionality is achieved through the following steps: Spark temperature information is obtained through infrared imaging equipment and temperature sensors; Capture the trajectory information of sparks using a high-speed camera; Spark concentration information is obtained online using a laser scattering particulate counter. Information on spark carbon content was obtained using Raman spectroscopy. Spark surface temperature features are extracted from spark temperature information, spark velocity features are extracted from spark trajectory information, and spark number concentration features are extracted from spark concentration information.

[0023] The process of inferring combustion anomalies based on spark state characteristics and establishing a mapping relationship between spark state characteristics and combustion anomalies can be achieved through the following steps: Performing a quantitative diagnosis of combustion anomalies includes the following steps: Establish a combustion anomaly index : ; in, ( t () indicates the surface temperature of Mars. Indicates the reference value for combustion temperature. ( t () indicates the speed of Mars's motion; Indicates the design flue gas velocity; ( t () indicates spark concentration; Indicates the spark concentration threshold; This represents the 50% cumulative particle size of the spark, i.e., the median particle size. Indicates the particle size threshold; ( t () indicates the carbon content of the spark; if If so, then combustion is considered normal; if If so, it is determined that the combustion is abnormal.

[0024] After identifying abnormalities in the hot blast stove combustion based on the characteristics of the sparks, it is necessary to further determine the type of abnormality and establish a mapping between the characteristics of the sparks and the combustion abnormalities. Specifically: Constructing a combined sequence of Martian feature changes: ; if and If so, the combustion anomaly type is determined to be oxygen-deficient combustion; Indicates the time interval for data collection; Constructing the second combination sequence of Martian feature changes: ; if and If so, the combustion anomaly type is determined to be local overheating; Constructing the third sequence of combined changes in Martian features: ; if and If so, the combustion anomaly is determined to be due to improper air distribution; Constructing the fourth combination sequence of Martian feature changes: ; if and If so, the combustion anomaly type is determined to be a fuel anomaly.

[0025] For example, the spark characteristic monitoring data of a certain coke oven gas hot blast stove is as follows: , ; ; ; ; ; ; , , , , , .

[0026] Calculation obtained =0.68; due to To determine if there is an abnormality in the combustion.

[0027] again and The problem was determined to be due to improper air distribution, which caused unburned particulate fuel to be carried away, forming sparks and resulting in a significant increase in spark concentration.

[0028] By increasing the secondary wind ratio from 25% to 35%, after 24 hours When the concentration was reduced to 0.18, combustion returned to normal.

[0029] Based on the mapping relationship between spark state characteristics and combustion anomalies, a graded blocking strategy is used to achieve graded blocking and improve system safety. This is achieved through the following steps: A combustion risk level assessment is conducted, with the comprehensive combustion risk index set as follows: ;in, Indicates safe flow rate. Represents the step function; This indicates the risk value for slagging and the risk value for unburned volatile matter; ;in, This indicates the ratio of SiO2 content to CaO content; Indicates the sphericity of the spark. >0.85; ;in, Indicates particle size is smaller than The concentration of the spark; Classify combustion risks: when At that time, the overall combustion risk is Level 1; a warning signal is issued; the blocking measure is to adjust the air-fuel ratio. when At that time, the overall combustion risk was level two; the blocking measures were cyclone interception and water mist interception. when At that time, the overall combustion risk was level three; the containment measures were to stop combustion, implement emergency inerting, and detonation containment.

[0030] Example 2: The combustion anomaly index in Example 1 reflects the linear relationship between combustion state and spark state characteristics. When the hot blast stove is under constant load and the fuel-air ratio is stable, the combustion state of the hot blast stove exhibits linear characteristics. However, under turbulent mixing, abrupt changes in chemical reaction kinetics, and thermoacoustic oscillations, the combustion process exhibits strong nonlinearity, making it impossible to construct the combustion anomaly index using the method described in Example 1. A method combining nonlinear dynamics modeling and machine learning is needed to improve the accuracy of the combustion anomaly index and ultimately ensure the combustion safety of the hot blast stove. Therefore, based on Example 1, we propose the following technical solution: Based on the characteristics of the Martian state, the anomalies in the combustion state can be inferred, and a mapping relationship between the characteristics of the spark state and the combustion anomalies can be established. This can also be achieved through the following steps: The phase space of Martian surface temperature is reconstructed using Takens' embedding theorem: ];in, Indicates the embedding dimension. 'Indicates a delay time; ;in, Indicates the data collection period; Constructing a recursion graph and performing recursive quantitative analysis includes: The state vector in phase space Compare these elements to generate a binary matrix, i.e., a recursion graph; if any element in the recursion graph... 1 ( < ), indicating that the state vectors are similar, otherwise it is 0; including: Calculate the recursion rate This is used to quantify the probability of repeating spark temperature states and reflect combustion stability. Among them, the similarity threshold ; Represents the Heaviside jump function; computational determinism This is to indicate whether periodic oscillations exist in the phase space; ;in, Represents any element value in the recursive graph. This represents the length of the diagonal in the recursive graph. The length of the diagonal in the recursive graph is... The number of line segments; The Martian state characteristics were acquired during the data acquisition period, a time series of Martian state characteristics was constructed, and the Hurst index was used to analyze them. Long-range correlations describing time series of Martian state characteristics include: ;in, The series range representing the time series characteristics of Mars' state; Indicates standard deviation; Indicates the length of the time window; ;in, Indicates the amount of time window adjustment; Taking the time series of spark concentration characteristics as an example, if If the state fluctuates randomly, it indicates that combustion is normal; if If this is the case, it indicates that the persistence of the state change is enhanced, suggesting that the state is becoming unstable. if If the state change is determined to be non-persistent, it indicates a tendency for sudden engine shutdown.

[0031] Establishing a nonlinear mapping of spark states using a hybrid kernel support vector machine (SVR) includes: Add a non-linear term to the original combustion anomaly index, namely: ; The kernel function for SVR is: ;in, Indicates the Gaussian kernel weights. Indicates the Gaussian kernel bandwidth. Indicates the order of a polynomial; Indicates SVR input variables; Through a self-attention mechanism, the weights are adaptively adjusted, that is: , =1, 2, 3, 4, 5; where, Indicates the sharpening factor; This indicates the sensitivity of the output to the input parameters.

[0032] When the fuel is pulverized, in order to make the combustion anomaly index suitable for the characteristics of the fuel, a combustion characteristic adaptation layer is introduced into the combustion anomaly index to adapt to the characteristics of pulverized fuel, including: Obtain fuel information, which includes: the sieve aperture size through which 90% of the powdered fuel particles pass, i.e., the 90% cumulative particle size of the fuel particles; the volatile matter content of the fuel sensing ash-free machine; the ash softening temperature and the moisture content of the received basis; Constructing fuel characteristic vectors ;in, This indicates the 90% cumulative particle size of the fuel particles. Indicates the reference particle size of the fuel; This indicates that the fuel is free of ash-based volatiles. Indicates the ash softening temperature. Indicates the moisture content of the fuel received; Constructing the fuel-spark transfer function: ; Based on the corrected parameters , Calculate the combustion anomaly index to obtain the combustion anomaly index of the fused fuel characteristics. ; Adding a fuel phase to the existing phase space creates a new phase space: ;in, Indicates the time constant of fuel characteristics; Add a fuel similarity metric to the SVR kernel function to obtain the corrected kernel function: ;in, Indicates Manhattan distance; when switching fuel, i.e. ; It automatically reduces the weight of historical data, thereby reducing the impact of fuel characteristics on the combustion anomaly index.

[0033] When implementing staged blocking, the blocking measures should be adjusted according to the characteristics of the fuel (especially powdered fuel) to improve the blocking effect. Specifically: Adjusting blocking measures according to fuel characteristics to improve blocking effectiveness includes: Adjust the water mist concentration based on the ash-free volatile matter content of the fuel; the adjusted water mist concentration ;in, The baseline water mist concentration; Adjust the inert gas concentration according to the fuel particle size; the adjusted inert gas concentration is: .

[0034] The present invention also provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the hot blast stove spark real-time monitoring and automatic blocking system.

[0035] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. The embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this invention. It should be noted that the computer-readable medium described above in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical cables, RF, etc., or any suitable combination thereof.

[0036] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0037] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and explained in the embodiments. Without departing from the principles described, the implementation of the present invention may have any changes or modifications.

Claims

1. A real-time monitoring and automatic blocking system for hot blast stove sparks, characterized in that, include: A spark monitoring module is configured to collect spark status information and extract spark status features from the status information. A combustion monitoring module, configured to acquire fuel combustion status data and fuel information; A safety control module is configured to acquire data from the spark monitoring module and the combustion monitoring module, and to infer combustion state anomalies based on spark state characteristics, thereby establishing a mapping relationship between spark state characteristics and combustion anomalies. The self-blocking control module is configured to implement graded blocking based on the mapping relationship between spark state characteristics and combustion anomalies, thereby improving system safety.

2. The real-time monitoring and automatic blocking system for hot blast stove sparks according to claim 1, characterized in that, The spark monitoring module is configured to collect spark state information and extract spark state features from the state information, including: Spark temperature information is obtained through infrared imaging equipment and temperature sensors; Capture the trajectory information of sparks using a high-speed camera; Spark concentration information is obtained online using a laser scattering particulate counter. Information on spark carbon content was obtained using Raman spectroscopy. Spark surface temperature features are extracted from spark temperature information, spark velocity features are extracted from spark trajectory information, and spark number concentration features are extracted from spark concentration information.

3. The real-time monitoring and automatic blocking system for hot blast stove sparks according to claim 2, characterized in that, The method of inferring combustion state anomalies based on spark state characteristics and establishing a mapping relationship between spark state characteristics and combustion anomalies includes: Performing a quantitative diagnosis of combustion anomalies includes the following steps: Establish a combustion anomaly index : ； in, ( t () indicates the surface temperature of Mars. Indicates the reference value for combustion temperature. ( t () indicates the speed of Mars's motion; Indicates the design flue gas velocity; ( t () indicates spark concentration; Indicates the spark concentration threshold; This represents the 50% cumulative particle size of the spark, i.e., the median particle size. Indicates the particle size threshold; ( t () indicates the carbon content of the spark; if If so, then combustion is considered normal; if If so, it is determined that the combustion is abnormal.

4. The real-time monitoring and automatic blocking system for hot blast stove sparks according to claim 3, characterized in that, After identifying combustion anomalies, combustion anomaly type identification is performed to establish a mapping relationship between spark state characteristics and combustion anomalies, including: Constructing a combined sequence of Martian feature changes: ; if and If so, the combustion anomaly type is determined to be oxygen-deficient combustion; Indicates the time interval for data collection; Constructing the second combination sequence of Martian feature changes: ; if and If so, the combustion anomaly type is determined to be local overheating; Constructing the third sequence of combined changes in Martian features: ; if and If so, the combustion anomaly is determined to be due to improper air distribution; Constructing the fourth combination sequence of Martian feature changes: ; if and If so, the combustion anomaly type is determined to be a fuel anomaly.

5. The real-time monitoring and automatic blocking system for hot blast stove sparks according to claim 4, characterized in that, The method of inferring combustion state anomalies based on spark state characteristics and establishing a mapping relationship between spark state characteristics and combustion anomalies also includes: The phase space of Martian surface temperature is reconstructed using Takens' embedding theorem: ];in, Indicates the embedding dimension. 'Indicates a delay time; ;in, Indicates the data collection period; Constructing a recursion graph and performing recursive quantitative analysis includes: The state vector in phase space The comparisons are performed to generate a binary matrix, i.e., a recursive graph; The spark state characteristics within the data acquisition period are obtained, a time series of spark state characteristics is constructed, and the Hurst exponent is used to analyze them. Describe the long-range correlation of time series of spark state characteristics.

6. The real-time monitoring and automatic blocking system for hot blast stove sparks according to claim 5, characterized in that, The state vector in phase space The comparisons generate a binary matrix, i.e., a recursion graph, which includes: Calculate the recursion rate This is used to quantify the probability of repeating spark temperature states and reflect combustion stability. Among them, the similarity threshold ; Represents the Heaviside jump function; computational determinism This is to indicate whether periodic oscillations exist in the phase space; ;in, Represents any element value in the recursive graph. This represents the length of the diagonal in the recursive graph. The length of the diagonal in the recursive graph is... The number of line segments; The spark state characteristics within the data acquisition period are obtained, a time series of spark state characteristics is constructed, and the Hurst exponent is used to analyze them. Long-range correlations describing the time series of spark state characteristics include: ;in, The range of the time series representing the characteristics of spark state; Indicates standard deviation; Indicates the length of the time window; ;in, Indicates the amount of time window adjustment; Establishing a nonlinear mapping of spark states using a hybrid kernel support vector machine (SVR) includes: Add a non-linear term to the original combustion anomaly index, namely: ； The kernel function for SVR is: ;in, Indicates the Gaussian kernel weights. Indicates the Gaussian kernel bandwidth. Indicates the order of a polynomial; Indicates SVR input variables; Through a self-attention mechanism, the weights are adaptively adjusted, that is: , =1, 2, 3, 4, 5; where, Indicates the sharpening factor; This indicates the sensitivity of the output to the input parameters.

7. The real-time monitoring and automatic blocking system for hot blast stove sparks according to claim 6, characterized in that, The method of mapping the characteristics of spark states to combustion anomalies and using a graded blocking strategy to achieve graded blocking and improve system safety includes: A combustion risk level assessment is conducted, with the comprehensive combustion risk index set as follows: ;in, Indicates safe flow rate. Represents the step function; This indicates the risk value for slagging and the risk value for unburned volatile matter; ;in, This indicates the ratio of SiO2 content to CaO content; Indicates the sphericity of the spark. >0.85; ;in, Indicates particle size is smaller than The concentration of the spark; Classify combustion risks: when At that time, the overall combustion risk is Level 1; a warning signal is issued; the blocking measure is to adjust the air-fuel ratio. when At that time, the overall combustion risk was level two; the blocking measures were cyclone interception and water mist interception. when At that time, the overall combustion risk was level three; the containment measures were to stop combustion, implement emergency inerting, and detonation containment.

8. The real-time monitoring and automatic blocking system for hot blast stove sparks according to claim 7, characterized in that, The method of inferring combustion state anomalies based on spark state characteristics and establishing a mapping relationship between spark state characteristics and combustion anomalies also includes: A fuel characteristic adaptation layer is introduced into the combustion anomaly index to accommodate the characteristics of powdered fuels, including: Obtain fuel information, which includes: the sieve aperture size through which 90% of the powdered fuel particles pass, i.e., the 90% cumulative particle size of the fuel particles; the volatile matter content of the fuel sensing ash-free machine; the ash softening temperature and the moisture content of the received basis; Constructing fuel characteristic vectors ;in, This indicates the 90% cumulative particle size of the fuel particles. Indicates the reference particle size of the fuel; This indicates that the fuel is free of ash-based volatiles. Indicates the ash softening temperature. Indicates the moisture content of the fuel received; Constructing the fuel-spark transfer function: ； Based on the corrected parameters , Calculate the combustion anomaly index to obtain the combustion anomaly index of the fused fuel characteristics. ; Adding a fuel phase to the existing phase space creates a new phase space: ;in, Indicates the time constant of fuel characteristics; Add a fuel similarity metric to the SVR kernel function to obtain the corrected kernel function: ;in, Indicates Manhattan distance; when switching fuel, i.e. ; It automatically reduces the weight of historical data, thereby reducing the impact of fuel characteristics on the combustion anomaly index.

9. The real-time monitoring and automatic blocking system for hot blast stove sparks according to claim 8, characterized in that, The method of mapping the characteristics of spark states to combustion anomalies and using a graded blocking strategy to achieve graded blocking and improve system safety also includes: Adjusting blocking measures according to fuel characteristics to improve blocking effectiveness includes: Adjust the water mist concentration based on the ash-free volatile matter content of the fuel; the adjusted water mist concentration ;in, The baseline water mist concentration; Adjust the inert gas concentration according to the fuel particle size; the adjusted inert gas concentration is: .

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the real-time monitoring and automatic blocking system for hot blast stove sparks as described in any one of claims 1-9.

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