Hot-blast stove mars edge calculation grading response method and cloud platform early warning system

Abstract

The invention relates to the technical field of hot blast stoves, in particular to a hot blast stove mars edge calculation hierarchical response method and a cloud platform early warning system, and the method comprises the steps: obtaining a mars flowing path and flowing equipment information from a database; constructing a segmented and graded early warning strategy, positioning abnormities at different positions on a Mars flow path in real time through edge calculation, and triggering warnings of corresponding grades; and the early warning occurrence position and level are obtained, motion characteristics and temperature characteristics of the sparks are identified to distinguish the composition of the sparks, the fault type of the hot blast stove is diagnosed according to the composition of the sparks, and corresponding response measures are taken.

Description

Technical Field

[0001] The present invention relates to the technical field of hot blast stoves, and particularly to a method for hierarchical response of edge computing for hot blast stove sparks and a cloud platform warning system. Background Art

[0002] Hot blast stoves have become replacement products for electric heat sources and traditional steam power heat sources in many industries. For example, they provide hot air for drying agricultural crops and provide high-temperature combustion-supporting air with a continuous temperature for blast furnaces. The reasons for the phenomenon of "sparks" or "flashes" generated by hot blast stoves usually include: 1. Refractory materials fall off or are damaged. When these fallen refractory brick particles are carried out by high-temperature hot air or waste gas flow, they present a "spark" state in the air flow due to their extremely high temperature (exceeding 1000°C). 2. Unburned fuel particles. Due to improper adjustment of the burner (imbalance of air-fuel ratio or poor air distribution), large fluctuations in gas pressure / heating value, etc., the fuel particles may not be fully burned, forming "sparks".

[0003] The movement path of sparks generated by the fall or damage of refractory materials: from the checker brick channel in the regenerator to the hot air outlet to the hot air main pipe to the blast furnace tuyere (during the air supply period). Or from the checker brick channel in the regenerator to the flue to the chimney.

[0004] The movement path of sparks generated by unburned fuel particles: from the combustion chamber to the waste gas valve to the flue (mainly appears during the switching from the end of the combustion period to the air supply period or the waste gas discharge period).

[0005] The appearance of sparks in a hot blast stove is a fault signal. Especially the sparks ejected from the hot air valve and pipe flange are likely to be caused by leakage of the hot air valve or pipe damage; the dark red sparks floating out of the chimney sporadically are probably due to the fall of refractory materials or ash carried by waste gas, and fault troubleshooting must be carried out.

[0006] In reality, usually professional personnel conduct inspections, and combine the location, color, and shape of the sparks found to judge the fault and take corresponding treatment measures. However, the appearance of these characteristics means that the fault has already occurred. How to monitor the movement state and temperature state of sparks inside the hot blast stove, accurately understand the internal state of the system, predict the occurrence of faults, and reduce the occurrence of risks is an urgent problem to be solved. Summary of the Invention

[0007] In the present invention, according to the nodes of changes in the movement state and temperature state of sparks, combined with the key equipment passed through, the movement path of sparks is divided into multiple segments. In each segment, edge computing is used to predict the average temperature and movement state of sparks, and different levels of warnings are given based on the size of the comprehensive risk index.

[0008] The technical solution proposed by the present invention is: a method for hierarchical response of hot blast stove spark edge computing, the method comprising: Obtain the spark flow path and the information of the equipment through which the spark flows from the database; Construct a segmented hierarchical warning strategy, and through edge computing, locate anomalies at different positions on the spark flow path in real time and trigger warnings at corresponding levels; Obtain the location and level of the warning, identify the motion characteristics and temperature characteristics of the spark to distinguish the composition of the spark, and diagnose the fault type of the hot blast stove according to the composition of the spark, and take corresponding response measures.

[0009] Preferably, the construction of the segmented hierarchical warning strategy includes: Segment the spark flow path with key equipment on the spark flow path, the inflection points of the spark temperature and speed, and the potential hazard degree of the spark as boundaries; Obtain the motion characteristics, temperature and shape characteristics of the spark in each segment, where the motion characteristics include the spark motion speed vector and acceleration vector; the temperature and shape characteristics include the spark temperature and shape factor; Set 3 levels of warnings for each segment, including: Calculate the comprehensive risk index of the corresponding segment by using the spark speed and the average spark temperature of each segment ; where respectively represent the temperature weight, speed weight, static risk weight and exclusive risk factor of the respectively represent the average spark temperature, reference temperature, maximum allowable temperature deviation, spark speed, reference speed and maximum allowable speed deviation of the Determine the warning level according to the region where the comprehensive risk index is located, including: When it is a first-level warning; When it is a second-level warning; When it is a third-level warning.

[0010] Preferably, the real-time positioning of anomalies at different positions on the spark flow path through edge computing and triggering warnings at corresponding levels includes: Construct a spark motion trajectory model , where respectively represent the position vector, speed vector, acceleration vector, time step, Gaussian noise term, gas density, spark particle density, spark particle diameter, drag coefficient, flow velocity vector and gravitational acceleration vector of the spark at time Spark temperature decay prediction model , where represents the regression coefficient, represents the average temperature, ambient temperature and gas constant of Mars at a certain moment, represents the edge computing LSTM model; represents the standard activation energy; According to the Mars trajectory prediction model and the Mars temperature decay prediction model, predict the velocity vector and temperature of Mars at each segment, and use the predicted temperature value and the magnitude of the velocity vector as input variables, and substitute them into to output the comprehensive risk prediction value for each segment; According to the magnitude of the comprehensive risk prediction value, output a warning of the corresponding level.

[0011] Preferably, the method of real-time positioning of anomalies at different positions on the Mars flow path through edge computing and triggering warnings of corresponding levels further includes: Improve the prediction accuracy of the Mars trajectory and the temperature measurement accuracy under the rotating air flow by constructing a rotating flow field model, including: Construct a three-dimensional eddy current field analysis model ; where represents the flow velocity vector, represents the air pressure, represents the kinematic viscosity, represents the swirl angular velocity vector, represents the Coriolis force term; Flow velocity vector ; represents the spatial position coordinates of Mars, represents the POD mode function, represents the number of POD modes; the time coefficient is obtained by inverting the data of the pipeline wall pressure sensor; Optimize the acceleration vector in the Mars motion trajectory model and add a virtual mass force term: ; where represents the Mars particle velocity vector, represents the particle density; represents the fluid material derivative; ; Solve the updated Mars motion trajectory model through extended Kalman filtering to obtain the accurate position of Mars; Construct a radiation transmission correction model to correct the temperature measurement error caused by the radiation coefficient of fly ash in the flue gas; including: True temperature of Mars where represents the measured temperature of the infrared thermometer or the predicted temperature of the Mars temperature decay prediction model, represents the fly ash absorption coefficient; Indicates the fly ash concentration; Indicates the radiation propagation path, Indicates the path integration unit; Construct a dynamic response compensation model to supplement the dynamic response delay caused by the thermal inertia of the thermocouple, resulting in a delay in temperature measurement; including: Thermocouple response model ; Measurement lag caused by thermocouple thermal inertia ; where, Indicates The true gas temperature, thermocouple strategy temperature, and thermal response time at the moment; Then, the final compensated temperature is ; Recalculate the comprehensive risk index based on the final compensated temperature, and output early warnings at different levels according to the magnitude of the recalculated risk index.

[0012] Preferably, before outputting the corresponding level of early warning according to the magnitude of the comprehensive risk prediction value, it further includes: Evaluate the treatment effect of the upstream segment on the sparks, including: Obtain the number of sparks at the inlet and outlet of the segment , and calculate the spark capture efficiency ; Obtain the temperatures of the sparks at the inlet and outlet of the segment , and calculate the spark cooling efficiency ; Obtain the velocity vectors of the sparks at the inlet and outlet of the segment , and calculate the spark deceleration efficiency ; Obtain the spark flow rates at the inlet and outlet of the segment , and calculate the spark residual flux ; Construct a pass judgment function: ; where, Indicates the Heaviside step function, ; Respectively indicate the spark capture rate threshold, cooling efficiency threshold, deceleration efficiency threshold, and residual flux threshold; If the value of the pass judgment function of the segment is greater than the preset pass threshold, it is judged that the treatment effect of the segment on the sparks is qualified; otherwise, it is unqualified.

[0013] Preferably, before outputting the corresponding level of early warning according to the magnitude of the comprehensive risk prediction value, it further includes: Judge whether the abnormal parameters of the upstream segment will cause false alarms in the downstream segment; Quantify the impact of abnormal parameter changes in the upstream segment on downstream monitoring.

[0014] Build an upstream-downstream impact analysis model, including: Build the transfer equations for temperature and velocity ; where Calculate at this time through the true temperature of Mars; Build the constraint equations for temperature and velocity ; where, the temperature safety margin , the velocity safety margin ; respectively represent the downstream temperature alarm threshold and the downstream velocity alarm threshold; represent the temperature safety factor and the velocity safety factor; represents the Mars velocity under the rated working condition of the downstream segment; Coupling transfer matrix in , respectively represent the temperature self-transfer coefficient, the influence coefficient of temperature on velocity, the influence coefficient of velocity on temperature, and the velocity self-transfer coefficient; The safe range of Mars temperature within the segment is , where ; The safe range of Mars velocity within the segment is , where ; If the change range of the upstream abnormal parameter is within the temperature safety range and the velocity safety range, it is judged that the abnormality of the upstream parameter will not cause false alarms downstream; Quantify the impact of abnormal parameter changes in the upstream segment on downstream monitoring, including: Set the temperature influence index , the velocity influence index ; Obtain the comprehensive influence degree ; where respectively represent the temperature influence weight and the velocity influence weight, represents the average Mars temperature under the rated working condition of the downstream segment; If , it is judged that the impact degree on the downstream is level one; If , it is judged that the impact degree on the downstream is level two; If , it is judged that the impact degree on the downstream is level three; If , it is judged that the impact degree on the downstream is level four.

[0015] Preferably, the method for obtaining the warning occurrence position and level, identifying the motion characteristics and temperature characteristics of Mars to distinguish the composition of Mars, diagnosing the fault type of the hot blast stove according to the composition of Mars, and taking corresponding response measures includes: Obtain the warning level of the corresponding segment. If it is a level-three warning, obtain the real-time data stream of the corresponding segment , and extract the inertial characteristic index , temperature decay factor and radiation spectral entropy to form an input feature vector; Input the input feature vector into a pre-trained classification decision tree model to output the type of Mars particles; the types of Mars particles include refractory brick particles and unburned fuel particles; According to the output type of Mars particles, conduct afterburning characteristic detection, including: Obtain the temperature of Mars particles, oxygen concentration, particle activation energy, and the time for Mars particles to flow through the segment within the current segment; Calculate and obtain the afterburning probability of Mars particles ; where represents the particle ignition temperature and oxygen concentration, represents the time for Mars particles to flow through the segment, respectively represent the temperature change weight coefficient, environmental weight coefficient, and time weight coefficient; If , it means the particles will not afterburn; If , it means the particles will afterburn; If , it means there is a possibility of afterburn for the particles; Combined with the afterburning probability, determine the type of Mars particles, that is: If and and , determine that the Mars particles within the current segment are refractory brick particles, and the fault type is refractory brick fault; If and , determine that the Mars particles within the current segment are unburned fuel particles, and the fault type is insufficient combustion; If and or or , determine that the Mars particles within the current segment are a mixture of fuel particles and refractory brick particles, and the fault types are refractory brick fault and combustion fault; According to the fault type and the segment where it is located, conduct hierarchical response.

[0016] The present invention also provides a hot blast stove spark edge computing hierarchical response cloud platform warning system, which includes a plurality of edge computing units and a communication module connected to the edge computing units. The system is used to execute the hot blast stove spark edge computing hierarchical response method described above.

[0017] The present invention also provides a computer-readable storage medium storing a computer program, and the computer program is executed by a processor to implement the hot blast stove spark edge computing hierarchical response method.

[0018] Advantages of the present invention: 1. The present invention divides the movement path of the spark into multiple segments, monitors the movement state and temperature state of the spark through edge computing, and determines whether there is a fault risk in the segment according to the average temperature and speed of the spark in the segment, that is, different levels of warnings are triggered according to the size of the comprehensive risk index.

[0019] 2. In the present invention, when monitoring the movement state and temperature state of the spark, the composition of the spark (refractory brick particles and unburned fuel particles) can be distinguished, so as to identify the type of fault and take corresponding different countermeasures. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of the hot blast stove spark edge computing hierarchical response method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious deformations. The basic principles defined in the following description can be applied to other implementation schemes, deformation schemes, improvement schemes, equivalent schemes, and other technical schemes that do not depart from the spirit and scope of the present invention.

[0022] It can be understood that the term "one" should be understood as "at least one" or "one or more". That is, in one embodiment, the number of an element can be one, and in other embodiments, the number of the element can be multiple. The term "one" cannot be understood as a limitation on the number.

[0023] Embodiment 1: Refer to Figure 1 The technical solution provided by the present invention is: a hot blast stove spark edge computing hierarchical response method, including the following steps: Step 1: Obtain the spark flow path and the information of the equipment passed through from the database; Step 2: Construct a segmented hierarchical warning strategy, and real-time locate the anomalies at different positions on the spark flow path through edge computing and trigger corresponding level warnings; Among them, constructing a segmented and hierarchical early warning strategy includes the following steps: Taking key equipment on the Mars flow path, the inflection points of Mars temperature and Mars speed, and the potential hazard degree of Mars as boundaries, segment the Mars flow path; for example, in this embodiment, the Mars movement path is divided into 7 segments: the first segment is the combustion injection segment, from the burner outlet to the regenerator inlet; the second segment is the regenerator deceleration segment, from the regenerator inlet to the middle of the checker brick; the third segment is the regenerator acceleration segment, from the middle of the checker brick to the regenerator outlet; the fourth segment is the branch pipe transportation segment, from the regenerator outlet to the inlet of the collector; the fifth segment is the collector treatment segment, from the inlet of the collector to the outlet of the collector; the sixth segment is the main pipe stabilization segment, from the outlet of the collector to the middle of the main pipe; the seventh segment is the tuyere impact segment, from the middle of the main pipe to the blast furnace tuyere.

[0024] Obtain the Mars movement characteristics, temperature and shape characteristics of each segment. The movement characteristics include the Mars movement speed vector and acceleration vector, which can be obtained by a laser Doppler velocimeter sensor; the temperature and shape characteristics include the Mars temperature and shape factor, and the Mars temperature is obtained by a high-speed polarized infrared camera and a thermocouple.

[0025] Set 3 levels of early warning for each segment, including: Calculate the comprehensive risk index corresponding to each segment by using the Mars speed and the average Mars temperature of each segment ; where respectively represent the temperature weight, speed weight, static risk weight and exclusive risk factor of the th segment; respectively represent the average Mars temperature, reference temperature, maximum allowable temperature deviation, Mars speed, reference speed and maximum allowable speed deviation of the th segment of Mars; Determine the early warning level according to the region where the comprehensive risk index is located, including: When it is a first-level early warning; When it is a second-level early warning; When it is a third-level early warning.

[0026] Among them, real-time positioning of anomalies at different positions on the Mars flow path through edge computing and triggering corresponding-level warnings includes the following steps: Construct a Mars movement trajectory model , where respectively represent The position vector, velocity vector, acceleration vector, time step, Gaussian noise term, gas density, Martian particle density, Martian particle diameter, drag coefficient, flow velocity vector, and gravitational acceleration vector of Mars at a moment; the motion vector of Mars can be used to identify whether Mars will collide with the pipe wall or the inner wall of the equipment. The collision of Mars with the pipe wall indicates that there is Mars in the pipeline, and the local temperature of the pipeline is abnormally high due to the collision of Mars, causing damage to the pipeline.

[0027] Martian temperature decay prediction model , where represents the regression coefficient, represents the average temperature of Mars, the ambient temperature, and the gas constant at a moment, represents the edge computing LSTM model; represents the standard activation energy; According to the Mars trajectory prediction model and the Martian temperature decay prediction model, predict the velocity and temperature of Mars in each segmented velocity vector, and use the predicted temperature value and the modulus of the velocity vector as input variables, and substitute them into , and output the comprehensive risk prediction value for each segment; according to the magnitude of the comprehensive risk prediction value, output a warning of the corresponding level.

[0028] In this embodiment, the specific configuration of the edge computing resources is as follows: deploy an edge computing unit ECU at each segmented node (combustion chamber outlet, regenerator inlet, Mars catcher, etc.). The hardware configuration of each ECU can be: a multi-core processor, a dedicated FPGA (for accelerating algorithms), sensors, and a local storage module.

[0029] Step 3: Obtain the location and level of the warning, identify the motion characteristics and temperature characteristics of Mars to distinguish the composition of Mars, and diagnose the fault type of the hot blast stove according to the composition of Mars, and take corresponding response measures, including the following steps: Obtain the warning level of the corresponding segment. If it is a third-level warning, obtain the real-time data stream of the corresponding segment , and extract the inertial feature index feature , the temperature decay factor and the radiation spectral entropy to form an input feature vector; Input the input feature vector into the pre-trained classification decision tree model to output the type of Martian particles; the types of Martian particles include refractory brick particles and unburned fuel particles; According to the output type of Martian particles, perform afterburning feature detection, including the following steps: Obtain the temperature of Martian particles, oxygen concentration, particle activation energy, and the time for Martian particles to flow through the segment in the current segment; Calculate and obtain the afterburning probability of Martian particles ; wherein, represents the ignition temperature of the particles and the oxygen concentration, represents the time for the spark particles to flow through the section, respectively represent the temperature change weight coefficient, the environmental weight coefficient, and the time weight coefficient; If , it means that the particles will not reignite; If , it means that the particles will reignite; If , it means that there is a possibility of particle re-ignition; Combining the re-ignition probability, determine the type of spark particles, and according to the type of fault and the section where it is located, classify the response, including the following steps: If and and When, it is determined that the spark particles in the current section are refractory brick particles, and the type of fault is refractory brick fault; the response measures taken are to reduce the furnace temperature, repair the inner lining of the pipeline in the current section or the refractory layer in the hot blast stove; If and When, it is determined that the spark particles in the current section are unburned fuel particles, and the type of fault is insufficient combustion; the response measures taken are to adjust the air-fuel ratio or clean the burner nozzle; If and or or When, it is determined that the spark particles in the current section are a mixture of fuel particles and refractory brick particles, and the types of faults are refractory brick faults and combustion faults; then the above two disposal measures are adopted simultaneously.

[0030] Embodiment 2: In reality, in order to reduce sparks, a spark catcher is usually set in the hot blast stove system. Through the rotating airflow and using centrifugal force, the sparks settle after hitting the inner wall.

[0031] As a key device (section node) in the movement path of the sparks, it is necessary to improve the detection accuracy of the movement state and temperature state of the sparks under the rotating airflow to accurately predict the spark temperature and movement state at this position. For this purpose, we propose the following technical solutions: By constructing a rotating flow field model, improve the prediction accuracy of the spark trajectory and the temperature measurement accuracy under the rotating airflow, including: Construct a three-dimensional eddy current field analysis model ; wherein, represents the flow velocity vector, represents the air pressure, represents the kinematic viscosity, represents the swirl angular velocity vector, represents the Coriolis force term; flow velocity vector ; represents the spatial position coordinates of Mars, represents the POD mode function, represents the number of POD modes; time coefficient is obtained by inverting the data of the pipeline wall pressure sensor; Optimize the acceleration vector in the Mars motion trajectory model and add the virtual mass force term: ; where, represents the Mars particle velocity vector, represents the particle density; represents the fluid material derivative; ; The physical meaning of the virtual mass force lies in the additional force required to accelerate the fluid, which cannot be ignored when is greater than 0.1; Solve the updated Mars motion trajectory model through the extended Kalman filter to obtain the accurate position of Mars; Construct a radiation transfer correction model to correct the temperature measurement error caused by the radiation coefficient of fly ash in the flue gas; including the following steps: True temperature of Mars , where, represents the measured temperature of the infrared thermometer or the predicted temperature of the Mars temperature decay prediction model, represents the fly ash absorption coefficient; represents the fly ash concentration; represents the radiation propagation path, represents the path integration unit; Construct a dynamic response compensation model to supplement the dynamic response delay caused by the thermal inertia of the thermocouple, resulting in a delay in temperature measurement; including: Thermocouple response model ; Measurement lag caused by thermocouple thermal inertia ; where, represents the true gas temperature, thermocouple strategy temperature, and thermal response time at time Then, the compensated final temperature is ; Recalculate the comprehensive risk index based on the supplemented final temperature, and output warnings at different levels according to the magnitude of the recalculated risk index.

[0032] Example Three: After segmenting the movement path of Mars, abnormal fluctuations occur in the upstream segment parameters, which may cause false alarms in the downstream segment. To reduce false alarms and ensure the robustness of early warning, we propose the following technical solutions: Before outputting early warnings of corresponding levels according to the magnitude of the comprehensive risk prediction value, evaluate the processing effect of the upstream segment on Mars, including the following steps: Obtain the number of Mars at the segment inlet and outlet , and calculate the Mars capture efficiency ; In this embodiment, the Mars number information can be obtained through a laser particle calculator.

[0033] Obtain the temperatures of Mars at the segment inlet and outlet , and calculate the Mars cooling efficiency ; In this embodiment, the Mars temperature information can be obtained through the deployed infrared thermal imager array.

[0034] Obtain the velocity vectors of Mars at the segment inlet and outlet , and calculate the Mars deceleration efficiency ; In this embodiment, the Mars velocity vector information can be obtained through a cross-correlation flowmeter.

[0035] Obtain the Mars flow rates at the segment inlet and outlet , and calculate the Mars residual flux ; In this embodiment, the Mars flow rate can be obtained through a weighing dust meter.

[0036] Construct a qualified judgment function: ; where represents the Heaviside step function ; respectively represent the Mars capture rate threshold, cooling efficiency threshold, deceleration efficiency threshold, and residual flux threshold; If the value of the qualified judgment function of the segment is greater than the preset qualified threshold, it is judged that the Mars processing effect of the segment is qualified; otherwise, it is unqualified.

[0037] Then, judge whether the abnormal upstream segment parameters will cause false alarms in the downstream segment and quantify the impact of the change in abnormal upstream segment parameters on downstream monitoring.

[0038] The specific steps are as follows: Construct an upstream and downstream impact analysis model, including the following steps: Construct the transfer equations of temperature and velocity ; where is calculated by the true temperature of Mars at this time; Construct the constraint equations of temperature and velocity ; where the temperature safety margin , speed safety margin ; respectively represent the downstream temperature alarm threshold and the downstream speed alarm threshold; represent the temperature safety factor and the speed safety factor; represents the Mars speed under the rated working condition of the downstream section; the safety margin is adaptively adjusted according to the working condition to meet the requirements of different section temperature changes and speed changes.

[0039] coupling transfer matrix in the , respectively represent the temperature self-transfer coefficient, the influence coefficient of temperature on speed, the influence coefficient of speed on temperature, and the speed self-transfer coefficient; the temperature-speed cross influence is reflected through the non-diagonal terms, and the environmental temperature coefficient ensures the model regression.

[0040] The safe range of Mars temperature within the section is , where ; The safe range of Mars speed within the section is , where ; If the change range of the upstream abnormal parameter is within the temperature safety range and the speed safety range, it is judged that the abnormality of the upstream parameter will not cause false alarms in the downstream; Quantify the influence of the change of the upstream section abnormal parameter on the downstream monitoring, including: Set the temperature influence index , the speed influence index ; Obtain the comprehensive influence degree ; where respectively represent the temperature influence weight and the speed influence weight, represents the average Mars temperature under the rated working condition of the downstream section; If , it is judged that the influence degree on the downstream is level one; If , it is judged that the influence degree on the downstream is level two; If , it is judged that the influence degree on the downstream is level three; If , it is judged that the influence degree on the downstream is level four.

[0041] If the influence degree is level one, keep the existing data acquisition status; if the influence degree is level two, increase the monitoring frequency and the data acquisition frequency; if the influence degree is level three, send an alarm message and check whether there is a fault in the upstream section; if the influence degree is level four, trigger an alarm and isolate the upstream section and the downstream section at this position.

[0042] The present invention also provides a warning system for the hot blast stove spark edge computing hierarchical response cloud platform, which includes a plurality of edge computing units and a communication module connected to the edge computing units, and the system is used to execute the hot blast stove spark edge computing hierarchical response method described above.

[0043] The present invention also provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the hot blast stove spark edge computing hierarchical response method.

[0044] In the embodiments disclosed by the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. The embodiments disclosed by the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed. It should be noted that the computer-readable medium in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wire segments, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, apparatus, or device. In the present application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program codes. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable medium can send, propagate, or transmit a program for use by or combined with an instruction execution system, apparatus, or device. The program codes contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: a wireless segment, a wire segment, an optical cable, RF, etc., or any suitable combination of the above.

[0045] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0046] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and illustrated in the embodiments. Without departing from the said principles, any changes or modifications can be made to the embodiments of the present invention.

Claims

1. A method for hierarchical response of hot blast stove spark edge computing, characterized in that, The method includes: Obtaining the Mars flow path and the information of the equipment through which it flows from the database; Constructing a segmented and hierarchical early warning strategy to real-time locate anomalies at different positions on the Mars flow path through edge computing and trigger warnings at corresponding levels; Obtaining the location and level of the early warning, identifying the motion characteristics and temperature characteristics of the Mars to distinguish its composition, diagnosing the fault type of the hot blast stove according to the Mars composition, and taking corresponding response measures.

2. The method for calculating and grading the response of the hot blast stove to the edge of the spark according to claim 1, wherein, The constructing of the segmented and hierarchical early warning strategy includes: Segmenting the Mars flow path with key equipment on the Mars flow path, the inflection points of Mars temperature and velocity, and the potential hazard degree of the Mars as boundaries; Obtaining the motion characteristics, temperature and shape characteristics of each segment, where the motion characteristics include the Mars motion velocity vector and acceleration vector; the temperature and shape characteristics include the Mars temperature and shape factor; Three levels of early warnings are set for each segment, including: Calculate the comprehensive risk index for each corresponding segment using the Martian velocity and average Martian temperature for each segment ; where respectively represent the temperature weight, velocity weight, static risk weight, and exclusive risk factor for the th segment; respectively represent the average Martian temperature, reference temperature, maximum allowable temperature deviation, Martian velocity, reference velocity, and maximum allowable velocity deviation for the th segment of Mars; Determining the early warning level according to the region where the comprehensive risk index is located, including: When it is a first-level warning; When it is a secondary warning; When it is a third-level warning.

3. The method for calculating and grading the response of the hot blast stove to the edge of the spark according to claim 2, wherein, The real-time locating of anomalies at different positions on the Mars flow path through edge computing and triggering warnings at corresponding levels includes: Construct a model of the Mars trajectory , where respectively represent the position vector, velocity vector, acceleration vector, time step, Gaussian noise term, gas density, Mars particle density, Mars particle diameter, drag coefficient, flow velocity vector, and gravitational acceleration vector of Mars at a certain moment; Mars Temperature Decay Prediction Model , where represents the regression coefficient represents the average temperature, ambient temperature and gas constant of Mars at time represents the edge computing LSTM model represents the standard activation energy According to the Mars trajectory prediction model and the Mars temperature decay prediction model, predict the Mars velocity vector and temperature at each segment. Use the predicted temperature value and the magnitude of the velocity vector as input variables and substitute them into to output the comprehensive risk prediction value for each segment; Outputting warnings at corresponding levels according to the magnitude of the comprehensive risk prediction value.

4. The method for calculating and grading the response of the hot blast stove to the edge of the spark according to claim 3, wherein The real-time locating of anomalies at different positions on the Mars flow path through edge computing and triggering warnings at corresponding levels also includes: Improving the Mars trajectory prediction accuracy and temperature measurement accuracy under rotating airflows by constructing a rotating flow field model, including: Construct a three-dimensional eddy current field analysis model ; among them, represents the flow velocity vector, represents the air pressure, represents the kinematic viscosity, represents the swirl angular velocity vector, represents the Coriolis force term; Flow velocity vector ; represents the spatial position coordinates of Mars, represents the POD modal function, represents the number of POD modes; the time coefficient is obtained by inverting the data of the pipeline wall pressure sensor; Optimize the acceleration vector in the Mars motion trajectory model and add a virtual mass force term: ; where represents the Mars particle velocity vector, represents the particle density; represents the fluid material derivative; ; Solving the updated Mars motion trajectory model through extended Kalman filtering to obtain the accurate position of the Mars; Constructing a radiation transmission correction model to correct the temperature measurement error caused by the radiation coefficient of fly ash in the flue gas; including: True temperature of Mars , where represents the measured temperature of the infrared thermometer or the predicted temperature of the Mars temperature attenuation prediction model, represents the fly ash absorption coefficient; represents the fly ash concentration; represents the radiation propagation path, represents the path integration unit; Constructing a dynamic response compensation model to supplement the dynamic response delay caused by the thermal inertia of the thermocouple, resulting in a delay in temperature measurement; including: Thermocouple response model ; The thermal inertia of the thermocouple causes a measurement lag ; among them, represents the true gas temperature, the thermocouple strategy temperature, and the thermal response time at a moment; Then, the final temperature after compensation is ; Recalculating the comprehensive risk index based on the supplemented final temperature, and outputting warnings at different levels according to the magnitude of the recalculated risk index.

5. The method for calculating and grading the response of the hot blast stove to the edge of the spark according to claim 4, wherein Before outputting warnings at corresponding levels according to the magnitude of the comprehensive risk prediction value, it also includes: Evaluating the treatment effect of the upstream segment on the Mars, including: Obtain the number of segmented inlet and outlet martians , calculate the martian capture efficiency ; Obtain the temperatures at the segmented entrances and exits of Mars , and calculate the Mars cooling efficiency ; Obtain the velocity vectors of the segmented entry and exit from Mars , and calculate the Mars deceleration efficiency ; Obtain the segmented inlet and outlet Martian fluxes , and calculate the Martian residual flux ; Constructing a qualified judgment function: ; wherein, represents the Heaviside step function, ; respectively represent the Mars capture rate threshold, the cooling efficiency threshold, the deceleration efficiency threshold, and the residual flux threshold; If the value of the qualified judgment function of the segment is greater than the preset qualified threshold, it is judged that the Mars treatment effect of the segment is qualified; otherwise, it is unqualified.

6. The method for calculating and grading the response of the hot blast stove to the edge of the spark according to claim 5, characterized in that, Before outputting warnings at corresponding levels according to the magnitude of the comprehensive risk prediction value, it also includes: Judging whether the abnormal parameters of the upstream segment will cause false alarms in the downstream segment; Quantifying the impact of the change in abnormal parameters of the upstream segment on downstream monitoring.

7. The method for calculating and grading the response of the hot blast stove to the edge of the spark according to claim 6, characterized in that, The judging whether the abnormal parameters of the upstream segment will cause false alarms in the downstream segment includes: Constructing an upstream and downstream impact analysis model, including: Construct the transfer equations for temperature and velocity ; among which, Calculate at this time through the true temperature of Mars; Construct the constraint equations for temperature and velocity ; where, the temperature safety margin , the velocity safety margin ; respectively represent the downstream temperature alarm threshold and the downstream velocity alarm threshold; represent the temperature safety factor and the velocity safety factor; represents the Martian velocity under the downstream segmented rated operating conditions Coupling transfer matrix in , respectively representing the temperature self-transfer coefficient, the influence coefficient of temperature on velocity, the influence coefficient of velocity on temperature, and the velocity self-transfer coefficient; The safe range of Martian temperature within a segment is , where ; The safe range of the Mars velocity within a segment is , where ; If the change range of the upstream abnormal parameters is within the temperature safety range and velocity safety range, it is judged that the abnormal parameters of the upstream will not cause false alarms in the downstream; Quantifying the impact of the change in abnormal parameters of the upstream segment on downstream monitoring, including: Set the temperature influence index , the speed influence index ; Obtain the comprehensive influence degree ; among them, respectively represent the temperature influence weight and the speed influence weight, represents the average temperature of Mars under the rated conditions of the downstream segmented operation; If , it is determined that the degree of influence on the downstream is level one; If , then it is determined that the degree of influence on the downstream is level two; If , it is determined that the impact on the downstream is at level three; If , it is determined that the degree of impact on the downstream is level four.

8. The method for calculating and grading the response of the hot blast stove to the edge of the spark according to claim 7, wherein The obtaining the location and level of the early warning, identifying the motion characteristics and temperature characteristics of the Mars to distinguish its composition, diagnosing the fault type of the hot blast stove according to the Mars composition, and taking corresponding response measures includes: Obtain the early warning level of the corresponding segment. If it is a level-three early warning, then obtain the real-time data stream of the corresponding segment , extract the inertial characteristic index feature , temperature decay factor and radiation spectral entropy , and form an input feature vector; Input the input feature vector into the pre-trained classification decision tree model to output the type of Martian particles; the types of the Martian particles include refractory brick particles and unburned fuel particles; According to the output type of the Martian particles, the re-ignition feature detection includes: Obtain the temperature of the Martian particles, oxygen concentration, particle activation energy, and the time for the Martian particles to flow through the segment within the current segment; Calculating and obtaining the probability of re-ignition of Martian particles ; among them, represents the ignition temperature of the particle and the oxygen concentration, represents the time for the Martian particle to flow through the segment, respectively represent the temperature change weight coefficient, the environmental weight coefficient, and the time weight coefficient; If , it means that the particles will not reignite; If , it means that the particles will reignite; If , it indicates the possibility of re-ignition of the particles; Combine the re-ignition probability to determine the type of Martian particles, that is: If and and At this time, it is determined that the Martian particles in the current segment are refractory brick particles, and the fault type is refractory brick fault; If and At this time, it is determined that the Martian particles in the current segment are unburned fuel particles, and the fault type is insufficient combustion; If And Or Or When it is judged that the Martian particles in the current segment are a mixture of fuel particles and refractory brick particles, the fault types are refractory brick faults and combustion faults; According to the type of fault and the segment where it is located, perform hierarchical response.

9. The hot blast stove spark edge computing hierarchical response cloud platform warning system includes multiple edge computing units and a communication module connected to the edge computing units, and is characterized in that, The system is used to execute the hot blast stove Martian edge calculation hierarchical response method described in any one of claims 1-8 above.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the hot blast stove Martian edge calculation hierarchical response method described in any one of claims 1-8 above.

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