Intelligent combustion control device and method based on CAN bus

Abstract

The application discloses a CAN bus-based intelligent combustion control device and method, and relates to the field of combustion control, and comprises a coordination module, which is used for interacting with real-time data of each combustion node through a CAN protocol stack, dynamically generating and broadcasting a matching coefficient frame and a control instruction based on a global working condition, and synchronously receiving state information fed back by the nodes to isolate faults; the application applies the CAN bus to construct an efficient communication architecture, dynamically adjusts the matching of fuel and combustion air based on the global working condition, realizes that the combustion efficiency is stably above 94%, and even when the fuel characteristics fluctuate by ±15%, the high efficiency can still be maintained; meanwhile, the nitrogen oxide emission is effectively controlled to be below 110mg / m 3 , and the application has the advantages of environmental protection and energy saving.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of combustion control, in particular to an intelligent combustion control device and method based on a CAN bus. BACKGROUND

[0002] Industrial combustion control is a technology that realizes efficient combustion and reduces pollution by adjusting fuel, air ratio and combustion parameters. It covers sensor monitoring and intelligent algorithm control, and is applied to boilers, kilns and the like to balance energy consumption and environmental protection and ensure safe and stable operation of equipment.

[0003] An application for an invention patent with the application number 202411305307.2 discloses an atmosphere furnace intelligent combustion control system, which comprises: an atmosphere furnace combustion related data acquisition module for acquiring a time queue of air flow values and a time queue of waste gas temperature values; an atmosphere furnace combustion related data time sequence encoding module for inputting the time queue of air flow values and the time queue of waste gas temperature values into a sequence encoder respectively to obtain a sequence of air flow time sequence associated feature vectors and a sequence of waste gas temperature time sequence associated feature vectors; an atmosphere furnace combustion parameter time sequence propagation aggregation module for inputting the sequence of air flow time sequence associated feature vectors and the sequence of waste gas temperature time sequence associated feature vectors into a node energy attenuation driven time sequence feature forward aggregation network to obtain an air flow time sequence propagation aggregation representation vector and a waste gas temperature time sequence propagation aggregation representation vector; an air flow-waste gas temperature global interaction module for inputting the air flow time sequence propagation aggregation representation vector and the waste gas temperature time sequence propagation aggregation representation vector into a feature decoupling local interaction fine-grained aggregation network to obtain an air flow-waste gas temperature decoupling reinforced interaction global representation vector as an air flow-waste gas temperature decoupling reinforced interaction global representation feature; and an optimization instruction generation module for obtaining an optimization instruction based on the air flow-waste gas temperature decoupling reinforced interaction global representation feature, the optimization instruction being used to represent that an air flow value at a next time point should be increased, decreased or kept unchanged. The application aims to solve the problem of "traditional combustion control is based on manual adjustment by operating personnel, the operating personnel have a high operation intensity when manually controlling, and often have the phenomenon of losing one to gain the other, adjustment is not timely, and manual control cannot maintain a good atmosphere ratio, there is under-oxygen or over-oxygen combustion, resulting in low combustion efficiency, and large fluctuation of operating parameters, in particular, too low atmosphere ratio, incomplete fuel combustion, which pollutes the surrounding environment. Too high atmosphere ratio will make the combustion-supporting atmosphere take away too much heat, also causing a large amount of energy loss, affecting the operation quality of the unit".

[0004] However, the existing combustion equipment has the following defects:

[0005] Most of the combustion equipment uses analog signal (4-20mA) or RS-485 communication, which has the problems of weak anti-interference ability, low transmission rate, complex wiring and the like; the main controller failure will lead to the paralysis of the whole system, and the reliability is low; the fuel flow, air ratio, temperature and the like are independently adjusted, the dynamic response is slow, the combustion efficiency is low, and the pollutant (NOx, CO) emission is high; it is difficult to monitor the burner state (such as flameout, nozzle blockage) in real time, and the safety hidden danger is large;

[0006] Therefore, the intelligent combustion control device and method based on CAN bus are provided. SUMMARY

[0007] In view of the above defects of the prior art, the intelligent combustion control device and method based on CAN bus are provided, which can effectively solve the problems of the prior art.

[0008] To achieve the above purpose, the following technical solutions are used.

[0009] The intelligent combustion control device based on CAN bus comprises:

[0010] A coordination module is used for interacting with real-time data of each combustion node through a CAN protocol stack, dynamically generating and broadcasting a ratio coefficient frame and a control instruction based on a global working condition, and synchronously receiving state information fed back by the node for fault isolation; an adjustment module is used for receiving the ratio coefficient frame broadcast by a main controller of a combustion system, combining real-time data of a local fuel flow sensor, and completing combustion air volume calculation and damper driving within a preset time threshold to complete dynamic proportion optimization of fuel and combustion air; a protection module is used for immediately cutting off a local fuel valve when flame anomaly is detected at a node level, sending an emergency shutdown frame with the highest priority, receiving a global shutdown instruction of the system level and linking a safety instrument system to execute a shutdown operation; a diagnosis module is used for monitoring state parameters of nozzle blockage, ignition failure, flame sensor failure, fuel valve jamming and combustion air fan overload in real time through a preset self-diagnosis data frame structure, analyzing error codes and node state information and feeding back to the main controller; a communication module is used for setting a main communication line pair and a redundant communication line pair in accordance with electrical characteristics of the ISO11898 standard, constructing physical layer redundancy through a double-line time-frequency division multiplexing mechanism, controlling a transmission rate to be greater than or equal to 500kbps, and controlling a communication delay to be less than or equal to 5ms; and a learning module is used for constructing a dynamic learning model based on a combustion working condition feature vector set, generating a PID parameter dynamic adjustment matrix through an improved particle swarm optimization algorithm with a combustion stability penalty term, and after closed loop verification of more than 5 typical working conditions, sending an encrypted configuration frame to an adjustment node to control the combustion system to maintain a combustion efficiency of not less than 94% and a nitrogen oxide emission of at least less than 110mg / m 3 .

[0011] Further, the coordination module generates a matching coefficient frame containing a timestamp accurate to the millisecond level and a node address field, and the matching coefficient K is calculated based on global working condition parameters, i.e.:

[0012] K = K0 × (T target -T avg ) / T target + K base ;

[0013] wherein K0 is a dynamic adjustment coefficient; T target is a target combustion temperature; T avg is an average value of real-time temperatures of each node; and K base is a basic matching coefficient;

[0014] wherein each node includes a fuel supply node, a combustion air adjustment node, a flame monitoring node, and a temperature and pressure sensing node;

[0015] The fault isolation is achieved by continuously comparing the deviation of temperature, pressure, and flow state information fed back by the nodes from preset threshold values, starting a timer when the deviation exceeds the threshold value, marking the node as offline when the deviation of the node exceeds the threshold value for three consecutive communication periods, prohibiting the node from participating in global matching calculation, and generating an isolation frame containing the fault node ID and the deviation value and broadcasting the frame to other nodes to update the global node list.

[0016] Further, the combustion air flow calculation of the adjustment module uses a dynamic correction formula:

[0017] Q = K × Q f × [1 + α × (T - T0)] × [1 + β × (P - P0) / P0];

[0018] wherein Q is the combustion air flow; K is the matching coefficient; Q f is the real-time flow collected by a local fuel flow sensor; α and β are temperature and pressure correction coefficients; T is the current ambient temperature; T0 is the reference ambient temperature; P is the current combustion air pressure; and P0 is the reference combustion air pressure;

[0019] wherein the preset time threshold in the adjustment module is ≤10 ms, the damper drive is completed by a pulse width modulation signal controlling a servo motor, and the linear correspondence between the duty cycle D of the drive signal and the calculated flow Q satisfies:

[0020]

[0021] wherein D max and D min are the maximum duty cycle when the damper is fully open, 95% ± 1%, and the minimum duty cycle when the damper is fully closed, 5% ± 1%; and Q maxThe maximum opening degree of the air door corresponds to the rated air volume, and the duty ratio adjustment step is set to be less than or equal to 0.1%.

[0022] Further, the highest priority emergency shutdown frame sent by the protection module adopts a fixed frame ID 0x18F00001.

[0023] The frame structure includes an 8-byte data field, the first byte is the node ID of the triggered fault, the second byte is the fault type code, and the remaining 6 bytes are real-time parameter snapshots when the fault occurs.

[0024] The execution delay of the node-level cut-off local fuel valve is less than or equal to 5ms, and the cut-off action is triggered by a hardware circuit independent of the microprocessor.

[0025] The system-level global shutdown instruction is generated by the main controller within 10ms after receiving the emergency shutdown frame, adopts a broadcast frame form facing all distributed combustion control nodes, the frame ID of the broadcast frame is set to 0x18F00002, the frame data field includes 2-byte shutdown instruction code and 4-byte timestamp accurate to milliseconds, and a 24V on-off signal is sent to the safety instrument system through hardwiring for double triggering, when the safety instrument system executes the shutdown operation, the total fuel valve is cut off first and then each node sub-valve is closed in turn, and the completion time of the entire shutdown process is less than or equal to 50ms.

[0026] The fault type code includes 0x01 representing flame loss, 0x02 representing over-temperature, 0x03 representing pressure anomaly, and 0x04 representing flow limit, and the parameter snapshot includes 16-bit binary code values of fuel flow, combustion air pressure and current temperature.

[0027] Further, the self-diagnosis data frame of the diagnosis module adopts an extended frame format conforming to the ISO11898 standard, the frame ID is set to 0x18Fxxxxx, and the frame data field includes 8 bytes of information, wherein byte 0 stores an error code, byte 1 stores a node state, bytes 2-3 store temperature values in the form of 16-bit signed integers, and bytes 4-7 store custom data.

[0028] The error code E is generated according to a hierarchical coding rule, i.e., E=(F<<12)|(S<<8)|L.

[0029] F represents a 4-bit fault type code, S is a 3-bit node state code, and L is a 1-bit fault level code, the diagnosis module monitors state parameters such as nozzle blockage and ignition failure at a frequency of not less than 100Hz, and the diagnosis result is encapsulated into a self-diagnosis data frame every 1 second and uploaded to the coordination module, and when an emergency fault of L=1 is detected, the diagnosis module immediately triggers the protection module to execute the shutdown operation.

[0030] The custom data of bytes 4-7 includes pressure overrun flag and flow abnormal threshold, the fault type code includes 0001 representing nozzle blockage, 0010 representing ignition failure, 0011 representing flame sensor failure, 0100 representing fuel valve sticking, 0101 representing combustion air fan overload, the node state code includes 001 representing operation, 010 representing standby, 011 representing fault, 100 representing communication interruption, and the fault level code is 0-warn, 1-emergency.

[0031] Further, in the time-frequency division multiplexing mechanism of the communication module, the time slot allocation ratio of the main communication line to the redundant communication line is 3:1, the main line transmission period T1=2ms, and the redundant line transmission period T2=6ms.

[0032] The dynamic adjustment formula of the transmission rate is:

[0033]

[0034] In the formula, N is the current number of online nodes.

[0035] The physical layer redundancy is switched by real-time comparison of line signal strength, and when the main line signal strength is lower than-80dBm, the redundant line is automatically switched.

[0036] Further, in the improved particle swarm optimization algorithm of the learning module, the particle position update formula and the velocity update formula are:

[0037]

[0038] In the formula, X i (t+1) is the updated particle position; X i (t) is the position of the i-th particle at time t; V i (t+1) is the velocity of the i-th particle at time t+1; ω is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers in the interval [0,1]; p i is the individual optimal position of the i-th particle; p g is the global optimal position of all particles; λ is the combustion stability penalty coefficient; and σ is the flame fluctuation standard deviation.

[0039] In the formula, the inertia weight ω is in the range of 0.4≤ω≤0.9, the learning factors c1 and c2 are in the range of 1.5≤c1=c2≤2.5, r1 and r2 are used to increase the randomness of the search, the combustion stability penalty coefficient λ is in the range of 0.2≤λ≤0.5, and the PID parameter dynamic adjustment matrix dimension is 3×N, where 3 represents the PID parameter dimension and N represents the number of adjustment nodes.

[0040] Further, the node-level flame anomaly detection of the protection module uses UV sensor signal continuous monitoring, and when the signal strength is less than the normal flame signal strength threshold in 3 consecutive sampling periods, it is determined that there is a flame anomaly.

[0041] The data field of the highest priority emergency shutdown frame contains 8 bytes of fault information, the first 2 bytes are node ID, and the last 6 bytes are the fuel flow and combustion air flow values at the time of fault occurrence.

[0042] The linkage of the global shutdown instruction and the safety instrument system is double-confirmed through a hard-wired dry contact signal and a CAN frame check code, and the check code is a combination of the instruction frame data and the time stamp.

[0043] Each sampling period is 1ms, and the normal flame signal strength threshold has a value range of [500mV, 800mV].

[0044] On the other hand, the intelligent combustion control method based on the CAN bus comprises the following steps:

[0045] Real-time data interaction is implemented with each combustion node through the CAN protocol stack, the matching coefficient frame and the control instruction are dynamically generated based on the global working condition and are broadcasted, the state information fed back by the node is synchronously received to complete fault isolation; the matching coefficient frame broadcasted by the main controller of the combustion system is received, the combustion air flow is calculated and the air door is driven within a preset time threshold based on the real-time data of the local fuel flow sensor, so as to complete dynamic proportion optimization of the fuel and the combustion air; when a flame anomaly is detected at the node level, the local fuel valve is immediately shut off, the highest priority emergency shutdown frame is sent, the global shutdown instruction of the system level is received and the safety instrument system is linked to execute the shutdown operation; the state parameters of nozzle blockage, ignition failure, flame sensor failure, fuel valve jamming and combustion air fan overload are monitored in real time through a preset self-diagnosis data frame structure, error codes and node state information are analyzed and fed back to the main controller of the combustion system; a dynamic learning model is constructed based on a combustion working condition feature vector set, a PID parameter dynamic adjustment matrix is generated through an improved particle swarm optimization algorithm by introducing a combustion stability penalty term, and after being verified in a closed loop for more than 5 typical working conditions, the matrix is issued to the adjustment node in an encrypted configuration frame, so that the combustion system can maintain a combustion efficiency of not less than 94% and a nitrogen oxide emission of at least less than 110mg / m 3 ;

[0046] In the communication process, a master communication line pair and a redundant communication line pair meeting the electrical characteristics of the ISO11898 standard are set, a physical layer redundancy is constructed by using a two-line time-frequency division multiplexing mechanism, the transmission rate is controlled to be greater than or equal to 500kbps, and the communication delay is less than or equal to 5ms.

[0047] Compared with the known prior art, the technical scheme provided by the application has the following beneficial effects:

[0048] The application provides a CAN bus-based intelligent combustion control device and method. In the execution process, the device and method apply the CAN bus to build an efficient communication architecture, dynamically adjust the fuel and combustion air ratio through global working conditions, realize stable combustion efficiency of more than 94%, and can maintain high efficiency even if the fuel characteristics fluctuate by ±15%. At the same time, the nitrogen oxide emission is effectively controlled to be less than 110 mg / m 3 , has the advantages of environmental protection and energy saving, has a fast response safety mechanism, can cut off the fuel within 5 ms at the node level, the whole process of system-level shutdown is not more than 50 ms, double protection improves the operation safety, in addition, a self-diagnosis function is added to monitor various faults in real time and isolate quickly, double-line redundant communication ensures stable data transmission, the delay is less than or equal to 5 ms, and the combustion parameters are optimized through dynamic learning to adapt to various working conditions, effectively improving the reliability and economy of the combustion system. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0050] Figure 1 It is a structure schematic diagram of the CAN bus-based intelligent combustion control device.

[0051] Figure 2 It is a flowchart of the CAN bus-based intelligent combustion control method. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0053] The present application will be further described below in combination with the embodiments.

[0054] Embodiment 1:

[0055] The CAN bus-based intelligent combustion control device of the present embodiment, as shown in Figure 1 , comprises:

[0056] A coordination module is configured to interact with real-time data of each combustion node through a CAN protocol stack, dynamically generate and broadcast a matching coefficient frame and a control instruction based on a global working condition, and receive state information fed back by the nodes to isolate faults;

[0057] The matching coefficient frame generated by the coordination module contains a time stamp accurate to a millisecond level and a node address field, and the matching coefficient K is calculated based on global working condition parameters, i.e.,

[0058] K=K0×(T target -T avg ) / T target +K base ;

[0059] In the formula, K0 is a dynamic adjustment coefficient, T target is a target combustion temperature, T avg is an average value of real-time temperatures of the nodes, and K base is a basic matching coefficient.

[0060] Each node includes a fuel supply node, a combustion air adjustment node, a flame monitoring node, and a temperature and pressure sensing node.

[0061] The above formula takes the basic matching coefficient as a reference, introduces a dynamic adjustment coefficient based on a deviation of the target combustion temperature and the average value of the real-time temperatures of the nodes, and dynamically corrects the matching coefficient based on global working condition parameters. In order to realize global cooperation of the combustion system, the matching coefficient can respond to temperature differences of the nodes in real time, the average value of global temperatures collected by the CAN bus is included in the calculation, the limitations of traditional fixed matching or single-node local adjustment are broken through, the matching coefficient has basic stability and global adaptability through dynamic correction, and real-time data interaction of the coordination module is combined to realize precise cooperative control of each combustion node, and to provide a calculation basis for cooperation of nodes after fault isolation.

[0062] Fault isolation is realized by continuously comparing deviations of temperature, pressure, and flow state information fed back by the nodes from preset thresholds. When the deviation exceeds the threshold, timing is started. When the deviation of three consecutive communication periods exceeds the threshold, the node is marked as offline, and is prohibited from participating in global matching calculation. Meanwhile, an isolation frame containing the ID and deviation value of the faulty node is broadcast to other nodes to update a global node list.

[0063] A regulation module is configured to receive the matching coefficient frame broadcast by a main controller of the combustion system, combine real-time data of a local fuel flow sensor, and complete combustion air volume calculation and air door driving within a preset time threshold to complete dynamic proportion optimization of fuel and combustion air.

[0064] The combustion air volume calculation of the regulation module adopts a dynamic correction formula:

[0065] Q = K x Q f x [1 + a x (T - T0)] x [1 + b x (P - P0) / P0];

[0066] In the formula: Q is the combustion air volume; K is the matching coefficient; Q f is the real-time flow collected by the local fuel flow sensor; a and b are the temperature correction coefficient and the pressure correction coefficient; T is the current ambient temperature; T0 is the reference ambient temperature; P is the current combustion air pressure; P0 is the reference combustion air pressure;

[0067] The temperature correction coefficient and the pressure correction coefficient a and b are valued as follows:

[0068] Taking 25℃ as the reference, when the temperature is higher than the reference, the temperature correction coefficient increases with the increase of temperature, and when the temperature is lower than the reference, the temperature correction coefficient decreases with the decrease of temperature, and the value is between 0.8 and 1.2;

[0069] Taking the system rated pressure as the reference, when the pressure is higher than the reference, the pressure correction coefficient decreases with the increase of pressure, and when the pressure is lower than the reference, the pressure correction coefficient increases with the decrease of pressure, and the value is between 0.9 and 1.1;

[0070] The above formula is based on the matching coefficient and the real-time fuel flow, introduces the temperature correction coefficient and the pressure correction coefficient, and comprehensively calculates the combustion air volume. The design principle is to eliminate the influence of environmental temperature and combustion air pressure fluctuation on the actual effective air volume, ensure that the ratio of fuel to combustion air remains accurate under complex working conditions, compensate for environmental interference through double-parameter correction, make the air volume calculation more in line with the actual working condition, and at the same time, combined with the rapid response time of the adjustment module ≤10ms, realize the dynamic real-time optimization of air volume, and provide a key guarantee for the stability of combustion efficiency;

[0071] Among them, the preset time threshold in the adjustment module is ≤10ms, the damper drive is completed through pulse width modulation signal control of the servo motor, and the duty cycle D of the drive signal and the linear corresponding relationship of the calculated air volume Q satisfy:

[0072]

[0073] In the formula: D max , D min is the maximum duty cycle when the damper is fully open, 95% ± 1%, and the minimum duty cycle when the damper is fully closed, 5% ± 1%; Q max is the rated air volume corresponding to the maximum opening of the damper, and the duty cycle adjustment step is set to ≤0.1%;

[0074] The above formula establishes a linear relationship between the duty cycle D of the damper driving PWM (pulse width modulation) signal and the calculated air volume, with the minimum duty cycle and the maximum duty cycle as the boundaries, and the actual duty cycle is determined by the air volume ratio. The design purpose is to convert the abstract air volume calculation result into a physical signal that the actuator can directly respond to, to ensure that the damper opening degree accurately matches the required air volume, and compared with the traditional rough driving control, the accuracy and consistency of damper regulation are greatly improved. At the same time, the linear relationship design simplifies the signal conversion logic, and cooperates with the regulation time threshold ≤10ms, realizes the fast and accurate conversion from air volume calculation to physical action;

[0075] The protection module is used for cutting off the local fuel valve immediately when detecting flame anomaly at the node level, and sending the highest priority emergency shutdown frame, while receiving the system level global shutdown instruction and cooperating with the safety instrument system to execute the shutdown operation;

[0076] The highest priority emergency shutdown frame sent by the protection module adopts a fixed frame ID 0x18F00001;

[0077] Among them, the frame structure contains 8 bytes of data field, the first byte is the node ID of the triggered fault, the second byte is the fault type code, and the remaining 6 bytes are the real-time parameter snapshot when the fault occurs;

[0078] The execution delay of cutting off the local fuel valve at the node level is ≤5ms, and the cutting action is triggered by a hardware circuit independent of the microprocessor;

[0079] The system level global shutdown instruction is generated by the main controller within 10ms after receiving the emergency shutdown frame, adopts a broadcast frame form facing all distributed combustion control nodes, the frame ID of the broadcast frame is set to 0x18F00002, the frame data field contains 2 bytes of shutdown instruction code and 4 bytes of timestamp, accurate to millisecond, and at the same time, a 24V on-off signal is sent to the safety instrument system through hardwiring for double triggering, when the safety instrument system executes the shutdown operation, the total fuel valve is cut off first and then each node valve is closed in turn, and the completion time of the whole shutdown process is ≤50ms;

[0080] Among them, the fault type code includes: 0x01 represents flame loss, 0x02 represents overtemperature, 0x03 represents pressure anomaly, and 0x04 represents flow limit; The parameter snapshot includes: 16-bit binary code value of fuel flow, combustion air pressure and current temperature;

[0081] The node level flame anomaly detection of the protection module adopts UV sensor signal continuous monitoring, and when the signal strength is less than the flame normal signal strength threshold for 3 consecutive sampling periods, it is determined that the flame is abnormal;

[0082] The data field of the highest priority emergency shutdown frame contains 8 bytes of fault information, the first 2 bytes are node ID, and the last 6 bytes are the fuel flow and combustion air flow values at the time of fault occurrence;

[0083] The linkage of the global shutdown instruction and the safety instrument system is confirmed by a hard-wired dry contact signal and a CAN frame check code, and the check code is a combination of the instruction frame data and the time stamp;

[0084] Each sampling period is 1 ms, and the value range of the normal flame signal intensity threshold is [500 mV, 800 mV];

[0085] The diagnostic module is used to monitor the state parameters of nozzle blockage, ignition failure, flame sensor failure, fuel valve jamming and combustion air fan overload in real time through a preset self-diagnosis data frame structure, analyze error codes and node state information and feed back to the main controller;

[0086] The self-diagnosis data frame of the diagnostic module adopts an extended frame format conforming to the ISO11898 standard, the frame ID is set to 0x18Fxxxxx, and the frame data field contains 8 bytes of information, of which byte 0 stores error codes, byte 1 stores node states, bytes 2-3 store temperature values in the form of 16-bit signed integers, and bytes 4-7 store custom data;

[0087] The error code E is generated according to a hierarchical coding rule, i.e. E=(F<<12)|(S<<8)|L;

[0088] F represents a 4-bit fault type code, S is a 3-bit node state code, and L is a 1-bit fault level code. The diagnostic module monitors the state parameters such as nozzle blockage and ignition failure at a frequency not less than 100 Hz, and the diagnostic results are packaged into a self-diagnosis data frame every 1 second and uploaded to the coordination module. When an emergency fault of L=1 is detected, the diagnostic module immediately triggers the protection module to perform a shutdown operation;

[0089] The custom data of bytes 4-7 includes a pressure overrun flag and a flow abnormality threshold, the fault type code includes 0001 for nozzle blockage, 0010 for ignition failure, 0011 for flame sensor failure, 0100 for fuel valve jamming, and 0101 for combustion air fan overload, the node state code includes 001 for running, 010 for standby, 011 for fault, and 100 for communication interruption, and the fault level code is 0 for warning and 1 for emergency.

[0090] The communication module is used to set a pair of main communication lines and a pair of redundant communication lines with electrical characteristics conforming to the ISO11898 standard, to build physical layer redundancy through a two-line time-frequency division multiplexing mechanism, to control the transmission rate to be greater than or equal to 500 kbps, and the communication delay to be less than or equal to 5 ms;

[0091] In the dual-line time-frequency multiplexing mechanism of the communication module, the time slot allocation ratio of the main communication line to the redundant communication line is 3:1, the main line transmission period T1=2 ms, and the redundant line transmission period T2=6 ms.

[0092] The dynamic adjustment formula of the transmission rate is:

[0093]

[0094] In the formula, N is the current number of online nodes.

[0095] The physical layer redundancy is switched by real-time comparison of line signal strength, and the redundant line is automatically switched when the main line signal strength is lower than -80 dBm.

[0096] The learning module is configured to construct a dynamic learning model based on a set of combustion condition feature vectors, generate a PID parameter dynamic adjustment matrix through an improved particle swarm optimization algorithm with a combustion stability penalty term, and after closed-loop verification of more than 5 typical conditions, the PID parameter dynamic adjustment matrix is downloaded to the adjustment node in an encrypted configuration frame to control the combustion system to maintain a combustion efficiency of not less than 94% and a nitrogen oxide emission of at least lower than 110 mg / m 3 ;

[0097] In the improved particle swarm optimization algorithm of the learning module, the particle position update formula and the velocity update formula are as follows:

[0098]

[0099] In the formula, X i (t+1) is the updated particle position; X i (t) is the position of the i-th particle at time t; V i (t+1) is the velocity of the i-th particle at time t+1; ω is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers in the interval [0, 1]; p i is the individual optimal position of the i-th particle; p g is the global optimal position of all particles; λ is the combustion stability penalty coefficient; and σ is the standard deviation of flame fluctuation.

[0100] In the formula, the inertia weight ω is in the range of 0.4≤ω≤0.9, the learning factors c1 and c2 are in the range of 1.5≤c1=c2≤2.5, r1 and r2 are used to increase the randomness of the search, the combustion stability penalty coefficient λ is in the range of 0.2≤λ≤0.5, and the PID parameter dynamic adjustment matrix has a dimension of 3×N, where 3 represents the dimension of the PID parameters and N represents the number of adjustment nodes.

[0101] The above formula introduces a combustion stability penalty term on the basis of the traditional particle swarm optimization algorithm, balances local and global search through inertia weight, learning factor and random number, realizes dynamic update of particle position and velocity, aims to optimize PID parameters while considering combustion efficiency, emission indicators and combustion stability, avoids system instability caused by single index optimization, takes flame fluctuation standard deviation as a penalty term into the optimization process, breaks through the limitation of traditional particle swarm optimization algorithm which only focuses on efficiency or emission, makes the optimization result still maintain high efficiency (≥94%) and low emission (nitrogen oxide ≤110mg / m 3 ) when fuel characteristics fluctuate ±15%, and ensures the robustness of PID parameters through closed-loop verification of more than 5 typical working conditions, providing algorithm support for adaptive control of the combustion system;

[0102] It should be noted that:

[0103] The value of inertia weight ω needs to be dynamically adjusted according to the degree of fuel characteristic fluctuation: when the fuel characteristic fluctuation is close to ±15%, take 0.7-0.9 to enhance the global search ability and quickly adapt to new working conditions; when the fluctuation is small, take 0.4-0.6 to strengthen local optimization and maintain combustion efficiency ≥94% and ammonia oxide emission ≤110mg / m 3 ;

[0104] The learning factors c1 and c2 are usually set to equal values, in the interval of 1.5-2.5, if the system requires higher node cooperativity (such as multi-node synchronous regulation scene), it can be biased to 2.0-2.5 to strengthen global experience learning; if you need to highlight the node local adaptability, you can be biased to 1.5-2.0 to retain more individual experience weight;

[0105] The combustion stability penalty coefficient λ needs to be adjusted in combination with the flame fluctuation standard deviation σ: when the flame stability requirement is strict (such as low ammonia combustion scene), take 0.4-0.5 to increase the punishment strength; when you need to prioritize the guarantee of combustion efficiency, take 0.2-0.3 to weaken the punishment;

[0106] In one specific embodiment, the combustion control device can adopt a double-layer communication architecture, such as Figure 1As shown, the solid line represents the CAN bus connection for core control data transmission, in which the coordination module is connected to the adjustment module and the protection module in turn through the CAN bus to form a main control link, and the communication module is connected to the learning module through the CAN bus to realize parameter optimization; the dashed line represents auxiliary communication connection, and each functional module (coordination module, adjustment module, protection module, communication module, learning module) is connected to the diagnosis module through auxiliary communication, and the diagnosis module serves as the center of system state information aggregation to collect the running state, fault information and other non-real-time data of each module; in this architecture, the communication module not only provides CAN bus communication services for the system internally, but also provides a wireless network communication interface for the system and external devices.

[0107] In this embodiment, the coordination module runs to interact with the real-time data of each combustion node through the CAN protocol stack, dynamically generates and broadcasts the matching coefficient frame and the control instruction based on the global working condition, synchronously receives the state information fed back by the node for fault isolation, the adjustment module is post-operated to receive the matching coefficient frame broadcast by the combustion system main controller, combines the real-time data of the local fuel flow sensor, and completes the calculation of the combustion air volume and the damper driving within a preset time threshold to complete the dynamic proportion optimization of fuel and combustion air, and the protection module immediately cuts off the local fuel valve when detecting flame abnormalities at the node level, sends an emergency shutdown frame with the highest priority, receives the global shutdown instruction of the system level and synchronizes the safety instrument system to execute the shutdown operation, and through the diagnosis module, the state parameters of nozzle blockage, ignition failure, flame sensor failure, fuel valve jamming and combustion air fan overload are monitored in real time through the preset self-diagnosis data frame structure, error codes and node state information are analyzed and fed back to the main controller, the communication module further sets the main communication line pair and redundant communication line pair in accordance with the electrical characteristics of the ISO11898 standard, constructs physical layer redundancy through double-line time-frequency division multiplexing mechanism, controls the transmission rate to be ≥500kbps, and the communication delay to be ≤5ms, and finally, through the learning module, a dynamic learning model is constructed based on the combustion condition characteristic vector set, an improved particle swarm optimization algorithm with a combustion stability penalty term is introduced to generate a PID parameter dynamic adjustment matrix, which is verified through closed-loop verification of more than 5 typical working conditions, and then is distributed to the adjustment node in an encrypted configuration frame to control the combustion system to maintain a combustion efficiency of not less than 94% and a nitrogen oxide emission of at least lower than 110mg / m 3 .

[0108] Through the system operation in the above embodiment, the combustion ratio of the combustion equipment (such as a gas boiler) can be dynamically optimized, the combustion efficiency is still maintained to be not less than 94% when the fuel characteristics fluctuate ±15%, the nitrogen oxide emission is lower than 110mg / m 3 ; the communication response is fast and stable, the fault can be quickly isolated; the shutdown is rapid in emergency, effectively improving the combustion safety and stability, and reducing the pollutant emission.

[0109] Supplementary explanation on "closed-loop verification of more than 5 typical working conditions":

[0110] I. Selection criteria for typical working conditions

[0111] Typical working conditions need to cover the most common and critical dynamic scenarios in the actual operation of the combustion system, combined with the characteristics of the combustion system and the variables of fuel, environment, and load, and usually include but are not limited to the following 5 types of core working conditions:

[0112] Fuel property fluctuation condition: simulates random fluctuations of fuel heat value and composition within ±15% (such as changes in methane content in natural gas, fluctuations in coal heat value, etc.), verifying the adaptability of the system to changes in fuel properties.

[0113] High / low load switching condition: covers 30% (low load), 70% (medium load), 100% (full load) of rated load and load step changes (such as sudden increase from 50% to 80%), verifying the system's response capability under different energy output demands.

[0114] Environmental parameter variation condition: includes extreme and gradual scenarios of environmental temperature (-10℃ to 40℃) and atmospheric pressure (90kPa to 110kPa), focusing on verifying the actual effect of temperature correction coefficient (α) and pressure correction coefficient (β) in the adjustment module.

[0115] Start-stop transition condition: simulates the whole process of cold start (from normal temperature to target temperature) and hot shutdown (from running state to safe shutdown), verifying the ignition success rate, temperature rise rate, and shutdown safety redundancy.

[0116] Local fault disturbance condition: simulates the scenario of a single combustion node (such as fuel valve, combustion air fan) recovering to normal after a short-term abnormality (such as flow fluctuation ±20%, pressure drop 10%), verifying the fault isolation and global reconfiguration capability of the coordination module.

[0117] Long-term stable operation condition: continuous 8-hour rated load operation, verifying the parameter drift in steady state (such as whether the PID parameters remain effective, whether the emission indicators are stable).

[0118] II. Core process of closed-loop verification

[0119] Closed-loop verification is a cyclic mechanism of "setting working conditions → system adjustment → indicator monitoring → parameter optimization → re-verification", ensuring that the PID parameter dynamic adjustment matrix generated by the learning module has universality, with the following specific steps:

[0120] Working condition parameter pre-setting: in the test environment, accurately set the parameters of the target working condition (such as fuel flow, target temperature, environmental temperature, etc.) through simulation tools or physical platforms, and record the initial state.

[0121] System autonomous operation: The PID parameter matrix to be verified is issued to the adjustment node, and the system automatically runs under the preset working condition. The learning module collects real-time data of each node (temperature, flow, pressure, and emission concentration, etc.).

[0122] Key indicator monitoring: Focus on monitoring three core indicators:

[0123] Combustion efficiency: needs to be continuously ≥94% (calculated by heat balance method or flue gas analysis method);

[0124] Nitrogen oxide emissions: needs to be continuously ≤110mg / m 3 (detected by online flue gas monitoring instrument);

[0125] Stability index: the standard deviation of flame fluctuation ( ) ≤ preset threshold (usually ≤5%), without flame loss, over-temperature and other abnormal protection triggering.

[0126] Parameter iterative optimization: if the indicators are not up to standard under certain working conditions (such as efficiency below 94%), the PID parameter matrix is adjusted again through the improved particle swarm optimization algorithm, and the "operation-monitoring" process is repeated until the indicators meet the requirements.

[0127] Verification pass standard: each typical working condition needs to meet the above indicators for 3 consecutive closed-loop cycles, and the indicator fluctuation amplitude in a single cycle is ≤2% (such as efficiency is stable between 94% and 96%), which can be determined as the working condition verification pass.

[0128] III. Purpose and role of verification

[0129] Ensure parameter reliability: through verification covering multiple scenarios, ensure that the generated PID parameter matrix not only fits the model in theory, but also works stably in actual complex working conditions, avoiding the problem of "theoretical optimization but actual failure".

[0130] Strengthen system robustness: the verification process forces to expose potential defects of the system under extreme working conditions (such as low load + low temperature environment), and improves the resistance of the system to multi-variable coupled disturbance through parameter optimization.

[0131] Safety redundancy confirmation: in fault disturbance working conditions, the linkage effect of the protection module and the diagnosis module is verified simultaneously (such as whether it is quickly turned off in abnormal conditions, and whether the fault location is accurate), to ensure the synergy of safety mechanism and adjustment mechanism.

[0132] After the above verification, the encrypted configuration frame generated by the learning module can be stably issued to the adjustment node, finally realizing the core performance indicators of "combustion efficiency ≥94% and nitrogen oxide emissions ≤110mg / m 3 " within the range of ±15% of fuel property fluctuation required in the document.

[0133] Embodiment 2:

[0134] In a specific implementation, on the basis of Embodiment 1, the present embodiment refers to Figure 2 The CAN bus-based intelligent combustion control device in Embodiment 1 is further specifically described:

[0135] The CAN bus-based intelligent combustion control method comprises:

[0136] Real-time data interaction is implemented with each combustion node through a CAN protocol stack, a matching coefficient frame and a control instruction are dynamically generated based on a global working condition and are broadcast, and state information fed back by the node is synchronously received to complete fault isolation;

[0137] The matching coefficient frame broadcast by the main controller of the combustion system is received, real-time data of a local fuel flow sensor is combined, and air volume calculation and air door driving are completed within a preset time threshold to complete dynamic proportion optimization of fuel and combustion air;

[0138] When flame abnormality is detected at the node level, the local fuel valve is immediately shut off, a highest-priority emergency shutdown frame is sent, a system-level global shutdown instruction is received and a safety instrument system is linked to perform a shutdown operation;

[0139] Through a preset self-diagnosis data frame structure, state parameters of nozzle blockage, ignition failure, flame sensor failure, fuel valve jamming and combustion air fan overload are monitored in real time, error codes and node state information are analyzed and fed back to the main controller of the combustion system;

[0140] A dynamic learning model is constructed based on a combustion working condition feature vector set, an improved particle swarm optimization algorithm with a combustion stability penalty term is introduced to generate a PID parameter dynamic adjustment matrix, after closed-loop verification of more than 5 typical working conditions, the matrix is issued to the adjustment node in an encrypted configuration frame, and the combustion system is controlled to maintain a combustion efficiency of not less than 94% and nitrogen oxide emissions of at least less than 110 mg / m 3 ;

[0141] Among them, the communication process sets a main communication line pair and a redundant communication line pair conforming to the electrical characteristics of the ISO11898 standard, uses a two-line time-frequency division multiplexing mechanism to construct a physical layer redundancy, controls the transmission rate to be greater than or equal to 500 kbps, and the communication delay to be less than or equal to 5 ms.

[0142] In summary, in the above-mentioned embodiments, the device and method apply a CAN bus to construct an efficient communication architecture, dynamically adjust the matching of fuel and combustion air based on a global working condition, realize a stable combustion efficiency of more than 94%, and still maintain high efficiency even if the fuel characteristics fluctuate by ±15%. At the same time, nitrogen oxide emissions are effectively controlled to be less than 110 mg / m 3, with the advantages of environmental protection and energy saving, and a fast response safety mechanism, node-level abnormalities can be cut off within 5ms, and the system-level shutdown process does not exceed 50ms, double protection to improve operation safety, in addition, self-diagnosis function is added to monitor various faults in real time and isolate quickly, double-line redundant communication ensures stable data transmission, delay ≤5ms, and through dynamic learning to optimize combustion parameters, adapt to various working conditions, effectively improve the reliability and economy of the combustion system.

[0143] The above examples are only used to illustrate the technical solutions of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A CAN bus based intelligent combustion control device, characterized in that, Comprise: A coordination module for interacting with real-time data of each combustion node through a CAN protocol stack, dynamically generating and broadcasting a proportioning coefficient frame and a control instruction based on a global operating condition, and synchronously receiving state information fed back by the node for fault isolation; The coordination module generates a matching coefficient frame containing a time stamp accurate to the millisecond level and a node address field, and the matching coefficient The calculation of the matching coefficient is based on global working condition parameters, namely: ； In the formula: is a dynamic adjustment coefficient; is a target combustion temperature; is an average value of real-time temperatures of each node; is a basic proportion coefficient; Wherein, each node comprises a fuel supply node, a combustion air adjustment node, a flame monitoring node and a temperature and pressure sensing node; The fault isolation is achieved by continuously comparing the deviation of temperature, pressure and flow state information fed back by the node from a preset threshold value, and when the deviation exceeds the threshold value, a timer is started, and when the deviation of 3 consecutive communication periods exceeds the threshold value, the node is marked as offline, and is prohibited from participating in global proportioning calculation, and a isolation frame containing the fault node ID and the deviation value is generated and broadcast to other nodes to update the global node list; An adjustment module for receiving the proportioning coefficient frame broadcast by the main controller of the combustion system, combining the real-time data of the local fuel flow sensor, and completing the combustion air flow calculation and damper driving within a preset time threshold to complete the dynamic proportioning optimization of fuel and combustion air; A protection module for immediately shutting off the local fuel valve when detecting flame abnormalities at the node level, and sending an emergency shutdown frame with the highest priority, while receiving a global shutdown instruction at the system level and cooperating with the safety instrument system to execute the shutdown operation; A diagnosis module for monitoring state parameters of nozzle blockage, ignition failure, flame sensor failure, fuel valve jamming and combustion air fan overload in real time through a preset self-diagnosis data frame structure, analyzing error codes and node state information and feeding back to the main controller; A communication module for setting a pair of main communication lines and a pair of redundant communication lines that meet the electrical characteristics of ISO11898 standard, building physical layer redundancy through double-line time-frequency division multiplexing mechanism, controlling the transmission rate to be greater than or equal to 500kbps, and the communication delay to be less than or equal to 5ms; A learning module for constructing a dynamic learning model based on a set of combustion condition feature vectors, generating a dynamic PID parameter adjustment matrix through an improved particle swarm optimization algorithm with a combustion stability penalty term, and after closed-loop verification of more than 5 typical conditions, issuing an encrypted configuration frame to the adjustment node to control the combustion system to maintain a combustion efficiency of not less than 94% and a nitrogen oxide emission of at least less than 110mg / m³ within a fuel characteristic fluctuation range of ±15%; Wherein, the typical conditions include fuel characteristic fluctuation condition, high / low load switching condition, environmental parameter variation condition, start-stop transition condition, local fault disturbance condition and long-term stable operation condition.

2. The CAN bus based intelligent combustion control device as claimed in claim 1 wherein, The combustion air flow calculation of the adjustment module uses a dynamic correction formula: ； In the formula: is the combustion air flow rate; is the matching coefficient; is the real-time flow rate collected by the local fuel flow sensor; is the temperature correction coefficient, the pressure correction coefficient; is the current ambient temperature; is the reference ambient temperature; is the current combustion air pressure; is the reference combustion air pressure; Wherein, the preset time threshold in the adjustment module is less than or equal to 10ms, the damper driving is completed by a pulse width modulation signal controlling a servo motor, and the duty cycle D of the driving signal and the linear correspondence of the calculated flow Q satisfy: ； In the formula: is the maximum duty ratio when the air door is fully open, 95%±1%, and the minimum duty ratio when the air door is fully closed, 5%±1%; is the rated air volume corresponding to the maximum opening degree of the air door, and the duty ratio adjustment step is set to be ≤0.1%.

3. The CAN bus based intelligent combustion control device as claimed in claim 1 wherein, The highest priority emergency shutdown frame sent by the protection module uses a fixed frame ID 0x18F00001; Wherein, the frame structure contains 8 bytes of data field, the first byte is the node ID of the triggering fault, the second byte is the fault type code, and the remaining 6 bytes are real-time parameter snapshots when the fault occurs; The execution delay of the local fuel valve cut-off at the node level is less than or equal to 5ms, and the cut-off action is triggered by a hardware circuit independent of the microprocessor; The system-level global shutdown instruction is generated by the main controller within 10 ms after receiving the emergency shutdown frame, adopts a broadcast frame form facing all distributed combustion control nodes, the frame ID of the broadcast frame is set as 0x18F00002, the frame data field contains a 2-byte shutdown instruction code and a 4-byte timestamp accurate to milliseconds, and a 24V switching signal is sent to the safety instrument system through hard wiring for double triggering; when the safety instrument system performs the shutdown operation, the total fuel valve is cut off first and then the node valves are closed in sequence, and the completion time of the whole shutdown process is less than or equal to 50 ms; The fault type code includes 0x01 for flame loss, 0x02 for over-temperature, 0x03 for pressure abnormality and 0x04 for flow over-limit, and the parameter snapshot includes 16-bit binary code values of fuel flow, combustion air pressure and current temperature.

4. The CAN bus based intelligent combustion control device as claimed in claim 1 wherein, The self-diagnosis data frame of the diagnosis module adopts an extended frame format conforming to the ISO11898 standard, the frame ID is set as 0x18Fxxxxx, and the frame data field contains 8 bytes of information, wherein byte 0 stores an error code, byte 1 stores a node state, bytes 2-3 store a temperature value in the form of a 16-bit signed integer, and bytes 4-7 store custom data; The error code E is generated according to a hierarchical coding rule, i.e. E = (F << 12) | (S << 8) | L; F represents a 4-bit fault type code, S is a 3-bit node state code, and L is a 1-bit fault level code; the diagnosis module monitors state parameters such as nozzle blockage and ignition failure at a frequency of not less than 100 Hz, encapsulates diagnosis results into a self-diagnosis data frame every 1 second and uploads the self-diagnosis data frame to the coordination module, and when an emergency fault of L = 1 is detected, the diagnosis module immediately triggers the protection module to perform a shutdown operation; The custom data of bytes 4-7 includes a pressure over-limit flag and a flow abnormal threshold, the fault type code includes 0001 for nozzle blockage, 0010 for ignition failure, 0011 for flame sensor failure, 0100 for fuel valve jamming and 0101 for combustion air fan overload, the node state code includes 001 for running, 010 for standby, 011 for fault and 100 for communication interruption, and the fault level code is 0 for warning and 1 for emergency.

5. The CAN bus based intelligent combustion control device as claimed in claim 1 wherein, In the time-frequency division multiplexing mechanism of the communication module, the time slot allocation ratio of the main communication line to the redundant communication line is 3:1, the transmission period T1 of the main line is 2 ms, and the transmission period T2 of the redundant line is 6 ms; The dynamic adjustment formula of the transmission rate is: ； In the formula, N is the current number of online nodes. The physical layer redundancy is switched by real-time comparison of line signal strengths, and when the signal strength of the main line is lower than -80 dBm, the system is automatically switched to the redundant line.

6. The CAN bus based intelligent combustion control device as claimed in claim 1 wherein, In the improved particle swarm optimization algorithm of the learning module, the particle position update formula and the velocity update formula are: ； wherein: is the updated particle position; is the position of the i-th particle at time t; is the velocity of the i-th particle at time t+1; is the inertia weight; , is the learning factor; , is a random number in the interval [0, 1]; is the individual optimal position of the i-th particle; is the global optimal position of all particles; is the combustion stability penalty coefficient; is the flame fluctuation standard deviation; wherein the inertia weight is in the range 0.4≤ ≤0.9, the learning factor , is in the range 1.5≤ = ≤2.5, , for increasing the randomness of the search, the combustion stability penalty factor is in the range 0.2≤ ≤0.5, the PID parameter dynamic adjustment matrix has a dimension of 3×N, 3 representing the PID parameter dimension and N representing the number of adjustment nodes.

7. The CAN bus based intelligent combustion control device as claimed in claim 1 wherein, The node-level flame anomaly detection of the protection module adopts continuous monitoring of UV sensor signals, and when the signal strength is less than the normal flame signal strength threshold for 3 consecutive sampling periods, the flame is determined to be abnormal. The data field of the highest priority emergency shutdown frame contains 8 bytes of fault information, the first 2 bytes are node ID, and the last 6 bytes are fuel flow and combustion air flow values at the time of fault occurrence; The linkage of the global shutdown instruction and the safety instrument system is confirmed by a hard-wired dry contact signal and a CAN frame check code, and the check code is a combination of the instruction frame data and the time stamp. Each sampling period is 1 ms, and the flame normal signal intensity threshold value ranges from 500 mV to 800 mV.

8. A method of intelligent combustion control based on CAN bus, the method being a method of implementing the intelligent combustion control device based on CAN bus as claimed in any one of claims 1 to 7, characterized in that, It comprises: Real-time data interaction with each combustion node is implemented through a CAN protocol stack, and the matching coefficient frame and the control instruction are dynamically generated based on the global working condition and broadcasted, and the state information fed back by the node is received synchronously to complete fault isolation; The matching coefficient frame broadcasted by the main controller of the combustion system is received, and the local fuel flow sensor real-time data is combined to complete the combustion air flow calculation and the damper driving within a preset time threshold, so as to complete the dynamic proportion optimization of fuel and combustion air; When flame anomaly is detected at the node level, the local fuel valve is immediately cut off, the highest priority emergency shutdown frame is sent, the system-level global shutdown instruction is received, and the safety instrument system is linked to execute the shutdown operation; Through a preset self-diagnosis data frame structure, the state parameters of nozzle blockage, ignition failure, flame sensor failure, fuel valve jamming and combustion air fan overload are monitored in real time, error codes and node state information are analyzed and fed back to the main controller of the combustion system; Based on the combustion condition characteristic vector set, a dynamic learning model is constructed, an improved particle swarm optimization algorithm with a combustion stability penalty term is used to generate a PID parameter dynamic adjustment matrix, and after closed-loop verification of more than 5 typical working conditions, an encrypted configuration frame is issued to the adjustment node to control the combustion system to maintain a combustion efficiency of not less than 94% and a nitrogen oxide emission of at least less than 110 mg / m³ within a fuel characteristic fluctuation range of ±15%; In the communication process, a main communication line pair and a redundant communication line pair meeting the electrical characteristics of the ISO11898 standard are set, a physical layer redundancy is constructed by using a two-line time-frequency division multiplexing mechanism, the transmission rate is controlled to be greater than or equal to 500 kbps, and the communication delay is less than or equal to 5 ms.

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