Distributed biomass power generation real-time monitoring method fused with Internet of Things

Abstract

The invention relates to the field of power equipment data processing, in particular to a distributed biomass power generation real-time monitoring method fusing the Internet of Things, and aims to solve the problem of time sequence offset caused by sampling frequency difference and network transmission jitter of multi-source heterogeneous data streams. A neural process model is adopted to predict fuel calorific value space-time distribution characteristics, a causal convolutional network is combined to generate a high-frequency pseudo sequence synchronized with temperature field data, and data stream space-time consistency is maintained through forward error correction coding redundancy compensation and MQTT protocol hierarchical transmission; a combustion efficiency confidence coefficient matrix is constructed, ash accumulation characteristics and flue gas oxygen content dynamic changes are fused, a space-time cross attention optimization algorithm is used for generating an anti-disturbance control instruction set, nonlinear deviation and overshoot oscillation of an emission closed-loop control system are effectively restrained, and the control precision and operation economy of the biomass energy conversion process are improved.

Description

Technical Field

[0001] The present invention relates to the field of power equipment data processing, and particularly to a real-time monitoring method for distributed biomass power generation integrating the Internet of Things. Background Art

[0002] Real-time monitoring of biomass power generation is a technical system that continuously and dynamically tracks the physical parameters, chemical components, and equipment status of the biomass energy conversion process by integrating a sensor network, a data acquisition system, and intelligent analysis algorithms. This system relies on a multi-channel sensor network to collect key parameters such as boiler temperature, steam pressure, oxygen content in flue gas, fuel calorific value, and ash residue rate in real time, and transmits them to the central processing unit through a redundant communication protocol; the edge computing node constructs a thermodynamic equilibrium equation based on the combustion kinetics model and historical operation data, dynamically optimizes the fuel ratio and combustion efficiency, and synchronously combines spectral analysis technology to detect the concentrations of pollutants such as NOx and SO2, and feeds back to the control system to adjust the opening of the secondary air damper and the injection amount of the denitration agent; the fault diagnosis module uses a convolutional neural network to identify abnormal vibration spectra or thermal stress distribution deviations, predict the risk of equipment wear or coking, trigger hierarchical warnings and generate maintenance decisions, so as to balance power generation efficiency, emission indicators, and equipment life under thermodynamic constraints, and improve the robustness and economy of the energy conversion link, which conforms to the technical path of efficient and clean utilization of biomass energy.

[0003] In a distributed Internet of Things architecture, the biomass power generation monitoring system faces the problem of time synchronization of multi-source heterogeneous data streams. Specifically, in the process of optimizing the parameters of the combustion kinetics model, there is an order-of-magnitude difference in the sampling frequencies of the fuel calorific value spectral data and the boiler temperature field distribution data received by the edge computing node (the former is sampled at the second level, and the latter needs to be refreshed at a high frequency of milliseconds), and the combustion efficiency optimization algorithm based on the thermodynamic equilibrium equation requires strict timing alignment of the input parameters; when the distributed data caching mechanism cannot eliminate the timing offset caused by network transmission jitter, the dynamic optimization module will generate a spatio-temporal mismatch between the oxygen concentration feedback control quantity in the combustion chamber and the current fuel supply state, resulting in the accumulation of calculation errors in the thermal cycle efficiency. This error will cause a non-linear deviation between the predicted value and the actual emission value of NOx concentration during the variable operating conditions of the gas turbine, and ultimately cause overshoot oscillation of the emission closed-loop control system. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a real-time monitoring method for distributed biomass power generation integrating the Internet of Things, which solves the problem that the biomass power generation monitoring system needs to solve the timing offset caused by sampling frequency differences and network transmission jitter of multi-source heterogeneous data streams in a distributed architecture. This offset leads to spatio-temporal mismatch of the combustion efficiency optimization algorithm, and further causes non-linear deviation and overshoot oscillation of the emission closed-loop control system.

[0005] To solve the above technical problems, the specific technical solution of the present invention is as follows: The real-time monitoring method for distributed biomass power generation integrating the Internet of Things provided by the present invention includes: Collecting multi-source heterogeneous data streams of distributed biomass power generation equipment, where the multi-source heterogeneous data streams include millisecond-level real-time high-frequency data, second-level medium-low frequency data, and control instruction streams; Performing cross-frequency domain time series alignment on the medium-low frequency data through a fuel calorific value dynamic interpolation processing module to generate a synchronized high-frequency fuel calorific value pseudo-sequence, and the fuel calorific value dynamic interpolation processing module realizes frequency domain upsampling processing based on a neural process model and a causal convolutional network; Inputting the synchronized high-frequency fuel calorific value pseudo-sequence and the real-time high-frequency data stream into a network transmission channel optimized by a dynamic bandwidth allocation algorithm, and allocating real-time transmission priorities through a DDPG reinforcement learning algorithm to output a spatio-temporally synchronized multi-source data stream; Based on the spatio-temporally synchronized multi-source data stream, constructing a combustion efficiency confidence matrix for ash adhesion perception in the combustion chamber through a graph attention network, where the combustion efficiency confidence matrix associates the topological coordinates of the combustion chamber with the time series data of the oxygen content in the flue gas; Inputting the combustion efficiency confidence matrix into a spatio-temporal cross-attention optimization algorithm to generate a dynamic control instruction set for the denitration agent injection amount and the secondary air damper opening, and the spatio-temporal cross-attention optimization algorithm integrates real-time sulfur content data and gas turbine vibration spectrum data; Aggregating the control effect feedback data and encrypted model gradients of edge nodes through a federated collaboration module to update the global optimization model parameters, and the federated collaboration module uses a differential privacy protection algorithm to process the encrypted model gradients; Issuing the dynamic control instruction set to the actuator, and adjusting the secondary air damper opening and the denitration agent injection amount in the combustion chamber according to the dynamic control instruction set to form a closed-loop control link.

[0006] Further, in the real-time monitoring method for distributed biomass power generation integrating the Internet of Things of the present invention, the cross-frequency domain time series alignment of the fuel calorific value dynamic interpolation processing module includes: Receiving the fuel component detection results and the temperature field historical data, and predicting the spatio-temporal distribution characteristics of the fuel calorific value through the neural process model; Performing frequency domain upsampling processing on the fuel calorific value spectral data by using a causal convolutional network to generate a high-frequency pseudo-sequence synchronized with the millisecond-level temperature field data; Encapsulating the high-frequency pseudo-sequence into a streaming data format and transmitting it to the combustion state modeling module through the gRPC protocol.

[0007] Furthermore, for the real-time monitoring method of distributed biomass power generation integrating the Internet of Things according to the present invention, the optimization of the network transmission channel by the dynamic bandwidth allocation algorithm includes: Monitor the network bandwidth status and the time-series data of the combustion chamber pressure gradient, and dynamically allocate the transmission priorities of real-time data streams and non-real-time data streams through the DDPG reinforcement learning algorithm; Adjust the forward error correction coding parameters and the sliding window size according to the network jitter rate, and perform redundant transmission compensation on the synchronized high-frequency fuel calorific value pseudo-sequence generated; Transmit the high-frequency fuel calorific value pseudo-sequence through the MQTT 5.0 protocol in a hierarchical manner, so that the end-to-end delay of the temperature field data stream is lower than the preset threshold.

[0008] Furthermore, for the real-time monitoring method of distributed biomass power generation integrating the Internet of Things according to the present invention, the construction of the combustion efficiency confidence matrix includes: Receive the spatio-temporally synchronized multi-source data streams and the combustion chamber topology coordinate data, and extract the ash accumulation characteristics of the local area of the combustion chamber through the graph attention network; Perform a spatial association mapping on the ash accumulation characteristics and the time-series data of the oxygen content in the flue gas to generate a combustion stability scoring matrix; Push the combustion stability scoring matrix to the rolling optimization control module through the Kafka streaming processing platform.

[0009] Furthermore, for the real-time monitoring method of distributed biomass power generation integrating the Internet of Things according to the present invention, the execution of the spatio-temporal cross-attention optimization algorithm includes: Analyze the local ash thickness over-limit area in the combustion stability scoring matrix, and extract the corresponding real-time sulfur content data; Calculate the denitration agent demand weights of different areas of the combustion chamber through the spatio-temporal attention mechanism to generate the injection amount allocation ratio; Fuse the vibration spectrum data of the gas turbine, correct the integral control term weight of the secondary air damper opening command, generate a dynamic control instruction set under the anti-disturbance control mode, and feedback the instruction set to the network transmission optimization module.

[0010] Furthermore, for the real-time monitoring method of distributed biomass power generation integrating the Internet of Things according to the present invention, the update of the global model parameters of the federated cooperation module includes: Receive the encrypted edge node model gradient data and the control effect feedback log, and perform gradient noise injection through the differential privacy protection algorithm; Aggregate the PID adjustment instructions of each edge node based on the transfer reinforcement learning strategy to generate global optimization parameters; Distribute the global optimization parameters to the fuel calorific value dynamic interpolation processing module and the rolling optimization control module through the HTTPS encrypted channel.

[0011] Furthermore, the real-time monitoring method for distributed biomass power generation integrating the Internet of Things according to the present invention further includes a step of ensuring the temporal consistency of the service grid architecture: Inject a logical clock synchronization protocol into the communication link between microservices and attach a Lamport timestamp to the data packet; Verify the cross-service call temporal causality of the network transmission channel through a gRPC interceptor and discard expired instructions caused by network out-of-order; When the response delay of the combustion state modeling module exceeds the threshold, switch to a pre-stored combustion efficiency empirical value to maintain the control closed-loop.

[0012] Furthermore, the real-time monitoring method for distributed biomass power generation integrating the Internet of Things according to the present invention further includes a microservice hot update step when the fuel type is switched: When the fuel delivery system switches the raw material type, trigger the model loading interface of the fuel calorific value dynamic interpolation processing module; Synchronously update the fuel diffusion coefficient matrix of the combustion state modeling module and associate the thermodynamic characteristic parameters of the new fuel type; Record the fuel type label through the federated collaboration module for the model generalization training of the migration reinforcement learning strategy.

[0013] Furthermore, the real-time monitoring method for distributed biomass power generation integrating the Internet of Things according to the present invention further includes an ash accumulation linkage control step: When the combustion state modeling module detects that the local ash thickness exceeds the limit, trigger the soot blower linkage signal through a Kafka message; The rolling optimization control module switches to the disturbance rejection control mode to improve the priority of temperature data transmission in the corresponding combustion chamber area; The network transmission optimization module dynamically adjusts the bandwidth allocation strategy and preferentially transmits the real-time high-frequency data stream associated with the ash accumulation area.

[0014] Furthermore, the real-time monitoring method for distributed biomass power generation integrating the Internet of Things according to the present invention further includes an elastic expansion step of edge-cloud collaboration: Deploy the fuel calorific value dynamic interpolation processing module and the rolling optimization control module to the edge lightweight cluster; Synchronize the combustion chamber physical topology metadata and the network transmission quality indicators through the edge-cloud collaboration bus; Dynamically expand the instances of the combustion state modeling module based on the Consul service discovery mechanism to respond to the computational load fluctuations triggered by the change in the ash accumulation degree.

[0015] Advantages of the present invention; The present invention significantly improves the control accuracy and operation stability of the biomass power generation monitoring system through multi-dimensional technology collaboration, effectively solving the control mismatch problem caused by the time-series offset of multi-source data. The dynamic interpolation processing module combines the neural process model and the causal convolutional network to achieve high-precision time-series alignment of cross-frequency domain data, eliminate the sampling frequency difference between the fuel calorific value data and the temperature field data, and provide spatio-temporal consistent input parameters for the combustion efficiency optimization algorithm; the dynamic bandwidth allocation algorithm and the hierarchical transmission mechanism cooperate to suppress the influence of network jitter, and maintain the time-series integrity of the multi-source data stream transmission through priority scheduling driven by reinforcement learning and forward error correction redundancy compensation, avoiding spatio-temporal mismatch in the feedback control of the oxygen concentration in the combustion chamber; the combustion efficiency confidence matrix fuses the ash accumulation characteristics and the dynamic change of oxygen content, and generates anti-disturbance control instructions by combining the spatio-temporal cross-attention optimization algorithm, reducing the non-linear prediction deviation of the sulfur dioxide emission concentration; the federated collaboration module realizes knowledge sharing and global optimization between edge nodes through the aggregation of model parameters protected by differential privacy, suppressing the control oscillation caused by local working condition fluctuations; the service grid architecture and the edge-cloud elastic expansion mechanism cooperate to ensure the system robustness, the logical clock synchronization and the fuse degradation strategy maintain the time-series causal consistency of the control closed-loop, and the Consul-driven dynamic load balancing effectively responds to the sudden change in the computing resource demand caused by ash accumulation, forming a full-link optimization system from data acquisition, transmission, modeling to execution, reducing the overshoot of the emission closed-loop control system to an acceptable threshold range. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can also be obtained according to the drawings without creative efforts.

[0017] Figure 1 It is a flowchart of the distributed biomass power generation real-time monitoring method integrating the Internet of Things provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments and corresponding drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below with reference to the drawings. To better understand the objectives of the present invention, the present invention is further described in detail below.

[0019] Please refer to Figure 1, the real-time monitoring method for distributed biomass power generation integrating the Internet of Things provided by the present invention includes: Step S101, collecting multi-source heterogeneous data streams of distributed biomass power generation equipment, where the multi-source heterogeneous data streams include millisecond-level real-time high-frequency data, second-level medium-low frequency data, and control instruction streams; Step S102, performing cross-frequency domain time series alignment on the medium-low frequency data through a fuel calorific value dynamic interpolation processing module to generate a synchronized high-frequency fuel calorific value pseudo-sequence. The fuel calorific value dynamic interpolation processing module realizes frequency domain upsampling processing based on a neural process model and a causal convolutional network; Step S103, inputting the synchronized high-frequency fuel calorific value pseudo-sequence and the real-time high-frequency data stream into a network transmission channel optimized by a dynamic bandwidth allocation algorithm, and allocating real-time transmission priorities through a DDPG reinforcement learning algorithm to output a spatio-temporally synchronized multi-source data stream; Step S104, based on the spatio-temporally synchronized multi-source data stream, constructing a combustion efficiency confidence matrix for detecting ash adhesion in the combustion chamber through a graph attention network. The combustion efficiency confidence matrix associates the topological coordinates of the combustion chamber with the time series data of the oxygen content in the flue gas; Step S105, inputting the combustion efficiency confidence matrix into a spatio-temporal cross-attention optimization algorithm to generate a dynamic control instruction set for the denitration agent injection amount and the secondary damper opening. The spatio-temporal cross-attention optimization algorithm integrates real-time sulfur content data and the vibration spectrum data of the gas turbine; Step S106, aggregating the control effect feedback data and encrypted model gradients of edge nodes through a federated collaboration module to update the global optimization model parameters. The federated collaboration module uses a differential privacy protection algorithm to process the encrypted model gradients; Step S107, sending the dynamic control instruction set to the actuator, adjusting the secondary damper opening and the denitration agent injection amount in the combustion chamber according to the dynamic control instruction set to form a closed-loop control link, collecting the control effect feedback data of the actuator (including the actual opening of the secondary damper and the actual injection amount of the denitration agent), and uploading the feedback data to the federated collaboration module.

[0020] Neural Process: Adopts a variational autoencoder structure with a latent space dimension of 128, generates a probability map of the calorific value distribution in the combustion chamber space through Monte Carlo sampling, and makes predictions in combination with the time series correlation of the temperature field data.

[0021] Neural Process: The input is fuel component spectral data (dimension 128) and temperature field historical data (dimension 256), and the output is the fuel calorific value probability distribution of the combustion chamber grid (dimension 1024). The model optimizes the variational lower bound through the KL divergence loss function.

[0022] Differential privacy protection: The gradient noise follows a Gaussian distribution with a mean of 0 and a standard deviation of 0.01 - 0.03. The gradient is clipped by L2 norm (threshold 1.0) before noise injection.

[0023] Causal convolutional network: It consists of 5 layers of dilated convolutional structures with a convolutional kernel size of 3×3, and the dilation factor increases exponentially (1, 2, 4, 8, 16) to avoid future information leakage and achieve frequency-domain upsampling from seconds to milliseconds.

[0024] The real-time monitoring method for distributed biomass power generation integrating the Internet of Things provided by the present invention realizes the real-time optimization and stable operation of the biomass power generation system through the coordination of multi-level data processing and control strategies. The logical relationship and technical details of each step of the specific technical solution are as follows: Distributed biomass power generation equipment collects high-frequency data streams in milliseconds and medium-low frequency data streams in seconds through a multi-channel sensor network deployed in boilers, gas turbines, and fuel delivery systems. Among them, the high-frequency data stream in milliseconds includes the boiler temperature field distribution, flue gas oxygen content, and gas turbine vibration spectrum, and is transmitted to the edge computing node through a redundant communication protocol; the medium-low frequency data stream in seconds covers fuel calorific value spectral data, ash residue rate, and sulfur content detection results, which are reported by industrial Internet of Things terminal devices at a fixed cycle; the control instruction stream includes secondary air damper opening instructions, denitration agent injection amount instructions, and soot blower linkage signals for real-time adjustment of the combustion chamber operating state. During the data collection process, data streams in different frequency domains are marked with timestamps and managed by a cache queue to provide basic timing information for subsequent cross-frequency domain alignment.

[0025] The fuel calorific value dynamic interpolation processing module receives medium-low frequency fuel calorific value spectral data and historical temperature field data, and predicts the distribution characteristics of fuel calorific value in the spatial dimension of the combustion chamber based on the neural process model. The neural process model learns the non-linear mapping relationship of combustion kinetic parameters through variational inference, and combines the temporal correlation of historical temperature field data to generate a latent variable representation of the spatio-temporal distribution of fuel calorific value. Subsequently, the causal convolutional network performs frequency-domain upsampling processing on the fuel calorific value spectral data, captures long-distance temporal dependence relationships through dilated convolutional kernels, upsamples the second-level sampled data to a millisecond-level pseudo-sequence, and synchronizes the timing with the high-frequency temperature field data. The upsampled high-frequency pseudo-sequence is encapsulated into a streaming data format through the gRPC protocol and transmitted to the combustion state modeling module.

[0026] The network transmission channel optimized by the dynamic bandwidth allocation algorithm receives the synchronized high-frequency fuel calorific value pseudo-sequence and real-time high-frequency data stream, and uses the DDPG reinforcement learning algorithm to dynamically allocate transmission resources. The DDPG algorithm takes the network bandwidth status, the time-series data of the combustion chamber pressure gradient, and the end-to-end delay requirement as the state input, outputs the priority weights of the real-time data stream and the non-real-time data stream through the Actor network, and the Critic network evaluates the Q value of the transmission strategy to optimize the bandwidth utilization. Aiming at the problem of network transmission jitter, the forward error correction coding parameters and the sliding window size are dynamically adjusted according to the real-time network quality, and redundant coding compensation is performed on the high-frequency data stream to reduce the packet loss rate. The processed multi-source data stream is hierarchically transmitted through the MQTT 5.0 protocol. Among them, the temperature field data stream is transmitted with QoS level 2 to ensure stable control of the end-to-end delay.

[0027] Based on the spatio-temporally synchronized multi-source data stream, the combustion state modeling module constructs a combustion efficiency confidence matrix using the graph attention network. The graph attention network takes the combustion chamber topology coordinates as nodes, establishes a grid space relationship graph of the combustion chamber, and extracts the ash accumulation characteristics of the local area through the multi-head attention mechanism. The ash thickness feature vector and the time-series data of the flue gas oxygen content are subjected to spatial correlation mapping, and the combustion stability score of each grid area is calculated by graph convolution operation to generate a score matrix whose dimension matches the physical structure of the combustion chamber. This matrix is pushed to the rolling optimization control module through the Kafka streaming processing platform to trigger the real-time control instruction generation process.

[0028] The spatio-temporal cross-attention optimization algorithm analyzes the local ash overlimit area in the combustion stability score matrix, and calculates the weight of the denitration agent demand in combination with the real-time sulfur content data. The spatio-temporal attention mechanism captures the correlation between the ash distribution in the spatial dimension of the combustion chamber and the sulfur concentration fluctuation in the time dimension through query-key value matching, and generates the distribution ratio of the denitration agent injection amount divided by region. At the same time, the vibration spectrum data of the gas turbine is input into the PID control model to correct the integral term weight of the secondary air damper opening instruction and suppress the mechanical vibration disturbance caused by ash accumulation. The generated dynamic control instruction set is fed back to the network transmission optimization module to adjust the temperature data transmission priority in the ash accumulation area, forming a closed-loop linkage between the control instruction and the data transmission.

[0029] The federated collaboration module receives the encrypted model gradients and control effect feedback logs uploaded by the edge nodes, injects Laplace noise into the gradient data using the differential privacy protection algorithm to eliminate the risk of sensitive information leakage. The transfer reinforcement learning strategy aggregates the PID adjustment instructions of each edge node through cross-node policy distillation to generate global optimization parameters. The optimized model parameters are distributed to the fuel calorific value dynamic interpolation processing module and the rolling optimization control module through the HTTPS encrypted channel to achieve collaborative learning and control strategy generalization of distributed nodes.

[0030] The dynamic control instruction set is sent to the actuator through the Modbus-TCP protocol to adjust the opening degree of the secondary air damper servo motor and the injection volume of the denitration agent metering pump. The status feedback data of the actuator re-enters the multi-source data acquisition process. Through the continuous update of the combustion efficiency confidence matrix and the iteration of the optimization algorithm, a complete closed-loop link from data acquisition, processing, control to feedback is formed. This closed-loop system effectively suppresses the combustion efficiency calculation error caused by data timing offset through multi-module collaboration and maintains the stable operation of the emission closed-loop control system.

[0031] Specifically, for the real-time monitoring method of distributed biomass power generation integrating the Internet of Things described in the present invention, the cross-frequency domain time series alignment of the fuel calorific value dynamic interpolation processing module includes: Receiving the fuel component detection results and the temperature field historical data, and predicting the spatio-temporal distribution characteristics of the fuel calorific value through the neural process model; Performing frequency domain upsampling processing on the fuel calorific value spectral data by using a causal convolutional network to generate a high-frequency pseudo-sequence synchronized with the millisecond-level temperature field data; Encapsulating the high-frequency pseudo-sequence into a streaming data format and transmitting it to the combustion state modeling module through the gRPC protocol.

[0032] The fuel calorific value dynamic interpolation processing module realizes accurate time series alignment of cross-frequency domain data through multi-source data fusion and algorithm collaboration. This module receives the fuel component detection results and the temperature field historical data. The fuel component detection results are from the real-time scanning of fuel particles by a near-infrared spectroscopy analyzer, and the temperature field historical data is collected by a distributed temperature sensor network at a millisecond-level frequency. The neural process model is based on a variational inference framework, jointly models the second-level sampling sequence of the fuel calorific value spectral data and the millisecond-level high-frequency sequence of the temperature field data, extracts the heat conduction characteristics of the combustion chamber space dimension through a latent space encoder, and uses a decoder to predict the dynamic distribution of the fuel calorific value in the time and space dimensions. The model output includes the predicted value of the fuel calorific value in the grid area of the combustion chamber and its confidence interval, providing a prior distribution constraint for subsequent frequency domain upsampling.

[0033] The causal convolutional network performs frequency domain upsampling processing on the fuel calorific value spectral data, and adopts an dilated convolutional structure to capture long-distance time series dependence relationships. The network input layer receives the spatio-temporal distribution characteristics generated by the neural process model, gradually expands the receptive field through multiple layers of causal convolutional kernels, and upsamples the second-level sampled fuel calorific value data to a millisecond-level pseudo-sequence on the premise of avoiding future information leakage. During the upsampling process, the network fuses the local gradient characteristics of the temperature field high-frequency data through residual connections, dynamically adjusts the convolutional kernel weights, and makes the generated pseudo-sequence synchronize with the temperature field data in the time domain fluctuation characteristics. The output layer normalizes the upsampling result to eliminate the calorific value dimension difference of different fuel batches.

[0034] The high-frequency pseudo-sequence enters the downstream processing flow through streaming data encapsulation and protocol transmission. The encapsulation module divides the upsampled pseudo-sequence into data blocks according to time windows. Each data block is appended with a millisecond-level timestamp and combustion chamber grid coordinate metadata to form a structured streaming data packet. The data packet is transmitted to the combustion state modeling module through the gRPC protocol. During the transmission process, a bidirectional streaming call mode is adopted to provide real-time feedback on the data reception status. The gRPC interceptor compensates for the timing offset caused by network jitter while verifying the continuity of timestamps, maintaining strict timing alignment between the pseudo-sequence and the temperature field data at the combustion state modeling end. After the transmission is completed, the data packets are reorganized in chronological order in the cache queue of the combustion state modeling module, providing synchronous input for the construction of the combustion efficiency confidence matrix.

[0035] Specifically, for the real-time monitoring method of distributed biomass power generation integrating the Internet of Things described in the present invention, the dynamic bandwidth allocation algorithm for optimizing the network transmission channel includes: Monitoring the network bandwidth status and the combustion chamber pressure gradient time series data, and dynamically allocating the transmission priorities of real-time data streams and non-real-time data streams through the DDPG reinforcement learning algorithm; Adjusting the forward error correction coding parameters (including the proportion of redundant data packets and the coding block length) and the sliding window size according to the network jitter rate, and performing redundant transmission compensation on the synchronized high-frequency fuel calorific value pseudo-sequence; Transmitting the high-frequency fuel calorific value pseudo-sequence through the MQTT 5.0 protocol in a hierarchical manner, so that the end-to-end delay of the temperature field data stream is lower than the preset threshold.

[0036] The dynamic bandwidth allocation algorithm optimizes the resource utilization rate of the network transmission channel through multi-dimensional parameter perception and adaptive strategies. The network bandwidth status data comes from the Prometheus monitoring system of distributed nodes, which collects the available bandwidth, packet loss rate, and queue depth of each transmission link in real time; the combustion chamber pressure gradient time series data is reported by the high-frequency pressure sensor array at a millisecond-level frequency, reflecting the dynamic change characteristics of the gas flow inside the combustion chamber. The DDPG reinforcement learning algorithm constructs a state space including network bandwidth utilization rate, pressure gradient change rate, and historical transmission delay, and defines the action space as the bandwidth allocation weight ratio of real-time data streams and non-real-time data streams. The Critic network evaluates the long-term benefits of the transmission strategy through the Bellman equation, and the Actor network outputs the priority allocation strategy to achieve the coordinated optimization of transmission resources and the dynamic conditions of the combustion chamber.

[0037] The forward error correction coding parameters and the sliding window size are dynamically adjusted according to the network jitter rate, which is obtained by calculating the variance of the time delay within the sliding time window. When the network jitter rate exceeds the threshold, the proportion of redundant data packets in the forward error correction coding is increased to enhance the packet loss resistance of the pseudo-sequence of the high-frequency fuel calorific value; at the same time, the sliding window size is reduced to reduce the data recombination delay caused by network out-of-order. The coding parameter adjustment module is linked with the transmission priority allocation strategy, and the Turbo product code coding scheme is enabled for the temperature field data stream with the highest real-time requirement to achieve a balance between redundancy and computational overhead and maintain the timing integrity of data transmission.

[0038] The pseudo-sequence of the high-frequency fuel calorific value is hierarchically transmitted through the MQTT 5.0 protocol, and the protocol stack divides the quality of service levels according to the real-time requirements of the data stream. The temperature field data stream is configured with a QoS 2-level transmission mode, adopting the message persistent storage and end-to-end confirmation mechanism to eliminate the retransmission delay caused by network fluctuations; the control instruction stream uses QoS 1-level transmission and allows a limited number of retry operations when the bandwidth is limited; the non-real-time meta-data stream uses QoS 0-level transmission to release network resources to the greatest extent. The protocol adapter performs load fragmentation before data transmission, splitting large-size data packets into transmission units that meet the MTU limit, avoiding the end-to-end delay accumulation caused by fragmentation and recombination, and realizing the stable control of the preset delay threshold.

[0039] Specifically, for the real-time monitoring method of distributed biomass power generation integrating the Internet of Things described in the present invention, the construction of the combustion efficiency confidence matrix includes: Receiving multi-source data streams with spatio-temporal synchronization and the combustion chamber topology coordinate data, and extracting the ash accumulation characteristics of the local area of the combustion chamber through a graph attention network; Performing spatial correlation mapping on the ash accumulation characteristics and the time series data of the oxygen content in the flue gas to generate a combustion stability scoring matrix; Pushing the combustion stability scoring matrix to the rolling optimization control module through the Kafka streaming processing platform.

[0040] Specifically, for the real-time monitoring method of distributed biomass power generation integrating the Internet of Things described in the present invention, the execution of the spatio-temporal cross-attention optimization algorithm includes: The spatio-temporal cross-attention optimization algorithm performs multi-head attention calculation through a three-dimensional feature tensor (the spatial dimension corresponds to the combustion chamber grid coordinates, the time dimension covers the sliding time window, and the channel dimension fuses the ash thickness, sulfur content, and temperature gradient characteristics) to generate the spatial allocation weight of the denitration agent injection amount.

[0041] Analyzing the local ash thickness over-limit area in the combustion stability scoring matrix and extracting the corresponding real-time sulfur content data; Calculate the denitrant demand weights for different regions of the combustion chamber through a spatio-temporal attention mechanism to generate the injection volume allocation ratio. Fuse the vibration spectrum data of the gas turbine, correct the weight of the integral control term of the secondary air damper opening command, generate a dynamic control instruction set under the anti-disturbance control mode, and feedback the instruction set to the network transmission optimization module.

[0042] The process of constructing the combustion efficiency confidence matrix realizes the refined evaluation of the combustion state through spatial feature extraction and multi-source data fusion. The spatio-temporally synchronized multi-source data stream comes from the high-frequency pseudo-sequence output by the fuel calorific value dynamic interpolation module and the real-time temperature field data processed by the network transmission optimization module. The combustion chamber topology coordinate data is obtained through the grid space coordinate mapping table generated by the laser scanner and the CAD modeling tool. The graph attention network constructs graph structure data based on the combustion chamber topology coordinates, where the nodes represent the grid partitions of the combustion chamber, and the edge weights reflect the differences in the heat conduction coefficients of adjacent regions. The attention weights of the ash accumulation characteristics of each node are calculated through the multi-head attention mechanism to extract the spatio-temporal correlation characteristics of the ash thickness distribution in the local area.

[0043] The ash accumulation feature vector and the time series data of the flue gas oxygen content are subjected to spatial correlation mapping, and the data features in the spatial dimension and the time dimension are fused by graph convolution operation. The time series data of the flue gas oxygen content is segmented into segment sequences through a sliding time window. The oxygen content value of each time segment is tensor-concatenated with the ash thickness feature vector of the corresponding combustion chamber grid area, and input into the fully connected layer to calculate the combustion stability score. The dimension of the score matrix is consistent with the grid division of the combustion chamber topology coordinates, and the score value of each grid unit reflects the combustion efficiency confidence of the area under the dynamic balance of ash accumulation and oxygen supply.

[0044] The combustion stability score matrix is pushed to the rolling optimization control module through the Kafka streaming processing platform. The Kafka producer slices the matrix data into data blocks according to the time window, and each data block is appended with a millisecond-level timestamp and the combustion chamber grid coordinate index. The Kafka Topic is sliced according to the physical partition of the combustion chamber, and the consumer side realizes parallel data pulling by subscribing to the partition Topic to ensure that the rolling optimization control module can obtain the combustion state scores of each region in real time. The Exactly-Once semantics is adopted during the data push process to avoid duplicate data ingestion caused by network retransmission and maintain the temporal consistency of the control instruction generation link.

[0045] Specifically, for the distributed biomass power generation real-time monitoring method integrating the Internet of Things of the present invention, the global model parameter update of the federated cooperation module includes: Receive the encrypted edge node model gradient data and the control effect feedback log, and inject gradient noise through the differential privacy protection algorithm; Aggregate the PID adjustment instructions of each edge node based on the transfer reinforcement learning strategy to generate global optimization parameters; Distribute the global optimization parameters to the fuel calorific value dynamic interpolation processing module and the rolling optimization control module through the HTTPS encrypted channel.

[0046] The spatio-temporal cross-attention optimization algorithm realizes the accurate generation of denitration control instructions through multi-modal data fusion and dynamic weight allocation. The identification of the over-limit area of the local ash thickness in the combustion stability scoring matrix is based on grid threshold detection, and the threshold setting module dynamically adjusts the critical value according to the heat resistance coefficient of the combustion chamber material and historical operation data. The real-time sulfur content data comes from the spectral analyzer installed in the fuel delivery pipeline and is uploaded to the edge computing node at a sampling frequency of seconds. In the data preprocessing link, the sulfur content time series is aligned with the time window of the scoring matrix, and the sulfur concentration peak characteristics of the time segment corresponding to the over-limit area are extracted.

[0047] The spatio-temporal attention mechanism constructs a three-dimensional feature tensor. The spatial dimension corresponds to the combustion chamber grid coordinates, the time dimension covers the data slices within the sliding time window, and the channel dimension fuses the ash thickness, sulfur content, and temperature gradient characteristics. The multi-head attention layer calculates the correlation weights of each grid area in the spatio-temporal dimension and generates a denitration agent demand weight matrix through Softmax normalization. The weight matrix is input into the fully connected neural network, and combined with the physical distribution parameters of the denitration agent nozzles, an allocation ratio map matching the spatial layout of the injection device is generated, and the ratio value is mapped to the duty cycle of the pulse width modulation signal of each nozzle.

[0048] The vibration spectrum data of the gas turbine is collected by the acceleration sensor, and the characteristic frequency components are extracted through the fast Fourier transform. The vibration energy distribution and the combustion stability scoring matrix are subjected to cross-domain feature fusion. The fused feature vector is input into the PID controller to dynamically adjust the integral term gain coefficient of the secondary air damper opening instruction to suppress the low-frequency mechanical vibration disturbance caused by ash accumulation. The generated dynamic control instruction set is encapsulated into a Modbus-TCP protocol data frame. The instruction frame header contains the physical address identifier and priority flag bit of the target actuator, and is sent to the underlying PLC controller through the edge-cloud collaboration bus. At the same time, the instruction metadata is fed back to the network transmission optimization module to trigger the transmission priority improvement mechanism of the temperature data stream in the corresponding area.

[0049] Specifically, the real-time monitoring method for distributed biomass power generation integrating the Internet of Things described in the present invention further includes the step of ensuring the temporal consistency of the service grid architecture: Inject a logical clock synchronization protocol into the communication link between microservices and attach a Lamport timestamp to the data packet; Verify the cross-service call temporal causality of the network transmission channel through the gRPC interceptor and discard the expired instructions caused by network out-of-order; When the response delay of the combustion state modeling module exceeds the threshold, switch to the pre-stored combustion efficiency empirical value to maintain the control closed-loop.

[0050] The logical clock synchronization protocol uses Lamport timestamps to append an incrementing logical clock counter to each data packet. The gRPC interceptor verifies the temporal causality by comparing the Lamport timestamp of the data packet with the current maximum timestamp of the service instance. If the timestamp lags, it triggers the retransmission of the data packet.

[0051] The temporal consistency guarantee mechanism of the service mesh architecture maintains the causal orderliness of data interaction between microservices through distributed clock synchronization and dynamic fault tolerance strategies. The logical clock synchronization protocol injects Lamport timestamps into the headers of data packets on the microservice communication link. The timestamp generation module performs incremental encoding based on the local event counter of the service instance and the number of cross-service message transmissions. The combustion state modeling module compares the continuity of the timestamp sequence when processing data packets and starts the cache queue reordering mechanism for data packets with lagging timestamps to eliminate the disorder of event order caused by network partitioning.

[0052] The gRPC interceptor embeds causal verification logic in the transport layer of the service mesh. The interceptor parses the Lamport timestamp in the data packet and compares it with the current maximum timestamp of the service instance. When a break or reverse order is detected in the timestamp sequence, the interceptor triggers the message discard policy and generates a retransmission request. The network transmission optimization module adjusts the redundant coding level of the corresponding data stream according to the metadata of the discard instruction. For messages of combustion chamber control instruction type, the interceptor enables a strong consistency verification mode and rejects historical instructions with timestamp deviations exceeding the sliding window threshold to prevent expired instructions from interfering with the real-time control loop.

[0053] The response delay monitoring of the combustion state modeling module is based on the Prometheus monitoring system of the service mesh. The delay threshold is dynamically adjusted according to the change rate of the combustion chamber pressure gradient. When the response delay exceeds the threshold, the fuse triggers the degradation strategy and loads the pre-stored combustion efficiency empirical value dataset from the edge storage node. The empirical value dataset is generated through the cluster analysis of historical operation data and contains the combustion efficiency benchmark values and their confidence intervals under different working conditions. The control module uses the interpolation algorithm to dynamically match the optimal empirical value according to the current temperature field distribution to maintain the continuity of the control amount of the secondary air damper. During the degradation process, the service mesh bus synchronously updates the model training labels of the federated collaboration module to avoid the cumulative model drift caused by the use of empirical values.

[0054] Specifically, the distributed biomass power generation real-time monitoring method integrating the Internet of Things according to the present invention further includes a microservice hot update step when the fuel type is switched: When the fuel delivery system switches the raw material type, it triggers the model loading interface of the fuel calorific value dynamic interpolation processing module; Synchronously update the fuel diffusion coefficient matrix of the combustion state modeling module and associate the thermodynamic characteristic parameters of the new fuel type; Record the fuel type label through the federated collaboration module for the model generalization training of the migration reinforcement learning strategy.

[0055] The microservice hot update step during fuel type switching realizes the seamless adaptation of the combustion control strategy through the dynamic model loading and parameter collaborative update mechanism. The raw material type switching signal of the fuel delivery system is jointly triggered by an optoelectronic sensor and a mass flowmeter. When a step change in the fuel particle size distribution or mass flow rate is detected, the edge computing node generates a fuel type switching event. The event bus pushes the switching instruction to the fuel calorific value dynamic interpolation processing module through the Kafka message queue, triggering the model loading interface call of the module. The interface call process uses a declarative API to declare the target fuel type identifier, loads the pre-trained neural process model parameter file from the edge storage node, and realizes the hot replacement of the model instance through the memory mapping technology after loading, avoiding data processing interruption caused by the restart of the service process.

[0056] The combustion state modeling module receives the fuel type identifier and retrieves the calorific value coefficient, volatile content, and ash melting point parameters of the new fuel from the fuel characteristic database. The update of the fuel diffusion coefficient matrix uses an incremental matrix decomposition algorithm to perform singular value decomposition on the new fuel characteristic parameters and the basis matrix generated from historical combustion experiment data, and updates the diffusion coefficient weights in the combustion chamber grid area. During the update process, the module maintains the isolated operation of the real-time data processing thread and the parameter update thread, and switches the old and new coefficient matrices through a double-buffer mechanism to ensure the continuity of the combustion efficiency confidence matrix calculation.

[0057] After the fuel switching event is triggered, the federated collaboration module constructs a metadata label containing the timestamp, fuel type code, and switching phase identifier. The label data is encrypted and transmitted to the cloud model repository through the HTTPS channel and associated with the encrypted model gradients uploaded by the edge node for storage. The migration reinforcement learning strategy introduces a label-aware domain adaptation loss function during the model training stage, aligns the feature distribution differences between the old and new fuel types through contrastive learning, freezes the neural network layer parameters unrelated to the new fuel during the global model update, and retains the control strategy knowledge of the historical fuel type to achieve the balance between model generalization ability and control stability.

[0058] Specifically, the distributed biomass power generation real-time monitoring method integrating the Internet of Things according to the present invention further includes an ash accumulation linkage control step: When the combustion state modeling module detects that the local ash thickness exceeds the limit, it triggers a soot blower linkage signal through a Kafka message; The rolling optimization control module switches to the disturbance rejection control mode to improve the priority of temperature data transmission in the corresponding combustion chamber area; The network transmission optimization module dynamically adjusts the bandwidth allocation strategy and preferentially transmits the real-time high-frequency data stream associated with the ash accumulation area.

[0059] The ash accumulation linkage control step realizes the dynamic management of the abnormal ash state in the combustion chamber through a multi-module collaborative response mechanism. The combustion state modeling module uses the sliding window threshold detection algorithm to identify the local ash thickness over-limit area based on the combustion efficiency confidence matrix output by the graph attention network. The threshold setting module dynamically adjusts the critical value according to the thermal stress limit of the combustion chamber material and the historical soot blowing cycle data, and generates an alarm event containing grid coordinates, ash thickness, and timestamp after detecting the over-limit area. The alarm event is encapsulated as a Protobuf format message body by the Kafka producer and written into the preset soot blower linkage Topic. The consumer subscribes to this Topic and parses the message content to trigger the control signal of the soot blower solenoid valve in the corresponding grid area.

[0060] After receiving the soot blower linkage signal, the rolling optimization control module switches to the disturbance rejection control mode, which enhances the system robustness by adjusting the differential gain coefficient of the PID controller. The disturbance rejection control parameter library stores the control parameter combinations corresponding to different ash thickness intervals. The parameter selector matches the optimal parameter group according to the real-time ash thickness, and at the same time increases the data acquisition frequency of the temperature sensors in the over-limit area. The temperature data transmission priority management module assigns the QoS level 2 service quality level to the data stream in the over-limit area in the MQTT protocol stack, and ensures the reliability of data transmission through the message persistence and end-to-end confirmation mechanism.

[0061] The network transmission optimization module dynamically reconstructs the bandwidth allocation strategy according to the priority instruction issued by the rolling optimization control module. The DDPG reinforcement learning algorithm adds the ash over-limit area identification feature to the state space, evaluates the influence degree of the bandwidth allocation strategy on the control loop stability through the Critic network, and outputs the bandwidth weight increase coefficient for the over-limit area. The forward error correction coding module configures a high redundancy coding scheme for the real-time high-frequency data stream associated with the over-limit area, and compresses the sliding window size to 50% of the standard value to reduce the transmission delay caused by network jitter. The optimized data stream is preferentially transmitted to the combustion state modeling module through the edge-cloud collaborative bus to form a closed-loop feedback link for ash management and data transmission.

[0062] Specifically, the distributed biomass power generation real-time monitoring method integrating the Internet of Things according to the present invention further includes an elastic expansion step of edge-cloud collaboration: Deploy the fuel calorific value dynamic interpolation processing module and the rolling optimization control module to the edge lightweight cluster; Synchronize the physical topology metadata of the combustion chamber and the network transmission quality indicators through the edge-cloud collaboration bus; Dynamically expand the instances of the combustion state modeling module based on the Consul service discovery mechanism, and respond to the computing load fluctuations triggered by the change in the ash accumulation degree.

[0063] The elastic expansion step of edge-cloud collaboration realizes the dynamic adaptation of computing resources through the distributed resource scheduling and load awareness mechanism. The fuel calorific value dynamic interpolation processing module and the rolling optimization control module are encapsulated as Docker container images and deployed to the K3s nodes of the edge lightweight cluster. During the deployment process, resource quota limits and service dependency relationships are defined through Helm Chart. The edge lightweight cluster realizes node status monitoring based on the Rancher management platform, dynamically adjusts the CPU core binding policy of the module instances according to the fuel delivery rate, and maintains the timeliness of millisecond-level data processing. After the module is deployed locally, the local cache pool stores the operation data of the combustion chamber in the past three days, reducing the network overhead of cloud data calls.

[0064] The edge-cloud collaboration bus adopts a two-way data synchronization architecture. The physical topology metadata of the combustion chamber is transmitted through the persistent Topic of Apache Pulsar. The metadata includes the three-dimensional grid coordinates of the combustion chamber, the material thermal conductivity table, and the sensor deployment topology map. The network transmission quality indicators are collected through the Prometheus time series database. The indicator data covers bandwidth utilization, end-to-end delay, and packet loss rate, and is compressed before transmission using the Snappy compression algorithm. The bus synchronization service establishes a long connection session between the edge side and the cloud side. The session keep-alive mechanism is maintained through heartbeat packets, and the data consistency check uses the CRC32 cyclic redundancy check code. When data corruption is detected, incremental retransmission is triggered.

[0065] The Consul service discovery mechanism is linked with the instance expansion strategy of the combustion state modeling module. The Consul health check endpoint collects the CPU utilization rate and memory occupancy rate of the module instances in real time. When the ash accumulation degree index exceeds the dynamic threshold, the Horizontal Pod Autoscaler (HPA) issues an instance expansion instruction to the K3s cluster based on the predefined elastic scaling strategy. During the expansion process, the new instance loads the cached snapshot of the combustion efficiency confidence matrix from the edge storage node and shares the real-time data stream with the existing instances through a lock-free queue. When the instance is scaled down, the node to be destroyed completes the transfer of unprocessed data packets through the graceful termination mechanism, maintaining the continuity of the control instruction generation process. The expansion strategy collaborates with the bandwidth allocation strategy of the network transmission optimization module to synchronously adjust the transmission priority weights of the corresponding data streams when the number of instances changes.

[0066] In the specific implementation of the present invention, through the coordination of multi-level technical solutions, the problems of multi-source data time series alignment and closed-loop control stability in a distributed biomass power generation system are solved. The system is deployed in the boilers, gas turbines, and fuel delivery devices of a biomass power plant. The sensor network includes 128 temperature sensors, 16 groups of flue gas composition analyzers, and 8-channel vibration monitoring units. The temperature sensors collect data on the temperature field distribution in the combustion chamber at a sampling frequency of 500 Hz. The flue gas analyzers output data on oxygen content and sulfur content at a frequency of 1 Hz. The vibration sensors capture the vibration spectrum of the gas turbine at a sampling rate of 10 kHz. The data acquisition end accesses the edge computing node through the OPC UA protocol. The node is configured with dual gigabit Ethernet interfaces, which are respectively connected to the fuel delivery control system and the cloud monitoring platform.

[0067] The fuel calorific value dynamic interpolation processing module runs on the K3s lightweight cluster of the edge node, receives the second-level calorific value data uploaded by the fuel spectral analyzer and the historical temperature field data. The neural process model, based on the variational autoencoder architecture, generates a probability map of the calorific value distribution in the spatial dimension of the combustion chamber. Combining the temporal fluctuation characteristics of the temperature field data, it outputs a millisecond-level pseudo-sequence of the fuel calorific value. The causal convolutional network adopts a 5-layer dilated convolutional structure with a convolutional kernel size of 3×3, and the dilation factor increases exponentially to capture long-distance temporal dependence relationships. The upsampled pseudo-sequence is pushed to the combustion state modeling module through the gRPC streaming interface with a 50 ms time window. The network transmission optimization module is deployed at the bypass mirror port of the core switch. The DDPG reinforcement learning algorithm updates the transmission policy with a period of 15 seconds. The Actor network outputs the weight of the real-time data stream bandwidth occupancy ratio. The Critic network evaluates the comprehensive utility value of delay-packet loss of the policy, and dynamically adjusts the redundancy packet ratio of the forward error correction coding in the range of 10%-30%. The MQTT 5.0 protocol stack assigns QoS level 2 transmission guarantee to the temperature field data stream, and the end-to-end delay is controlled within 5 ms.

[0068] The combustion state modeling module constructs a 512×512 grid-based combustion chamber space model. The graph attention network adopts an 8-head attention mechanism. The ash thickness feature vector and the oxygen content time series data are fused through the graph convolutional layer to generate a combustion stability scoring matrix. The rolling optimization control module analyzes the coordinates of the out-of-limit area in the scoring matrix. The spatio-temporal cross-attention mechanism calculates the distribution weights of the denitration agent injection amount. Combining with the 125Hz characteristic frequency component of the vibration spectrum, the integral time constant of the PID controller is adjusted to 2.5 seconds to generate an anti-disturbance control instruction. The federated collaboration module establishes a TLS 1.3 encrypted channel between edge nodes. The differential privacy algorithm adds Gaussian noise with a mean of 0 and a standard deviation of 0.01 to the model gradient. The global model is aggregated and updated every 30 minutes and synchronized to each node through the edge-cloud collaboration bus. The service mesh architecture configures a circuit breaker policy in the Istio control plane. When the response delay of the combustion modeling module exceeds 50ms, it switches to a pre-stored combustion efficiency empirical value data set to maintain the control instruction output frequency not lower than 20Hz, forming a millisecond-level closed-loop control link from data collection, processing to execution, effectively suppressing the overshoot oscillation of the emission control system.

[0069] The embodiments of the present invention are deployed and implemented in the boiler combustion chamber, gas turbine and fuel delivery system of a biomass power plant. The specific technical solutions are as follows: The distributed sensor network consists of 256 K-type thermocouples, 32 groups of laser spectroscopy analyzers and 16-channel vibration accelerometers. The thermocouples collect the combustion chamber temperature field data at a sampling frequency of 500Hz. The spectroscopy analyzer outputs the detection results of the fuel calorific value and sulfur content at a frequency of 1Hz. The vibration sensor monitors the vibration spectrum of the gas turbine bearing at a sampling rate of 10kHz. The data acquisition terminal is connected to the edge computing node through an industrial Ethernet switch. The node is configured with an Intel Xeon D-2145NT processor and 64GB of memory, and runs a microservice architecture based on Kubernetes. The data stream processing link delay is controlled within 15ms.

[0070] The fuel calorific value dynamic interpolation processing module is deployed in the edge node container cluster, and receives the second-level fuel calorific value data of the spectroscopy analyzer and the temperature field historical data stream. The neural process model adopts a variational autoencoder structure, and the latent space dimension is set to 128. The probability map of the calorific value distribution in the combustion chamber space is generated through Monte Carlo sampling. Combining with the millisecond-level fluctuation characteristics of the temperature field data, the output frequency is increased to 500Hz to generate a pseudo sequence of the fuel calorific value. The causal convolutional network is configured with a 5-layer dilated convolutional structure, the convolutional kernel size is 3×3, and the dilation factor increases in sequence of 1, 2, 4, 8, 16 to capture the time series dependence relationship up to 160ms. The upsampled pseudo sequence is pushed through the gRPC bidirectional stream interface, and the data packet is attached with a Lamport logical timestamp and strictly aligned with the temperature field data.

[0071] The dynamic bandwidth allocation algorithm is integrated into the edge switch data plane development kit. The DDPG reinforcement learning algorithm updates the transmission policy every 10 seconds. The state space includes network bandwidth utilization rate, packet loss rate, and the change rate of combustion chamber pressure gradient. The action space outputs the real-time data stream occupancy ratio weight interval as [0.6, 0.9]. The forward error correction coding adopts the Reed-Solomon(255,223) coding scheme, and the redundancy is dynamically adjusted between 12% - 28% according to the network jitter rate. The MQTT 5.0 protocol stack allocates QoS level 2 transmission for the temperature field data stream, compressing the end-to-end delay fluctuation range to ±2ms and reducing the jitter rate to below 0.5%.

[0072] The combustion state modeling module constructs a 1024×1024 grid combustion chamber space model. The graph attention network configures 12-head attention mechanisms. The ash thickness feature vector and the time series data of flue gas oxygen content are fused through the graph convolutional layer to generate a combustion stability scoring matrix. The rolling optimization control module analyzes the abnormal areas in the scoring matrix where the ash thickness exceeds 3mm. The spatio-temporal cross-attention mechanism calculates the weight of the denitration agent injection amount allocation, combines the energy distribution of the 80 - 200Hz characteristic frequency band of the vibration spectrum, and dynamically adjusts the integral time constant of the PID controller to the interval of 1.2 - 3.5 seconds to generate an anti-disturbance control instruction set. The control instructions are sent down through the Modbus-TCP protocol, shortening the instruction frame transmission period to 20ms and reducing the standard deviation of the response delay to 0.8ms.

[0073] The federated collaboration module establishes a TLS 1.3 encryption channel among 8 edge nodes. The differential privacy algorithm adds Gaussian noise with a mean of zero and a standard deviation of 0.015 to the model gradients. The global model is aggregated and updated once per hour, and the parameter synchronization error is controlled within 0.3%. The service mesh architecture configures the Istio 1.18 control plane. The fuse triggers degradation when the response delay of the combustion modeling module exceeds 45ms, loads the pre-stored empirical value dataset, and maintains the control instruction output frequency not lower than 20Hz. The edge-cloud collaboration bus adopts the Apache Pulsar message middleware. The synchronization period of the combustion chamber topology metadata is set to 5 minutes. The Consul service discovery mechanism automatically expands the modeling module instances to 3 times the base number when the ash accumulation index exceeds 0.7, and the computing resource utilization rate is stabilized in the interval of 65% - 75%, forming a full-link closed-loop optimization system to eliminate the non-linear deviation and overshoot oscillation of the emission control system.

[0074] The technical features of the present invention are explained as follows: Fuel calorific value dynamic interpolation processing module: It refers to a subsystem that performs high-frequency reconstruction on low-frequency fuel calorific value data through an algorithm model. Its core function is to eliminate the sampling frequency difference between second-level fuel spectral data and millisecond-level temperature field data. The specific implementation paths include spatio-temporal prediction of the neural process model and frequency-domain upsampling of the causal convolutional network.

[0075] Neural Process (NP): A probabilistic model that combines neural networks and Gaussian processes, and learns the latent variable representation of the data distribution through variational inference. It is used in the present invention to predict the dynamic distribution characteristics of fuel calorific value in the three-dimensional space of the combustion chamber, overcoming the dependence on fixed sampling points of traditional interpolation methods.

[0076] Causal Convolution: An improved structure of convolutional neural networks, whose convolutional kernels only cover the input data at the current and historical moments, avoiding the leakage of future information. In this solution, dilated causal convolution is used to expand the temporal receptive field and achieve the capture of long-range dependencies.

[0077] DDPG Reinforcement Learning Algorithm (Deep Deterministic Policy Gradient): A type of deep reinforcement learning algorithm that includes an Actor network (policy generation) and a Critic network (value evaluation). In the network transmission optimization of the present invention, it is used to dynamically allocate the bandwidth weights of real-time / non-real-time data streams, with the combustion chamber pressure gradient as the environmental feedback signal.

[0078] Combustion Efficiency Confidence Matrix: A two-dimensional matrix data structure generated based on a graph attention network, where the matrix elements correspond to the combustion stability scores of the grid partitioned areas of the combustion chamber. The score values are comprehensively calculated from the ash thickness, oxygen content in the flue gas, and temporal fluctuation characteristics, serving as the core input for generating control commands.

[0079] Spatio-Temporal Cross Attention Optimization Algorithm: An extended application of the attention mechanism in the spatio-temporal dimension. Through the interactive calculation of Query, Key, and Value, it captures the correlation between the spatial distribution and time series data of the combustion chamber. It is used to generate the spatial allocation weights of the denitration agent injection amount.

[0080] gRPC (Google Remote Procedure Call): A high-performance RPC framework developed by Google, which uses the Protocol Buffers data serialization protocol. This solution is used for the streaming transmission of high-frequency pseudo-sequences, and realizes low-latency data exchange through the bidirectional streaming mode.

[0081] MQTT 5.0 (Message Queuing Telemetry Transport): A lightweight Internet of Things message transmission protocol that supports QoS (Quality of Service) classification. In this solution, QoS 2 (Exactly-Once transmission guarantee) is configured for the temperature field data stream, and data integrity after reconnection is achieved through session state maintenance.

[0082] Kafka streaming processing platform: A distributed message queue system with high throughput and low latency characteristics. In this solution, its Topic partitioning mechanism is used to achieve parallel pushing of the combustion stability scoring matrix, and load balancing is achieved through the consumer group (ConsumerGroup).

[0083] Consul service discovery mechanism: A distributed service mesh solution developed by HashiCorp that provides health check and service registration and discovery functions. In this solution, combined with the horizontal pod autoscaler (HPA) strategy, the number of instances of the combustion state modeling module is dynamically adjusted.

[0084] Differential Privacy: A data privacy protection technology that adds noise (Gaussian noise is used in this solution) to the model gradient, making the contribution of a single data sample impossible to be deduced inversely. It ensures the security of edge node data during the federated learning process.

[0085] Temporal alignment and transmission optimization collaboration: The neural process model solves the sampling frequency difference (time domain problem), and the DDPG algorithm optimizes network jitter (space domain problem). The two form a spatio-temporal joint processing link through gRPC timestamp synchronization.

[0086] Closed-loop control instruction generation: The combustion efficiency confidence matrix (spatial feature) and the spatio-temporal cross-attention mechanism (temporal feature) jointly drive the adjustment of PID controller parameters, and the federated collaboration module realizes the distributed optimization of control strategies.

[0087] Elastic architecture guarantee: Consul service discovery and K3s lightweight cluster achieve dynamic expansion of computing resources, and Istio service mesh ensures the stability of microservice calls, forming an elastic control system from the edge to the cloud.

[0088] Through a multi-level data processing and collaborative control architecture, the present invention systematically solves the problems of timing offset and control mismatch in a distributed biomass power generation monitoring system. First, aiming at the sampling frequency differences of multi-source heterogeneous data, a dynamic interpolation processing module for fuel calorific value is designed to achieve cross-frequency domain timing alignment based on a neural process model and a causal convolutional network. The neural process model learns the spatio-temporal distribution law of fuel calorific value through variational inference, and combines the temporal correlation characteristics of historical temperature field data to generate a high-frequency fuel calorific value pseudo-sequence; the causal convolutional network adopts an expanded convolutional kernel structure to complete the frequency domain upsampling of data from seconds to milliseconds without leaking future information, so that the low-frequency fuel calorific value spectral data and the high-frequency temperature field data are strictly synchronized in time, eliminating the timing misalignment of the input parameters of the combustion efficiency optimization algorithm.

[0089] Secondly, aiming at the spatio-temporal mismatch caused by network transmission jitter, a dynamic bandwidth allocation algorithm and a network transmission optimization channel are constructed. The DDPG reinforcement learning algorithm takes the network bandwidth state, the combustion chamber pressure gradient, and the historical transmission delay as inputs to dynamically adjust the transmission priority weights of real-time data streams and non-real-time data streams; the forward error correction coding parameters adaptively adjust the redundancy according to the network jitter rate to perform redundant transmission compensation on the high-frequency fuel calorific value pseudo-sequence; the MQTT 5.0 protocol hierarchical transmission mechanism configures QoS level 2 service for the temperature field data stream, and reduces the impact of transmission jitter through message persistence and end-to-end confirmation. The above measures ensure the transmission consistency of multi-source data streams in the spatio-temporal dimension, and avoid the spatio-temporal mismatch of the combustion chamber oxygen concentration feedback control amount caused by network out-of-order.

[0090] Finally, the closed-loop control stability is achieved through a combustion efficiency confidence matrix and a spatio-temporal cross-attention optimization algorithm. The graph attention network extracts the spatio-temporal characteristics of ash accumulation in the combustion chamber to generate a grid-based combustion stability scoring matrix; the spatio-temporal cross-attention mechanism fuses sulfur content data and vibration spectrum characteristics to dynamically generate an anti-disturbance control instruction set for denitration agent injection amount and damper opening; the federated collaborative module aggregates the control feedback of edge nodes and updates the global model parameters using differential privacy protection to suppress the accumulation of emission prediction errors. At the same time, the service mesh architecture maintains the data causality consistency between microservices through Lamport timestamp synchronization and gRPC interceptor verification, and dynamically balances the computing load in combination with the edge-cloud elastic expansion mechanism, forming a closed-loop optimization link from data acquisition, transmission, modeling to control, effectively eliminating overshoot oscillations.

Claims

1. A real-time monitoring method for distributed biomass power generation integrating the Internet of Things, characterized in that: include: Collecting multi-source heterogeneous data streams of distributed biomass power generation equipment, wherein the multi-source heterogeneous data streams include millisecond-level real-time high-frequency data, second-level medium- and low-frequency data, and control instruction streams; The medium and low frequency data are aligned in time series across frequency domains by a dynamic interpolation processing module for fuel calorific value to generate a synchronized high frequency fuel calorific value pseudo sequence, wherein the dynamic interpolation processing module for fuel calorific value implements frequency domain upsampling processing based on a neural process model and a causal convolutional network; Input the synchronized high-frequency fuel calorific value pseudo-sequence and the real-time high-frequency data stream into a network transmission channel optimized by a dynamic bandwidth allocation algorithm, allocate real-time transmission priority through a DDPG reinforcement learning algorithm, and output a multi-source data stream synchronized in time and space; Based on the spatiotemporally synchronized multi-source data stream, a combustion efficiency confidence matrix of combustion chamber ash adhesion perception is constructed through a graph attention network, wherein the combustion efficiency confidence matrix associates the combustion chamber topological coordinates with the flue gas oxygen content time series data; Inputting the combustion efficiency confidence matrix into a spatiotemporal cross-attention optimization algorithm to generate a dynamic control instruction set of the denitrification agent injection amount and the secondary air door opening, wherein the spatiotemporal cross-attention optimization algorithm integrates the real-time sulfur content data and the gas turbine vibration spectrum data; Aggregating the control effect feedback data of edge nodes and the encrypted model gradient through a federated collaboration module to update the global optimization model parameters. The federated collaboration module processes the encrypted model gradient using a differential privacy protection algorithm. The dynamic control instruction set is sent to the actuator, and the secondary air door opening of the combustion chamber and the denitrification agent injection amount are adjusted according to the dynamic control instruction set to form a closed-loop control link.

2. The real-time monitoring method for distributed biomass power generation integrated with the Internet of Things according to claim 1 is characterized in that: The cross-frequency domain timing alignment of the fuel calorific value dynamic interpolation processing module includes: Receiving fuel composition detection results and temperature field historical data, and predicting the spatiotemporal distribution characteristics of the fuel calorific value through the neural process model; A causal convolutional network is used to perform frequency domain upsampling processing on the fuel calorific value spectrum data to generate a high-frequency pseudo sequence synchronized with the millisecond-level temperature field data; The high-frequency pseudo sequence is encapsulated into a streaming data format and transmitted to the combustion state modeling module via the gRPC protocol.

3. The real-time monitoring method for distributed biomass power generation integrating the Internet of Things according to claim 1 is characterized in that: The dynamic bandwidth allocation algorithm optimizes the network transmission channel including: Monitor the network bandwidth status and combustion chamber pressure gradient time series data, and dynamically allocate the transmission priority of real-time data streams and non-real-time data streams through the DDPG reinforcement learning algorithm; The forward error correction coding parameters and sliding window size are adjusted according to the network jitter rate to compensate for redundant transmission of the generated synchronized high-frequency fuel calorific value pseudo sequence; The high-frequency fuel calorific value pseudo-sequence is transmitted hierarchically through the MQTT 5.0 protocol, so that the end-to-end delay of the temperature field data stream is lower than a preset threshold.

4. The real-time monitoring method for distributed biomass power generation integrated with the Internet of Things according to claim 1 is characterized in that: The construction of the combustion efficiency confidence matrix comprises: receiving real-time sulfur content detection data of a fuel delivery system and performing spatial correlation mapping with the combustion stability scoring matrix; Receive time-space synchronized multi-source data streams and combustion chamber topology coordinate data, and extract ash accumulation features in the local area of ​​the combustion chamber through a graph attention network; Performing spatial correlation mapping between the ash accumulation characteristics and the flue gas oxygen content time series data to generate a combustion stability scoring matrix; The combustion stability scoring matrix is ​​pushed to the rolling optimization control module through the Kafka streaming platform.

5. The real-time monitoring method for distributed biomass power generation integrated with the Internet of Things according to claim 4 is characterized in that: The execution of the spatiotemporal cross attention optimization algorithm includes: Analyze the local ash thickness exceeding limit area in the combustion stability scoring matrix and extract the corresponding sulfur content real-time data; The denitrification agent demand weights of different areas in the combustion chamber are calculated through the spatiotemporal attention mechanism to generate the injection amount distribution ratio; The gas turbine vibration spectrum data is integrated, the integral control item weight of the secondary air door opening instruction is corrected, a dynamic control instruction set under the anti-disturbance control mode is generated, and the instruction set is fed back to the network transmission optimization module.

6. The real-time monitoring method for distributed biomass power generation integrated with the Internet of Things according to claim 1 is characterized in that: The global model parameter update of the federated collaboration module includes: Receive encrypted edge node model gradient data and control effect feedback logs, and perform gradient noise injection through the differential privacy protection algorithm; Aggregate the PID adjustment instructions of each edge node based on the transfer reinforcement learning strategy to generate global optimization parameters; The global optimization parameters are distributed to the fuel calorific value dynamic interpolation processing module and the rolling optimization control module through the HTTPS encrypted channel.

7. The real-time monitoring method for distributed biomass power generation integrated with the Internet of Things according to claim 1 is characterized in that: It also includes the steps to ensure the timing consistency of the service mesh architecture: Inject a logical clock synchronization protocol into the communication link between microservices and add Lamport timestamps to data packets; Verify the cross-service call timing causality of the network transmission channel through the gRPC interceptor, and discard expired instructions caused by network disorder; When the response delay of the combustion state modeling module exceeds a threshold, the control closed loop is maintained by switching to a pre-stored combustion efficiency empirical value.

8. The real-time monitoring method for distributed biomass power generation integrated with the Internet of Things according to claim 1 is characterized in that: It also includes the hot update steps of microservices when the fuel type is switched: When the fuel delivery system switches the raw material type, the model loading interface of the fuel calorific value dynamic interpolation processing module is triggered; Synchronously updating the fuel diffusion coefficient matrix of the combustion state modeling module and associating the thermodynamic characteristic parameters of the new fuel type; The fuel type labels are recorded through the federal collaboration module for model generalization training of transfer reinforcement learning strategies.

9. The real-time monitoring method for distributed biomass power generation integrated with the Internet of Things according to claim 1 is characterized in that: It also includes the ash accumulation linkage control steps: When the combustion state modeling module detects that the local ash thickness exceeds the limit, a soot blower linkage signal is triggered through a Kafka message; The rolling optimization control module switches to the anti-disturbance control mode to increase the priority of temperature data transmission in the corresponding combustion chamber area; The network transmission optimization module dynamically adjusts the bandwidth allocation strategy to give priority to transmitting the real-time high-frequency data stream associated with the ash accumulation area.

10. The real-time monitoring method for distributed biomass power generation integrated with the Internet of Things according to claim 1 is characterized in that: It also includes the elastic expansion steps of edge-cloud collaboration: Deploy the fuel calorific value dynamic interpolation processing module and the rolling optimization control module to the edge lightweight cluster; Synchronize the combustion chamber physical topology metadata and the network transmission quality index through the edge-cloud collaborative bus; The combustion state modeling module instance is dynamically expanded based on the Consul service discovery mechanism to respond to the computing load fluctuation triggered by the change of ash accumulation degree.

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