Biomass boiler denitration catalyst protection system with replaceable protective screen

Abstract

The present application belongs to the technical field of denitration catalyst protection system, and particularly relates to a biomass boiler denitration catalyst protection system with replaceable protective mesh cover, which is suitable for pollution protection of denitration catalyst components in the biomass boiler flue gas treatment process. The system comprises a boiler body, a denitration catalyst component, a replaceable protective mesh cover and an intelligent monitoring unit. The intelligent monitoring unit integrates a multi-source detection module, a signal processing module, a pollution level determination module and a communication interaction module. By real-time collection of microwave dielectric constant, acoustic emission signal and multi-spectral reflectance and other parameters, a pollution characteristic matrix is constructed, and a working condition correction mechanism is combined to realize dynamic determination of the pollution level, and prompt information is sent to a remote platform to guide the replacement time of the protective mesh cover. The system has high reliability, scalability and industrial adaptability.

Description

Technical Field

[0001] This invention belongs to the technical field of denitrification catalyst protection systems, specifically relating to a biomass boiler denitrification catalyst protection system with a replaceable protective mesh cover. Background Technology

[0002] Nitrogen oxides (NO) x Emission control is a critical aspect of biomass boiler operation. Selective catalytic reduction (SCR) technology, due to its high denitrification efficiency and low byproduct production, has been widely applied in the flue gas treatment systems of small, medium, and large biomass boilers. The core component of the SCR denitrification system is the catalyst, and its performance directly determines the denitrification efficiency and operational stability of the entire system.

[0003] However, due to the high ash content, numerous impurities, and high proportion of alkali metals in biomass fuels, boiler exhaust gas is prone to carrying a large amount of particulate matter and impurities. These impurities are very likely to collide with the catalyst surface during high-speed flow, causing catalyst ash accumulation, wear, blockage, or even structural damage, which seriously affects denitrification efficiency and service life.

[0004] In existing technologies, static grids or coarse baffles are typically installed at the front end of the SCR reactor. However, after long-term use, dust easily accumulates on the surface, making maintenance and replacement difficult. This causes airflow disturbance and increased pressure drop, further affecting the thermal efficiency and stable operation of the boiler system. Therefore, there is an urgent need for a denitrification catalyst system that can intelligently prompt for the replacement of the protective screen, while also meeting the long-term stable operation requirements of biomass boilers under high dust and high temperature conditions. Summary of the Invention

[0005] To address the above problems, the present invention aims to provide a biomass boiler denitrification catalyst protection system with a replaceable protective mesh cover, comprising: a boiler body, a denitrification catalyst assembly arranged in the boiler flue gas passage, a protective mesh cover detachably fixed to the inlet side of the catalyst assembly, and an intelligent monitoring unit related to the monitoring of the protective mesh cover.

[0006] The intelligent monitoring unit includes a multi-source parameter detection module, a status analysis module, an operating condition correction module, a pollution determination module, a communication interaction module, and a prompt control module.

[0007] The multi-source parameter detection module includes:

[0008] a) A microwave dielectric constant sensor was used to acquire the dielectric constant variation curve of the deposits on the surface of the protective mesh cover;

[0009] b) Acoustic emission signal acquisition device, which collects the acoustic wave signal generated by particle impact when the flue gas flows through the protective net cover and extracts the waveform features;

[0010] c) A multispectral reflectance measurement device to determine the reflectance distribution of the protective mesh surface in different wavelength bands;

[0011] The state analysis module is used to combine data acquired by different types of sensors in chronological order into a pollution feature matrix, and generate a pollution determination vector in the multidimensional feature space of the pollution feature matrix;

[0012] The operating condition correction module obtains the pollution correction vector based on the operating condition mapping correction logic;

[0013] The contamination determination module determines the contamination level of the protective mesh cover based on multi-level threshold determination logic;

[0014] When the pollution level meets the preset replacement conditions, the communication interaction module will transmit a data packet containing the judgment result to the external monitoring terminal or operation and maintenance scheduling platform, prompting the control module to execute the local prompt signal output.

[0015] As a preferred technical solution, the method for generating the pollution determination vector includes: extracting dielectric constant values ​​from the time-series data of the dielectric constant sensor at preset equidistant sampling points, and performing linear interpolation calculations between adjacent sampling points to generate a complete curve; performing bandpass filtering and segmented spectrum analysis on the acoustic emission signal to extract the main frequency and the amplitude of the first three harmonics; normalizing the wavelength channel values ​​of the multispectral reflectance and calculating the difference vector of the reflectance of adjacent channels; after time synchronization and alignment, concatenating the dielectric constant curve, acoustic spectrum parameters, and spectral difference vector into a pollution feature matrix; and obtaining the main eigenvector through covariance matrix eigenvalue decomposition as the pollution determination vector.

[0016] As a preferred technical solution, the operating condition mapping correction logic is as follows: collect operating condition parameters such as boiler operating load, flue gas temperature, relative humidity, and flow rate; find the reference node closest to the current operating condition in the pre-established operating condition mapping table, and extract the correction coefficient set corresponding to the node; adjust each element of the pollution judgment vector proportionally according to the correction coefficient set to obtain the pollution correction vector; input the pollution correction vector into the multi-level threshold judgment logic to output the pollution level code matching the current operating state.

[0017] As a preferred technical solution, the multi-level threshold determination logic includes: applying weight coefficients to each component of the pollution correction vector based on the weight coefficients obtained from historical sample analysis, and calculating the magnitude of the weighted vector; comparing the magnitude with the multi-level threshold table level by level, with the threshold table arranged from low to high according to pollution level; when the magnitude is within a certain level range, writing the corresponding pollution level code into the status cache, and triggering the communication interaction module to read it when the level code is updated.

[0018] As a preferred technical solution, acoustic emission signal processing includes: acquiring the original acoustic waveform within a fixed sampling period; performing bandpass filtering on the waveform within a specified range to suppress signals in non-target frequency bands; generating an amplitude variation curve through envelope detection, and segmenting acoustic events in the curve according to a set amplitude threshold; performing a fast Fourier transform on each acoustic event to generate an event spectrum feature table, and transmitting data such as the dominant frequency position and amplitude ratio in the table to the state analysis module.

[0019] As a preferred technical solution, multispectral reflectance data processing includes: dark current subtraction of the original values ​​of each band; spectral smoothing filtering of the subtraction results to reduce noise; calculation of the difference in reflectance between adjacent bands to obtain a reflectance gradient vector; and combination of the gradient vector and the original reflectance vector to form an extended spectral feature vector, which is used as the spectral data part of the pollution feature matrix and input to the state analysis module.

[0020] As a preferred technical solution, the dielectric constant data processing procedure is as follows: the dielectric constant sampling sequences of the inlet and outlet sides of the protective mesh are recorded respectively; the second-order difference of each sequence at equal time intervals is calculated to obtain the change acceleration curve; the acceleration curves of the inlet and outlet sides are subtracted to generate the deposition difference curve; the deposition change rate anomalies are marked on the difference curve by peak search, and the time index of the anomalies is recorded.

[0021] As a preferred technical solution, time synchronization alignment is achieved by: generating a global timestamp in each sampling period; attaching timestamp tags to the output data of various sensors; merging data from different sensors in the order of timestamps to form multi-channel data frames; and sending the data frames to the status analysis module in batches to ensure the consistency of the pollution feature matrix on the time axis.

[0022] As a preferred technical solution, when generating the replacement prompt data packet, the communication interaction module first combines the pollution correction vector, pollution level code, and timestamp into a structured data frame; it then performs adaptive compression encoding by selecting a compression ratio based on the real-time network bandwidth status; the compressed data frame is transmitted to the remote monitoring platform via the IoT communication protocol; after receiving the platform's receipt information, the receipt content and sending time are written into the operation and maintenance log, and the prompt record is stored locally.

[0023] Beneficial effects

[0024] This invention, by setting up a multi-source parameter detection module, can collect pollution-related data from three dimensions: microwave dielectric constant, acoustic emission characteristics, and multispectral reflectivity. The data is then assembled in timestamp order to construct a pollution feature matrix, and further, a pollution determination vector is generated for subsequent judgment and control processing. Unlike traditional methods that rely on a single differential pressure or particle concentration, this matrix integrates multimodal information, enhancing the dimensional basis for pollution state identification.

[0025] This invention retrieves correction coefficients from a preset mapping table, adjusts the contamination determination vector, and obtains a contamination correction vector. The contamination determination module employs multi-level threshold determination logic, applies weight coefficients obtained from training with historical samples to the contamination correction vector, calculates the magnitude of the weighted vector, and outputs a contamination level code after comparing the magnitude value with the hierarchical threshold table. This structure can adapt to dynamic changes in different operating environments, improving the accuracy and real-time performance of contamination identification.

[0026] This invention employs a global timestamp mechanism to align multi-source data over time, ensuring the temporal consistency of the pollution feature matrix. When the pollution level reaches a set condition, the communication module combines the pollution correction vector, level code, and timestamp into a structured data frame, adaptively compresses it according to network status, uploads it via IoT protocol, and records it in the maintenance log upon receiving a confirmation. This system enables continuous monitoring, accurate determination, and efficient alerts regarding the pollution status of protective mesh covers, providing stable and controllable front-end protection conditions for the denitrification catalyst. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0028] Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0029] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0030] Example 1: A method for protecting the denitrification catalyst in a biomass boiler with a replaceable protective mesh cover.

[0031] This embodiment applies to a biomass boiler system consisting of a boiler body, a denitrification catalyst assembly, a replaceable protective mesh installed on its inlet side, and a monitoring system integrating intelligent sensing, analysis, and control functions.

[0032] This method utilizes multi-source data fusion, feature matrix construction, operating condition adaptation correction, and a hierarchical early warning mechanism to achieve dynamic monitoring and proactive maintenance alerts for the contamination status of protective mesh covers, ensuring the safe operation and thermal efficiency of the denitrification catalyst. For example... Figure 1 As shown, it specifically includes:

[0033] S1. Start the multi-source parameter detection module:

[0034] This step activates the multi-source detection system installed in the front-end area of ​​the boiler denitrification catalyst assembly. This system consists of three sub-modules, which work together to achieve three-dimensional perception of pollution characteristics:

[0035] 1) Microwave Dielectric Constant Sensor Module: This module employs a low-power microwave transmitter and a dual-channel receiver array, deployed approximately 15 cm upstream and downstream of the protective mesh cover, respectively, forming a difference comparison structure. The system acquires data 5 times per minute, outputting the dielectric constant values ​​of the upstream and downstream regions in 32-bit floating-point format each time, and recording their difference curves to determine the trend of sediment thickness variation.

[0036] 2) Acoustic emission signal acquisition module: This module employs an integrated electroacoustic transducer array and a fitted strain gauge combination structure, with multiple transducers evenly distributed on the metal support around the protective mesh cover. The module features self-calibration, uses a 16-bit ADC to sample signals in a frequency range of 5–100 kHz, and incorporates preamplifier and anti-interference shielding circuits to input the signal into an embedded processing chip for real-time buffering and peak marking.

[0037] 3) Multispectral reflectance measurement module: This module includes a multi-band LED illumination source (wavelength range covering ultraviolet to near-infrared), projected forward through a low-temperature coated quartz window; a photoelectric detection array is positioned in the opposite direction of illumination to form a reflected light receiving path, and, in conjunction with optical signal synchronous sampling logic, measures the reflection intensity and calculates the reflectance ratio of each band. The system is equipped with an automatic cleaning mechanism (e.g., a film scraper or compressed gas nozzle) to maintain stability during long-term measurements.

[0038] S2, Time stamping and cache loading:

[0039] After all sensor output data is acquired, the main control processing unit adds a 64-bit high-precision timestamp (with microsecond-level accuracy) to it. A distributed buffering mechanism is used to store and manage the data from the three types of sensors in a synchronized structure, ensuring the alignment basis for subsequent matrix construction.

[0040] S3, Signal Preprocessing:

[0041] To ensure the accuracy and continuity of pollution data analysis, multiple preprocessing operations are performed on various types of data:

[0042] 1) Dielectric constant data processing: Due to the possibility of short-term interference or acquisition anomalies during actual boiler operation, linear interpolation is used to fill in missing points, and continuous abnormal segments are marked for later use to ensure that the sequence can be used for subsequent statistics.

[0043] 2) Acoustic signal processing: For acoustic emission signals, a digital bandpass filter (20–80 kHz) is used to remove low-frequency mechanical noise from the environment, the signal envelope is extracted to identify the main impact events, and then the ratio of the first three frequencies and the main peak energy of each signal segment is calculated using Fast Fourier Transform (FFT) to generate a spectral feature set.

[0044] 3) Spectral signal processing: After eliminating dark current interference and noise, the multi-band reflectance data is smoothed using wavelet filtering technology. Then, the reflectance difference between adjacent bands is calculated and combined into a gradient vector to reflect the difference in sediment color or material.

[0045] S4. Time alignment and matrix concatenation:

[0046] The processed three types of data are aligned with time as the horizontal axis. Using the dielectric constant data as the reference master sequence, temporal interpolation matching is performed on the acoustic feature vector and the spectral gradient data, and finally concatenated into a pollution feature matrix with dimension T×P, where T is the number of sampling periods and P is the total parameter dimension.

[0047] S5, Execution Vector Correction:

[0048] The following key operating parameters were collected as a reference basis for pollution characteristic correction: boiler load (steam mass flow rate, unit: kg / h); flue gas temperature (°C); relative humidity (%); flue gas velocity (m / s).

[0049] All parameters are collected through the DCS system or PLC controller, with a sampling period of 30 seconds. Abnormal data is filtered out through a three-point median filtering and redundancy verification mechanism before being input to the operating condition analysis unit.

[0050] The system pre-sets multiple typical working condition node libraries, and quickly locates the reference node that is closest to the current working condition through the KD-Tree index structure, and obtains its corresponding pollution correction factor set. Each factor represents the correction weight of the corresponding pollution dimension.

[0051] After mapping the feature matrix to a decision vector X using a dimensionality reduction algorithm (such as Principal Component Analysis (PCA), a component multiplication is performed with the pollution correction factor set to output a pollution correction vector. This operation significantly enhances the system's robustness to fluctuations in pollution characteristics under different operating conditions.

[0052] S6, Matching pollution level range:

[0053] The system loads the weight set β and the upper limit of the threshold λ according to the preset pollution level classification model (such as the five-level pollution model), and dynamically configures them in YAML or JSON format, supporting on-site adjustment or remote distribution.

[0054] The pollution correction vector is weighted and its modulus value is calculated. This modulus represents the comprehensive pollution index, which can intuitively reflect the degree of pollution of the protective net.

[0055] The module length corresponds to the grade interval, and the grade code is output. If a grade jump occurs, the subsequent prompt and communication modules are immediately triggered.

[0056] S7. Generate structured data frames:

[0057] The system packages the contamination correction vector, grade code, and timestamp into structured data frames. Data compression employs Huffman dynamic coding or run-length encoding to adapt to low-bandwidth wireless communication environments.

[0058] Structured data frames are sent to the cloud monitoring platform via a 4G / NB-IoT module or Ethernet interface. Upon receiving the data, the platform sends a confirmation of receipt, and the device synchronously records logs and updates its status cache. Simultaneously, the prompt control module triggers a local warning in the form of sound and light (e.g., LED color change, intermittent buzzer beeping), and displays level Y and recommended maintenance measures on the touchscreen, such as "Currently at level 4 pollution, please replace the protective mesh cover."

[0059] Through the multi-step process described in the above embodiments, the system not only achieves multi-dimensional perception and dynamic judgment of pollution status, but also effectively improves the ability to suppress false judgments under complex operating conditions by introducing operating condition correction factors and modulus weighting mechanisms. Furthermore, all monitoring modules support modular replacement, facilitating maintenance and upgrades; all data formats are compatible with the OPC-UA protocol, enabling seamless integration with mainstream industrial control systems; AI edge judgment modules can be deployed to further enhance the level of intelligence; and in terms of platform linkage, it can achieve data interoperability with energy consumption management and maintenance management systems, realizing comprehensive collaborative optimization.

[0060] Example 2: A biomass boiler denitrification catalyst protection system with replaceable protective mesh cover

[0061] This embodiment relates to an intelligent protection device applied to a biomass boiler flue gas treatment system, and in particular to a system-level solution that senses pollution status, performs feature analysis, triggers remote prompts, and provides maintenance recommendations.

[0062] The system is particularly suitable for front-end protection scenarios of denitrification catalyst components, and is used to monitor the contamination status of the protective mesh and provide intelligent replacement guidance.

[0063] like Figure 2 As shown, this system consists of the following core modules: boiler body, denitrification catalyst assembly, replaceable protective mesh cover installed at the front end of the catalyst assembly, multi-source detection subsystem for pollution status perception, intelligent main control module for signal processing and pollution assessment, prompt control module for remote communication and local interaction, and communication interface module for interaction with cloud platform.

[0064] The modules work together organically through a bus structure, local control units, and data transfer logic to form a complete, adaptive, scalable, and highly integrated boiler front-end protection system.

[0065] Structurally, the basic components of this system are the boiler body and its associated flue structure. The boiler body is typically an integrated design of the combustion chamber and convection heat transfer zone, with its outlet connected to the flue gas passage.

[0066] The denitrification catalyst assembly is installed in the duct section between the boiler flue gas outlet and the induced draft fan. It contains a removable catalyst module, which can be fitted with different carrier materials depending on the actual flue gas composition. To prevent physical blockage of the catalyst surface by large particles or corrosion by highly corrosive gases, a protective mesh cover is installed on the inlet side of the catalyst assembly. This protective mesh cover features a quick-disassembly design, including a frame, a metal filter layer, and auxiliary reinforcement mechanisms. The design of the protective mesh cover must ensure smooth airflow while also possessing sufficient strength to withstand the physical impacts and deposition loads during long-term operation.

[0067] From a functional perspective, the core of the system lies in the intelligent detection subsystem located in the upstream and downstream areas of the protective mesh. This subsystem contains multiple heterogeneous sensor nodes, which are used to collect information such as particle deposition status, pollution intensity, and material reflectivity.

[0068] Among them, the microwave sensing nodes are deployed on both the upstream and downstream sides of the mesh cover. Their working principle is based on the fact that changes in dielectric properties can reflect changes in sediment thickness. The acoustic emission nodes adopt an attached structure to sense vibration signals caused by particle impact. The multispectral reflectance measurement nodes analyze changes in sediment surface material and color through active illumination and reflectance capture.

[0069] All of the above sensor nodes are connected to the local data acquisition unit via anti-interference cables or bus interfaces, and are equipped with digital buffer circuits to prevent high-frequency signal overflow.

[0070] During system operation, boiler operating parameters constantly change over time. For example, load, flue gas temperature, humidity, and flow rate can all affect the pollution status assessment. To address this issue, the system is equipped with an independent operating condition sensing module. This module, through its connection to the boiler control system's data interface, collects these operating parameters in real time and sends them to the processing unit for correction and analysis. This step is crucial for ensuring the accuracy of pollution status assessment, preventing misjudgments or missed reports due to changes in operating conditions.

[0071] All sensor data first enters the edge processing chip for preprocessing, including filtering, normalization, time alignment, and feature extraction. For acoustic signals, the system employs envelope analysis and event recognition mechanisms to identify representative impact events, followed by further frequency domain analysis.

[0072] For microwave and optical data, interpolation and differential processing are performed based on the sampling time window to ensure that various types of data can be fused under the same time reference.

[0073] After processing, the various feature data are integrated into a pollution feature vector set according to the time series, which is used for subsequent pollution determination logic.

[0074] In the pollution status judgment module, the system compares the above feature data with a pre-set reference threshold model and outputs the corresponding pollution level through the built-in pollution classification algorithm.

[0075] This grading system is typically divided into five levels, corresponding to five operating states: clean, slightly contaminated, moderately contaminated, severely contaminated, and extremely clogged. Each level of contamination corresponds to a different response mechanism and alert strategy.

[0076] For example, when the pollution level reaches moderate or above, the system will automatically trigger the alarm mechanism, activate the prompt control module, and remind maintenance personnel to conduct inspections through local audio-visual devices; when the level continues to rise to severe or extreme conditions, the system will not only emit a bright red indicator light, but also display replacement instructions and expected risk trends on the local operation screen.

[0077] In the communication module, the system supports multiple communication protocols and physical interfaces, including wired Ethernet, 4G cellular network, and NB-IoT Internet of Things access.

[0078] The structured data packets are assembled by the main control unit and include pollution characteristic parameters, judgment results, timestamps, operating condition information, etc., and the transmission bandwidth is reduced through compression algorithms.

[0079] After receiving confirmation from the remote platform, the system synchronously records local logs, updates the device status cache, and sends feedback to the human-machine interface.

[0080] This remote platform can be deployed on private servers or in public cloud environments, and features functions such as device management, trend analysis, data visualization, and maintenance schedule generation. The system allows multiple devices to connect to the same platform, supporting batch management and group scheduling.

[0081] Through the platform, users can remotely view the current pollution level, historical trend curves, and system prompts of any device, and can remotely issue adjustment commands to achieve remote optimization of system operation strategies.

[0082] In terms of maintainability, the structural design of the protective mesh cover has also been optimized. The mesh cover adopts a modular frame structure, which allows for replacement and cleaning without disassembling the main flue.

[0083] Each protective mesh cover is equipped with a unique number and RFID tag. The system can identify its replacement time and cumulative usage time to help determine the contamination rate and protection life.

[0084] In addition, to further enhance the system's adaptability, an scalable edge computing module is designed in the embodiment, which supports loading lightweight inference models to predict pollution trends.

[0085] This model can determine whether there is a trend of worsening pollution in the next few hours based on information such as changes in pollution level, fluctuation range of operating parameters, and rate of change of average collected features under a sliding time window.

[0086] If the predicted value exceeds the threshold, the system will generate an early warning message so that the operations and maintenance team has enough time to complete the preprocessing or replacement work.

[0087] To improve system security, the implementation also introduces multiple fault-tolerant mechanisms.

[0088] A verification mechanism is used during data transmission to ensure data consistency; the main control module has a built-in watchdog circuit to prevent the system from crashing for a long time; the communication module supports dual-channel redundant backhaul; when the local judgment module fails, the system automatically enters a simplified operation mode, retaining the minimum data acquisition and prompting functions until manual repair is completed.

[0089] This embodiment has been deployed and verified at a biomass power plant. During 3000 hours of continuous operation, it demonstrated stable performance without data interruptions, misjudgments, or control failures. The average replacement cycle for the protective mesh screens was extended by more than two times, and the workload for operation and maintenance was significantly reduced. Through the deployment of the system, the plant's denitrification efficiency remained consistently above the design value, without any issues such as catalyst performance degradation or flue gas deviation.

[0090] In summary, this system, with its reliable structural design, intelligent data processing mechanism, and efficient communication interaction, enables dynamic monitoring and maintenance alerts for the front-end pollution status of biomass boiler denitrification catalysts. It has high engineering adaptability and practical value, can significantly reduce operation and maintenance costs, improve catalyst utilization efficiency and overall system performance, and is suitable for deployment in biomass boiler flue gas treatment systems of various sizes.

[0091] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A biomass boiler denitration catalyst protection system with replaceable protective screen, characterized in that, The application relates to a boiler body, a denitration catalyst assembly arranged in a boiler flue gas channel, a protective mesh cover detachably fixed to an inlet side of the catalyst assembly, and an intelligent monitoring unit related to monitoring of the protective mesh cover. The intelligent monitoring unit comprises a multi-source parameter detection module, a state analysis module, a working condition correction module, a pollution determination module, a communication interaction module and a prompt control module. The multi-source parameter detection module comprises: a) a microwave dielectric constant sensor for obtaining a dielectric constant change curve of deposits on the surface of the protective mesh cover; b) a sound emission signal collector for collecting sound wave signals generated by particle impact when flue gas flows through the protective mesh cover and performing waveform feature extraction; c) a multi-spectral reflectance measuring device for measuring reflectance distribution of the surface of the protective mesh cover at different wave bands; The state analysis module is used for combining data obtained by different types of sensors into a pollution feature matrix in time sequence, and generating a pollution determination vector in a multi-dimensional feature space of the pollution feature matrix. The pollution determination vector is generated in the following manner: extracting dielectric constant values from time sequence data of the dielectric constant sensor at preset equidistant sampling points, and performing linear interpolation calculation between adjacent sampling points to generate a complete curve; performing band-pass filtering and segmented frequency spectrum analysis on the sound emission signals to extract main frequency and first three order harmonic amplitudes; performing normalization processing on each wavelength channel value of the multi-spectral reflectance, and calculating a difference vector of adjacent channel reflectances; after time synchronization alignment, the dielectric constant curve, the acoustic spectrum parameters and the spectral difference vector are spliced into the pollution feature matrix; a principal component vector is obtained by eigenvalue decomposition of a covariance matrix, and is used as the pollution determination vector; the working condition correction module obtains a pollution correction vector based on working condition mapping correction logic; the working condition mapping correction logic is as follows: collecting working condition parameters such as boiler operating load, flue gas temperature, relative humidity and flow rate; finding a reference node closest to the current working condition in a pre-established working condition mapping table, and extracting a correction coefficient set corresponding to the node; proportionally adjusting each element of the pollution determination vector according to the correction coefficient set to obtain the pollution correction vector; inputting the pollution correction vector into a multi-level threshold value determination logic to output a pollution level code matching the current operating state; the pollution determination module determines the pollution level of the protective mesh cover based on the multi-level threshold value determination logic; the multi-level threshold value determination logic comprises: applying a weight coefficient to each component of the pollution correction vector based on a weight coefficient obtained by historical sample analysis, and calculating the module length of the weighted vector; comparing the module length with a multi-level threshold value table in stages, and arranging the threshold value table from low to high according to pollution grading; when the module length is located in a certain level interval, a corresponding pollution level code is written into a state buffer area, and the communication interaction module is triggered to read when the level code is updated; when the pollution level meets preset replacement conditions, the communication interaction module transmits a data packet containing the determination result to an external monitoring terminal or an operation and maintenance scheduling platform, and the prompt control module outputs a local prompt signal. The sound emission signal processing adopts the following process:

2. The biomass boiler De-NOx catalyst protection system with replaceable protective screen according to claim 1, characterized in that, obtaining an original acoustic waveform in a fixed sampling period; and performing band-pass filtering on the waveform in a specified range to suppress signals in non-target frequency bands. ​ The amplitude variation curve is generated by envelope detection, and the acoustic events are segmented in the curve according to the set amplitude threshold value; A fast Fourier transform is performed on each acoustic event to generate an event spectrum feature table, and the main frequency position and amplitude ratio data in the table are transmitted to the state analysis module.

3. The biomass boiler De-NOx catalyst protection system with replaceable protective screen according to claim 1, characterized in that, The multispectral reflectance data processing includes: Dark current is deducted from the original values of each wave band, and spectral smoothing filtering is performed on the deduction results to reduce noise; The difference value of reflectance is calculated between adjacent wave bands to obtain a reflectance gradient vector; The gradient vector and the original reflectance vector are combined into an extended spectral feature vector, which is input into the state analysis module as the spectral data part of the pollution feature matrix.

4. The biomass boiler De-NOx catalyst protection system with replaceable protective screen according to claim 1, characterized in that, The dielectric constant data processing process is: The dielectric constant sampling sequences of the inlet side and the outlet side of the protective screen are recorded respectively; The second-order difference of each sequence at equal time intervals is calculated to obtain a change acceleration curve; The acceleration curves of the inlet side and the outlet side are subtracted to generate a deposition difference curve; Abnormal points of deposition rate are marked on the difference curve by peak value search, and the time index of the abnormal points is recorded.

5. The biomass boiler De-NOx catalyst protection system with replaceable protective screen according to claim 1, characterized in that, Time synchronization alignment is: A global time stamp is generated at each sampling period; Each type of sensor adds a time stamp label when outputting data; The data acquisition module merges different sensor data in timestamp order to form a multi-channel data frame; The data frame is sent to the state analysis module in batches to ensure the consistency of the pollution feature matrix on the time axis.

6. The biomass boiler De-NOx catalyst protection system with replaceable protective screen according to claim 5, characterized in that, When generating the replacement prompt data packet, the communication interaction module first combines the pollution correction vector, the pollution level code and the time stamp into a structured data frame; According to the real-time network bandwidth state, the adaptive compression encoding is performed with a compression ratio; The compressed data frame is transmitted to the remote monitoring platform through the Internet of Things communication protocol; After receiving the reply information of the receiving platform, the reply content and the sending time are written into the operation and maintenance log, and the prompt record is stored locally.

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