Biomass boiler denitration catalyst protection system with replaceable protection net cover

Abstract

The invention belongs to the technical field of denitration catalyst protection systems, and particularly relates to a biomass boiler denitration catalyst protection system with a replaceable protection net cover, which is suitable for pollution protection of a denitration catalyst assembly in a biomass boiler flue gas treatment process. The system comprises a boiler body, a denitration catalyst assembly, a replaceable protective net cover and an intelligent monitoring unit, and the intelligent monitoring unit integrates a multi-source detection module, a signal processing module, a pollution level judgment module and a communication interaction module. Parameters such as microwave dielectric constants, acoustic emission signals and multispectral reflectivity are collected in real time, a pollution characteristic matrix is constructed, dynamic judgment of the pollution level is achieved in combination with a working condition correction mechanism, prompt information is sent to a remote platform, and the replacement opportunity of the protective net cover is guided. The system has high reliability, expandability and industrial adaptability.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of denitration catalyst protection systems, and particularly relates to a biomass boiler denitration catalyst protection system with replaceable protective mesh cover. BACKGROUND

[0002] Nitrogen oxide (NO x ) emission control is a key link in the operation of biomass boilers. Selective catalytic reduction (SCR) technology has been widely used in the tail gas treatment system of small and large biomass boilers due to its high denitration efficiency and few by-products. The core component of the SCR denitration system is the catalyst, and its performance directly determines the denitration efficiency and operational stability of the entire system.

[0003] However, due to the characteristics of biomass fuel, such as high ash content, many impurities, and a large proportion of alkali metals, the boiler tail gas is easily entrained with a large amount of particulate matter and impurities. These impurities are extremely prone to impact the catalyst surface during high-speed flow, causing catalyst ash deposition, wear, blockage, and even structural damage, thereby seriously affecting the denitration efficiency and service life.

[0004] In the prior art, a static grid or a rough baffle is usually provided at the front end of the SCR reactor, but the surface is prone to ash deposition after long-term use, making it difficult to maintain and replace, causing airflow disturbance and increased pressure drop, and further affecting the thermal efficiency and stable operation of the boiler system. Therefore, there is an urgent need for a denitration catalyst system that can intelligently prompt the replacement of the protective mesh cover, while meeting the long-term stable operation requirements of biomass boilers under high dust and high temperature conditions. SUMMARY

[0005] To solve the above problems, the present application proposes a biomass boiler denitration catalyst protection system with replaceable protective mesh cover, which includes: a boiler body, a denitration 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; The intelligent monitoring unit includes 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 includes: a) a microwave dielectric constant sensor to obtain the dielectric constant change curve of the deposits on the surface of the protective mesh cover; b) an acoustic emission signal collector to collect the acoustic signals generated by particle impact when the flue gas flows through the protective mesh cover and extract the waveform features; c) a multi-spectral reflectance measurement device to measure the reflectance distribution of the protective mesh cover surface at different wavebands; The state analysis module is configured to combine data acquired by different types of sensors in time sequence into a pollution feature matrix, and generate a pollution judgment vector in a multi-dimensional feature space of the pollution feature matrix; The working condition correction module obtains a pollution correction vector based on working condition mapping correction logic. The pollution judgment module determines the pollution level of the protective cover based on multi-level threshold judgment logic. When the pollution level meets preset replacement conditions, the communication interaction module transmits a data packet containing the judgment result to an external monitoring terminal or an operation and maintenance scheduling platform, prompting the control module to output a local prompt signal.

[0006] As a preferred technical solution, the pollution judgment vector is generated in the following manner: dielectric constant values are extracted from time series data of the dielectric constant sensor at preset equidistant sampling points, and a complete curve is generated by linear interpolation between adjacent sampling points; band-pass filtering and segmented frequency spectrum analysis are performed on the acoustic emission signal to extract the main frequency and the amplitude of the first three harmonics; each wavelength channel value of the multi-spectral reflectivity is normalized, and a difference vector of adjacent channel reflectivities is calculated; after time synchronization alignment, the dielectric constant curve, the acoustic spectrum parameters and the spectral difference vector are spliced into a pollution feature matrix; the principal component vector is obtained by eigenvalue decomposition of the covariance matrix, which is used as the pollution judgment vector.

[0007] As a preferred technical solution, the working condition mapping correction logic is as follows: working condition parameters such as boiler operating load, flue gas temperature, relative humidity and flow rate are collected; the reference node closest to the current working condition is found in the pre-established working condition mapping table, and the correction coefficient set corresponding to the node is extracted; each element of the pollution judgment vector is proportionally adjusted according to the correction coefficient set to obtain a pollution correction vector; the pollution correction vector is input into the multi-level threshold judgment logic to output a pollution level code matching the current operating state.

[0008] As a preferred technical solution, the multi-level threshold judgment logic includes: based on the weight coefficients obtained by analyzing historical samples, the components of the pollution correction vector are multiplied by the weight coefficients to calculate the module length of the weighted vector; the module length is compared with a multi-level threshold table in stages, and the threshold table is arranged from low to high according to the pollution classification; when the module length is located in a certain level interval, the corresponding pollution level code is written into the state buffer area, and the communication interaction module is triggered to read when the level code is updated.

[0009] As a preferred technical solution, the acoustic emission signal processing includes: acquiring the original acoustic waveform in a fixed sampling period; performing band-pass filtering in a specified range on the waveform to suppress non-target frequency band signals; generating an amplitude change curve through envelope detection, and dividing the acoustic events in the curve according to the set amplitude threshold; performing fast Fourier transform on each acoustic event to generate an event spectrum feature table, and transferring the main frequency position and amplitude ratio data in the table to the state analysis module.

[0010] As a preferred technical solution, the multi-spectral reflectance data processing includes: dark current subtraction on each waveband original value; spectral smoothing filtering on the subtraction result to reduce noise; calculating the difference value of reflectance between adjacent wavebands to obtain a reflectance gradient vector; combining the gradient vector with the original reflectance vector into an extended spectral feature vector, which is input into the state analysis module as the spectral data part of the pollution feature matrix.

[0011] As a preferred technical solution, the dielectric constant data processing process is: recording the dielectric constant sampling sequence of the inlet side and the outlet side of the protective screen respectively; calculating the second-order difference of each sequence at equal time intervals to obtain a change acceleration curve; subtracting the acceleration curves of the inlet side and the outlet side to generate a deposition difference curve; marking the deposition change rate abnormal points on the difference curve through peak value search, and recording the time index of the abnormal points.

[0012] As a preferred technical solution, the time synchronization alignment is: generating a global time stamp 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 time stamp order to form a multi-channel data frame; the data frame is sent to the state analysis module in batch form to ensure the consistency of the pollution feature matrix on the time axis.

[0013] As a preferred technical solution, 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; adaptive compression encoding is performed according to the real-time network bandwidth state to select the compression ratio; the compressed data frame is transmitted to the remote monitoring platform through the Internet of Things communication protocol; after receiving the receipt information of the receiving platform, the receipt content and the sending time are written into the operation and maintenance log, and the prompt record is stored locally.

[0014] Advantageous effects The present application can collect data related to pollution from three dimensions of microwave dielectric constant, acoustic emission characteristics and multi-spectral reflectance by setting a multi-source parameter detection module, splice and construct a pollution feature matrix according to the time stamp order, and further generate a pollution judgment vector for subsequent judgment and control processing. Unlike the traditional method relying on single differential pressure or particle concentration, this matrix integrates multi-modal information, enhancing the dimensional basis of pollution state recognition.

[0015] The present invention retrieves correction coefficients from a preset mapping table and adjusts the pollution determination vector to generate a pollution correction vector. The pollution determination module employs multi-level threshold decision logic, applying weighting coefficients derived from historical sample training to the pollution correction vector. The module then calculates the modulus of the weighted vector, compares the modulus value with a grading threshold table, and outputs a pollution level code. This structure adapts to dynamic changes in different operating environments, improving the accuracy and real-time performance of pollution identification.

[0016] The global timestamp mechanism of this invention aligns multi-source data, ensuring temporal consistency across the pollution signature matrix. When the pollution level reaches a set condition, the communication interaction module combines the pollution correction vector, level code, and timestamp into a structured data frame. This data is adaptively compressed based on network status and uploaded via the IoT protocol. Upon receipt, it is recorded in the operation and maintenance log. This system enables continuous monitoring, accurate assessment, and efficient notification of the pollution status of protective screens, providing stable and controllable front-end protection for denitrification catalysts. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematic diagram of the method flow of the present invention; Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0018] In order to deepen the understanding of the present invention, the present invention will be further described in detail below with reference to the examples. The examples are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0019] Example 1: A method for protecting a biomass boiler denitrification catalyst with a replaceable protective screen This embodiment is applicable to a biomass boiler equipment consisting of a boiler body, a denitrification catalyst assembly, a replaceable protective mesh installed on its inlet side, and a monitoring system with integrated intelligent sensing, analysis and control functions.

[0020] This method realizes dynamic monitoring of the pollution status of the protective net and proactive maintenance reminders through multi-source data fusion, feature matrix construction, operating condition adaptation and correction, and hierarchical early warning mechanism, thus ensuring the safe operation and thermal efficiency of the denitrification catalyst. Figure 1 As shown, specifically including: S1. Start the multi-source parameter detection module: This step activates the multi-source detection system installed in the front area of ​​the boiler denitrification catalyst assembly. The system consists of the following three sub-modules, which work together to achieve three-dimensional perception of pollution characteristics: 1) Microwave dielectric constant sensor module: This module uses a low-power microwave transmitter and a double-channel receiving array, which are respectively deployed about 15 cm upstream and downstream of the protective mesh cover, forming a difference comparison structure. The system collects data 5 times per minute, each time outputting the dielectric constant values of the upstream and downstream areas in 32-bit floating-point format, and recording the difference curve for determining the trend of sediment thickness changes.

[0021] 2) Acoustic emission signal acquisition module: An integrated electro-acoustic transducer array and a combination structure with a conformal strain gauge are used, with multiple transducers evenly distributed on the metal supports around the protective mesh cover. The module has a self-calibration function, uses 16-bit ADC to sample signals, with a sampling frequency range of 5-100 kHz, and combines preamplification and anti-interference shielding circuit to input signals into an embedded processing chip for real-time caching and peak marking.

[0022] 3) Multi-spectral reflectance measurement module: This module includes multi-band LED illumination sources (wavelength range covering ultraviolet to near-infrared), which are projected forward through low-temperature coated quartz windows; a photodetector array is set up in the opposite direction of the illumination to form a reflected light receiving path, and cooperates with a light signal synchronous sampling logic to measure the reflected intensity and calculate the reflectance ratio of each band. The system is equipped with an automatic cleaning mechanism (such as a membrane scraper or compressed gas nozzle) to maintain the stability of long-term measurement.

[0023] S2, time marking and caching: All sensor output data is marked with a 64-bit high-precision timestamp (precision up to microseconds) by the main control processing unit after collection is completed. A distributed buffering mechanism is used to manage the three types of sensor data in a synchronous structure, and to ensure alignment in the subsequent matrix construction process.

[0024] S3, signal preprocessing: To ensure the accuracy and continuity of pollution data analysis, multiple preprocessing operations are performed on each type of data: 1) Dielectric constant data processing: Due to possible short-term interference or collection abnormalities in actual boiler operation, linear interpolation is used to fill in missing points, and continuous abnormal sections are marked for later use to ensure that the sequence can be used for subsequent statistics.

[0025] 2) Acoustic signal processing: For acoustic emission signals, a digital bandpass filter (20-80 kHz) is used to remove environmental mechanical low-frequency noise, the signal envelope is extracted to identify major impact events, and the first three frequencies and the main peak energy ratio of each signal segment are calculated using Fast Fourier Transform (FFT), thereby generating a spectrum feature group.

[0026] 3) Spectral signal processing: After eliminating the dark current interference and noise, the multi-band reflectivity data is smoothed using wavelet filtering technology, and then the reflectivity difference between adjacent bands is calculated to form a gradient vector reflecting the color or material difference of the sediment.

[0027] S4, Time alignment and matrix splicing: The processed three types of data are aligned with time as the horizontal axis. The dielectric constant data is used as the reference main sequence to time-interpolate and match the acoustic feature vector and spectral gradient data. Finally, a pollution feature matrix with dimensions T x P is spliced, where T is the number of sampling periods and P is the total parameter dimension.

[0028] S5, Perform vector correction: The following key operating parameters are collected as the reference basis for pollution feature correction: boiler load (steam mass flow, unit: kg / h); flue gas temperature (°C); relative humidity (%); flue gas flow rate (m / s).

[0029] 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 filter and a redundancy checking mechanism before being input to the working condition analysis unit.

[0030] The system has a plurality of typical working condition node libraries preset, which can quickly locate the closest reference node to the current working condition through the KD-Tree index structure, and obtain its corresponding pollution correction factor set. Each factor represents the correction weight of the corresponding pollution dimension.

[0031] According to the dimension reduction algorithm (such as principal component analysis PCA), the feature matrix is mapped to a decision vector X, and the component multiplication is performed with the pollution correction factor set to output the pollution correction vector. This operation significantly enhances the robustness of the system to pollution feature fluctuations under different operating conditions.

[0032] S6, Match pollution level interval: The system loads the weight set β and threshold upper limit λ according to the preset pollution level classification model (such as a five-level pollution model), and dynamically configures them in YAML or JSON format, supporting on-site adjustment or remote issuance.

[0033] The pollution correction vector is weighted and its module length value is calculated. The module length represents the pollution comprehensive index, which can directly reflect the pollution level of the protective screen.

[0034] The module length corresponds to the classification interval, and outputs the level code. If the level jumps, the subsequent prompt and communication module is triggered immediately.

[0035] S7, Generate structured data frame: The system packs the pollution correction vector, level code and timestamp into a structured data frame. Data compression uses Huffman dynamic coding or Run-Length Encoding to adapt to low-bandwidth wireless communication environment.

[0036] The structured data frame is sent to the cloud monitoring platform through the 4G / NB-IoT module or Ethernet interface. After receiving the data, the platform feeds back the reception confirmation, and the device synchronously records the log and updates the state cache. At the same time, the prompt control module triggers the local audible and visual alarm (such as LED color change, intermittent beeping of the buzzer), and displays the level Y and recommended maintenance measures on the touch screen, such as "current pollution level 4, please replace the protective screen cover".

[0037] Through the multi-step process in the above embodiments, the system not only realizes multi-dimensional perception and dynamic determination of the pollution state, but also effectively improves the misjudgment suppression ability under complex working conditions by introducing the working condition correction factor and the module length weighting mechanism. In addition: all monitoring modules support modular replacement, facilitating maintenance and upgrading; all data formats are compatible with OPC-UA protocol, allowing seamless access to mainstream industrial control systems; AI edge judgment modules can be expanded and deployed to further improve the intelligent level; in terms of platform linkage, data intercommunication can be realized with energy management and maintenance management systems to achieve overall collaborative optimization.

[0038] Embodiment Two: A biomass boiler denitration catalyst protection system with replaceable protective screen cover The present embodiment relates to an intelligent protection device applied to a biomass boiler flue gas treatment system, in particular to a system-level solution for sensing pollution state, performing feature analysis, triggering remote prompts and implementing maintenance recommendations.

[0039] The system is particularly suitable for denitration catalyst component front-end protection scenarios, for monitoring the pollution state of the protective screen cover and providing intelligent replacement guidance.

[0040] As shown in Figure 2 The system is composed of the following core component modules: boiler body, denitration catalyst component, replaceable protective screen cover arranged at the front end of the catalyst component, multi-source detection subsystem for pollution state perception, intelligent master control module for signal processing and pollution evaluation, prompt control module for remote communication and local interaction, and communication interface module for interaction with the cloud platform.

[0041] The modules are organically coordinated through bus structure, local control unit and data relay logic to form a complete self-adaptive, scalable and highly integrated boiler front-end protection system.

[0042] From the structural point of view, the basic components of the system are the boiler body and its associated flue structure. The boiler body is usually designed as an integrated type of combustion chamber and convection heat transfer zone, with the outlet connected to the flue gas passage.

[0043] The denitration catalyst assembly is installed in the pipeline section between the boiler smoke outlet and the induced draft fan, and has an extractable catalyst module inside. Different types of carrier materials can be selected for the module according to the actual composition of the flue gas. To prevent the catalyst surface from being physically blocked by large particles or corroded by strongly corrosive gases, a set of protective mesh covers is provided on the inlet side of the catalyst assembly. The protective mesh cover is designed with a quick detachable structure, including a frame, a metal filter mesh layer, and an auxiliary reinforcement mechanism. The design of the protective mesh cover not only ensures smooth airflow, but also has a certain strength to withstand physical impact and deposition load during long-term operation.

[0044] From the functional implementation point of view, the core of the system is the intelligent detection subsystem set in the upstream and downstream areas of the protective mesh cover. The subsystem contains multiple heterogeneous sensor nodes, which are used to collect information such as particle deposition state, pollution intensity, and material reflectivity.

[0045] Among them, the microwave sensing node is deployed on both sides of the upstream and downstream of the mesh cover, and its working principle is based on the change of dielectric properties to reflect the change of sediment thickness; the acoustic emission node adopts a pasting structure to perceive the vibration signals caused by particle impact; the multi-spectral reflection measurement node analyzes the changes of sediment surface material and color through active illumination and reflection capture.

[0046] All sensor nodes are connected to the local data acquisition unit through anti-interference cables or bus interfaces, and are equipped with digital buffer circuits to prevent high-frequency signal overflow.

[0047] During system operation, boiler operating parameters will change over time, such as load, flue gas temperature, humidity, flow rate, etc., which may affect the judgment results of pollution state. To solve this problem, the system is equipped with an independent operating condition sensing module. The module connects to the data interface of the boiler control system, collects the above operating parameters in real time, and sends them to the processing unit for correction analysis. This step is crucial to ensure the accuracy of pollution state judgment and can avoid false positives or false negatives caused by changes in operating conditions.

[0048] All sensor data first enter the edge processing chip for preprocessing, including filtering, normalization, time alignment, and feature extraction. For acoustic signals, the system uses envelope analysis and event recognition mechanism to identify representative impact events, and then performs further frequency domain analysis.

[0049] For microwave and optical data, interpolation and difference processing are performed according to the sampling time window to ensure that all types of data can be fused under the same time reference.

[0050] The processed feature data of various types are integrated into a pollution feature vector set in time sequence, for subsequent pollution determination logic.

[0051] In the pollution state determination module, the system compares the above feature data with the pre-set reference threshold model, and outputs the corresponding pollution level through the built-in pollution grading algorithm.

[0052] The level system is usually divided into five levels, corresponding to clean, slight pollution, moderate pollution, severe pollution and extreme congestion.

[0053] For example, when the pollution level reaches moderate or above, the system will automatically trigger the alarm mechanism, start the prompt control module, and remind the operation and maintenance personnel through the local sound and light device; when the level continues to rise to severe or extreme state, the system not only sends out a red highlight indicator light, but also displays the replacement instructions and the expected risk trend on the local operation screen.

[0054] In the communication module part, the system supports multiple communication protocols and physical interfaces, including wired Ethernet, 4G cellular network, NB-IoT Internet of Things access, etc.

[0055] The structured data packet is assembled by the main control unit, including pollution feature parameters, judgment results, time stamp, operating condition information, etc., and the transmission bandwidth is reduced through compression algorithm.

[0056] After receiving the remote platform confirmation information, the system synchronously records the local log, updates the device state cache, and feeds back to the human-computer interaction interface.

[0057] The remote platform can be deployed on a private server or a public cloud environment, and has functions of device management, trend analysis, data visualization and maintenance schedule generation, etc. The system allows multiple devices to access the same platform, supports batch management and grouping scheduling.

[0058] Through the platform, users can remotely view the current pollution level, historical trend curve and system prompt content of any device, and can remotely issue adjustment instructions to realize remote optimization of system operation strategy.

[0059] In terms of maintenance, the system also optimizes the structural design of the protective screen. The screen adopts a modular frame structure, which supports replacement and cleaning without disassembling the main flue.

[0060] Each set of protective screen is provided with a unique number and an RFID tag, and the system can identify the replacement time and cumulative use time to assist in judging the pollution rate and protection life.

[0061] In addition, to further improve the adaptive ability of the system, an extensible edge computing module is designed in the embodiment to support loading of a lightweight inference model and predicting pollution trends.

[0062] The model can determine whether there is a pollution deterioration trend in the future several hours based on information such as pollution level changes, working condition parameter fluctuation amplitudes, and average rate of change of collected characteristics in a sliding time window.

[0063] If the predicted value exceeds the threshold value, the system will generate an early warning message in advance, so that the operation and maintenance team has enough time to complete the pretreatment or replacement work.

[0064] To improve the safety of the system, the embodiment also introduces a multiple fault tolerance mechanism.

[0065] A check mechanism is used in the data transmission process to ensure data consistency; a watchdog circuit is built into the main control module to prevent the system from crashing for a long time; the communication module supports dual-channel redundant return; when the local judgment module fails, the system automatically enters a simplified operation mode, retaining the minimum data collection and prompting functions until manual repair is completed.

[0066] The embodiment has been verified through field deployment in a certain biomass power plant and has shown stable performance in 3000 hours of continuous operation without data interruption, misjudgment, or control failure. The average protective net cover replacement period has been extended by more than twice, and the operation and maintenance workload has been significantly reduced. Through the deployment of the system, the denitration efficiency of the plant has been stably maintained above the design value, and there have been no problems of catalyst performance degradation or flue gas deflection.

[0067] In summary, the system, with reliable structural design, intelligent data processing mechanism, and efficient communication interaction mode, realizes dynamic monitoring and maintenance reminders of the pollution state in front of the denitration catalyst of a biomass boiler, has high engineering adaptability and practical value, can significantly reduce operation and maintenance costs, improve catalyst use efficiency and system comprehensive performance, and is suitable for deployment in biomass boiler flue gas treatment systems of various scales.

[0068] The above shows and describes the basic principles, main features, and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A biomass boiler denitrification catalyst protection system with a replaceable protective mesh, characterized in that: include: The boiler body, the denitrification catalyst assembly arranged in the boiler flue gas channel, the protective screen that is detachably fixed to the inlet side of the catalyst assembly, and the intelligent monitoring unit related to the monitoring of the protective screen; The intelligent monitoring unit includes 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; Among them, the multi-source parameter detection module includes: a) Microwave dielectric constant sensor, to obtain the dielectric constant variation curve of the deposits on the surface of the protective mesh; b) Acoustic emission signal collector, which collects the acoustic wave signals generated by the impact of particles when the flue gas flows through the protective mesh and extracts the waveform features; c) Multispectral reflectivity measuring device, which measures the reflectivity distribution of the protective mesh surface in different wavelength bands; The state analysis module is used to combine the data obtained by different types of sensors into a pollution feature matrix in chronological order, and generate a pollution judgment vector in the multidimensional feature space of the pollution feature matrix; The working condition correction module obtains the pollution correction vector based on the working condition mapping correction logic; The pollution determination module determines the pollution level of the protective screen based on the multi-level threshold determination logic; When the pollution level meets the preset replacement conditions, the communication interaction module transmits the 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.

2. A biomass boiler denitrification catalyst protection system with a replaceable protective mesh cover according to claim 1, characterized in that: The generation method of the pollution determination vector includes: Extract the dielectric constant value from the time series data of the dielectric constant sensor according to the preset equidistant sampling points, and perform linear interpolation calculation between adjacent sampling points to generate a complete curve; Perform bandpass filtering and segmented spectrum analysis on the acoustic emission signal to extract the main frequency and the amplitude of the first three harmonics; Normalize the values ​​of each wavelength channel of the multispectral reflectance and calculate the difference vector of the reflectance of adjacent channels; After time synchronization and alignment, the dielectric constant curve, acoustic spectrum parameters and spectral difference vector are spliced ​​into a pollution feature matrix; The main eigenvector is obtained by eigendecomposition of the covariance matrix as the contamination judgment vector.

3. The biomass boiler denitrification catalyst protection system with a replaceable protective mesh cover according to claim 1 is characterized in that: The working condition mapping correction logic is: Collect operating parameters such as boiler load, flue gas temperature, relative humidity, and flow rate; search for 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; Proportionally adjusting each element of the pollution determination vector according to the correction coefficient set to obtain a pollution correction vector; The pollution correction vector is input into the multi-level threshold decision logic to output the pollution level code that matches the current operating state.

4. The biomass boiler denitrification catalyst protection system with a replaceable protective mesh cover according to claim 1 is characterized in that: The multi-level threshold decision logic includes: Based on the weight coefficients obtained from historical sample analysis, the weight coefficients are applied to each component of the pollution correction vector and the modulus of the weighted vector is calculated; Compare the modulus length with the multi-level threshold table step by step, and the threshold table is arranged from low to high according to the pollution level; When the module length is within a certain level range, the corresponding pollution level code is written into the status buffer area, and the communication interaction module is triggered to read it when the level code is updated.

5. The biomass boiler denitrification catalyst protection system with a replaceable protective mesh cover according to claim 1 is characterized in that: Acoustic emission signal processing uses the following process: Acquire the original acoustic waveform within a fixed sampling period; perform bandpass filtering within a specified range on the waveform to suppress non-target frequency band signals; Generate an amplitude change curve through envelope detection, and segment acoustic events in the curve according to the set amplitude threshold; A fast Fourier transform is performed on each acoustic event to generate an event spectrum feature table, and the data such as the main frequency position and amplitude ratio in the table are passed to the state analysis module.

6. The biomass boiler denitrification catalyst protection system with a replaceable protective mesh cover according to claim 1 is characterized in that: Multispectral reflectance data processing includes: Dark current is subtracted from the original value of each band; the subtraction result is spectrally smoothed and filtered to reduce noise; Calculate the difference value of reflectivity between adjacent bands to obtain the reflectivity gradient vector; The gradient vector and the original reflectivity vector are combined into an extended spectral feature vector, which is input into the state analysis module as the spectral data part in the pollution feature matrix.

7. The biomass boiler denitrification catalyst protection system with a replaceable protective mesh cover according to claim 1 is characterized in that: The dielectric constant data processing process is: Record the dielectric constant sampling sequences at the inlet and outlet sides of the protective mesh cover respectively; Calculate the second-order difference of each sequence at equal time intervals to obtain the changing acceleration curve; The acceleration curves at the inlet and outlet sides are subtracted to generate a deposition difference curve; The abnormal points of sedimentation change rate are marked on the difference curve by peak search, and the time index of the abnormal point is recorded.

8. The biomass boiler denitrification catalyst protection system with a replaceable protective screen according to claim 1, characterized in that: The time synchronization alignment is: Generate a global timestamp at each sampling period; Various sensors add timestamp tags when outputting data; The data acquisition module merges different sensor data in timestamp order to form a multi-channel data frame; The data frames are fed into the state analysis module in batches to ensure the consistency of the pollution feature matrix on the time axis.

9. A biomass boiler denitrification catalyst protection system with a replaceable protective screen according to claim 8, characterized in that: When generating a replacement prompt data packet, the communication interaction module first combines the pollution correction vector, pollution level code and timestamp into a structured data frame; Select compression ratio for adaptive compression encoding based on real-time network bandwidth status; The compressed data frame is transmitted to the remote monitoring platform via the Internet of Things communication protocol; After receiving the receipt information from the platform, the receipt content and sending time are written into the operation and maintenance log, and the reminder record is stored locally.

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