Method for controlling material level of small stock bin of denitration equipment

Abstract

The invention relates to the technical field of data processing, provides a material level control method for a small stock bin of denitration equipment, and aims to solve the problem that the denitration equipment cannot operate normally due to out-of-control feeding caused by distortion or failure of acquired data. Then, a material level anomaly recognition model is constructed, and anomaly is found through data processing, feature extraction and other training models. And comparing and analyzing, receiving standard parameters to generate a dynamic control parameter set, collecting real-time data, comparing and processing abnormal data. And image equipment is used for collecting and analyzing data to assist in judgment when a specific anomaly occurs. Finally, parameters are corrected according to image analysis results, it is verified that double control channels and the like are deployed to guarantee stable operation of the system, and accurate material level control is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a material level control method for a small silo of a denitration device. Background Art

[0002] The small silo of the denitrification equipment is a component used to store denitrification agents. Its function is to improve the stability and continuous supply of denitrification agents to the reaction zone of the denitrification system. The materials in the small silo of the denitrification equipment must meet the characteristics of high purity, low hygroscopicity, good fluidity, etc. For example, urea particles must avoid agglomeration, and the catalyst must maintain a specific particle size to ensure reaction efficiency; the silo design is often equipped with moisture-proof, arch-breaking (such as vibrators or airflow auxiliary devices), real-time material level monitoring (radar or ultrasonic sensors) and temperature and humidity control systems to prevent material compaction, blockage or deterioration. At the same time, the dosage is accurately adjusted through the automatic feeding device (screw conveyor or pneumatic conveying) to make the denitrification reaction efficient and reduce manual intervention.

[0003] The small silo level control technology of the denitrification equipment is to monitor and adjust the material height in the silo storing denitrification agents (such as urea, ammonia water or catalysts) in real time. The material level sensor collects material height or weight data in real time. After analysis by the PLC or DCS system, the pneumatic valve, screw feeder and other actuators are used to automatically replenish or stop the material to prevent empty silo from being cut off or full silo from overflowing. In order to address the problems of denitrification agents being prone to moisture absorption and agglomeration, as well as poor fluidity, some advanced systems also integrate vibration arch breaking, temperature and humidity monitoring or airflow auxiliary devices, combined with AI algorithms to predict material consumption trends and optimize replenishment strategies, thereby improving denitrification efficiency.

[0004] In the actual application of small silo level control technology for denitrification equipment, the core technical pain point is the interference or drift of the level sensor signal caused by high dust, high humidity and corrosive environment. Especially in working conditions where denitrification agents such as urea or ammonia are easy to volatilize, agglomerate or generate dust, contact or non-contact sensors (such as radar, ultrasonic) are easily affected by material adhesion, aerosol obstruction or chemical corrosion, resulting in distortion or failure of the collected data, and then causing loss of control of feeding (such as shortage of material in empty silo or overflow of material in full silo), causing the denitrification equipment to fail to operate normally. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for controlling the material level of a small silo of a denitrification device, so as to solve the problem that the denitrification device cannot operate normally due to the distortion or failure of the collected data, which causes the feed to be out of control (such as material shortage in an empty silo or material overflow in a full silo).

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows: The present invention provides a method for controlling the material level of a small silo of a denitrification device, comprising: Step S101, obtaining silo operation data, which includes the original signal of the radar level meter, the original signal of the ultrasonic level meter, the temperature and humidity values ​​in the silo, the speed of the screw conveyor and the opening of the pneumatic valve; Step S102, extracting data features from the silo operation data to obtain a high-dimensional feature set of signal strength fluctuation coefficient, temperature and humidity correlation factor, and equipment state vector, and training a random forest based on the high-dimensional feature set of signal strength fluctuation coefficient, temperature and humidity correlation factor, and equipment state vector to obtain a material level abnormality recognition model; Step S103, receiving standard material level control parameters, substituting the standard material level control parameters into a preset material level control model to obtain standard material level control parameters, sending the standard material level control parameters to the silo equipment end, collecting operation data generated after the silo equipment end executes the standard material level control parameters, obtaining real-time silo operation data, substituting the standard material level control parameters into a preset silo simulation model to obtain silo operation prediction data, comparing the real-time silo operation data with the silo operation prediction data to obtain a real-time silo data comparison result, comparing the real-time silo data comparison result with a preset silo data error range, if the real-time silo data comparison result exceeds the preset silo data error range, then retrieving data exceeding the preset silo data error range in the real-time silo data comparison result, and using the data exceeding the preset silo data error range as data to be simulated; Step S104, when the real-time silo data comparison result exceeds the preset silo data error range, the silo image data is acquired, and the silo image data is substituted into the preset silo operation image analysis model to obtain the silo operation image analysis result, and the silo operation image analysis result includes the silo operation is normal and the silo operation is abnormal; Step S105, when the result of the silo operation image analysis is that the silo operation is abnormal, the data features of the data to be simulated are extracted, and data simulation is performed using a preset silo control data simulation model based on the data features of the data to be simulated to generate silo simulation control parameters, and the silo simulation control parameters are replaced in the standard material level control parameters to obtain corrected material level control parameters, and the corrected material level control parameters are used as the real-time execution control parameters of the silo.

[0007] Furthermore, in the method for controlling the material level of a small silo of a denitration device described in the present invention, step S101 comprises: The original signal of the radar level meter includes the microwave emission time, microwave reception time and microwave reflection intensity; The original signal of the ultrasonic level meter includes the emission time of the ultrasonic wave and the reception time of the ultrasonic wave; The temperature and humidity values ​​inside the silo include the temperature data inside the silo and the humidity data inside the silo.

[0008] Furthermore, in the method for controlling the material level of a small silo of a denitration device described in the present invention, step S102 includes: Preprocessing the silo operation data to obtain preprocessed silo operation data, and extracting data features from the preprocessed silo operation data to obtain silo operation data features; Perform data analysis on the silo operation data characteristics to obtain the signal strength fluctuation coefficient, perform environmental coupling analysis based on the signal strength fluctuation coefficient, and obtain the coupling influence data of the material level measurement; The third harmonic component of the screw conveyor driving current is extracted as the mechanical load feature, and combined with the overshoot parameter of the pneumatic valve step response, a three-dimensional device state vector with mixed characteristics in the time and frequency domain is constructed. The three-dimensional equipment state vector with mixed characteristics in time and frequency domain, the coupling influence data of material level measurement and the silo operation data characteristics are used to train the random forest model to obtain the material level anomaly recognition model.

[0009] Furthermore, in the method for controlling the material level of a small silo of a denitration device described in the present invention, the step S103 comprises: Analyze the standard material level control parameters to obtain the target material level setting value, maximum material replenishment rate and allowable deviation threshold value in the standard material level control parameters; The parsed standard material level control parameters are input into a preset material level control model, and the preset material level control model generates a dynamic control parameter set including a target speed of the screw conveyor, a pneumatic valve opening curve, and a vibrator triggering timing sequence; During the execution of control instructions on the silo equipment side, the following real-time data streams are collected synchronously: The original echo signal of the radar level meter in dual-band scanning mode; The current harmonic spectrum and vibration acceleration waveform of the screw conveyor drive motor; LVDT feedback signal of pneumatic valve positioner and transient value of cylinder pressure; The three-dimensional temperature and humidity distribution matrix of the warehouse is updated in seconds; The IEEE 1588 time protocol is used to align the timestamps of real-time data streams to form a real-time silo operation data set including time series, equipment status, and environmental parameters.

[0010] Furthermore, in the method for controlling the material level of a small silo of a denitration device described in the present invention, step S103 includes: Sliding time window processing is applied to the real-time silo operation data stream, and the radar material level value, screw conveyor speed and the predicted data output by the silo simulation model are matched at the millisecond level based on the IEEE 1588 timestamp to eliminate the data phase difference caused by communication delay. The construction includes absolute deviation of material level, deviation of equipment status and difference in environmental coupling: Absolute deviation of material level: Euclidean distance between real-time radar measurement value and model prediction value; Equipment state deviation: the integral square error between the actual speed and theoretical speed of the screw conveyor; Environmental coupling difference: KL divergence between the measured temperature and humidity distribution matrix and the predicted matrix; Based on the material characteristics, the error threshold is dynamically adjusted to compare the data of absolute deviation of material level, deviation of equipment status and environmental coupling difference. The comparison results are marked with a three-level marking system: The comparison results are marked using a three-level marking system, including: Level 1 yellow warning: a single indicator exceeds the limit instantaneously but recovers within 30 seconds; Level 2 orange alarm: two indicators exceed the limit at the same time or a single indicator exceeds the limit continuously for more than 60 seconds; Level 3 red fault: three indicators exceed the limit or the material level parameter exceeds the threshold by 150%.

[0011] Furthermore, in the method for controlling the material level of a small silo of a denitration device described in the present invention, step S103 includes: Based on the three-level marking system of the real-time silo data comparison results, the red fault level and the orange alarm level data with continuous exceeding of the limit are screened out, and the isolated abnormal points caused by transient sensor interference are eliminated. The processed data is encapsulated into a data packet with a timestamp, stored in a ring buffer with version control, and written to the partition to be simulated in the database at the same time.

[0012] Furthermore, in the method for controlling the material level of a small silo of a denitration device described in the present invention, the step S104 comprises: When the red fault level mark in the real-time silo data comparison result is triggered 3 times cumulatively or the orange alarm exceeds the limit for 120 seconds, the synchronous acquisition instructions of the industrial camera, 3D laser radar and infrared thermal imaging equipment are activated; Industrial cameras capture the surface status of materials, infrared thermal imagers monitor areas with abnormal temperatures, and 3D laser radars construct point cloud models of the silo material surface; The data collected by the activated industrial camera, 3D laser radar and infrared thermal imaging equipment are preprocessed to obtain the preprocessed data collected by the activated industrial camera, 3D laser radar and infrared thermal imaging equipment.

[0013] Furthermore, in the method for controlling the material level of a small silo of a denitration device described in the present invention, the step S104 comprises: Extract the material surface texture complexity index, morphological characteristics of agglomeration areas, and temperature gradient characteristics of thermal anomaly areas from the pre-processed data collected by activated industrial cameras, 3D laser radars, and infrared thermal imaging equipment; The material surface texture complexity index, morphological characteristics of the agglomeration area, and temperature gradient characteristics of the thermal anomaly area are input into the pre-trained silo operation image analysis model to generate the silo operation image analysis results.

[0014] Furthermore, in the method for controlling the material level of a small silo of a denitration device described in the present invention, the step S105 comprises: The data to be simulated is preprocessed, and based on the preprocessed data to be simulated, the feature vector signal time-frequency joint features, environmental coupling factors, equipment health indicators and image-assisted features are constructed; the feature vector signal time-frequency joint features, environmental coupling factors, equipment health indicators and image-assisted features are fused to obtain fused features, and the fused features are used as the data features of the data to be simulated.

[0015] Furthermore, in the method for controlling the material level of a small silo of a denitration device described in the present invention, the step S105 comprises: Input the silo simulation control parameters into the PLC hardware-in-the-loop simulation system to verify the protocol compatibility between the silo simulation control parameters and the DCS control logic; Deploy AB dual control channels in the DCS system: A channel runs the corrected material level control parameters; Channel B maintains original standard parameters for hot backup; After executing the corrected material level control parameters, the sliding window mean of the material level control deviation is continuously calculated. If the deviation rate is <2% for 5 consecutive cycles, the corrected material level control parameters are locked as the new standard benchmark.

[0016] Beneficial effects of the present invention: The method for controlling the material level of a small silo of a denitrification device of the present invention has many beneficial effects, which are mainly reflected in the following aspects: Precise material level control: Through step S101, the silo operation data is fully acquired, including the original signal of the radar level meter, the original signal of the ultrasonic level meter, the temperature and humidity values ​​in the silo, the speed of the screw conveyor, the opening of the pneumatic valve and other multi-source data, which provides a rich and comprehensive data basis for the material level control, making the grasp of the silo operation status more accurate, helping to achieve more precise material level control, effectively avoiding problems such as empty silo shortage or full silo overflow, and ensuring the stable operation of the denitrification equipment.

[0017] Discover and handle abnormal situations in a timely manner: Constructing a material level abnormality recognition model: In step S102, after preprocessing the silo operation data, extracting features, analyzing the data, and constructing a three-dimensional equipment state vector by combining various equipment-related features, the material level abnormality recognition model is trained using these data. The model can detect material level abnormalities in a timely manner and warn of potential problems in advance, so that operators can take corresponding measures before the problems worsen, reducing the impact of failures such as uncontrolled feeding caused by material level abnormalities on the normal operation of the denitrification equipment.

[0018] Comparative analysis and data processing: The operations in step S103, including parsing the standard material level control parameters to generate dynamic control parameter sets, synchronously collecting real-time data streams, performing data time series matching, constructing comparative indicators, and dynamically adjusting the error threshold based on material characteristics for comparative analysis, can clearly determine whether the silo operation status is normal and promptly discover various degrees of abnormal conditions (from level 1 yellow warning to level 3 red fault). By screening out key abnormal data and properly storing them, accurate data support is provided for subsequent in-depth analysis and processing, which helps to quickly locate the root cause of the problem and take targeted solutions.

[0019] Multi-dimensional auxiliary judgment of silo operation status: Image data assisted judgment: Step S104: When the comparison result of the silo operation data shows a serious abnormality (the red fault level mark is triggered 3 times cumulatively or the orange alarm exceeds the limit for 120 seconds), the industrial camera, 3D laser radar and infrared thermal imaging equipment are activated to collect data, and a variety of key features are extracted after preprocessing, and then the pre-trained silo operation image analysis model is used to generate analysis results, and the silo operation status is assisted in judging the silo operation status from multiple dimensions (material surface status, temperature distribution, material agglomeration, etc.). This multi-dimensional analysis method can provide a more comprehensive and in-depth understanding of the internal situation of the silo, further improve the accuracy of the judgment of the silo operation status, and provide a more reliable basis for subsequent parameter correction.

[0020] Parameter optimization and system stability improvement: Parameter correction and execution: In step S105, when the result of the silo operation image analysis is abnormal, the simulated data is preprocessed and multi-dimensional features are constructed. After fusion, the silo simulation control parameters are generated, and the standard material level control parameters are replaced to obtain the corrected parameters as real-time execution control parameters. The protocol compatibility is verified by inputting the silo simulation control parameters into the PLC hardware-in-the-loop simulation system, so that the new parameters can run normally in the actual system. At the same time, AB dual control channels are deployed in the DCS system, channel A runs the corrected material level control parameters, and channel B maintains the original standard parameters for hot backup. This redundant design improves the reliability and stability of the system. In addition, the new standard benchmark is locked according to the material level control deviation after executing the corrected parameters, and the dynamic optimization and adjustment of the material level control parameters are realized, so that the silo material level control is carried out in a relatively stable and effective state, further ensuring the normal operation of the denitrification equipment.

[0021] Adapt to complex environment and material characteristics: In the entire technical solution, the high dust, high humidity and corrosive environment of the small silo of the denitrification equipment and the material characteristics of the denitrification agent, such as easy hygroscopic agglomeration and poor fluidity, are fully considered. For example, in the process of data collection and analysis, the data characteristics collected by different equipment are processed, and the various comparative indicators and analysis methods constructed are also dynamically adjusted in combination with the material characteristics (such as dynamically adjusting the error threshold). This enables the material level control method of the present invention to better adapt to complex actual working conditions, effectively respond to the challenges brought by changes in the environment and material characteristics to the material level control, and improve the accuracy and stability of the material level control.

[0022] To sum up, the small silo material level control method for denitrification equipment of the present invention effectively solves the problem of uncontrolled feeding caused by distortion or failure of collected data through multi-step and multi-model collaborative working, comprehensively improves the accuracy of silo material level control, the stability of the system and the adaptability to complex environments and material characteristics, ensures the normal operation of the denitrification equipment, and has significant economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0024] Figure 1 A schematic flow chart of a method for controlling a material level in a small silo of a denitrification device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are 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 in conjunction with the drawings.

[0026] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0027] The method for controlling the material level of a small silo of denitrification equipment provided by the present invention mainly includes the steps of acquiring silo operation data, performing data feature extraction and model training, comparing and analyzing data, using image data to assist judgment, and correcting and executing control parameters, so as to achieve precise control of the material level of the small silo.

[0028] The present invention provides a method for controlling the material level of a small silo of a denitrification device, comprising: Step S101, obtaining silo operation data, which includes the original signal of the radar level meter, the original signal of the ultrasonic level meter, the temperature and humidity values ​​in the silo, the speed of the screw conveyor and the opening of the pneumatic valve; Step S102, extracting data features from the silo operation data to obtain a high-dimensional feature set of signal strength fluctuation coefficient, temperature and humidity correlation factor, and equipment state vector, and training a random forest based on the high-dimensional feature set of signal strength fluctuation coefficient, temperature and humidity correlation factor, and equipment state vector to obtain a material level abnormality recognition model; Step S103, receiving standard material level control parameters, substituting the standard material level control parameters into a preset material level control model to obtain standard material level control parameters, sending the standard material level control parameters to the silo equipment end, collecting operation data generated after the silo equipment end executes the standard material level control parameters, obtaining real-time silo operation data, substituting the standard material level control parameters into a preset silo simulation model to obtain silo operation prediction data, comparing the real-time silo operation data with the silo operation prediction data to obtain a real-time silo data comparison result, comparing the real-time silo data comparison result with a preset silo data error range, if the real-time silo data comparison result exceeds the preset silo data error range, then retrieving data exceeding the preset silo data error range in the real-time silo data comparison result, and using the data exceeding the preset silo data error range as data to be simulated; Step S104, when the real-time silo data comparison result exceeds the preset silo data error range, the silo image data is acquired, and the silo image data is substituted into the preset silo operation image analysis model to obtain the silo operation image analysis result, and the silo operation image analysis result includes the silo operation is normal and the silo operation is abnormal; Step S105, when the result of the silo operation image analysis is that the silo operation is abnormal, the data features of the data to be simulated are extracted, and data simulation is performed using a preset silo control data simulation model based on the data features of the data to be simulated to generate silo simulation control parameters, and the silo simulation control parameters are replaced in the standard material level control parameters to obtain corrected material level control parameters, and the corrected material level control parameters are used as the real-time execution control parameters of the silo.

[0029] Step S101: Obtaining silo operation data: By collecting multi-source data such as the original signal of the radar level meter (including microwave emission, receiving time and reflection intensity), the original signal of the ultrasonic level meter (emission and receiving time), the temperature and humidity values ​​in the silo (temperature and humidity data), the speed of the screw conveyor and the opening of the pneumatic valve, a comprehensive and rich data foundation is provided for subsequent analysis, so as to accurately grasp the operation status of the silo.

[0030] Step S102: Constructing a material level abnormality recognition model: The silo operation data is preprocessed to remove noise, fill missing values, etc. to improve data quality, and then data feature extraction is performed to obtain the silo operation data characteristics.

[0031] Data analysis is performed on these characteristics to obtain the signal strength fluctuation coefficient. Based on this, environmental coupling analysis is performed to obtain coupling influence data of material level measurement in order to understand the interference of environmental factors on material level measurement.

[0032] The third harmonic component of the screw conveyor driving current is extracted as the mechanical load feature. Combined with the overshoot parameter of the pneumatic valve step response, a three-dimensional equipment state vector with mixed characteristics in time and frequency domains is constructed to fully reflect the equipment operation status.

[0033] The above data are used to train the random forest model to obtain a material level abnormality recognition model, which can detect material level abnormalities in a timely manner and provide a basis for subsequent control adjustments.

[0034] Step S103: Comparative analysis and data processing: Analyze the standard material level control parameters to obtain key information such as the target material level setting value, maximum feeding rate and allowable deviation threshold, input the preset material level control model, generate a dynamic control parameter set including the target speed of the screw conveyor, the opening curve of the pneumatic valve and the trigger timing of the vibrator, and send it to the silo equipment for execution.

[0035] In the process of executing control instructions on the silo equipment side, multiple real-time data streams are collected synchronously, including radar level meter echo signals, screw conveyor drive motor related data, pneumatic valve positioner feedback signals, and silo body three-dimensional temperature and humidity distribution matrix data, etc. The IEEE 1588 time protocol is used to align timestamps to form a complete real-time silo operation data set.

[0036] Sliding time window processing is applied to the real-time silo operation data stream, and the radar material level value, screw conveyor speed and the predicted data output by the silo simulation model are matched at the millisecond level based on the timestamp, eliminating the data phase difference caused by communication delay and improving the accuracy of data comparison.

[0037] Indicators such as material level absolute deviation, equipment status deviation, and environmental coupling difference are constructed. The error threshold is dynamically adjusted based on material characteristics for data comparison, and a three-level marking system is used to identify the comparison results. In this way, it is possible to clearly determine whether the silo operation status is normal and discover potential problems in a timely manner.

[0038] According to the three-level marking system, red fault-level and orange alarm-level data with continuous exceeding of limits are screened out, isolated abnormal points caused by transient sensor interference are eliminated, and the processed data are encapsulated into data packets with timestamps, stored in a ring buffer with version control, and written to the partition to be simulated in the database at the same time, providing accurate data support for subsequent analysis and processing.

[0039] Step S104: Image data auxiliary judgment: When the red fault level mark in the real-time silo data comparison result is triggered 3 times cumulatively or the orange alarm exceeds the limit for 120 seconds, it indicates that there may be major problems in the silo operation. At this time, the synchronous acquisition instructions of the industrial camera, 3D laser radar and infrared thermal imaging equipment are activated.

[0040] Industrial cameras capture the surface status of materials, infrared thermal imagers monitor areas with abnormal temperatures, and 3D lidar constructs a point cloud model of the silo material surface to obtain detailed information inside the silo from multiple angles.

[0041] The collected data is preprocessed to improve the data quality. Then, the material surface texture complexity index, the morphological characteristics of the agglomeration area, and the temperature gradient characteristics of the thermal anomaly area are extracted. These characteristics are input into the pre-trained silo operation image analysis model to generate the silo operation image analysis results to determine whether the silo operation status is normal.

[0042] Step S105: Parameter modification and execution: When the image analysis results of the silo operation are abnormal, the simulated data is preprocessed to construct multi-dimensional features such as signal time-frequency joint features, environmental coupling factors, equipment health indicators, and image auxiliary features. The data features are fused to obtain the fused features as the data features of the data to be simulated, providing more comprehensive feature information for subsequent data simulation.

[0043] Using the preset silo control data simulation model, data simulation is performed based on the above fusion features to generate silo simulation control parameters, which are replaced in the standard material level control parameters to obtain corrected material level control parameters, which are used as real-time execution control parameters of the silo.

[0044] The silo simulation control parameters are input into the PLC hardware-in-the-loop simulation system to verify its protocol compatibility with the DCS control logic so that the new parameters can operate normally in the system.

[0045] Deploy AB dual control channels in the DCS system, with channel A running the revised material level control parameters and channel B maintaining the original standard parameters for hot backup to improve system reliability and stability. After executing the revised material level control parameters, continuously calculate the sliding window mean of the material level control deviation. If the deviation rate is <2% for 5 consecutive cycles, lock the revised material level control parameters as the new standard benchmark to achieve dynamic optimization and adjustment of the material level control parameters.

[0046] Specifically, the method for controlling the material level of a small silo of a denitration equipment according to the present invention, step S101, comprises: The original signal of the radar level meter includes the microwave emission time, microwave reception time and microwave reflection intensity; The original signal of the ultrasonic level meter includes the emission time of the ultrasonic wave and the reception time of the ultrasonic wave; The temperature and humidity values ​​inside the silo include the temperature data inside the silo and the humidity data inside the silo.

[0047] Radar level meter original signal: The radar level meter measures the material level by emitting microwaves and receiving their reflected waves. The three elements of microwave emission time, reception time and reflection intensity contain rich information. The difference between the emission and reception time can directly reflect the distance of microwave propagation in the silo, and then calculate the distance between the material surface and the radar level meter to determine the material level. The reflection intensity of microwaves can reflect the characteristics of the material surface, such as the texture and density of the material. Materials with different characteristics have different reflection capabilities for microwaves, and the reflection intensity is also different. By obtaining these three parameters, the material level information fed back by the radar level meter can be understood more accurately and comprehensively.

[0048] Original signal of ultrasonic level meter: The working principle of ultrasonic level meter is based on the propagation characteristics of ultrasonic wave in air. By recording the emission time and reception time of ultrasonic wave and using the known constant of ultrasonic wave propagation speed in air, the distance traveled from emission to reception of ultrasonic wave can be calculated. This distance is closely related to the distance from the material surface in the silo to the ultrasonic level meter, thus realizing the measurement of material level. Compared with radar level meter, ultrasonic level meter may have unique advantages in certain working conditions. The data of the two level meters complement each other, which helps to improve the accuracy and reliability of material level measurement.

[0049] Temperature and humidity values ​​inside the silo: The temperature and humidity inside the silo have an important impact on the materials in the small silo of the denitrification equipment. Changes in temperature may cause changes in the physical properties of the materials. For example, thermal expansion and contraction may affect the volume of the materials, which in turn indirectly affects the accuracy of the material level. In terms of humidity, a high humidity environment may cause some denitrification agents (such as urea) to absorb moisture and agglomerate, changing the fluidity and stacking form of the materials, which also affects the material level. Therefore, obtaining the temperature and humidity data inside the silo can grasp the environmental conditions inside the silo in real time, and provide the necessary environmental information reference for accurate judgment of the material level and subsequent material level control. This is of great significance for timely adjusting the control strategy to avoid material level measurement errors and uncontrolled feeding caused by changes in temperature and humidity.

[0050] Specifically, in the method for controlling the material level of a small silo of a denitration device according to the present invention, the step S102 comprises: Preprocessing the silo operation data to obtain preprocessed silo operation data, and extracting data features from the preprocessed silo operation data to obtain silo operation data features; Perform data analysis on the silo operation data characteristics to obtain the signal strength fluctuation coefficient, perform environmental coupling analysis based on the signal strength fluctuation coefficient, and obtain the coupling influence data of the material level measurement; The third harmonic component of the screw conveyor driving current is extracted as the mechanical load feature, and combined with the overshoot parameter of the pneumatic valve step response, a three-dimensional device state vector with mixed characteristics in the time and frequency domain is constructed. The three-dimensional equipment state vector with mixed characteristics in time and frequency domain, the coupling influence data of material level measurement and the silo operation data characteristics are used to train the random forest model to obtain the material level anomaly recognition model.

[0051] Purpose of preprocessing: The collected silo operation data may contain noise, missing values, inconsistent data formats, etc. These problems will affect the accuracy and effect of subsequent data analysis and model training. Therefore, the silo operation data is first preprocessed, and the data is converted into a form more suitable for analysis through operations such as denoising, filling missing values, and data standardization to obtain the preprocessed silo operation data.

[0052] Significance of feature extraction: Although the quality of preprocessed data has been improved, the original data dimension is high and the information is relatively scattered. The role of data feature extraction is to extract the key information that best represents the data characteristics and internal laws from the preprocessed silo operation data to obtain the silo operation data characteristics. These features can more concisely and effectively reflect the silo operation status, providing a basis for subsequent in-depth analysis and model building.

[0053] Signal strength fluctuation coefficient: The signal strength fluctuation coefficient is one of the important analysis results of the data analysis of the extracted silo operation data characteristics. This coefficient reflects the change of signal strength over time or other factors, such as the fluctuation degree of signal strength of radar level meter or ultrasonic level meter. The instability of signal strength may indicate some abnormal conditions in the silo, such as uneven surface of the material, interference of aerosol, etc.

[0054] Environmental coupling analysis: Environmental coupling analysis is performed based on the signal strength fluctuation coefficient to find out the comprehensive impact of environmental factors on level measurement. Because in the actual denitrification equipment small silo environment, environmental factors such as temperature, humidity, and dust may interfere with the signal of the level sensor, thereby affecting the accuracy of the level measurement. Through environmental coupling analysis, the coupling impact data of the level measurement can be obtained. These data help to understand how environmental factors act on the level measurement and the degree of their influence on the measurement results, providing an important basis for the subsequent accurate judgment of level abnormalities.

[0055] Construct a three-dimensional device state vector: Extracting mechanical load characteristics: The third harmonic component of the screw conveyor drive current can reflect the mechanical load of the screw conveyor. When the screw conveyor works under different material conveying amounts, material characteristics and other conditions, the harmonic components of its drive current will change. The third harmonic component, as one of the key characteristics, can effectively characterize the operating status of the screw conveyor.

[0056] Combined with pneumatic valve parameters: The overshoot parameter of the pneumatic valve step response reflects the dynamic characteristics of the pneumatic valve during the opening or closing process. The size of the overshoot is related to factors such as valve performance, pipeline resistance, and material flow characteristics. Combining this parameter with the third harmonic component of the screw conveyor drive current can more comprehensively describe the operating status of the silo equipment.

[0057] Constructing a three-dimensional equipment state vector: By combining the above two different features, a three-dimensional equipment state vector with mixed features in the time and frequency domain is constructed. This vector form can reflect the operating status of the silo equipment from different angles in a comprehensive dimension, providing richer and more comprehensive equipment operation information for subsequent model training, and helping to improve the model's ability to identify equipment operation anomalies.

[0058] Training the abnormal material level recognition model: Model input data: The three-dimensional equipment state vector of the time-frequency domain mixed characteristics, the coupling influence data of the material level measurement, and the silo operation data characteristics are provided as input to the random forest model. These data describe the operation status of the silo from different angles, including the operation of the equipment itself, the impact of environmental factors on the material level measurement, and the overall silo operation characteristics.

[0059] Purpose of model training: The random forest model is a powerful machine learning algorithm. By training it with these rich data, the model can learn the patterns and laws of these data features under normal and abnormal material levels. After sufficient training, the model can judge whether the current operation status of the silo is normal based on the new input data, thereby obtaining a material level abnormality recognition model. This model will play an important role in the subsequent material level control process, and can detect material level abnormalities in a timely manner, provide a basis for taking corresponding control measures, and ensure the stable operation of the small silo of the denitrification equipment.

[0060] Specifically, in the method for controlling the material level of a small silo of a denitration device according to the present invention, the step S103 comprises: Analyze the standard material level control parameters to obtain the target material level setting value, maximum material replenishment rate and allowable deviation threshold value in the standard material level control parameters; The parsed standard material level control parameters are input into a preset material level control model, and the preset material level control model generates a dynamic control parameter set including a target speed of the screw conveyor, a pneumatic valve opening curve, and a vibrator triggering timing sequence; During the execution of control instructions on the silo equipment side, the following real-time data streams are collected synchronously: The original echo signal of the radar level meter in dual-band scanning mode; The current harmonic spectrum and vibration acceleration waveform of the screw conveyor drive motor; LVDT feedback signal of pneumatic valve positioner and transient value of cylinder pressure; The warehouse's three-dimensional temperature and humidity distribution matrix is ​​updated in seconds; The IEEE 1588 time protocol is used to align the timestamps of real-time data streams to form a real-time silo operation data set including time series, equipment status, and environmental parameters.

[0061] Analysis of standard material level control parameters: Purpose: Standard material level control parameters are the key basis for effective control of silo material level, but these parameters may exist in a more complex form and need to be analyzed to clarify the specific values ​​of each key information.

[0062] Specific operations and results: Analyze the standard material level control parameters to obtain important parameters such as the target material level setting value, maximum feeding rate and allowable deviation threshold. The target material level setting value specifies the material level height that the silo is expected to reach; the maximum feeding rate specifies the maximum speed limit when replenishing materials, which is crucial for the reasonable control of the material replenishment amount and replenishment rhythm; the allowable deviation threshold defines the acceptable deviation range between the actual material level value and the set value. If it exceeds this range, corresponding adjustment measures may need to be taken.

[0063] Generate a dynamic control parameter set: Input based on analytical results: Input the standard material level control parameters obtained through analysis into the preset material level control model. This model is pre-designed and adjusted, and can generate specific control instruction parameters that meet the actual operation requirements of the silo based on the input standard parameters.

[0064] Generated dynamic control parameter content: The preset material level control model will generate a dynamic control parameter set including the screw conveyor target speed, pneumatic valve opening curve and vibrator trigger timing. The screw conveyor target speed determines the speed of material transportation, which directly affects the amount of material in and out of the silo; the pneumatic valve opening curve describes the degree to which the pneumatic valve should be opened at different times, accurately controlling the size of the material flow channel; the vibrator trigger timing specifies when the vibrator starts and how long it continues to work, which helps prevent material agglomeration and blockage in the silo, allowing the material to flow smoothly. These dynamic control parameter sets work together to achieve refined control of the silo equipment end.

[0065] Synchronous acquisition of real-time data streams: Collection timing: When executing the control instructions generated by the material level control model at the silo equipment end, it is necessary to collect multiple real-time data streams simultaneously. This is because in the actual operation process, the operating status and environmental conditions of each device in the silo are constantly changing. Real-time data collection can capture these changes in time and provide a basis for subsequent analysis and adjustment.

[0066] Specific collected data stream content: The original echo signal of the radar level meter in dual-band scanning mode: The radar level meter measures the material level by transmitting and receiving microwave signals. The dual-band scanning mode can obtain more abundant material level information. The original echo signal can reflect the distance between the material surface and the radar level meter, and is one of the important bases for judging the real-time status of the material level.

[0067] The current harmonic spectrum and vibration acceleration waveform of the screw conveyor drive motor: The current harmonic spectrum of the screw conveyor drive motor can reflect the operating status of the motor, such as whether there are abnormal conditions such as overload and failure. The vibration acceleration waveform can reflect the vibration of the screw conveyor during operation, which is very important for monitoring the health of the equipment and the stability of material transportation.

[0068] LVDT feedback signal of pneumatic valve positioner and transient value of cylinder pressure: The LVDT feedback signal of pneumatic valve positioner can accurately feedback the actual opening position of the valve, so that the valve can operate according to the preset opening curve. The transient value of cylinder pressure can reflect the pressure change of pneumatic valve during opening and closing, which is of great significance for judging the working performance of the valve and whether there is leakage.

[0069] Second-level update data of the three-dimensional temperature and humidity distribution matrix of the silo: The temperature and humidity environment inside the silo affects the properties of the material (such as whether it is agglomerated, fluidity, etc.) and the accuracy of the material level measurement. Collecting the second-level update data of the three-dimensional temperature and humidity distribution matrix of the silo can grasp the changes in temperature and humidity in the silo in real time, so as to adjust the control strategy in time to adapt to environmental changes.

[0070] Form a real-time silo operation data set: Data alignment operation: Use the IEEE 1588 time protocol to align the timestamps of the various real-time data streams collected synchronously. Since the frequency and time of data collection by different devices may be different, aligning the timestamps through a unified time protocol can improve the consistency of each data stream in time, making it possible to perform subsequent correlation analysis and comprehensive judgment of different data.

[0071] Dataset composition and significance: After timestamp alignment, a real-time silo operation data set is formed, including time series, equipment status, environmental parameters and other information. This data set comprehensively covers the operation status information of the silo at a certain moment, including the change of the operation parameters of each device over time (time series), the current actual operation status of each device (equipment status) and the environmental conditions of the silo (environmental parameters). It provides a rich and accurate data basis for further analysis of the silo operation, judgment of whether there are abnormalities and taking corresponding adjustment measures, and is an important source of data-driven decision-making in the entire silo level control method.

[0072] Through the operations in the above step S103, the process from parsing the standard material level control parameters to generating specific control instruction parameters, and then to synchronously collecting real-time operation data to form a complete data set is realized, which lays a solid data foundation for the subsequent accurate analysis, comparison and abnormal processing of the silo operation status, thereby ensuring the effectiveness and stability of the small silo level control of the denitrification equipment.

[0073] Specifically, in the method for controlling the material level of a small silo of a denitration device according to the present invention, the step S103 comprises: Sliding time window processing is applied to the real-time silo operation data stream, and the radar material level value, screw conveyor speed and the predicted data output by the silo simulation model are matched at the millisecond level based on the IEEE 1588 timestamp to eliminate the data phase difference caused by communication delay. The construction includes absolute deviation of material level, deviation of equipment status and difference in environmental coupling: Absolute deviation of material level: Euclidean distance between real-time radar measurement value and model prediction value; Equipment state deviation: the integral square error between the actual speed and theoretical speed of the screw conveyor; Environmental coupling difference: KL divergence between the measured temperature and humidity distribution matrix and the predicted matrix; Based on the material characteristics, the error threshold is dynamically adjusted to compare the data of absolute deviation of material level, equipment status deviation and environmental coupling difference, and the comparison results are marked with a three-level marking system: The comparison results are marked using a three-level marking system, including: Level 1 yellow warning: a single indicator exceeds the limit instantaneously but recovers within 30 seconds; Level 2 orange alarm: two indicators exceed the limit at the same time or a single indicator exceeds the limit continuously for more than 60 seconds; Level 3 red fault: three indicators exceed the limit or the material level parameter exceeds the threshold by 150%.

[0074] Sliding time window processing matches millisecond-level timing: Purpose of sliding time window processing: The real-time silo operation data stream is continuous and dynamically changing. Sliding time window processing can divide the continuous data stream into windows of fixed length, which is convenient for segmented analysis and processing of data. In this way, the changes in the silo operation status can be observed and analyzed in different time segments.

[0075] Significance of millisecond timing matching: In actual silo systems, due to factors such as communication delays, the data collected by different devices may deviate in time, which will affect the accuracy of data comparison and analysis. Based on the IEEE 1588 time protocol, the radar material level value, screw conveyor speed and the predicted data output by the silo simulation model are matched at the millisecond level, which can accurately align data from different sources in the time dimension and eliminate the data phase difference caused by communication delays. At the same time, the actual measurement data and the predicted data can be accurately compared, providing a reliable data basis for subsequent analysis.

[0076] Constructing comparative indicators: Absolute deviation of material level: It is determined by calculating the Euclidean distance between the real-time radar measurement value and the model prediction value. Euclidean distance is a commonly used method to measure the distance between two vectors. It is used here to quantify the degree of deviation between the actual material level and the predicted material level. This indicator can intuitively reflect the difference between the current material level measurement value and the model expected value, helping to determine whether the material level is within the normal range.

[0077] Equipment state deviation: It is measured by the integral square error between the actual speed and the theoretical speed of the screw conveyor. The integral square error can comprehensively consider the difference between the actual speed and the theoretical speed over a period of time. It not only focuses on the deviation at a certain moment, but also reflects the operating stability of the equipment over a period of time. This indicator is used to evaluate the degree of deviation between the actual operating state of the screw conveyor and the theoretical set state, reflecting whether the equipment is working properly.

[0078] Environmental coupling difference: It is represented by the KL divergence of the measured temperature and humidity distribution matrix and the predicted matrix. KL divergence is an indicator used to measure the difference between two probability distributions. Here, the measured temperature and humidity distribution and the predicted distribution are regarded as two probability distributions. The difference between the actual environmental temperature and humidity distribution and the predicted environmental temperature and humidity distribution is quantified by calculating the KL divergence. This helps to understand the degree of deviation between the current environmental conditions and the expected environmental conditions, because environmental factors have an important impact on silo operation and material level control.

[0079] Dynamically adjust the error threshold based on material characteristics and perform data comparison: Reasons for dynamically adjusting the error threshold: Different materials have different physical properties and flow characteristics. For example, factors such as the particle size, humidity, and viscosity of the material will affect the operating status of the silo and the range of data changes. Therefore, in order to more accurately determine whether the silo is operating normally, it is necessary to dynamically adjust the error threshold according to the characteristics of the material. This can make the comparison results more consistent with the actual situation and avoid misjudgment or missed judgment due to a fixed error threshold.

[0080] Data comparison operation: Based on the adjusted error threshold, data comparison is performed on the three indicators of material level absolute deviation, equipment status deviation, and environmental coupling difference. By comparing the actual measured values ​​of each indicator with the corresponding error threshold, it is determined whether the current silo operation status exceeds the normal range.

[0081] The comparison results are marked using a three-level marking system: Level 1 yellow warning: When a single indicator exceeds the limit momentarily but recovers within 30 seconds, it is marked as a level 1 yellow warning. This means that a short-term abnormality occurred during the operation of the silo, but the abnormality recovered by itself in a short time. It may be caused by some accidental factors, such as instantaneous changes in material flow, small fluctuations in sensors, etc. Although this situation may not have a serious impact on the operation of the silo at present, it still needs attention in order to promptly discover the development trend of potential problems.

[0082] Level 2 Orange Alarm: If two indicators are exceeded at the same time or a single indicator is exceeded for more than 60 seconds, it will be marked as a Level 2 Orange Alarm. This indicates that the silo operation has a relatively obvious abnormal condition, which may be a certain risk, and further analysis of the cause and corresponding measures are required. For example, it may be caused by factors such as equipment failure and changes in material characteristics. The problem needs to be checked in time to prevent the abnormal situation from further deteriorating.

[0083] Level 3 red fault: When the three indicators exceed the limit or the material level parameter exceeds the threshold by 150%, it is marked as a level 3 red fault. This means that there is a serious problem with the operation of the silo, which may cause the feed to be out of control, seriously affecting the normal operation of the denitrification equipment. At this time, emergency measures need to be taken immediately, such as stopping the operation of related equipment, troubleshooting and repairing, etc., to avoid causing greater damage to the entire denitrification system.

[0084] Through the above series of operations, this step can comprehensively and accurately evaluate the operating status of the silo. By constructing reasonable comparison indicators, dynamically adjusting the error threshold and adopting a clear marking system, it provides a clear basis for timely discovering problems and taking corresponding measures, thereby ensuring the stable operation of the small silo of the denitrification equipment.

[0085] Specifically, in the method for controlling the material level of a small silo of a denitration device according to the present invention, the step S103 comprises: Based on the three-level marking system of the real-time silo data comparison results, the red fault level and the orange alarm level data with continuous exceeding of the limit are screened out, and the isolated abnormal points caused by transient sensor interference are eliminated. The processed data is encapsulated into a data packet with a timestamp, stored in a ring buffer with version control, and written to the partition to be simulated in the database at the same time.

[0086] Data screening: Based on the three-level marking system of real-time silo data comparison results established previously, focus on screening out red fault-level and orange alarm-level data that continuously exceeds the limit. These two types of data represent more serious or continuous abnormal conditions that occur during silo operation. The red fault level indicates that there are serious problems in silo operation, which may have a significant impact on the normal operation of the denitrification equipment; the orange alarm-level data that continuously exceeds the limit also reflects that the abnormal condition does not occur by chance, but has a certain degree of persistence and requires in-depth analysis and processing. Through this screening method, we can focus on key data that has a significant impact on silo operation, providing core information for subsequent problem investigation and resolution.

[0087] Outlier processing: In the filtered data, there are isolated outliers caused by transient interference of sensors. These isolated outliers are not caused by real operating problems of the silo, but are erroneous data generated by the instantaneous interference of the sensor during data collection. If these data are not eliminated, they may mislead the subsequent data analysis and judgment. Therefore, it is necessary to use specific algorithms or rules to identify and eliminate these isolated outliers to improve the authenticity and reliability of the data and improve the accuracy of subsequent analysis results.

[0088] Data encapsulation and storage: After screening and removing abnormal points, the data will be encapsulated into a data packet with a timestamp. The addition of a timestamp gives the data a clear time mark, which is convenient for subsequent data analysis to accurately trace the time point when the data was generated and understand the changes in the silo's operating status over time. These data packets are stored in a ring buffer with version control. The design of the ring buffer can store data in a loop. In the case of limited storage space, it ensures that the latest data can be saved in time, and it can also retain data from a certain historical period for comparative analysis. Version control helps to record different versions of data, making it convenient to trace back and view the evolution of data when needed. In addition, the data will also be written to the database's to-be-simulated partition at the same time. The to-be-simulated partition provides a special storage area for subsequent simulation analysis of these data, so that the causes of abnormal silo operation can be deeply studied through simulation, providing strong data support for optimizing the material level control strategy.

[0089] Specifically, in the method for controlling the material level of a small silo of a denitration device according to the present invention, the step S104 comprises: When the red fault level mark in the real-time silo data comparison result is triggered 3 times cumulatively or the orange alarm exceeds the limit for 120 seconds, the synchronous acquisition instructions of the industrial camera, 3D laser radar and infrared thermal imaging equipment are activated; Industrial cameras capture the surface status of materials, infrared thermal imagers monitor areas with abnormal temperatures, and 3D laser radars construct point cloud models of the silo material surface; The data collected by the activated industrial camera, 3D laser radar and infrared thermal imaging equipment are preprocessed to obtain the preprocessed data collected by the activated industrial camera, 3D laser radar and infrared thermal imaging equipment.

[0090] Equipment activation conditions: Trigger mechanism: When the red fault level mark in the real-time silo data comparison result is triggered 3 times cumulatively or the orange alarm exceeds the limit for 120 seconds, the system determines that there may be a major problem in the silo operation. The red fault level mark is triggered 3 times cumulatively, indicating that serious abnormalities frequently occur during the silo operation; the orange alarm exceeds the limit for 120 seconds continuously, indicating that the abnormal state lasts for a long time. Both of these situations may have a serious impact on the normal operation of the denitrification equipment.

[0091] Activation purpose: In this case, the synchronous acquisition instructions of the industrial camera, 3D laser radar and infrared thermal imaging equipment are activated to obtain more detailed and comprehensive information inside the silo from multiple dimensions, so as to deeply analyze the reasons for abnormal operation of the silo.

[0092] Equipment collection function: Industrial cameras: Industrial cameras are used to capture the surface state of materials. The state of the material surface can reflect a lot of information, such as whether the material is agglomerated or unevenly stacked. This information is critical for determining the fluidity of the material in the silo and whether there is a potential blockage problem. Through the images taken by the industrial camera, the morphology of the material surface can be intuitively observed, providing a visual basis for subsequent analysis.

[0093] Infrared thermal imager: Infrared thermal imager mainly monitors abnormal temperature areas. In the small silo of denitrification equipment, abnormal temperature may mean that the material has undergone chemical reaction, local overheating or uneven cooling. These abnormal temperature areas may affect the properties and fluidity of the material, and then affect the material level control. Through infrared thermal imager, the abnormal temperature area can be quickly and accurately located, and its temperature distribution can be understood, which helps to timely discover potential safety hazards and operation problems.

[0094] 3D LiDAR: 3D LiDAR is used to construct a point cloud model of the silo material surface. The point cloud model can accurately describe the three-dimensional shape and spatial position information of the material surface in the silo. By comparing and analyzing the point cloud models at different times, we can understand the dynamic changes of the materials, such as the changes in the stacking shape of the materials, the movement speed of the materials, etc., so as to more accurately grasp the distribution and movement state of the materials in the silo, and provide more accurate data support for material level control.

[0095] Data preprocessing: Preprocessing objects: Preprocess the data collected by activated industrial cameras, 3D laser radars, and infrared thermal imaging devices. The data collected by these devices may have problems such as noise, missing data, and inconsistent resolution. Directly using these raw data may affect the accuracy and reliability of subsequent analysis.

[0096] Purpose of preprocessing: Through preprocessing operations such as denoising, data filling, normalization, etc., the quality of data is improved to make it more suitable for subsequent analysis and processing. The preprocessed data can more clearly reflect the actual situation inside the silo, laying a good foundation for extracting valuable information from these data and accurately judging the operation status of the silo.

[0097] In summary, step S104 uses a variety of devices to collect internal information of the silo from different angles through clear equipment activation conditions, and pre-processes the collected data, providing strong data support for comprehensive and accurate evaluation of the silo operating status. It is an indispensable and important part of the small silo level control method of the entire denitrification equipment.

[0098] Specifically, in the method for controlling the material level of a small silo of a denitration device according to the present invention, the step S104 comprises: Extract the material surface texture complexity index, morphological characteristics of agglomeration areas, and temperature gradient characteristics of thermal anomaly areas from the pre-processed data collected by activated industrial cameras, 3D laser radars, and infrared thermal imaging equipment; The material surface texture complexity index, morphological characteristics of the agglomeration area, and temperature gradient characteristics of the thermal anomaly area are input into the pre-trained silo operation image analysis model to generate the silo operation image analysis results.

[0099] Feature extraction: Purpose: After preprocessing the data collected by the activated industrial camera, 3D laser radar and infrared thermal imaging equipment, specific feature information is further extracted from it. These features can more accurately reflect the key aspects of the material and operating status inside the silo, and provide targeted data support for subsequent accurate judgment of the silo operation status.

[0100] Specific features: Material surface texture complexity index: The texture of the material surface can indicate a lot of information, such as the particle size distribution, uniformity, and whether the material has undergone special physical or chemical changes. By calculating the material surface texture complexity index, this texture feature can be quantified, so as to more accurately understand the properties of the material itself and possible problems, such as whether the surface texture of the material has changed due to moisture, chemical reaction, etc., which in turn affects its fluidity and material level.

[0101] Morphological characteristics of agglomeration areas: In small silos of denitrification equipment, material agglomeration is a common problem that may affect material level control. Extracting the morphological characteristics of the agglomeration area, including information such as the size, shape, and distribution of the agglomerates, can intuitively understand the specific situation of material agglomeration in the silo. These characteristics are crucial for determining the severity of agglomeration, analyzing the causes of agglomeration, and estimating its impact on material level measurement and material flow.

[0102] Temperature gradient characteristics of thermal anomaly areas: Temperature is a key factor in silo operation. Thermal anomaly areas may indicate uneven heat transfer, chemical reactions, or equipment failures inside the material. Temperature gradient characteristics further describe the speed and direction of temperature changes in thermal anomaly areas. By analyzing the temperature gradient characteristics of thermal anomaly areas, we can gain an in-depth understanding of the specific situation of temperature anomalies and their impact on material properties and material level control.

[0103] Generate analysis results using pre-trained models: Pre-trained silo operation image analysis model: This is a model that has been trained with a large amount of data in advance. It has learned the correspondence between the various features extracted above (material surface texture complexity index, morphological characteristics of agglomeration area, temperature gradient characteristics of thermal abnormal area, etc.) and the actual silo operation status (normal or abnormal) under different silo operation conditions. By training on a large amount of sample data, the model can capture the change patterns of these features and how they jointly affect the operation status of the silo.

[0104] Generate silo operation image analysis results: The extracted material surface texture complexity index, morphological characteristics of the agglomeration area, and temperature gradient characteristics of the thermal anomaly area are used as inputs to the pre-trained silo operation image analysis model. The model will conduct a comprehensive analysis of these input features based on the rules and patterns it has learned internally, and finally generate silo operation image analysis results. This result can clearly tell us whether the current operation status of the silo is normal or abnormal, and provide a key basis for subsequent decisions on whether to adjust the material level control parameters and other operations.

[0105] In summary, the operation of this part of step S104 realizes the transformation from the visual data inside the silo to the clear operation status judgment result by extracting key features from the pre-processed data collected by various devices and analyzing it with the pre-trained model. It plays an important auxiliary judgment role in the whole denitrification equipment small silo material level control method, and helps to more accurately ensure the normal operation of the silo and the effective control of the material level.

[0106] Specifically, in the method for controlling the material level of a small silo of a denitration device according to the present invention, the step S105 comprises: The data to be simulated is preprocessed, and based on the preprocessed data to be simulated, the feature vector signal time-frequency joint features, environmental coupling factors, equipment health indicators and image-assisted features are constructed; the feature vector signal time-frequency joint features, environmental coupling factors, equipment health indicators and image-assisted features are fused to obtain fused features, and the fused features are used as the data features of the data to be simulated.

[0107] Preprocess the simulated data: Purpose: The data to be simulated is determined after screening and judgment in the previous steps. There may be some factors that are not conducive to subsequent accurate analysis and processing, such as data noise, inconsistent data format, and some missing data. To preprocess the simulated data is to improve the data quality through a series of operations to make it more suitable for subsequent more in-depth feature construction and analysis.

[0108] Common preprocessing operations: The preprocessing process may involve denoising to remove random noise mixed in during data collection to improve data accuracy; reasonably fill in missing data to improve data integrity; it may also include data standardization operations to unify data of different magnitudes into a relatively reasonable range to facilitate subsequent calculations and analysis.

[0109] Construct multiple features: Construct feature vectors based on preprocessed data: After preprocessing, different types of features are constructed based on the quality-enhanced simulated data. These features reflect the relevant conditions of silo operation from different perspectives, as follows: Signal time-frequency joint features: Combines the characteristics of the signal in the time domain and frequency domain. During the operation of the silo, the changes in the signals emitted by equipment such as radar level meters and ultrasonic level meters at different time points and their performance in different frequency components contain information about the silo status. By extracting the signal time-frequency joint features, the details of the silo operation reflected by these equipment signals can be more comprehensively captured, such as the dynamic changes of materials and the stability of equipment operation.

[0110] Environmental coupling factor: Considering that the environment in which the silo is located has an important impact on the material level measurement and equipment operation, environmental factors such as temperature, humidity, dust, etc. may interfere with the sensor signal and affect the material properties. The environmental coupling factor is used to quantify the relationship between these environmental factors and the silo operation, so as to more accurately understand the role of environmental factors in abnormal silo operation.

[0111] Equipment health indicators: reflect the health of silo-related equipment (such as screw conveyors, pneumatic valves, etc.). For example, the current, vibration, temperature and other parameters of the equipment can be analyzed to determine whether the equipment has potential faults, whether the operation is normal, etc. These equipment health indicators are crucial for judging whether the overall operation of the silo is normal and for subsequent control parameter adjustments.

[0112] Image-assisted features: These are features extracted from the image data collected and analyzed by industrial cameras, 3D laser radars, and infrared thermal imaging equipment in the previous steps. These image data provide intuitive information such as the surface state and temperature distribution of materials inside the silo. The image-assisted features extracted from them can further supplement and improve the description of the silo's operating status, such as the texture characteristics of the material surface, agglomeration conditions, and other factors that affect material level control.

[0113] Data feature fusion: Purpose: The purpose of fusing the above-mentioned different types of features (signal time-frequency joint features, environmental coupling factors, equipment health indicators, and image-assisted features) is to comprehensively consider various factors and form a more comprehensive and more accurate fusion feature that reflects the characteristics of the data to be simulated. A single feature may only reflect the operation of the silo from one aspect, but by fusing these different dimensions of features, a more complete "portrait" can be obtained, thereby more accurately grasping the actual operation status of the silo represented by the data to be simulated.

[0114] Fusion method: The specific fusion method may involve a variety of mathematical and data processing techniques, such as weighted summation, principal component analysis, etc. The appropriate fusion method should be selected according to different application scenarios and data characteristics so that the fused features can retain the important information of each original feature and highlight the overall comprehensive features, so that they can be used as data features of the data to be simulated for subsequent data analysis and processing.

[0115] The preprocessing of the simulation data, construction of multiple features and data feature fusion operations in the above step S105 can provide a solid foundation for the subsequent use of these data features for accurate data simulation and generation of reasonable silo simulation control parameters, thereby further realizing effective control of the material level of the small silo of the denitrification equipment and ensuring the normal operation of the silo.

[0116] Specifically, in the method for controlling the material level of a small silo of a denitration device according to the present invention, the step S105 comprises: Input the silo simulation control parameters into the PLC hardware-in-the-loop simulation system to verify the protocol compatibility between the silo simulation control parameters and the DCS control logic; Deploy AB dual control channels in the DCS system: A channel runs the corrected material level control parameters; Channel B maintains original standard parameters for hot backup; After executing the corrected material level control parameters, the sliding window mean of the material level control deviation is continuously calculated. If the deviation rate is <2% for 5 consecutive cycles, the corrected material level control parameters are locked as the new standard benchmark.

[0117] Verify protocol compatibility: Purpose: After generating the silo simulation control parameters, it is necessary to improve these parameters to enable them to operate normally in the actual control system and be compatible with the existing control logic. The PLC (Programmable Logic Controller) hardware-in-the-loop simulation system is a platform for simulating the actual hardware operating environment and testing the feasibility of control parameters.

[0118] Operation and significance: Input the silo simulation control parameters into the PLC hardware-in-the-loop simulation system, and use the system to simulate the actual hardware operation process to verify the protocol compatibility of the silo simulation control parameters and the DCS (distributed control system) control logic. This step is crucial because if the parameters are incompatible with the DCS control logic, it may cause the control system to malfunction, fail to accurately control the silo material level, and even affect the normal operation of the denitrification equipment. Only by improving compatibility can the stability and effectiveness of the subsequent application of these parameters in the actual system be guaranteed.

[0119] Channel deployment in DCS system: AB dual control channel design idea: Deploying AB dual control channels in the DCS system is a redundant design that aims to improve the reliability and stability of the system. This design takes into account various unexpected situations that may occur during actual operation, such as undesirable effects after control parameter adjustment or temporary system failures.

[0120] Channel A runs the corrected material level control parameters: Channel A is responsible for running the corrected material level control parameters in the previous steps. These corrected parameters are based on detailed analysis, simulation and various abnormal situation judgments of the silo operation data. The purpose is to more accurately control the silo material level so that it can adapt to various changes in the actual operation of the silo, avoid problems such as empty silo shortage or full silo overflow, and ensure the normal operation of the denitrification equipment.

[0121] Channel B maintains the original standard parameters for hot backup: Channel B maintains the original standard parameters for hot backup. The so-called hot backup means that when the system is operating normally, channel B is ready to take over the work of channel A at any time. If channel A encounters problems during the operation of the revised material level control parameters, such as causing new abnormalities in the operation of the silo or failing to effectively control the material level, channel B can immediately switch to the original standard parameters to maintain the basic operation of the silo, avoiding serious consequences caused by improper parameter adjustment, and providing a reliable "insurance" for the system.

[0122] Calculate the deviation and lock the parameters: Continuously calculate the sliding window mean of the level control deviation: After implementing the revised level control parameters, it is necessary to continue to pay attention to the actual effect of the level control. By continuously calculating the sliding window mean of the level control deviation, the accuracy of the level control over a period of time (the time period covered by the sliding window) can be comprehensively evaluated. This mean can reflect whether the revised parameters are truly effective in controlling the level within the desired range and whether there is a continuous deviation trend.

[0123] Conditions for locking parameters as new standard benchmarks: If the deviation rate is <2% for 5 consecutive cycles, it means that the revised level control parameters have shown good control effects during this period and can stably and accurately control the silo level. At this time, locking the parameters as the new standard benchmark means that in the subsequent silo operation process, these locked parameters will be used as the main control basis until the need to readjust the parameters arises again. This allows the silo level control to be carried out in a relatively stable and effective state, further ensuring the normal operation of the denitrification equipment.

[0124] In summary, step S105 ensures the feasibility, reliability and stability of the silo simulation control parameters in practical applications from multiple aspects through a series of operations such as verifying protocol compatibility, deploying dual control channels and locking parameters according to deviation conditions. It is an important link in the entire denitrification equipment small silo level control method to achieve precise control and maintain the normal operation of the system.

[0125] The present invention effectively solves the problem of uncontrolled feeding caused by distortion or failure of collected data through multi-step and multi-model collaborative working, and ensures the normal operation of denitrification equipment. The details are as follows: Multi-source data acquisition: Step S101 is used to obtain comprehensive silo operation data, including the original signal of the radar level meter (microwave emission, receiving time and reflection intensity), the original signal of the ultrasonic level meter (emission and receiving time), the temperature and humidity values ​​in the silo (temperature and humidity data), the speed of the screw conveyor and the opening of the pneumatic valve, providing a rich data basis for subsequent analysis.

[0126] Constructing a material level abnormality recognition model: In step S102, the silo operation data is preprocessed and feature extracted to obtain the silo operation data features. The signal strength fluctuation coefficient is obtained through data analysis and environmental coupling analysis is performed to obtain the coupling influence data of the material level measurement; the third harmonic component of the screw conveyor drive current and the overshoot parameter of the pneumatic valve step response are extracted to construct a three-dimensional equipment state vector. The random forest is trained using these data to obtain a material level abnormality recognition model for timely detection of material level abnormalities.

[0127] Comparative analysis and data processing: Step S103: Receive standard material level control parameters, substitute them into the material level control model to generate a dynamic control parameter set and send it to the silo equipment end. Collect real-time silo operation data, substitute standard parameters into the silo simulation model to obtain predicted data, compare the two to obtain real-time comparison results, and compare with the preset error range. Data outside the error range is used as data to be simulated.

[0128] Sliding time window processing is applied to the real-time silo operation data stream to match the data time series, and the absolute deviation of material level, equipment status deviation, and environmental coupling difference indicators are constructed. The error threshold is dynamically adjusted based on material characteristics for comparison, and the results are identified using a three-level marking system. The red fault level and continuously exceeded orange alarm level data are screened out, and after processing, they are stored in the ring buffer and database partition to be simulated.

[0129] Image data assisted judgment: In step S104, when the red fault level mark in the real-time silo data comparison result is triggered 3 times cumulatively or the orange alarm exceeds the limit for 120 seconds, the industrial camera, 3D lidar and infrared thermal imaging equipment are activated to collect data. After pre-processing, the material surface texture complexity index, morphological characteristics of the agglomeration area and temperature gradient characteristics of the thermal abnormality area are extracted and substituted into the pre-trained silo operation image analysis model to judge the silo operation status.

[0130] Parameter correction and execution: In step S105, when the result of the silo operation image analysis is abnormal, the simulation data is preprocessed and multi-dimensional features are constructed. After fusion, it is used as data features, and the silo control data simulation model is used to generate silo simulation control parameters. The standard material level control parameters are replaced to obtain correction parameters and used as real-time execution control parameters. The silo simulation control parameters are input into the PLC hardware-in-the-loop simulation system to verify the protocol compatibility, and the AB dual control channels are deployed in the DCS system. After executing the correction parameters, the new standard benchmark is locked according to the deviation rate.

Claims

1. A method for controlling the material level of a small silo of a denitrification equipment, characterized in that: include: Step S101, obtaining silo operation data, which includes the original signal of the radar level meter, the original signal of the ultrasonic level meter, the temperature and humidity values ​​in the silo, the speed of the screw conveyor and the opening of the pneumatic valve; Step S102, extracting data features from the silo operation data and training the random forest to obtain a material level abnormality recognition model; Step S103, receiving standard material level control parameters, substituting the standard material level control parameters into a preset material level control model to obtain standard material level control parameters, collecting operation data generated after the silo equipment end executes the standard material level control parameters, obtaining real-time silo operation data, substituting the standard material level control parameters into a preset silo simulation model to obtain silo operation prediction data, comparing the real-time silo operation data with the silo operation prediction data to obtain a real-time silo data comparison result, if the real-time silo data comparison result exceeds the preset silo data error range, then retrieving data exceeding the preset silo data error range and using it as data to be simulated; Step S104, when the real-time silo data comparison result exceeds the preset silo data error range, the silo image data is acquired, and the silo image data is substituted into the preset silo operation image analysis model to obtain the silo operation image analysis result, and the silo operation image analysis result includes the silo operation is normal and the silo operation is abnormal; Step S105, when the result of the silo operation image analysis is that the silo operation is abnormal, the data features of the data to be simulated are extracted, and data simulation is performed using a preset silo control data simulation model based on the data features of the data to be simulated to generate silo simulation control parameters, and the silo simulation control parameters are replaced in the standard material level control parameters to obtain corrected material level control parameters, and the corrected material level control parameters are used as the real-time execution control parameters of the silo.

2. According to a method for controlling the material level of a small silo of a denitrification equipment as claimed in claim 1, it is characterized in that: The step S101 includes: The original signal of the radar level meter includes the microwave emission time, microwave reception time and microwave reflection intensity; The original signal of the ultrasonic level meter includes the emission time of the ultrasonic wave and the reception time of the ultrasonic wave; The temperature and humidity values ​​inside the silo include the temperature data inside the silo and the humidity data inside the silo.

3. According to a method for controlling the material level of a small silo of a denitration equipment as claimed in claim 1, it is characterized in that: The step S102 includes: Preprocessing the silo operation data to obtain preprocessed silo operation data, and extracting data features from the preprocessed silo operation data to obtain silo operation data features; Perform data analysis on the silo operation data characteristics to obtain the signal strength fluctuation coefficient, perform environmental coupling analysis based on the signal strength fluctuation coefficient, and obtain the coupling influence data of material level measurement; The third harmonic component of the screw conveyor driving current is extracted as the mechanical load feature, and combined with the overshoot parameter of the pneumatic valve step response, a three-dimensional device state vector with mixed characteristics in the time and frequency domain is constructed. The three-dimensional equipment state vector with mixed characteristics in time and frequency domain, the coupling influence data of material level measurement and the characteristics of silo operation data are used to train the random forest model to obtain the material level anomaly recognition model.

4. According to a method for controlling the material level of a small silo of a denitrification equipment as claimed in claim 1, it is characterized in that: The step S103 includes: Analyze the standard material level control parameters to obtain the target material level setting value, maximum material replenishment rate and allowable deviation threshold value in the standard material level control parameters; The parsed standard material level control parameters are input into a preset material level control model, and the preset material level control model generates a dynamic control parameter set including a target speed of the screw conveyor, a pneumatic valve opening curve, and a vibrator triggering timing sequence; During the execution of control instructions on the silo equipment side, the following real-time data streams are collected synchronously: The original echo signal of the radar level meter in dual-band scanning mode; The current harmonic spectrum and vibration acceleration waveform of the screw conveyor drive motor; LVDT feedback signal of pneumatic valve positioner and transient value of cylinder pressure; The warehouse's three-dimensional temperature and humidity distribution matrix is ​​updated in seconds; The IEEE 1588 time protocol is used to align the timestamps of real-time data streams to form a real-time silo operation data set including time series, equipment status, and environmental parameters.

5. According to a method for controlling the material level of a small silo of a denitrification equipment as claimed in claim 3, it is characterized in that: The step S103 includes: Sliding time window processing is applied to the real-time silo operation data stream, and the radar material level value, screw conveyor speed and the predicted data output by the silo simulation model are matched at the millisecond level based on the IEEE 1588 timestamp to eliminate the data phase difference caused by communication delay. The construction includes absolute deviation of material level, deviation of equipment status and difference in environmental coupling: Absolute deviation of material level: Euclidean distance between real-time radar measurement value and model prediction value; Equipment state deviation: the integral square error between the actual speed and theoretical speed of the screw conveyor; Environmental coupling difference: KL divergence between the measured temperature and humidity distribution matrix and the predicted matrix; Based on the material characteristics, the error threshold is dynamically adjusted to compare the data of absolute deviation of material level, deviation of equipment status and environmental coupling difference. The comparison results are marked with a three-level marking system: The comparison results are marked using a three-level marking system, including: Level 1 yellow warning: a single indicator exceeds the limit instantaneously but recovers within 30 seconds; Level 2 orange alarm: two indicators exceed the limit at the same time or a single indicator exceeds the limit continuously for more than 60 seconds; Level 3 red fault: three indicators exceed the limit or the material level parameter exceeds the threshold by 150%.

6. A method for controlling the material level of a small silo of a denitration equipment according to claim 5, characterized in that: The step S103 includes: Based on the three-level marking system of the real-time silo data comparison results, the red fault level and the orange alarm level data with continuous exceeding of the limit are screened out, and the isolated abnormal points caused by transient sensor interference are eliminated. The processed data is encapsulated into a data packet with a timestamp, stored in a ring buffer with version control, and written to the partition to be simulated in the database at the same time.

7. According to a method for controlling the material level of a small silo of a denitrification equipment as claimed in claim 1, it is characterized in that: The step S104 includes: When the red fault level mark in the real-time silo data comparison result is triggered 3 times cumulatively or the orange alarm exceeds the limit for 120 seconds, the synchronous acquisition instructions of the industrial camera, 3D laser radar and infrared thermal imaging equipment are activated; Industrial cameras capture the surface status of materials, infrared thermal imagers monitor areas with abnormal temperatures, and 3D laser radars construct point cloud models of the silo material surface; The data collected by the activated industrial camera, 3D laser radar and infrared thermal imaging equipment are preprocessed to obtain the preprocessed data collected by the activated industrial camera, 3D laser radar and infrared thermal imaging equipment.

8. A method for controlling the material level of a small silo of a denitration equipment according to claim 7, characterized in that: The step S104 includes: Extract the material surface texture complexity index, morphological characteristics of agglomeration areas, and temperature gradient characteristics of thermal anomaly areas from the pre-processed data collected by activated industrial cameras, 3D laser radars, and infrared thermal imaging equipment; The material surface texture complexity index, morphological characteristics of the agglomeration area, and temperature gradient characteristics of the thermal anomaly area are input into the pre-trained silo operation image analysis model to generate the silo operation image analysis results.

9. A method for controlling the material level of a small silo of a denitration equipment according to claim 1, characterized in that: The step S105 includes: The data to be simulated is preprocessed, and based on the preprocessed data to be simulated, the feature vector signal time-frequency joint features, environmental coupling factors, equipment health indicators and image-assisted features are constructed; the feature vector signal time-frequency joint features, environmental coupling factors, equipment health indicators and image-assisted features are fused to obtain fused features, and the fused features are used as the data features of the data to be simulated.

10. A method for controlling the material level of a small silo of a denitration equipment according to claim 9, characterized in that: The step S105 includes: Input the silo simulation control parameters into the PLC hardware-in-the-loop simulation system to verify the protocol compatibility between the silo simulation control parameters and the DCS control logic; Deploy AB dual control channels in the DCS system: A channel runs the corrected material level control parameters; Channel B maintains original standard parameters for hot backup; After executing the corrected material level control parameters, the sliding window mean of the material level control deviation is continuously calculated. If the deviation rate is <2% for 5 consecutive cycles, the corrected material level control parameters are locked as the new standard benchmark.

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