Energy-saving and environment-friendly tail gas treatment control system and method based on cremation machine

Abstract

The invention relates to the technical field of intelligent emission control, in particular to an energy-saving and environment-friendly tail gas treatment control system and method based on a cremation machine. The method comprises the following steps: collecting tail gas discharged by the cremation machine, and carrying out pollutant concentration detection to obtain pollutant concentration data; performing pollution grade division based on the pollutant concentration data to obtain first pollution grade data and second pollution grade data; performing multi-stage tail gas purification simulation according to the first pollution level data to obtain multi-stage tail gas purification data; detecting high-energy-consumption tail gas treatment equipment according to the multi-stage tail gas purification data to obtain high-energy-consumption tail gas treatment equipment data; and performing fan fault diagnosis based on the data of the high-energy-consumption tail gas treatment equipment to obtain fan fault data. Based on the intelligent emission control technology, the tail gas purification efficiency of the cremation machine and the accuracy and automation level of energy-saving and environment-friendly control are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent emission control, and in particular to an energy-saving and environmentally friendly exhaust gas treatment control system and method based on a cremator. Background Art

[0002] The traditional energy-saving and environmentally friendly exhaust gas treatment control method based on crematorium relies on a single pollutant concentration monitoring method, lacks a comprehensive perception of the diversity and dynamic changes of exhaust gas components, resulting in inaccurate classification of pollution levels, thus affecting the effect of subsequent purification treatment; the exhaust gas purification process is usually a preset fixed process, lacks the ability to differentiate and dynamically adjust for different pollution levels, and is difficult to achieve efficient and energy-saving exhaust gas treatment, resulting in energy waste and poor treatment effect; the existing methods are not in-depth enough in monitoring high-energy consumption exhaust gas treatment equipment, and often only focus on the surface operating parameters of the equipment, lack of internal status of the equipment such as filter bag clogging, powder Intelligent diagnosis of details such as dust adhesion and filter media aging leads to delayed fault warning and maintenance strategies, increasing the risk of equipment failure and maintenance costs; filter element blockage detection mostly relies on simple pressure difference monitoring, ignoring the impact of internal microstructural changes and particle morphology on blockage, making it difficult to accurately determine the location and severity of blockage in a timely manner, affecting dust removal efficiency and stable system operation; energy-saving control strategies are often single-dimensional start-stop control, lacking comprehensive linkage adjustment of fans, filters and purification parameters, and unable to achieve optimal energy consumption management and environmental protection goals in the exhaust gas treatment process, ultimately leading to low overall system energy efficiency and difficulty in meeting environmental emissions standards. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide an energy-saving and environmentally friendly exhaust gas treatment control system and method based on a cremator to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a method for controlling exhaust gas treatment based on a crematorium for energy conservation and environmental protection is provided, comprising the following steps:

[0005] Step S1: collecting exhaust gas emitted by a crematorium and performing pollutant concentration detection to obtain pollutant concentration data; classifying the pollution levels based on the pollutant concentration data to obtain first pollution level data and second pollution level data;

[0006] Step S2: performing a multi-stage exhaust gas purification simulation based on the first pollution level data to obtain multi-stage exhaust gas purification data; performing a high-energy consumption exhaust gas treatment equipment test based on the multi-stage exhaust gas purification data to obtain high-energy consumption exhaust gas treatment equipment data; performing a fan fault diagnosis based on the high-energy consumption exhaust gas treatment equipment data to obtain fan fault data;

[0007] Step S3: performing a medium-intensity exhaust gas purification simulation based on the second pollution level data to obtain medium-intensity exhaust gas purification data; performing a dust removal efficiency analysis based on the medium-intensity exhaust gas purification data to obtain dust removal efficiency data; performing a filter element clogging test based on the dust removal efficiency data to obtain filter element clogging data;

[0008] Step S4: Control the redundant fan start-stop strategy according to the fan fault data to obtain the redundant fan start-stop control data; dynamically adjust the exhaust gas treatment parameters according to the filter element blockage data to obtain the exhaust gas adjustment treatment parameters; perform exhaust gas energy-saving and environmental protection control according to the exhaust gas adjustment treatment parameters and the redundant fan start-stop control data to obtain the exhaust gas treatment control data.

[0009] By introducing a fine-grained detection and grading mechanism for pollutant concentration data, the present invention improves the ability to identify the complexity and dynamic changes of exhaust gas components, achieves accurate classification of exhaust gas pollution levels, and lays the foundation for subsequent differentiated treatment. Through multi-stage exhaust gas purification simulation and medium-intensity exhaust gas purification simulation, the purification process can be dynamically adjusted according to different pollution levels, avoiding the waste of resources caused by the use of fixed processes, and achieving more targeted and efficient purification control. The monitoring of high-energy exhaust gas treatment equipment starts with energy consumption data and extends to in-depth diagnosis of internal operating status, including fan failure, filter bag aging, dust adhesion and other dimensions. It can detect equipment anomalies in a timely manner and effectively reduce system failure rate and maintenance costs. In the dust removal efficiency analysis process, particle morphology analysis and filter element blockage mechanism identification are introduced to improve the multi-angle perception ability of the filter element status, break through the limitations of the traditional pressure difference method, accurately judge the blockage location and severity, and thus ensure the continuous and stable operation of the dust removal system. Redundant fan start and stop control combines fan fault data to achieve dynamic response, and accurately executes through load regulation and start and stop strategies to improve the redundant reliability of system operation. Exhaust treatment parameter adjustments are based on filter blockage status, further enhancing real-time response and energy-matching capabilities. Ultimately, exhaust treatment control integrates redundant fan control with dynamic parameter adjustment to achieve coordinated optimization of energy consumption, environmental protection, and stability, significantly improving the system's overall energy efficiency and pollutant treatment capabilities.

[0010] Preferably, this specification also provides an energy-saving and environmentally friendly exhaust gas treatment control system based on a cremator, which is used to execute the energy-saving and environmentally friendly exhaust gas treatment control method based on a cremator as described above. The energy-saving and environmentally friendly exhaust gas treatment control system based on a cremator includes:

[0011] The pollution level classification module is used to collect exhaust gas emitted by the crematorium and perform pollutant concentration detection to obtain pollutant concentration data; based on the pollutant concentration data, the pollution level is classified to obtain first pollution level data and second pollution level data;

[0012] A high-energy-consuming exhaust gas treatment equipment detection module is used to perform multi-stage exhaust gas purification simulation based on the first pollution level data to obtain multi-stage exhaust gas purification data; perform high-energy-consuming exhaust gas treatment equipment detection based on the multi-stage exhaust gas purification data to obtain high-energy-consuming exhaust gas treatment equipment data; and perform fan fault diagnosis based on the high-energy-consuming exhaust gas treatment equipment data to obtain fan fault data;

[0013] The dust removal efficiency analysis module is used to simulate medium-intensity exhaust gas purification based on the second pollution level data to obtain medium-intensity exhaust gas purification data; perform dust removal efficiency analysis based on the medium-intensity exhaust gas purification data to obtain dust removal efficiency data; and perform filter element blockage detection based on the dust removal efficiency data to obtain filter element blockage data;

[0014] The exhaust gas energy-saving and environmental protection control module is used to control the redundant fan start-stop strategy according to the fan fault data to obtain the redundant fan start-stop control data; dynamically adjust the exhaust gas treatment parameters according to the filter element blockage data to obtain the exhaust gas adjustment treatment parameters; and perform exhaust gas energy-saving and environmental protection control according to the exhaust gas adjustment treatment parameters and the redundant fan start-stop control data to obtain the exhaust gas treatment control data.

[0015] The energy-saving and environmentally friendly exhaust gas treatment control system based on a cremator of the present invention can realize any energy-saving and environmentally friendly exhaust gas treatment control method based on a cremator of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the energy-saving and environmentally friendly exhaust gas treatment control method based on a cremator. The internal modules of the system cooperate with each other to improve the exhaust gas purification efficiency of the cremator and the accuracy and automation level of energy-saving and environmental protection control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0017] Figure 1 This is a schematic flow chart of the steps of an energy-saving and environmentally friendly exhaust gas treatment control method based on a cremator according to the present invention;

[0018] Figure 2 Detailed flowchart of step S1 in the present invention;

[0019] Figure 3 Schematic diagram of the tail gas treatment equipment in the present invention;

[0020] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0021] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are 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 those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0022] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0023] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0024] To achieve this, please refer to Figures 1 to 3 The present invention provides an energy-saving and environmentally friendly tail gas treatment control method based on a cremator, the method comprising the following steps:

[0025] Step S1: collecting exhaust gas emitted by a crematorium and performing pollutant concentration detection to obtain pollutant concentration data; classifying the pollution levels based on the pollutant concentration data to obtain first pollution level data and second pollution level data;

[0026] In this embodiment, the exhaust gas emitted by the cremator is collected, and a gas sampler is used to continuously sample the exhaust gas in the exhaust pipe of the cremator. The sampling frequency is set to 1 time per minute, and the sampling time is not less than 10 seconds to ensure the representativeness of the sample. The sampling device is equipped with multiple sets of sensors, including but not limited to non-dispersive infrared sensors (NDIR) for CO and CO2 concentration detection, electrochemical sensors for NOx and SO2 concentration detection, and laser scattering particulate matter sensors for particulate matter (PM2.5 and PM10) concentration detection. The sensor measurement data is processed by signal amplification and filtering, and is collected to the data acquisition unit after eliminating environmental interference. The pollutant concentration data is sent to the pollution level classification module through the real-time data transmission module. The first pollution level threshold is set to a CO concentration of not less than 100 mg / m 3 、NOx concentration is not less than 80mg / m 3 、The concentration of particulate matter is not less than 50mg / m 3 The second pollution level threshold is CO concentration below 100 mg / m 3 and higher than 50 mg / m 3 、NOx concentration is lower than 80mg / m 3 and higher than 40 mg / m 3 、Particulate matter concentration is less than 50mg / m 3 and higher than 25 mg / m 3 Pollution level classification is achieved by comparing the threshold values ​​of multiple pollutant concentration data. The calculation process uses a logical judgment module to compare each pollutant concentration with the corresponding threshold value one by one, and combines it with a weighted average algorithm to calculate the overall pollution index. The data is divided into the first pollution level data and the second pollution level data based on the index size, and the pollution level data is finally output to the subsequent exhaust purification simulation module.

[0027] Step S2: performing a multi-stage exhaust gas purification simulation based on the first pollution level data to obtain multi-stage exhaust gas purification data; performing a high-energy consumption exhaust gas treatment equipment test based on the multi-stage exhaust gas purification data to obtain high-energy consumption exhaust gas treatment equipment data; performing a fan fault diagnosis based on the high-energy consumption exhaust gas treatment equipment data to obtain fan fault data;

[0028] In this embodiment, the operation of the bag dust removal module is triggered by control instructions based on the pollution level data. The working pressure of the module is controlled at 20kPa, the filter bag filtration area is set to 200 square meters, the filtration wind speed is limited to 1.2m / s-1.5m / s, and the particle concentration is monitored and fed back in real time to ensure that the particle purification rate reaches more than 80%. Subsequently, the desulfurization and denitrification module is triggered based on the output data of the dust removal module, and the desulfurization agent dosage concentration is set to 1500mg / m 3The denitrification catalyst's active temperature was maintained at 350°C ± 10°C, and the catalytic reaction time was 2 seconds. Desulfurization and denitrification efficiency was collected in real time via an online flue gas analyzer, generating gaseous pollutant purification data. The catalytic oxidation module was then activated, using a platinum-rhodium alloy catalyst. The catalytic temperature was controlled at 400°C, and the reactor residence time was set to 1.5 seconds. Catalytic efficiency was calculated by online measurement of changes in total organic matter (TVOC). Finally, the output data from each purification module was aggregated in real time and numerically integrated to calculate the overall multi-stage exhaust purification data. Based on this multi-stage purification data, the power consumption of the soot filter equipment was calculated. A power analyzer was used to measure equipment power, with a power threshold set at 2kW. Equipment exceeding this threshold was considered high-power soot filter equipment. Selected equipment was tested for bag blockage, using a differential pressure sensor to measure the pressure difference before and after the filter bag. A threshold of 1500 Pa was set, and a pressure difference exceeding this value was considered a blockage. Dust adhesion at the blocked site was determined using a laser particle size analyzer to measure particle size and morphology. Adhesion data was then calculated using a particle adhesion model. Abnormal filter media micropores are identified through scanning electron microscopy (SEM) observation of changes in the filter bag's micropore structure, combined with statistical analysis of pore size distribution to assess aging. The contribution of high energy consumption is calculated based on the filter bag's aging and power data, with a threshold set at a net power loss exceeding 10%. Ultimately, a comprehensive analysis identifies high-energy-consuming exhaust gas treatment equipment and outputs its data.

[0029] Step S3: performing a medium-intensity exhaust gas purification simulation based on the second pollution level data to obtain medium-intensity exhaust gas purification data; performing a dust removal efficiency analysis based on the medium-intensity exhaust gas purification data to obtain dust removal efficiency data; performing a filter element clogging test based on the dust removal efficiency data to obtain filter element clogging data;

[0030] In this embodiment, the second pollution level data is uploaded to the medium-intensity exhaust gas purification simulation platform. The simulation environment parameter settings include the purifier dosage of 0.3 kg / min, the reactor catalyst activity temperature is set to 300 ° C, the exhaust gas flow rate is controlled at 5 m / s, the flow field simulation refresh cycle is set to 3 seconds, and the dynamic calculation time step is 0.5 seconds. The flow field simulation module is used to simulate the exhaust gas flow and pollutant reaction process through the computational fluid dynamics (CFD) method, and the medium-intensity exhaust gas purification data is output. Then, a laser particle size analyzer is used to measure the particle size distribution of the smoke in the exhaust gas. The particle size range is set to 0.1 μm to 10 μm. The particle size data is used for morphological classification, and the fibrous and spherical particles are extracted separately. The fibrous smoke is subjected to fiber winding state analysis by a fiber winding detector, and the pressure drop change of the fiber web is measured. The pressure drop change value is calculated to evaluate the clogging trend. The sedimentation rate of spherical smoke is calculated based on Stokes' law. The sedimentation rate is related to the particle size and density of the particles. The gravity acceleration in the calculation formula is taken as 9.81 m / s 2 The fluid viscosity adopts the aerodynamic viscosity value of 1.81×10 at 20℃.-5 Pa·s is used to calculate the particle retention time in the purification system and estimate its removal rate. The overall dust removal efficiency is calculated using a weighted average method, combining the removal rates of fibrous and spherical dust. Finally, filter clogging detection is initiated based on this dust removal efficiency data. A pressure differential sensor before and after the dust channel measures the local pressure differential in the filter element. A threshold of 1200 Pa or above is set as a clogging warning, and filter clogging data is output.

[0031] Step S4: Control the redundant fan start-stop strategy according to the fan fault data to obtain the redundant fan start-stop control data; dynamically adjust the exhaust gas treatment parameters according to the filter element blockage data to obtain the exhaust gas adjustment treatment parameters; perform exhaust gas energy-saving and environmental protection control according to the exhaust gas adjustment treatment parameters and the redundant fan start-stop control data to obtain the exhaust gas treatment control data.

[0032] In this embodiment, based on the fan fault data, a PLC controller is used to execute the redundant fan start-stop strategy, and the redundant fan start threshold is set to the main fan operating power is less than 75% and the vibration sensor detects that the vibration amplitude exceeds 20mm / s, and the fan temperature exceeds 80°C to start the backup fan. The fan start-stop signal is transmitted in real time through the digital IO port to realize automatic control of the redundant fan. According to the filter element blockage data, the exhaust gas treatment parameters are dynamically adjusted, including fan speed, purifier dosage and filtration wind speed. The fan speed adjustment range is 800rpm to 1500rpm, the purifier dosage range is 0.1kg / min to 1.0kg / min, and the filtration wind speed is adjusted between 1.0m / s and 1.5m / s. The adjustment strategy is based on a closed-loop control algorithm, and a PID controller is used to calculate the deviation and adjust the parameters in real time to ensure that the exhaust gas treatment system operates in the optimal state. Finally, the system adjusts the exhaust gas processing parameters and redundant fan start-stop control data based on the exhaust gas, and uses the industrial control bus (such as Modbus TCP) to synchronously send exhaust gas energy-saving and environmental protection control instructions to complete the generation and execution of the overall exhaust gas treatment control data, realizing the energy-saving and environmentally friendly treatment process of the crematorium exhaust gas.

[0033] It is particularly important that step S4 includes the following steps:

[0034] Step S41: extracting abnormal operating condition trigger parameters based on wind turbine fault data;

[0035] In this embodiment, the three-axis acceleration data (unit: m / s) is extracted from the fan vibration monitoring system. 2), and combined with the fan speed sensor to obtain real-time RPM (revolutions per minute) data. The vibration intensity signal is spectrally analyzed through fast Fourier transform (FFT), and the main peak amplitude change in the frequency band of 300Hz to 1200Hz is extracted; if the main peak frequency shift exceeds ±10Hz, and the amplitude rise exceeds 30% of the baseline mean, it is marked as a shaft imbalance abnormality. At the same time, the bearing temperature sensor is used to collect temperature data. If the temperature exceeds 75°C (set threshold) and remains for more than 5 minutes, it is marked as a lubrication abnormality. Finally, the "frequency offset (Hz)", "vibration mean amplitude (m / s 2 )”, “bearing temperature (℃)” and “axial transient acceleration deviation rate”, a total of 4 abnormal operating condition trigger parameters.

[0036] Step S42: Matching redundant wind turbine configuration strategy based on abnormal operating condition trigger parameters;

[0037] In this embodiment, the abnormal operating condition parameters are compared with the preset fault response strategy table, which is divided into three levels of response intervals according to different fault levels. For example, when the frequency offset is greater than ±20Hz and the vibration amplitude exceeds 3.0m / s 2 , when the bearing temperature is greater than 80℃ and the vibration trend shows a continuous growth mode, the three-level redundant fan replacement solution is activated (activate the backup fan No. 1 and the air volume feedback control device at the same time). Select the corresponding redundant fan configuration strategy based on the matching results, including "activate fan number", "air volume setting (m 3 The policy is read by the control unit and cached in the Flash storage area on the device control board.

[0038] Step S43: Distribute fan loads based on the redundant fan configuration strategy to obtain redundant fan load control data;

[0039] In this embodiment, the main control PLC system sends a frequency adjustment command (in Hz) to the fan inverter, and the wind speed is matched based on the air volume target set in the configuration strategy. The air volume is determined by the wind speed sensor and the ventilation cross-sectional area (in m 2 ) is calculated in real time, for example, when the target air volume is 1200m 3 / h, the cross-sectional area of ​​the ventilation channel is 0.6m 2 , the expected wind speed is 5.56 m / s. The load is divided between the main and redundant fans in a 70:30 or 50:50 ratio, and the inverter adjusts the output frequency for precise control. This ultimately generates a set of data, including "Main Fan Speed ​​(RPM)", "Redundant Fan Speed ​​(RPM)", "Distributed Air Volume Ratio (%)", and "Load Adjustment Time (Seconds)", which is used to record the fan load distribution status.

[0040] Step S44: executing start / stop logic judgment based on the redundant fan load control data to obtain redundant fan start / stop control data;

[0041] In this embodiment, after reading the redundant fan load control data, the start-stop logic judgment is executed. If the redundant fan operating load is lower than 20% of the rated load for 5 consecutive minutes, and the main fan fault flag is cleared (0 signal), the redundant fan shutdown command is issued; if the load is stable at more than 50% and the fault signal exists, it continues to run. The start-stop control logic is executed based on the time reference cycle (the cycle is set to 10 seconds), and the load ratio and operating status identification bit are calculated in each cycle. The judgment variables include "load ratio threshold = 20%", "continuous ultra-low load time = 300s", "main fan fault bit = 1 / 0", etc. Finally, the redundant fan start-stop control data is obtained, including "start-stop signal (on = 1 / off = 0)", "control execution time (ms)", and "control target fan number".

[0042] Step S45: dynamically adjusting exhaust gas treatment parameters according to the filter element blockage data to obtain exhaust gas adjustment treatment parameters;

[0043] In this embodiment, the back-blowing cycle and back-blowing pressure are set based on the filter element pressure difference value (unit Pa) and the blockage degree level (level 1-level 5) contained in the filter element blockage data. If the filter element pressure difference is higher than 3000Pa and the blockage level is above level 4, the back-blowing interval is set to no more than 5 minutes, and the back-blowing pressure must reach 0.6MPa. The control system will read the pressure sensor and time controller data in real time, and dynamically adjust the working sequence of the dust removal module in combination with the historical blockage trend curve, including key parameters such as "back-blowing frequency (times / h)", "back-blowing duration (seconds)", and "air valve opening and closing time ratio (%)". The above parameters are dynamically adjusted by controlling the solenoid valve and the air door actuator through the programmable controller, and a complete set of exhaust gas adjustment processing parameters is output.

[0044] Step S46 performs exhaust gas energy-saving and environmental protection control according to the exhaust gas adjustment processing parameters and the redundant fan start-stop control data to obtain exhaust gas processing control data.

[0045] In this embodiment, the exhaust gas treatment parameters output from step S45 and the redundant fan start-stop control data of step S44 are input into the exhaust gas energy-saving control unit, and the control logic judgment module is called to execute the exhaust gas treatment control operation in conjunction with the fan operating status and the dust removal system status. The specific execution process includes: when the redundant fan is enabled and the backflush cycle is shortened to less than 3 minutes, the exhaust gas treatment main air duct pressure value is synchronously reduced by 5%-10% to prevent overpressure; if the fan is turned off, the treatment gas residence time is automatically extended to more than 7 seconds. The exhaust gas emission delay parameters, purifier dosage adjustment coefficient, exhaust gas diversion valve opening (controlled in the range of 30%-80%) set by the control module are all used as core execution control variables. All adjustment results form an exhaust gas treatment control data set, including "purification flow (m 3 / h)”, “Backflush execution status”, “Diverter valve angle (°)”, “Exhaust delayed emission setting (s)”.

[0046] Preferably, step S1 includes the following steps:

[0047] Step S11: collecting exhaust gas emitted by the crematorium to obtain exhaust gas sampling data;

[0048] In this embodiment, the exhaust gas emitted by the cremator is collected, and a gas sampling system is installed in the exhaust pipe of the cremator. The sampling probe is made of high-temperature resistant stainless steel with a temperature range of -40°C to 600°C. The sampling pipeline is equipped with a condenser and the temperature is controlled at 5°C to prevent water vapor from affecting the sample. The sampling frequency is set to 1 time per minute, the single sampling time is 10 seconds, the sampling flow rate is stable at 0.5L / min, and a constant volume sampling method is used to ensure consistent sampling volume. The sampling data is transmitted to the data acquisition module via wired or optical fiber. The sampling data includes gaseous pollutant components and particulate matter concentrations, and the data format is a real-time digital signal. The sampling system is calibrated regularly, and the calibration gas concentration is based on the national standard GB / T16157-1996 to ensure the accuracy and stability of the sampling data.

[0049] Step S12: performing pollutant pre-processing according to the exhaust gas sampling data to obtain purification pre-processing data;

[0050] In this embodiment, pollutant pretreatment is performed based on the exhaust gas sampling data. First, an automated condensation and dehydration device is used to reduce the water vapor content in the exhaust gas to below 5% to prevent moisture from affecting subsequent analysis. Subsequently, a multi-stage filter system is used, and the coarse filter mesh pore size is set to 10μm and the fine filter mesh pore size is set to 1μm to remove large suspended particles. The purification pretreatment data consists of preliminary separation data on moisture content, particulate matter concentration, and gas composition. Gaseous pollutants are chemically pretreated, and an alkaline solution spray device is used to neutralize SO2 and part of NOx by an absorption method. The pH value of the spray liquid is controlled at 9.0±0.2, and the spray liquid flow rate is dynamically adjusted according to the exhaust gas flow rate to ensure effective absorption of pollutants. Various parameters in the pretreatment process are monitored and fed back in real time by sensors to ensure stable operation of the pretreatment and output standardized purification pretreatment data.

[0051] Step S13: Separating multiple groups of pollutants based on the purification pre-processing data to obtain pollutant separation data;

[0052] In this embodiment, multiple groups of pollutants are separated based on the purification pretreatment data, and the pollutants in the exhaust gas are separated into multiple components using gas separation technology. A membrane separation device is used, and the membrane material is a polymer composite membrane. A membrane with high permeability and selectivity for SO2 and NOx is selected. The working pressure is controlled at 0.1MPa to 0.3MPa, and the membrane surface area is designed to be 50 square meters to ensure that the processing volume meets the exhaust gas flow requirements. Low-temperature condensation technology is used to physically separate heavy metals and organic matter. The condensation temperature is set at -10°C and the condensation time is fixed at 60 seconds to ensure that the pollutants are fully condensed and precipitated. Multiple groups of pollutant separation data include gas component concentration, separation efficiency and residual concentration, which are detected in real time by an online gas chromatograph with a detection accuracy of ±0.1ppm. The data are transmitted to the data processing unit in real time.

[0053] Step S14: performing concentration detection based on the pollutant separation data to obtain pollutant concentration data;

[0054] In this example, concentration detection is performed based on pollutant separation data, and a high-precision gas analyzer is used to measure the concentration of each group of pollutants. The instruments include a non-dispersive infrared detector (NDIR) for CO and CO2, a chemiluminescence detector for NOx, an ultraviolet fluorescence detector for SO2, and a laser light scattering method for particulate matter PM2.5 and PM10. The sampling period of each detection instrument is set to 5 seconds, and the data resolution reaches 0.01 mg / m 3The instrument's built-in temperature and pressure compensation systems ensure stable measurement data. This data is transmitted to the pollution classification system via industrial Ethernet. The instrument is calibrated weekly according to the relevant national standard GB / T16157-1996 before use. Concentration data includes both individual pollutant concentrations and combined concentrations of multiple indicators, all recorded in real-time digital form.

[0055] Step S15: performing pollution level classification based on the pollutant concentration data to obtain first pollution level data and second pollution level data.

[0056] In this embodiment, pollution levels are divided based on pollutant concentration data, pollutant concentrations are compared with standard limits, and threshold judgments are performed item by item. The judgment condition for the first pollution level is CO concentration ≥ 100 mg / m 3 , NOx concentration ≥80mg / m 3 , particle concentration ≥50mg / m 3 The second pollution level is determined to be 50 mg / m 3 ≤CO concentration <100 mg / m 3 , 40mg / m 3 ≤NOx concentration<80mg / m 3 , 25mg / m 3 ≤Particle concentration<50mg / m 3 A weighted pollution index calculation formula is used, with weights of 40% for CO, 35% for NOx, and 25% for particulate matter. The pollution index is calculated by multiplying the concentration of each pollutant by its corresponding weight and summing the results. Thresholds are set based on the pollution index: a pollution index ≥ 0.7 is designated as the first pollution level, and a pollution index ≤ 0.4 is designated as the second pollution level. The resulting pollution level classification is output as a digital signal for subsequent exhaust gas purification simulations.

[0057] Preferably, step S15 includes the following steps:

[0058] Step S151: determining a target pollution factor based on pollutant concentration data;

[0059] In this embodiment, the target pollution factor is determined based on the pollutant concentration data, using the real-time pollutant concentration data collected by the sensor network as input, mainly including five major pollutant indicators: CO, carbon monoxide, NOx, nitrogen oxides, SO2, sulfur dioxide, particulate matter PM2.5 and PM10. For example, the CO limit is 100mg / m 3 , NOx limit is 80mg / m 3 , SO2 limit is 60mg / m 3 , the PM2.5 limit is 35mg / m 3By comparison, it is determined whether the pollutant concentration exceeds the standard. Any pollutant that exceeds the limit is selected as the target pollution factor. At the same time, if the pollutant concentration is above 80% of the limit but does not reach the limit, it is also included in the target pollution factor of concern. This process is automatically executed by setting the threshold judgment unit. The specific value of the threshold used is the above-mentioned national standard limit and the corresponding percentage. The result of determining the target pollution factor is output in the data format of pollutant category and concentration value, providing an accurate basis for subsequent weight allocation.

[0060] Step S152: performing pollutant weight assignment based on the target pollution factor to obtain weighted pollution index data;

[0061] In this embodiment, pollutant weights are allocated based on target pollution factors, and weights are assigned to target pollution factors using a weighted method, with the sum of the weights being 1. Pollutant weights are determined based on their environmental impact and human health risk assessment results, and the weighting standard is based on environmental risk assessment reports and historical emission data issued by national environmental protection departments. For example, the CO weight is set to 0.4, the NOx weight is 0.35, the particulate matter PM2.5 weight is 0.15, and the SO2 weight is 0.1. The weight allocation process uses a weighted summation calculation to multiply the current concentration of each target pollution factor by the corresponding weight to calculate the weighted pollution index. The weighted pollution index calculation formula is: Index = ∑ (pollutant concentration × weight). The pollutant concentration value uses real-time measurement data, and the weight value is pre-set and solidified in the system database. The result is output in numerical form as input for pollution level classification.

[0062] Step S153: performing pollution level classification according to the weighted pollution index data to obtain first pollution level data and second pollution level data.

[0063] In this embodiment, pollution levels are classified based on weighted pollution index data. First, pollution level threshold standards are set, and the weighted pollution index is divided into three intervals: a weighted index ≥ 0.7 is defined as the first pollution level, indicating a high pollutant concentration; a weighted index between 0.4 and 0.7 is defined as the second pollution level, indicating a medium pollutant concentration; and a weighted index below 0.4 is defined as a low pollution level and is not processed. During the classification process, the system reads the weighted pollution index in real time, calculating and comparing the threshold intervals hour by hour. Time series statistics are performed on the index data for different time periods to ensure the stability and accuracy of the pollution level classification results. The pollution level classification results are transmitted to the exhaust gas purification simulation module in the form of a digital signal, which is used to initiate the corresponding processing flow. The specific operations of this step include reading the weighted pollution index value, applying the preset threshold conditions for judgment, and formatting the result into a standard pollution level code, such as "1" for the first level and "2" for the second level, for subsequent system processing.

[0064] Preferably, performing multi-stage exhaust gas purification simulation according to the first pollution level data in step S2 includes:

[0065] Performing a purification process determination based on the first pollution level data, thereby generating a multi-stage purification process start instruction;

[0066] In this embodiment, the system receives the first pollution level data obtained through pollution level classification. The data uses digital coding to represent the pollution level, ranging from 1 to 3 levels, where "1" represents a high pollution level. Based on this data, a preset control logic is used to determine the level of the purification process to be started. The specific judgment rules are: if the first pollution level data is equal to 1, the three-level purification process is started; if it is equal to 2, the two-level purification process is started; if it is equal to 3, the first-level purification process is started. This judgment logic is implemented by the judgment module built into the programmable logic controller (PLC), and the judgment result is converted into a corresponding start instruction code as an output signal. The start instruction is sent to the exhaust gas treatment equipment control unit in the form of a standard digital signal. The instruction includes the start and stop signal of the corresponding purification module and the operating parameter setting, such as the operating air volume is set to 5000 cubic meters per hour, the wind speed is set to 5 meters per second, and the temperature is maintained above 150°C to ensure the normal operation of subsequent modules.

[0067] The bag dust removal module is activated based on the multi-stage purification process start instruction to obtain the primary particulate matter purification data;

[0068] In this embodiment, the bag dust removal module is activated based on the multi-stage purification process start-up instruction to obtain primary particulate matter purification data. This step triggers the electric control valve of the bag dust collector to open and starts the pulse spray system by receiving the multi-stage purification process start-up instruction. The operating parameters of the bag dust removal module include filtration air volume (4000-6000 cubic meters / hour), filtration speed (1.5-2.5 meters / minute), spray pressure (0.4-0.6 MPa) and spray cycle (30-60 seconds). A differential pressure sensor is used to monitor the pressure difference before and after the bag filter area in real time. The pressure difference range is controlled within 250-500 Pascals. If it exceeds this range, the spray cleaning program is automatically triggered. During the operation of the bag dust removal module, a laser particle counter and a light scattering sensor are used to measure the concentration of particulate matter in the inlet and outlet flue gas, and data such as particle size distribution and particulate matter mass concentration are collected and uploaded through the data acquisition system to form primary particulate matter purification data. The data is expressed in mg / m 3 The unit is , and the time-sharing sampling period is 10 seconds. The value is used for the subsequent working condition adjustment of the desulfurization and denitrification modules.

[0069] Based on the primary particulate matter purification data, the desulfurization and denitrification modules are started to obtain gaseous pollutant purification data;

[0070] In this embodiment, the desulfurization and denitrification module is started based on the primary particulate matter purification data to obtain gaseous pollutant purification data. The desulfurization and denitrification module startup condition is that the particulate matter concentration in the primary particulate matter purification data is lower than 80mg / m 3 And the inlet SO2 concentration is higher than 60mg / m 3 The system automatically turns on the desulfurization spray tower and the selective catalytic reduction (SCR) device. In the desulfurization spray tower, the pH value of the spray liquid is controlled at 7.0-8.0, the spray liquid flow rate is 2-5 liters / minute, and the spray pressure is maintained at 0.2-0.5 MPa. The operating temperature of the SCR device is controlled at 320℃-400℃, and the ammonia dosage is adjusted in real time according to the NOx concentration, usually 5-20 mg / m 3 The concentration of gaseous pollutants is detected in real time by the Continuous Emission Monitoring System (CEMS), including the concentrations of SO2, NOx, NH3, etc., with a detection frequency of once per second. The collected gaseous pollutant concentration data is expressed in ppm or mg / m 3 The data is measured and transmitted to the control system through the collection device to generate gaseous pollutant purification data for reference by the catalytic oxidation module.

[0071] The catalytic oxidation module is started according to the gaseous pollutant purification data to obtain the organic matter treatment data;

[0072] In this embodiment, the catalytic oxidation module has a VOC concentration of more than 20 mg / m 3 The reactor temperature is maintained at 300-450°C during operation. The catalyst type is platinum-palladium based, and the catalyst packing density is 0.5-0.8 g / cm 3 The gas residence time in the reactor is set to 0.5-1 second, and the air volume is controlled at 4000 cubic meters per hour. When the module is started, the control unit sends a power-on signal to the electric heating device, and controls the temperature sensor feedback to adjust the heating power. The organic matter treatment data is detected by gas chromatograph to detect the change in VOC concentration before and after the reaction, and the data sampling cycle is once every 5 minutes. The measurement data is expressed as a percentage of concentration reduction, recorded and uploaded to the data management system for subsequent full-process exhaust gas output monitoring.

[0073] The whole process tail gas output is monitored based on the organic matter treatment data to obtain multi-stage tail gas purification data.

[0074] In this embodiment, the full-process exhaust output monitoring collects multiple indicators such as CO, NOx, SO2, particulate matter, etc. at the exhaust end through a multi-point flue gas sampling system, and uses electrochemical sensors and optical scattering sensors. The monitoring data is collected once per second. The exhaust temperature is kept monitored at 120℃-180℃, the flue gas flow rate measurement range is 0-20 m / s, and the flue gas flow rate is measured by a vortex flowmeter. The collected various exhaust indicators are transmitted to the control center in real time, and the data is recorded using a time series database. Based on the comparison of the concentration of each exhaust component and the emission limit, a multi-stage exhaust purification data report is formed. The report format includes fields such as timestamp, pollutant name, concentration value, and treatment process status. This data is used for feedback control of the overall exhaust treatment process and subsequent equipment maintenance decisions.

[0075] Preferably, the detection of high-energy consumption exhaust gas treatment equipment according to the multi-stage exhaust gas purification data in step S2 includes:

[0076] Statistical analysis of smoke filtration equipment power based on multi-stage exhaust purification data;

[0077] In this embodiment, the real-time current, voltage and operating time data of each smoke filter device in the multi-stage exhaust purification system are collected, and the power consumption of each device is monitored in real time using power monitoring instruments. (Where U is voltage, I is current, The actual power consumption of each soot filtration device is calculated using the power factor (value range: 0.85-0.95). Data is collected once per second, and hourly and daily power consumption is accumulated. During the statistical process, data is corrected based on equipment operating parameters (such as filtration air volume and inlet and outlet pressure differential) to eliminate power outliers during abnormal startup or maintenance periods, ensuring the accuracy of the statistical results. Finally, a table of power data for each device, expressed in kilowatts (kW), is generated, serving as the basis for subsequent equipment energy consumption analysis.

[0078] Identify high-power soot filter equipment based on the power of the soot filter equipment and obtain data of the high-power soot filter equipment;

[0079] In this embodiment, a high power threshold is set at 85% or higher of the device's rated power, based on the collected power data for the dust filter equipment. This threshold is then compared to the rated power threshold, screening out devices whose power consistently exceeds this threshold and whose operating time exceeds 30 minutes. This screening is performed automatically by the PLC's decision logic, which outputs a list of high-power devices, including device number, power value, and operating time. The system generates a high-power dust filter equipment data report based on the screening results, providing a target device range for subsequent bag blockage detection.

[0080] Carry out bag clogging detection based on the data of high-power smoke filter equipment to obtain bag clogging data;

[0081] In this embodiment, for the identified high-power equipment, a differential pressure transmitter is used to measure the flue gas pressure difference before and after the equipment filter bag, and the differential pressure range is set to 50-500 Pa. The filter bag blockage status judgment rule is: when the differential pressure exceeds 400 Pa continuously and is maintained for more than 10 minutes, it is determined to be a bag blockage. The differential pressure value is recorded in real time by the data acquisition system, and the sampling frequency of the differential pressure sensor is 1 second / time. Combined with the flue gas flow data and temperature and humidity parameters, the interference of environmental factors is eliminated, and the filter bag blockage position and blockage severity are accurately located. Generate bag blockage data, including the blockage position number, blockage degree level (mild, moderate, severe) and timestamp.

[0082] Perform dust adhesion analysis based on bag blockage data to obtain dust adhesion data;

[0083] In this example, dust samples collected from a clogged area of ​​the filter bag were tested for physical parameters using a surface tension tester and a dust adhesion measurement device. Test parameters included adhesion strength, measured over a range of 0.1 to 10 Newtons. The test environment temperature was controlled at 25°C ± 2°C, and the humidity was controlled at 50% ± 5%. The static contact angle method was used to measure the contact angle between the dust and the filter material surface, with values ​​ranging from 30° to 120°, reflecting the dust adhesion properties. During the test, data on the change in adhesion over time was collected to generate a dust adhesion time series curve. Data analysis software was used to calculate the average adhesion force and its standard deviation, generating a dust adhesion data report.

[0084] Perform filter material micro-abnormal detection based on dust adhesion data to obtain filter material micropore abnormality data;

[0085] In this embodiment, a high-resolution scanning electron microscope (SEM) is used to image the microporous structure of the filter bag filter material with a resolution of nanometers to detect changes in the size, shape and porosity of the micropores on the surface of the filter material. By comparing the microporous structure parameters of normal filter material (average pore diameter 3-5 microns, porosity 45%-55%), abnormal microporous areas are detected, and abnormal conditions such as blockage, deformation and damage are identified. An image processing algorithm is used to quantitatively analyze the area ratio of abnormal areas, and an abnormal area exceeding 5% is defined as a filter material micropore abnormality. Filter material micropore abnormality data is formed, including the type of abnormality, the degree of abnormality and the corresponding position coordinates.

[0086] Perform filter bag aging evaluation based on filter material micropore abnormality data to obtain filter bag aging data;

[0087] In this embodiment, the evaluation is performed using the filter bag aging evaluation standard based on the type and degree of abnormal micropores in the filter material, combined with the use time and operating conditions of the filter bag. The evaluation indicators include the micropore blockage rate, the percentage of porosity reduction, and the filter material strength loss ratio (measured by tensile test, the standard tensile strength is 10MPa, and it is judged to be obviously aged when it drops to below 7MPa after aging). The data acquisition system records the aging parameters, conducts a comprehensive analysis based on the equipment maintenance records and operating time, and outputs the filter bag aging level, which is divided into mild (0-20% aging), moderate (20-50% aging), and severe (more than 50% aging). A filter bag aging data report is generated, including the aging level, aging area, and recommended replacement time.

[0088] Based on the filter bag aging data, a high energy consumption contribution analysis is performed on the power of the smoke filtration equipment to obtain high energy consumption contribution data, and high energy consumption exhaust gas treatment equipment is determined based on the high energy consumption contribution data to obtain high energy consumption exhaust gas treatment equipment data.

[0089] In this embodiment, the effect of the increase in pressure difference caused by the aging of the filter bags on the power consumption of the equipment is calculated using the power-pressure difference relationship formula ΔP increase = K×ΔP×Q, where ΔP is the pressure difference change (unit: Pa), Q is the filtered air volume (cubic meters per second), and K is the equipment coefficient (value 0.7-1.0). Combined with the actual operating data, the additional power consumption caused by the aging of the filter bags is separated. The high energy consumption contribution value is compared with the total power of the equipment, and the equipment that accounts for more than 20% is determined to be a high-energy consumption exhaust gas treatment equipment. The judgment result includes the equipment number, high energy consumption contribution percentage, operating parameters and aging indicators, etc., which serve as the basis for subsequent fan fault diagnosis and maintenance plan, and generate a high-energy consumption exhaust gas treatment equipment data report.

[0090] Preferably, the fan fault diagnosis based on the high-energy-consuming exhaust gas treatment equipment data in step S2 includes:

[0091] Conduct bearing lubricant oil contamination detection based on data from high-energy-consuming exhaust gas treatment equipment to obtain bearing lubricant oil contamination data;

[0092] In this embodiment, a lubricating oil sampling device is installed at the bearing of the high-energy exhaust treatment equipment using a field acquisition system to automatically extract lubricating oil samples at regular intervals, and the sampling frequency is set to once every 12 hours. The collected lubricating oil samples are sent to a spectrometer for elemental composition detection, and infrared spectroscopy (FTIR) and Raman spectroscopy are used to analyze the content of water, metal particles, oxides and other pollutants in the lubricating oil. The moisture content threshold is set to 0.1% (mass ratio), and the metal particle concentration threshold is set to 10ppm (parts per million by weight). The data obtained by the analytical instrument is compared with the threshold, and the part exceeding the threshold is determined to be lubricating oil contamination. The collection process is carried out strictly in accordance with the lubricating oil sampling standards specified by the equipment manufacturer to ensure that the sampling location is accurate and the pollutant concentration data is representative. Finally, the bearing lubricating oil contamination data is output, and the data content includes the type of pollutant, concentration and timestamp.

[0093] Perform metal particle identification based on bearing lubricant oil contamination data to obtain metal particle data;

[0094] In this embodiment, the lubricating oil sample is processed by a magnetic separator, and a high-sensitivity magnetic detector is used to separate the ferrous and non-ferrous metal particles, and the detection particle size range is set to 0.5 microns to 100 microns. Subsequently, a scanning electron microscope (SEM) is used to perform morphological and composition analysis on the separated metal particles, with a resolution of nanometers, focusing on identifying the shape of the particles (such as particles, flakes, fibers) and elemental composition (Fe, Cu, Al, Cr, etc.). The particle morphology data is matched with the working state of the bearing, and the particle size distribution and number density are statistically analyzed to determine the activity and wear type of the metal particles. The data acquisition frequency is synchronized with the lubricating oil sampling, and the analysis results are output in the form of particle concentration (in units of particles / ml of lubricating oil) and particle size distribution diagram to form metal particle data.

[0095] Perform impeller wear assessment based on metal particle data to obtain impeller wear data;

[0096] In this embodiment, the elemental composition and morphological characteristics of the particles in the metal particle data are used in combination with the historical impeller material composition and wear mechanism database to determine the type of wear (such as pitting, abrasive wear, fatigue spalling, etc.). The wear severity threshold is set to a particle concentration of more than 5000 particles / ml and an iron element ratio of more than 70% as a high wear warning. The trend of wear aggravation is evaluated by calculating the rate of change of particle concentration per unit time. A comprehensive analysis is performed in combination with the impeller operating parameters (speed, load, temperature) to form an impeller wear degree grade (mild, moderate, severe) and an estimate of the wear location. Output impeller wear data, including wear grade, expected wear location and historical trend data.

[0097] Perform blade bending measurement based on impeller wear data to obtain blade bending data;

[0098] In this embodiment, a laser scanning measurement system is used to perform three-dimensional surface scanning of the impeller blades with a measurement accuracy of ±0.01 mm. By comparing with the reference blade CAD model, the deformation of the blade on each coordinate axis is calculated, with a focus on detecting the blade curvature. The blade curvature threshold is set to 0.1 mm exceeding the design tolerance and is considered abnormal. The measurement data is converted into bending angle and maximum deformation distance, and the deformation trend is analyzed in combination with the blade operating speed and load data. The measurement process must be carried out with the equipment stopped to ensure measurement stability, and a blade bending data report is output, including the blade number, maximum bending angle, deformation location, and measurement time.

[0099] Perform rotational imbalance analysis based on blade bending data to obtain rotational imbalance data;

[0100] In this embodiment, a high-precision three-axis acceleration sensor installed at the fan bearing is used to collect vibration signals. The sampling frequency is set to 5kHz, and time domain and frequency domain analysis is performed. The vibration amplitude corresponding to the rotation frequency is identified by fast Fourier transform (FFT). If the vibration amplitude exceeds the equipment operation standard limit of 0.5g (gravitational acceleration), it is determined that there is rotational imbalance. Combined with the blade bending data, the vibration characteristics and their correlation with blade deformation are analyzed, and the unbalanced mass and unbalanced torque are calculated. The rotational imbalance data is output, including the imbalance amplitude, imbalance phase angle, frequency spectrum and the quantitative relationship of the corresponding blade deformation.

[0101] Fan fault diagnosis is performed based on the rotation imbalance data to obtain fan fault data.

[0102] In this embodiment, the vibration analysis results and impeller wear and blade bending data are combined to identify faults based on preset fault thresholds. Fault classification criteria include bearing abnormalities (vibration frequency deviation ±5%), impeller wear (particle concentration exceeds the threshold), blade bending (deformation exceeds 0.1 mm), and rotational imbalance (vibration exceeds 0.5g). A fault report is automatically generated through a fault diagnosis algorithm, and the report content covers the fault type, severity level, fault start time, and warning level. The fan fault data is output, including the diagnostic conclusion, fault time node, and corresponding parameter values, to provide a basis for subsequent maintenance.

[0103] Preferably, step S3 includes the following steps:

[0104] Step S31: uploading the second pollution level data to the medium-intensity exhaust gas purification simulation platform;

[0105] In this embodiment, the second pollution level data is imported into the data interface of the exhaust gas purification simulation platform in a structured format (such as JSON or XML). The uploaded data includes the pollutant type, concentration value and timestamp. The data collection interval is 1 minute, and the pollutant concentration accuracy reaches 0.01mg / m 3 To ensure data integrity and accuracy, the data verification module verifies whether the format and range of the uploaded data meet the platform requirements. After the upload is completed, the data is archived and stored in the platform database for subsequent simulation calls.

[0106] Step S32: setting the purifier dosage range to 0.1 kg / min-1.0 kg / min and the reaction temperature adjustment range of the reactor catalyst activity to 200° C.-400° C.;

[0107] In this embodiment, the purifier dosage range is set to 0.1kg / min to 1.0kg / min, and the reaction temperature adjustment range of the reactor catalyst activity is 200℃ to 400℃. According to the exhaust gas composition and concentration characteristics, the acceleration rate of the automatic purifier dosing system is configured, and the dosage is monitored in real time using a flow meter to ensure that the dosage error is controlled within ±0.01kg / min. A thermocouple array is set in the reactor to monitor the catalyst activity reaction temperature in real time. The temperature controller adjusts the reactor heating system with a temperature adjustment accuracy of ±2℃. The above parameters are input through the control interface, and the parameter range is locked to prevent misoperation.

[0108] Step S33: setting the exhaust gas flow field simulation refresh period to 1s-5s and the purification reaction kinetics calculation time step to 0.1s-1s;

[0109] In this embodiment, the exhaust flow field simulation refresh cycle is set to 1 second to 5 seconds, and the purification reaction kinetics calculation time step is set to 0.1 second to 1 second. The simulation platform collects input exhaust parameters in real time and updates the flow field state according to the set refresh cycle to ensure that the simulation data reflects the latest operating conditions. The dynamic calculation uses the explicit time integration method, and the calculation time step is set to 0.1 second to ensure numerical stability, and the maximum does not exceed 1 second to avoid a decrease in simulation accuracy. The setting of the time step and refresh cycle is executed through the simulation system parameter configuration module and stored in the system configuration file.

[0110] Step S34: running the flow field simulation module in the exhaust gas purification simulation platform to obtain medium-intensity exhaust gas purification data;

[0111] In this embodiment, the flow field simulation module in the exhaust gas purification simulation platform is run, and the exhaust gas flow and reaction process are simulated using numerical calculation methods. The finite volume method is used to discretize the three-dimensional flow field of the exhaust gas in the reactor, and the number of computational grids is approximately 500,000 to 1 million units. The boundary conditions are set according to the actual equipment inlet and outlet parameters, and the inlet exhaust gas temperature, pressure, flow rate and pollutant concentration data are derived from real-time monitoring. During the simulation process, the purifier concentration distribution, temperature field and reactant concentration are dynamically calculated, and the purification efficiency, pollutant distribution and reaction temperature data are output to form medium-intensity exhaust gas purification data.

[0112] Step S35: performing dust removal efficiency analysis based on the medium-intensity exhaust gas purification data to obtain dust removal efficiency data;

[0113] In this example, dust removal efficiency analysis was performed based on medium-intensity exhaust gas purification data. The dust removal efficiency was calculated based on the rate of change in the captured particulate matter concentration. A particle size distribution analyzer was used to measure the particle size and number before and after purification. The particle size range covered 0.1 microns to 10 microns, with a sampling frequency of once per minute. The dust removal efficiency was calculated using the formula E = (C_in - C_out) / C_in × 100%, where C_in is the particle concentration before purification and C_out is the particle concentration after purification. The analysis results include dust removal efficiency for different particle size segments, and the data is recorded in real time to generate dust removal efficiency data.

[0114] Step S36: Perform filter element clogging detection based on the dust removal efficiency data to obtain filter element clogging data.

[0115] In this embodiment, filter clogging is detected based on dust removal efficiency data. The degree of filter clogging is obtained by measuring the pressure difference between the inlet and outlet of the smoke channel using a pressure differential sensor. The pressure difference threshold is set to 1200Pa. If this value is exceeded, the filter is considered clogged. The filter performance degradation rate is analyzed in combination with the dust removal efficiency data trend, and the efficiency drop critical point is identified using differential analysis. The filter clogging data includes the current pressure differential value, clogging level (mild, moderate, severe), filter usage time, and historical clogging records. The data is uploaded to the control system in real time to support filter replacement and maintenance decisions.

[0116] Preferably, step S35 includes the following steps:

[0117] Step S351: collecting smoke and dust based on the medium-intensity exhaust gas purification data to obtain smoke and dust data;

[0118] In this embodiment, a high-precision smoke sampler is used to install a sampling point in the outlet flue of the exhaust purification device. The sampling frequency is set to automatically collect samples every 1 minute, and the sampling time lasts for no less than 10 seconds to ensure that the sampling volume is stable and representative. The sampler uses a filter membrane to capture smoke. The pore size of the filter membrane is 0.3 microns to ensure the effective capture of fine particulate matter. After the sampling is completed, the mass difference before and after the filter membrane is measured by weight to calculate the mass concentration of smoke in the exhaust gas per unit volume. During the data collection process, the exhaust gas temperature, humidity and flow rate are recorded in real time, and the sampling volume is corrected to ensure data accuracy. The smoke data includes sampling time, sampling volume, smoke mass concentration and related environmental parameters.

[0119] Step S352: performing particle size analysis based on the smoke data to obtain smoke particle size data;

[0120] In this embodiment, a laser particle size analyzer (e.g., a laser scattering particle size analyzer) is used to measure the particle size distribution of the collected smoke samples. The measurement range covers 0.1 microns to 10 microns, and the particle size distribution is divided into 20 particle size segments. The samples are dried and the humidity is controlled below 5% to prevent moisture from affecting the measurement. The laser particle size analyzer is configured with a light source wavelength of 650 nm. The measurement is repeated at least three times, and the results are averaged. During the analysis, the volume distribution and number distribution of each particle size segment are recorded. The particle size data are mapped one-to-one with the sampling time and stored in a database, which serves as the basis for subsequent morphological classification.

[0121] Step S353: performing morphological classification based on the smoke particle size data to obtain fibrous smoke data and spherical smoke data;

[0122] In this embodiment, based on the particle size data and morphological feature database, particles with a particle size ranging from 0.1 micron to 1 micron and a large aspect ratio (aspect ratio ≥ 3) are defined as fibrous smoke, and particles with a particle size ranging from 0.1 micron to 10 microns and an aspect ratio close to 1 (0.9 to 1.1) are defined as spherical smoke. A scanning electron microscope (SEM) is used to verify the morphology of typical samples, with the scanning magnification set to 2000 to 5000 times and a resolution of 10 nanometers to identify the particle morphology and match it with the particle size data. According to the morphological feature database and the particle size threshold, the smoke data is classified and counted to obtain fibrous smoke data and spherical smoke data, including the number of particles, mass concentration and size distribution.

[0123] Step S354: performing a fiber winding test based on the fibrous smoke data to obtain fiber winding data; evaluating a pressure drop change based on the fiber winding data to obtain pressure drop change data; and calculating a fibrous smoke removal rate based on the pressure drop change data.

[0124] In this embodiment, a high-resolution optical microscope combined with image analysis software was used to scan the collected fiber samples from the filter element cross section. The magnification was set at 1000 times and the image resolution was 2048×2048 pixels. An image processing algorithm was used to identify the number and density of fiber intertwining points and calculate the entanglement index. The entanglement index range was defined as 0 to 1, with 0 indicating no entanglement and 1 indicating complete entanglement. Combined with the entanglement index, a differential pressure sensor was used to measure the pressure difference on both sides of the filter element. The sensor accuracy was ±1Pa. A linear correspondence model was established between the pressure difference change and the entanglement index to obtain pressure drop change data. Based on the pressure drop change data, the fibrous smoke removal rate was calculated using the formula E_f = 1-(ΔP_measured ÷ ΔP_initial), where ΔP_measured is the current pressure drop and ΔP_initial is the pressure drop of an unblocked filter element. The removal rate is expressed as a percentage.

[0125] Step S355: Calculating the settling velocity based on the spherical smoke data; determining the deposition location based on the settling velocity; calculating the particle retention time based on the deposition location; and calculating the spherical smoke removal rate based on the particle retention time.

[0126] In this embodiment, the settling velocity of spherical particles is calculated according to Stokes' law, using the formula v = (2 / 9) × (ρ_p - ρ_f) × g × d^2 ÷ μ, where ρ_p is the particle density (taken as 2500 kg / m 3 ), ρ_f is the exhaust gas density (1.2kg / m 3 ), g is the acceleration due to gravity 9.81m / s 2 , d is the particle diameter, μ is the tail gas dynamic viscosity (1.8×10^-5Pa·s). The sedimentation rate of spherical smoke dust of different particle sizes is calculated, and the unit of sedimentation rate is m / s. According to the sedimentation rate, combined with the geometric parameters of the tail gas pipeline, the particle deposition position is determined, and the deposition position is expressed as the pipeline length position coordinate. Using the particle deposition position, the retention time of the particles in the deposition area is calculated. The retention time is calculated based on the air flow velocity (2m / s) and the distance from the deposition position. Through the particle retention time, the particle removal efficiency formula E_s=1-exp(-k×t), where k is the removal rate constant and t is the retention time, is used to obtain the spherical smoke dust removal rate.

[0127] Step S356: Integrate the fibrous dust removal rate and the spherical dust removal rate to obtain dust removal efficiency data.

[0128] In this embodiment, the fibrous dust removal rate E_f and the spherical dust removal rate E_s are weighted averaged according to their respective dust mass concentration weights. The calculation formula is E_total = (C_f × E_f + C_s × E_s) ÷ (C_f + C_s), where C_f and C_s are the mass concentrations of fibrous and spherical dust, respectively. The calculation process utilizes real-time collected mass concentration data, and the weight ratio is dynamically adjusted. The final dust removal efficiency data is output as a percentage, including a timestamp and the corresponding efficiency value, which is used for subsequent filter blockage detection and exhaust gas treatment control.

[0129] Preferably, step S36 includes the following steps:

[0130] Step S361: determining a low dust removal efficiency period according to the dust removal efficiency data;

[0131] In this embodiment, real-time or historical dust removal efficiency data is obtained, and the data format includes a timestamp and the corresponding dust removal efficiency percentage, and the data sampling frequency is set to once per minute. Using the set low efficiency threshold of 30%, and taking this threshold as the judgment standard, when the dust removal efficiency is lower than 30%, the corresponding time period is recorded as a low dust removal efficiency period. This time period is considered valid only if it lasts for at least 5 minutes to avoid misjudgment of instantaneous fluctuations. The dust removal efficiency data is processed by sliding window averaging, and the window size is set to 3 minutes to smooth abnormal fluctuations. The start and end time of the low efficiency period is determined based on the sliding average. The low dust removal efficiency period data structure contains the start time, end time and the minimum dust removal efficiency value, which is used for targeted analysis in subsequent steps.

[0132] Step S362: detecting the pressure difference of the dust channel based on the low dust removal efficiency period;

[0133] In this embodiment, a differential pressure sensor is installed on the flue on both sides of the inlet and outlet of the smoke filter element. The sensor model should comply with industrial standards, with a measurement range of 0-5000 Pascal and an accuracy of ±1%. The sensor collects differential pressure data in real time, and the sampling frequency is set to once every 10 seconds to ensure that the trend of differential pressure changes is captured. During the identified period of low dust removal efficiency, the differential pressure data within the corresponding time period is collected. The ambient temperature and exhaust gas flow rate parameters are synchronously recorded by the data acquisition system, and the parameters are used to correct the differential pressure data. When the differential pressure value is greater than the preset normal operating upper limit of 500 Pascal, it is determined that there is an abnormal load in the smoke channel. The differential pressure data storage format includes a timestamp, a differential pressure value, and a corrected differential pressure value, which provides data support for subsequent load pressure calculations.

[0134] Step S363: determining the filter channel load pressure according to the smoke channel pressure difference;

[0135] In this embodiment, the load pressure of each filter channel is calculated by the pressure distribution model based on the smoke channel pressure difference data. The filter element structural parameters are preset to a total of 100 filter elements, a single filter channel cross-sectional area of ​​0.005 square meters, and an overall filter area of ​​0.5 square meters. The load pressure calculation formula is P_load = ΔP / N, where ΔP is the collected pressure difference value, N is the number of filter elements, and P_load is the load pressure of a single filter channel. During the calculation process, the pressure distribution is corrected in combination with the smoke concentration and flow rate, and the exhaust gas flow rate data (normal working range 2-5 meters / second) is used for pressure correction. The unit of the filter channel load pressure is Pascal, and the data is recorded in the form of a time series to ensure dynamic monitoring of filter element load changes.

[0136] Step S364: Identify the high-pressure area of ​​the filter element according to the filter channel load pressure;

[0137] In this embodiment, the load pressure of all filter channels is monitored in real time, and the high-pressure judgment threshold is set to 100 Pascals. Filter channels with a load pressure exceeding 100 Pascals are marked as high-pressure areas. A distributed pressure sensing network is used to transmit the pressure data of each filter channel to a centralized control unit for parallel calculation. The spatial position of the high-pressure area is identified by comparing the load pressure distribution of all filter channels. The spatial position is represented by the filter element number and its coordinates in the filter element array. The data structure contains the filter element number, load pressure value and coordinate information. The high-pressure area status is refreshed every 10 seconds, and the high-pressure area list is dynamically updated to provide real-time blockage risk prompts.

[0138] Step S365: Perform filter element clogging detection based on the high-pressure area of ​​the filter element to obtain filter element clogging data.

[0139] In this embodiment, a blockage detection is implemented on the filter element in the high-pressure area, and the blockage situation is evaluated by combining the differential pressure method and the impedance method. The differential pressure method uses the difference in pressure between the inlet and outlet of the filter element. The blockage judgment threshold is set to 150 Pascals. If the threshold is exceeded, the filter element is judged to be blocked. The impedance method detects particle blockage through the change of filter element resistance. The surface resistance value of the filter element is measured using a resistance sensor. The normal resistance range is 10-50 ohms, and it is judged to be blocked if it exceeds 70 ohms. The filter element blockage data includes the blocked filter element number, blockage level (mild, moderate, severe, distinguished by pressure and resistance thresholds), blockage time and location coordinates. The blockage detection results are uploaded to the control system in real time to guide the filter element cleaning or replacement operation.

[0140] Particularly importantly, step S365 includes:

[0141] The filter element pressure difference is measured based on the high pressure area of ​​the filter element to obtain the filter element pressure difference data;

[0142] In this embodiment, the differential pressure sensors distributed at both ends of the filter element are first used to collect the differential pressure. A capacitive micro differential pressure sensor (for example, a range of ±500Pa, a sensitivity of 0.1Pa) is selected, and its acquisition frequency is set to 1Hz, and the acquisition time is set to 60 consecutive seconds. The differential pressure value is collected by the sensor and transmitted to the data acquisition module, and an analog-to-digital converter with an A / D conversion accuracy of 12 bits is used to complete the conversion from analog to digital. The data transmission path is ensured by the CAN bus for communication stability. In order to improve data stability, a Savitzky–Golay filter is introduced into the system to suppress noise on the original differential pressure data, and the sliding window is set to 11 and the order is 3. The final output differential pressure data is saved in the form of a time series and forms a "filter element differential pressure data array", with data units in Pa and time units in seconds.

[0143] Calculate the pressure loss change of the filtration channel based on the filter element pressure difference data to obtain the pressure loss change data;

[0144] In this embodiment, after obtaining the filter element pressure difference time series data, the first-order difference method is used to calculate the rate of change of adjacent pressure difference values. The pressure difference change rate per second is expressed as ΔP(t) = P(t) - P(t-1). If the average change rate is greater than 5Pa / s within 10 consecutive seconds, it is determined to be a "pressure loss mutation." At the same time, multi-point linear regression is introduced to perform slope fitting on the pressure difference curve trend. The fitting window is set to 10 seconds to determine whether the fitting slope continues to rise by more than 0.3Pa / s. 2 The pressure loss change data is finally output in three forms: sliding window mean, slope value and differential volatility, with units of Pa / s and Pa / s 2 , which is used for subsequent determination of particle deposition trends.

[0145] Perform particle deposition trend analysis based on pressure loss change data to obtain deposition trend data;

[0146] In this embodiment, during the particle deposition trend analysis process, the continuous rising section in the pressure loss change is first extracted, and the peak growth amplitude and frequency are calculated using the envelope analysis method. The envelope calculation uses the Hilbert transform method to construct the instantaneous amplitude sequence. The goal of trend analysis is to identify the timing and position characteristics of particle deposition. Therefore, the pressure difference growth section is mapped to the filter element spatial area in combination with the spatial index of the filter element area. The deposition factor α is introduced = (pressure difference rise rate × rise duration) / filter element cross-sectional area (unit: Pa·s / cm 2 ) is normalized and calculated. When α exceeds 2.5 Pa·s / cm 2 The output results are in the form of a two-dimensional table indicating the sedimentation trend level (high, medium, low) and time stamp of each spatial segment.

[0147] Identify the filter element blockage characteristics based on the deposition trend data to obtain blockage characteristic data;

[0148] In this embodiment, based on the classification of deposition trend levels and combined with the spatial structure of the filter element (divided into 10 sections in the length direction, and each section is further divided into 3 layers), a blocking characteristic index matrix is ​​established in each area. The blocking characteristic indicators include: the maximum pressure difference rise value, rise slope, duration, and pressure difference fall time difference in the area. Each indicator is described in a multi-dimensional manner in the form of a matrix, and the comprehensive blocking factor β is calculated, which is defined as: β = max(ΔP)SlopeT_up / T_down, where T_up is the duration of the pressure difference rise and T_down is the time taken to fall back. A threshold value β≥250 (based on experimental calibration data) is set as the severe blockage identification standard. The output results include the β value and status identification (normal / slightly blocked / severely blocked) of each area.

[0149] Perform congestion degree clustering identification based on congestion feature data to obtain regional congestion degree data;

[0150] In this embodiment, the blockage degree clustering is performed using the K-Means unsupervised clustering algorithm. The blockage feature β values ​​of all regions are organized into a one-dimensional array and input into the clustering model. The number of clusters is set to k = 3, representing three categories: mild blockage, moderate blockage, and severe blockage. Before clustering, the β values ​​are normalized using Min-Max, and the mapping range is [0,1]. The upper limit of the number of iterations is set to 100, and the clustering convergence criterion is that the change in the center point is less than 0.001. After clustering is completed, each region is assigned to a specific category, and a regional blockage degree data table is formed, which includes: filter segment number, category number, category centroid value, and distance metric. The data is saved in a JSON structure for the visualization module to call.

[0151] The blockage status of the entire filter element is quantitatively evaluated based on the regional blockage degree data to obtain the filter element blockage data.

[0152] In this embodiment, the quantitative evaluation of the whole core state adopts the weighted accumulation method, and different weights are assigned according to each blockage category: the weight of mild blockage is 0.2, the weight of moderate blockage is 0.6, and the weight of severe blockage is 1.0. The whole core blockage index γ = ∑(W_i×N_i) / N_total, where W_i is the weight of the i-th category, N_i is the number of areas of this category, and N_total is the total number of areas. When γ≥0.65, it is judged as "critical state of overall blockage", and when γ≥0.85, it is judged as "overall serious blockage". The output filter element blockage data includes: the whole core blockage index γ, the blockage level identification (normal / warning / serious), the list of blocked areas and their corresponding weight values. The data results are used to trigger the redundant filter element switching or automatic dust cleaning control logic.

[0153] Preferably, this specification also provides an energy-saving and environmentally friendly exhaust gas treatment control system based on a cremator, which is used to execute the energy-saving and environmentally friendly exhaust gas treatment control method based on a cremator as described above. The energy-saving and environmentally friendly exhaust gas treatment control system based on a cremator includes:

[0154] The pollution level classification module is used to collect exhaust gas emitted by the crematorium and perform pollutant concentration detection to obtain pollutant concentration data; based on the pollutant concentration data, the pollution level is classified to obtain first pollution level data and second pollution level data;

[0155] A high-energy-consuming exhaust gas treatment equipment detection module is used to perform multi-stage exhaust gas purification simulation based on the first pollution level data to obtain multi-stage exhaust gas purification data; perform high-energy-consuming exhaust gas treatment equipment detection based on the multi-stage exhaust gas purification data to obtain high-energy-consuming exhaust gas treatment equipment data; and perform fan fault diagnosis based on the high-energy-consuming exhaust gas treatment equipment data to obtain fan fault data;

[0156] The dust removal efficiency analysis module is used to simulate medium-intensity exhaust gas purification based on the second pollution level data to obtain medium-intensity exhaust gas purification data; perform dust removal efficiency analysis based on the medium-intensity exhaust gas purification data to obtain dust removal efficiency data; and perform filter element blockage detection based on the dust removal efficiency data to obtain filter element blockage data;

[0157] The exhaust gas energy-saving and environmental protection control module is used to control the redundant fan start-stop strategy according to the fan fault data to obtain the redundant fan start-stop control data; dynamically adjust the exhaust gas treatment parameters according to the filter element blockage data to obtain the exhaust gas adjustment treatment parameters; and perform exhaust gas energy-saving and environmental protection control according to the exhaust gas adjustment treatment parameters and the redundant fan start-stop control data to obtain the exhaust gas treatment control data.

[0158] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0159] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. An energy-saving and environmentally friendly tail gas treatment control method based on a cremator, characterized in that: The following steps are involved: Step S1: collecting exhaust gas emitted by a crematorium and performing pollutant concentration detection to obtain pollutant concentration data; classifying the pollution levels based on the pollutant concentration data to obtain first pollution level data and second pollution level data; Step S2: performing a multi-stage exhaust gas purification simulation based on the first pollution level data to obtain multi-stage exhaust gas purification data; performing a high-energy consumption exhaust gas treatment equipment test based on the multi-stage exhaust gas purification data to obtain high-energy consumption exhaust gas treatment equipment data; performing a fan fault diagnosis based on the high-energy consumption exhaust gas treatment equipment data to obtain fan fault data; Step S3: performing a medium-intensity exhaust gas purification simulation based on the second pollution level data to obtain medium-intensity exhaust gas purification data; performing a dust removal efficiency analysis based on the medium-intensity exhaust gas purification data to obtain dust removal efficiency data; performing a filter element clogging test based on the dust removal efficiency data to obtain filter element clogging data; Step S4: Control the redundant fan start-stop strategy according to the fan fault data to obtain the redundant fan start-stop control data; dynamically adjust the exhaust gas treatment parameters according to the filter element blockage data to obtain the exhaust gas adjustment treatment parameters; perform exhaust gas energy-saving and environmental protection control according to the exhaust gas adjustment treatment parameters and the redundant fan start-stop control data to obtain the exhaust gas treatment control data.

2. The energy-saving and environmentally friendly tail gas treatment control method based on a crematorium according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting exhaust gas emitted by the crematorium to obtain exhaust gas sampling data; Step S12: performing pollutant pre-processing according to the exhaust gas sampling data to obtain purification pre-processing data; Step S13: Separating multiple groups of pollutants based on the purification pre-processing data to obtain pollutant separation data; Step S14: performing concentration detection based on the pollutant separation data to obtain pollutant concentration data; Step S15: performing pollution level classification based on the pollutant concentration data to obtain first pollution level data and second pollution level data.

3. The energy-saving and environmentally friendly tail gas treatment control method based on a crematorium according to claim 2 is characterized in that: Step S15 includes the following steps: Step S151: determining a target pollution factor based on pollutant concentration data; Step S152: performing pollutant weight assignment based on the target pollution factor to obtain weighted pollution index data; Step S153: performing pollution level classification according to the weighted pollution index data to obtain first pollution level data and second pollution level data.

4. The energy-saving and environmentally friendly tail gas treatment control method based on a crematorium according to claim 1 is characterized in that: The multi-stage exhaust gas purification simulation according to the first pollution level data in step S2 includes: Performing a purification process determination based on the first pollution level data, thereby generating a multi-stage purification process start instruction; The bag dust removal module is activated based on the multi-stage purification process start instruction to obtain the primary particulate matter purification data; Based on the primary particulate matter purification data, the desulfurization and denitrification modules are started to obtain gaseous pollutant purification data; The catalytic oxidation module is started according to the gaseous pollutant purification data to obtain the organic matter treatment data; The whole process tail gas output is monitored based on the organic matter treatment data to obtain multi-stage tail gas purification data.

5. The energy-saving and environmentally friendly tail gas treatment control method based on a crematorium according to claim 1 is characterized in that: In step S2, the high-energy consumption exhaust gas treatment equipment detection is performed based on the multi-stage exhaust gas purification data, including: Statistical analysis of smoke filtration equipment power based on multi-stage exhaust purification data; Identify high-power soot filter equipment based on the power of the soot filter equipment and obtain data of the high-power soot filter equipment; Carry out bag clogging detection based on the data of high-power smoke filter equipment to obtain bag clogging data; Perform dust adhesion analysis based on bag blockage data to obtain dust adhesion data; Perform filter material micro-abnormal detection based on dust adhesion data to obtain filter material micropore abnormality data; Perform filter bag aging evaluation based on filter material micropore abnormality data to obtain filter bag aging data; Based on the filter bag aging data, a high energy consumption contribution analysis is performed on the power of the smoke filtration equipment to obtain high energy consumption contribution data, and high energy consumption exhaust gas treatment equipment is determined based on the high energy consumption contribution data to obtain high energy consumption exhaust gas treatment equipment data.

6. The energy-saving and environmentally friendly tail gas treatment control method based on a crematorium according to claim 1 is characterized in that: In step S2, fan fault diagnosis based on the data of the high-energy-consuming exhaust gas treatment equipment includes: Conduct bearing lubricant oil contamination detection based on data from high-energy-consuming exhaust gas treatment equipment to obtain bearing lubricant oil contamination data; Perform metal particle identification based on bearing lubricant oil contamination data to obtain metal particle data; Perform impeller wear assessment based on metal particle data to obtain impeller wear data; Perform blade bending measurement based on impeller wear data to obtain blade bending data; Perform rotational imbalance analysis based on blade bending data to obtain rotational imbalance data; Fan fault diagnosis is performed based on the rotation imbalance data to obtain fan fault data.

7. The energy-saving and environmentally friendly tail gas treatment control method based on a crematorium according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: uploading the second pollution level data to the medium-intensity exhaust gas purification simulation platform; Step S32: setting the purifier dosage range to 0.1 kg / min-1.0 kg / min and the reaction temperature adjustment range of the reactor catalyst activity to 200° C.-400° C.; Step S33: setting the exhaust gas flow field simulation refresh period to 1s-5s and the purification reaction kinetics calculation time step to 0.1s-1s; Step S34: running the flow field simulation module in the exhaust gas purification simulation platform to obtain medium-intensity exhaust gas purification data; Step S35: performing dust removal efficiency analysis based on the medium-intensity exhaust gas purification data to obtain dust removal efficiency data; Step S36: Perform filter element clogging detection based on the dust removal efficiency data to obtain filter element clogging data.

8. The energy-saving and environmentally friendly tail gas treatment control method based on a crematorium according to claim 7 is characterized in that: Step S35 includes the following steps: Step S351: collecting smoke and dust based on the medium-intensity exhaust gas purification data to obtain smoke and dust data; Step S352: performing particle size analysis based on the smoke data to obtain smoke particle size data; Step S353: performing morphological classification based on the smoke particle size data to obtain fibrous smoke data and spherical smoke data; Step S354: performing a fiber winding test based on the fibrous smoke data to obtain fiber winding data; evaluating a pressure drop change based on the fiber winding data to obtain pressure drop change data; and calculating a fibrous smoke removal rate based on the pressure drop change data. Step S355: Calculating the settling velocity based on the spherical smoke data; determining the deposition location based on the settling velocity; calculating the particle retention time based on the deposition location; and calculating the spherical smoke removal rate based on the particle retention time. Step S356: Integrate the fibrous dust removal rate and the spherical dust removal rate to obtain dust removal efficiency data.

9. The energy-saving and environmentally friendly tail gas treatment control method based on a crematorium according to claim 7 is characterized in that: Step S36 includes the following steps: Step S361: determining a low dust removal efficiency period according to the dust removal efficiency data; Step S362: detecting the pressure difference of the dust channel based on the low dust removal efficiency period; Step S363: determining the filter channel load pressure according to the smoke channel pressure difference; Step S364: Identify the high-pressure area of ​​the filter element according to the filter channel load pressure; Step S365: Perform filter element clogging detection based on the high-pressure area of ​​the filter element to obtain filter element clogging data.

10. An energy-saving and environmentally friendly exhaust gas treatment control system based on a cremator, characterized in that: Used to execute the energy-saving and environmentally friendly exhaust gas treatment control method based on the crematorium according to claim 1, the energy-saving and environmentally friendly exhaust gas treatment control system based on the crematorium comprises: The pollution level classification module is used to collect exhaust gas emitted by the crematorium and perform pollutant concentration detection to obtain pollutant concentration data; based on the pollutant concentration data, the pollution level is classified to obtain first pollution level data and second pollution level data; A high-energy-consuming exhaust gas treatment equipment detection module is used to perform multi-stage exhaust gas purification simulation based on the first pollution level data to obtain multi-stage exhaust gas purification data; perform high-energy-consuming exhaust gas treatment equipment detection based on the multi-stage exhaust gas purification data to obtain high-energy-consuming exhaust gas treatment equipment data; and perform fan fault diagnosis based on the high-energy-consuming exhaust gas treatment equipment data to obtain fan fault data; The dust removal efficiency analysis module is used to simulate medium-intensity exhaust gas purification based on the second pollution level data to obtain medium-intensity exhaust gas purification data; perform dust removal efficiency analysis based on the medium-intensity exhaust gas purification data to obtain dust removal efficiency data; and perform filter element blockage detection based on the dust removal efficiency data to obtain filter element blockage data; The exhaust gas energy-saving and environmental protection control module is used to control the redundant fan start-stop strategy according to the fan fault data to obtain the redundant fan start-stop control data; dynamically adjust the exhaust gas treatment parameters according to the filter element blockage data to obtain the exhaust gas adjustment treatment parameters; and perform exhaust gas energy-saving and environmental protection control according to the exhaust gas adjustment treatment parameters and the redundant fan start-stop control data to obtain the exhaust gas treatment control data.

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