Device and method for monitoring and graded control of odor pollution in garbage disposal sites

Abstract

The present invention provides a device and method for monitoring and graded control of odor pollution in a garbage disposal site, relating to the technical field of odor control in garbage disposal sites. The device comprises: a multi-stage pretreatment module for performing graded dehumidification and dust removal treatment on the monitored gas to obtain pretreated gas; an integrated monitoring module for obtaining an electrical signal of the odor component concentration of the pretreated gas; a first data processing module for converting the electrical signal of the odor component concentration to obtain a chemical concentration monitoring value; a second data processing module for converting the chemical concentration monitoring value into an odor concentration monitoring value; a grading module for grading the odor concentration monitoring value to obtain different levels of odor pollution; and a deodorization module for generating different deodorization implementation plans based on the odor concentration level. The present invention solves the problems of low odor monitoring accuracy and high odor control costs in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of odor control in garbage disposal sites, and in particular to a device and method for monitoring and grading odor pollution in garbage disposal sites. Background Art

[0002] As an important facility for urban waste treatment, the odor emitted by garbage disposal sites has a serious impact on the surrounding environment and the health of residents. Existing technologies generally use methods that have not been accurately tested, and directly rely on single or combined technologies such as microbial degradation, activated carbon adsorption or chemical adsorption. This method fails to combine odor treatment with human perception of odor, and cannot directly respond to odor treatment plans based on the degree of odor stimulation to human sensory organs and the concentration of various pollutants. As a result, the odor removal efficiency is often low, and due to the lack of targeted and precise control, the economic cost remains high. In addition, the existing online monitoring system is easily interfered with in a high-humidity and dusty environment, and the monitoring accuracy is low, which affects the treatment effect. Therefore, when dealing with odor pollution in garbage dumps, the existing technology can neither ensure the treatment effect nor achieve cost-effectiveness optimization. Summary of the Invention

[0003] In order to overcome the deficiencies of the prior art, the present invention provides a device and method for monitoring and grading odor pollution in a garbage disposal site, which solves the problems of low odor monitoring accuracy and high odor control cost in the prior art.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A device for monitoring and controlling odor pollution in a garbage disposal site, comprising:

[0006] A power module and a multi-stage pre-processing module, an integrated monitoring module, a first data processing module, a second data processing module, a grading module and a deodorization module, all of which are connected to the power module;

[0007] The power supply module is used to supply power to the multi-stage preprocessing module, the integrated monitoring module, the first data processing module and the second data processing module. The multi-stage preprocessing module is used to perform graded dehumidification and dust removal treatment on the monitored gas to obtain pretreated gas. The integrated monitoring module is used to obtain an electrical signal of the odor component concentration of the pretreated gas. The first data processing module is used to convert the electrical signal of the odor component concentration to obtain a chemical concentration monitoring value. The grading module is used to grade the degree of human olfactory stimulation according to the odor concentration monitoring value to obtain different levels of odor pollution directly related to human olfactory perception. The deodorization module is used to generate different deodorization implementation plans according to the odor concentration level.

[0008] Preferably, the multi-stage pre-processing module includes:

[0009] A primary dust removal device, a primary dehumidification device, a final dust removal device and a final dehumidification device connected in sequence;

[0010] The primary dust removal device is used to perform primary dust removal on the gas to be monitored using a filter to obtain the first treated gas, the primary dehumidification device is used to perform primary dehumidification on the first treated gas using a condenser to obtain the second treated gas, the ultimate dust removal device is used to perform ultimate dust removal on the second treated gas using a gas purifier to obtain the third treated gas, and the ultimate dehumidification device is used to perform ultimate drying on the third treated gas using a desiccant to obtain pre-treated gas.

[0011] Preferably, the integrated monitoring module includes:

[0012] A hydrogen sulfide sensor, an ammonia sensor, a sulfur dioxide sensor, a trimethylamine sensor, and a volatile organic compound sensor, all connected to the multi-stage pretreatment module;

[0013] The hydrogen sulfide sensor is used to obtain the hydrogen sulfide concentration of the pretreated gas, the ammonia sensor is used to obtain the ammonia concentration of the pretreated gas, the sulfur dioxide sensor is used to obtain the sulfur dioxide concentration of the pretreated gas, the trimethylamine sensor is used to obtain the trimethylamine concentration of the pretreated gas, and the volatile organic compound sensor is used to obtain the volatile organic compound concentration of the pretreated gas.

[0014] Preferably, the first data processing module includes:

[0015] Signal conversion submodule, denoising submodule, normalization submodule, modeling submodule and monitoring submodule;

[0016] The signal conversion submodule is used to perform data conversion on the electrical signal of the odor component concentration of the pretreated gas to obtain a digital signal. The denoising submodule is used to perform noise reduction on the digital signal to obtain a noise-reduced signal. The normalization submodule is used to perform normalization on the noise-reduced signal to obtain a normalized signal. The modeling submodule is used to construct a multivariate linear regression model and calculate the chemical concentration monitoring value based on the normalized signal. The monitoring submodule is used to obtain the chemical concentration monitoring value in real time and store it.

[0017] Preferably, the second data processing module includes:

[0018] A CNN conversion submodule and a threshold management submodule, a dynamic model update submodule and an output submodule, all of which are connected to the CNN conversion submodule;

[0019] The threshold management submodule is used to store the odor thresholds of various pollutants and update the thresholds in the CNN conversion submodule according to the current temperature, humidity and air pressure to obtain updated thresholds. The dynamic model update submodule is used to update the model parameters of the CNN conversion submodule to obtain the optimal CNN model. The CNN conversion submodule is used to use the optimal CNN model and the updated threshold to convert the chemical concentration monitoring value to obtain the odor concentration monitoring value. The output submodule is used to output the odor concentration monitoring value to the classification module.

[0020] Preferably, the CNN conversion submodule includes:

[0021] Input build subunits, convolutional layers, activation layers, pooling layers, and fully connected layers;

[0022] The input construction subunit is used to construct a high-dimensional input tensor based on the chemical concentration monitoring value, the update threshold and the current environmental parameters; the convolution layer is used to extract the coupling features of the high-dimensional input tensor; the activation layer applies a nonlinear activation function; the pooling layer is used to reduce the dimension of the coupling features to obtain reduced-dimensional features; and the fully connected layer is used to obtain the odor concentration monitoring value based on the reduced-dimensional features.

[0023] Preferably, the denoising submodule includes:

[0024] Signal division unit, motion estimation unit, filtering unit, recognition unit, multi-level processing unit and feedback unit;

[0025] The signal division unit is used to perform block processing on the digital signal to obtain several digital block signals, the motion estimation unit is used to obtain motion estimation values ​​of the several digital block signals, the filtering unit is used to perform a first noise reduction process on the block signal according to the Kalman filtering method to obtain a first processed signal, the identification unit is used to construct a convolutional neural network model to perform a second noise reduction process on the first processed signal to obtain a second processed signal, the multi-level processing unit is used to identify the frequency band of the second processed signal and perform denoising according to different frequency bands to obtain a denoised signal, and the feedback unit is used to evaluate the denoising effect of the denoised signal and adjust the parameters of the filtering unit, the identification unit, and the multi-level processing unit in real time according to the evaluation results.

[0026] Preferably, the motion estimation unit comprises:

[0027] Data acquisition subunit, matching subunit, calculation subunit;

[0028] The data acquisition subunit is used to acquire image frames of the denoised images of the plurality of digital block signals and n frames of images of the historical denoised completed images; the matching subunit is used to determine different block sizes and corresponding historical denoised completed images according to an adaptive block size selection mechanism and perform matching calculations to obtain initial motion estimation values; and the calculation subunit is used to weight each initial motion estimation value according to the background of each digital block signal to obtain a final motion estimation value.

[0029] Preferably, the filtering unit includes:

[0030] A spatial filtering subunit, a temporal filtering subunit and a weighting subunit;

[0031] The temporal filtering subunit is used to filter and reduce noise on the block signal using the Kalman filtering method in the time domain to obtain a denoised image after Kalman filtering. The spatial filtering subunit is used to perform bilateral filtering and denoising on the block signal using a bilateral filter to obtain a bilaterally filtered image. The weighting subunit is used to obtain a first processed signal based on the final motion estimation value of the block signal, the denoised image after Kalman filtering and the image after bilateral filtering.

[0032] A method for monitoring and hierarchical control of odor pollution in a garbage disposal site, comprising:

[0033] Perform graded dehumidification and dust removal on the monitored gas to obtain pre-treated gas;

[0034] Obtaining an electrical signal of odor component concentration of the pretreated gas;

[0035] Converting the odor component concentration electrical signal to obtain a chemical concentration monitoring value;

[0036] Converting the chemical concentration monitoring value into an odor concentration monitoring value;

[0037] The degree of olfactory stimulation to humans is graded according to the monitored odor concentration values, resulting in different levels of odor pollution that are directly related to human olfactory perception;

[0038] Different deodorization implementation plans are generated according to the odor concentration level.

[0039] The present invention discloses the following technical effects:

[0040] The present invention provides a device and method for monitoring and grading odor pollution in a garbage disposal site. The device comprises a power module, a multi-stage pretreatment module, an integrated monitoring module, a first data processing module, a second data processing module, a grading module, and a deodorization module, all connected to the power module. The power module is used to power the multi-stage pretreatment module, the integrated monitoring module, the first data processing module, and the second data processing module. The multi-stage pretreatment module is used to perform graded dehumidification and dust removal on the monitored gas to obtain pretreated gas. The integrated monitoring module is used to obtain an electrical signal indicating the odor component concentration in the pretreated gas. The first data processing module is used to convert the electrical signal indicating the odor component concentration to obtain a chemical concentration monitoring value. The second data processing module is used to convert the chemical concentration monitoring value into an odor concentration monitoring value. The grading module is used to grade the degree of olfactory stimulation to the human sense of smell based on the odor concentration monitoring value, thereby obtaining different levels of odor pollution directly related to human olfactory perception. The deodorization module is used to generate different deodorization solutions based on the odor concentration levels. The present invention saves treatment costs by accurately measuring odor concentration and implementing graded treatment based on the measured concentration. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A schematic structural diagram of a device for monitoring and grading odor pollution in a garbage disposal site provided by an embodiment of the present invention.

[0043] Description of reference numerals:

[0044] 1-power supply module, 2-multi-stage pre-processing module, 3-integrated monitoring module, 4-first data processing module, 5-classification module, 6-deodorization module, 7-second data processing module. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] like Figure 1 As shown, the present invention provides a device for monitoring and hierarchical control of odor pollution in a garbage disposal site, comprising:

[0048] The power supply module 1 and the multi-stage pretreatment module 2, the integrated monitoring module 3, the first data processing module 4, the second data processing module 7, the grading module 5 and the deodorization module 6 are all connected to the power supply module 1; the multi-stage pretreatment module 2, the integrated monitoring module 3, the first data processing module 4, the second data processing module 7, the grading module 5 and the deodorization module 6 are connected in sequence.

[0049] The power supply module 1 is used to power the multi-stage preprocessing module 2, the integrated monitoring module 3, the first data processing module 4 and the second data processing module 7. The multi-stage preprocessing module is used to perform graded dehumidification and dust removal treatment on the monitored gas to obtain pretreated gas. The integrated monitoring module 3 is used to obtain the electrical signal of the odor component concentration of the pretreated gas. The first data processing module 4 is used to convert the electrical signal of the odor component concentration to obtain a chemical concentration monitoring value. The second data processing module 7 is used to convert the chemical concentration monitoring value into an odor concentration monitoring value. The grading module 5 is used to grade the degree of human olfactory stimulation according to the odor concentration monitoring value to obtain different levels of odor pollution directly related to human olfactory perception. The deodorization module 6 is used to generate different deodorization implementation plans according to the odor concentration level.

[0050] Specifically, the odor concentration monitoring value can be classified into low concentration state, medium concentration state and high concentration state. Low concentration state (0-1000ouE / m 3 ): The polluted gas is directly introduced into the microbial treatment area, and the odor is treated by microbial adsorption technology. Medium concentration state (1000-2500ouE / m 3 ): The polluted gas first enters the microbial treatment area and then enters the activated carbon adsorption unit. According to the monitored characteristic pollutant concentration, the appropriate activated carbon module is selected for targeted treatment. High concentration state (>2500ouE / m 3 ): The polluted gas is sequentially introduced into the microbial treatment area, activated carbon adsorption unit and absorption tank for graded treatment to ensure that the odor is fully controlled.

[0051] More specifically, regarding adsorbent selection, we select activated carbon with high-efficiency filtration, and utilize different types of activated carbon based on the properties of different gaseous pollutants (e.g., specialized activated carbon for treating acidic, alkaline, and reducing gases). Absorption tank design utilizes aqueous solutions such as sodium hydroxide, sodium hypochlorite, and hydrogen peroxide for gas absorption. The absorption tank is equipped with intelligent gas connection circuits, enabling the selection of appropriate absorption solutions for different pollutants.

[0052] Furthermore, the multi-stage pre-processing module 2 includes:

[0053] A primary dust removal device, a primary dehumidification device, a final dust removal device and a final dehumidification device connected in sequence;

[0054] The primary dust removal device is used to perform primary dust removal on the gas to be monitored using a filter to obtain the first treated gas, the primary dehumidification device is used to perform primary dehumidification on the first treated gas using a condenser to obtain the second treated gas, the ultimate dust removal device is used to perform ultimate dust removal on the second treated gas using a gas purifier to obtain the third treated gas, and the ultimate dehumidification device is used to perform ultimate drying on the third treated gas using a desiccant to obtain pre-treated gas.

[0055] Specifically, the primary dehumidification device: a condenser or dehumidifier is usually used to pre-treat the gas and reduce the moisture content in the gas. The condenser achieves the purpose of dehumidification by lowering the gas temperature and condensing water vapor into water. Desiccant: A desiccant (such as silica gel or molecular sieve) can be used in the system for further drying to adsorb residual moisture in the gas to ensure that the final sample is dry. Filter: Before the gas enters the system, an effective filter (such as a HEPA filter or activated carbon filter) is set to remove solid particles, dust and possible odor substances in the gas. This step can effectively prevent solid pollutants from affecting sensors and other equipment. Gas purifier: Some models may include a gas purification module that uses chemical reactions or adsorption technology to further remove pollutants in the gas, such as volatile organic compounds (VOCs) and other gas components. The structure can be modularly configured according to actual needs, flexibly responding to gas samples under different environmental conditions, and ensuring the applicability and effectiveness of the system.

[0056] For primary filtration, a primary-efficiency filter can be used. Filter materials typically include non-woven fabric, nylon mesh, activated carbon, or metal mesh. For secondary filtration, medium-efficiency and high-efficiency filters are used to remove fine particulate matter. Medium-efficiency filters typically use synthetic fiber, glass fiber, or non-woven fabric; high-efficiency filters typically use glass fiber, polypropylene, or other synthetic materials. The medium and high-efficiency filters can be selected based on actual conditions.

[0057] Furthermore, the integrated monitoring module 3 includes:

[0058] A hydrogen sulfide sensor, an ammonia sensor, a sulfur dioxide sensor, a trimethylamine sensor, and a volatile organic compound sensor, all connected to the multi-stage pretreatment module 2;

[0059] The hydrogen sulfide sensor is used to obtain the hydrogen sulfide concentration of the pretreated gas, the ammonia sensor is used to obtain the ammonia concentration of the pretreated gas, the sulfur dioxide sensor is used to obtain the sulfur dioxide concentration of the pretreated gas, the trimethylamine sensor is used to obtain the trimethylamine concentration of the pretreated gas, and the volatile organic compound sensor is used to obtain the volatile organic compound concentration of the pretreated gas.

[0060] Furthermore, the first data processing module 4 includes:

[0061] Signal conversion submodule, denoising submodule, normalization submodule, modeling submodule and monitoring submodule;

[0062] The signal conversion submodule is used to perform data conversion on the electrical signal of the odor component concentration of the pretreated gas to obtain a digital signal. The denoising submodule is used to perform noise reduction on the digital signal to obtain a noise-reduced signal. The normalization submodule is used to perform normalization on the noise-reduced signal to obtain a normalized signal. The modeling submodule is used to construct a multivariate linear regression model and calculate the chemical concentration monitoring value based on the normalized signal. The monitoring submodule is used to obtain the chemical concentration monitoring value in real time and store it.

[0063] Specifically, an analog-to-digital converter (ADC) is used to convert the analog electrical signal into a digital signal and the filtered signal is normalized to ensure the comparability of data of different gas concentrations.

[0064] Specifically, the expression of the multiple linear regression model is:

[0065] C=A+aB+d;

[0066] Among them, C is the chemical concentration monitoring value, A is the intercept, a is the regression coefficient, B is the normalized signal, and d is the error term.

[0067] Furthermore, the second data processing module 7 includes:

[0068] A CNN conversion submodule and a threshold management submodule, a dynamic model update submodule and an output submodule, all of which are connected to the CNN conversion submodule;

[0069] The threshold management submodule is used to store the odor thresholds of various pollutants and update the thresholds in the CNN conversion submodule according to the current temperature, humidity and air pressure to obtain updated thresholds. The dynamic model update submodule is used to update the model parameters of the CNN conversion submodule to obtain the optimal CNN model. The CNN conversion submodule is used to use the optimal CNN model and the updated threshold to convert the chemical concentration monitoring value to obtain the odor concentration monitoring value. The output submodule is used to output the odor concentration monitoring value to the classification module.

[0070] Specifically, the CNN conversion submodule is used to receive the chemical concentration monitoring values ​​output by the first data processing module, and construct a high-dimensional input tensor based on the odor thresholds of multiple pollutants and optional environmental parameters (such as temperature, humidity, etc.), automatically extract the correlation characteristics and nonlinear relationships between multiple pollutants, and output the odor concentration monitoring value after integrating the chemical concentration monitoring values ​​of various pollutants.

[0071] Preferably, the CNN conversion submodule includes:

[0072] Input build subunits, convolutional layers, activation layers, pooling layers, and fully connected layers;

[0073] The input construction subunit is used to construct a high-dimensional input tensor based on the chemical concentration monitoring value, the update threshold and the current environmental parameters; the convolution layer is used to extract the coupling features of the high-dimensional input tensor; the activation layer applies a nonlinear activation function; the pooling layer is used to reduce the dimension of the coupling features to obtain reduced-dimensional features; and the fully connected layer is used to obtain the odor concentration monitoring value based on the reduced-dimensional features.

[0074] Specifically, the CNN conversion submodule includes:

[0075] The input construction subunit is used to construct a high-dimensional input tensor based on the chemical concentration monitoring value, update threshold and current environmental parameters, and perform feature extraction on the chemical concentration data of different pollutants; the convolution layer, when the concentration values ​​of multiple pollutants are input at the same time, scans different channels or time series dimensions through the convolution kernel to learn the coupling effects of each pollutant on the overall odor concentration under different environmental conditions; the activation layer: uses activation functions such as ReLU, Sigmoid or Tanh to implement nonlinear mapping, which can capture the odor increase caused by a jump in the concentration of a single pollutant or the superposition of multiple pollutants; the pooling layer: performs dimensionality reduction operations on the intermediate features output by the convolution layer to reduce redundancy and enhance the robustness of the model, avoiding overfitting of small noise; the fully connected layer: before the final output layer, the features after pooling compression and convolution extraction are combined and regressed through the fully connected network to output the odor concentration monitoring value, which can be understood as the dilution multiple prediction value of "chemical concentration ÷ odor threshold".

[0076] More specifically, the conversion workflow is as follows: the CNN conversion submodule integrates the chemical concentration values ​​of multiple pollutants, odor thresholds, and environmental parameters into high-dimensional inputs, and obtains the odor concentration monitoring values ​​of each or more combined pollutants through convolution operations and full-connection outputs; the threshold management submodule dynamically updates the threshold information required by the CNN conversion submodule when it identifies the need to update the threshold database; if the system detects changes in sensor characteristics or large deviations in the actual olfactory feedback results, the dynamic model update submodule initiates an online or offline update process and iteratively trains the model parameters; the output submodule sends the calculated odor concentration monitoring values ​​to the grading module, and can visualize or archive the results for subsequent statistical analysis and deodorization solution optimization.

[0077] The updating process of the threshold is as follows:

[0078] Historical monitoring data: including chemical concentration monitoring values ​​of different pollutants under external environmental parameters such as different seasons, temperature and humidity, wind direction and speed, and predicted odor concentration values ​​for the same period; actual olfactory evaluation data: can come from subjective olfactory tests of on-site personnel or special odor analysis equipment such as electronic noses, which conduct quantitative or semi-quantitative olfactory evaluation of sample gases; regularly (such as at regular intervals or after collecting enough new samples), compare the error between the stored "odor concentration predicted by the CNN conversion submodule" and the "actual olfactory evaluation data". When the error exceeds the preset threshold (such as ±5%) or shows obvious deviation in multiple consecutive samplings, it indicates that the odor threshold or environmental correction factor may need to be updated.

[0079] Deviation trend analysis: If the deviation is concentrated in a certain pollutant or a certain time period, it can be determined whether the threshold setting for the pollutant does not match the actual scenario, or the threshold is inaccurate due to significant changes in external temperature and humidity.

[0080] Furthermore, the denoising submodule includes:

[0081] Signal division unit, motion estimation unit, filtering unit, recognition unit, multi-level processing unit and feedback unit;

[0082] The signal division unit is used to perform block processing on the digital signal to obtain several digital block signals, the motion estimation unit is used to obtain motion estimation values ​​of the several digital block signals, the filtering unit is used to perform a first noise reduction process on the block signal according to the Kalman filtering method to obtain a first processed signal, the identification unit is used to construct a convolutional neural network model to perform a second noise reduction process on the first processed signal to obtain a second processed signal, the multi-level processing unit is used to identify the frequency band of the second processed signal and perform denoising according to different frequency bands to obtain a denoised signal, and the feedback unit is used to evaluate the denoising effect of the denoised signal and adjust the parameters of the filtering unit, the identification unit, and the multi-level processing unit in real time according to the evaluation results.

[0083] Specifically, the values ​​of these matrices are dynamically adjusted according to the noise level of the real-time signal to optimize the filtering effect. This can be achieved by monitoring the quality of the denoised signal (e.g., the signal-to-noise ratio (SNR)).

[0084] More specifically, initial denoising: first, use the adaptive filtering method to perform preliminary noise reduction, which can significantly reduce the high-frequency noise in the signal; deep learning post-processing: introduce the adaptively filtered signal into the deep learning model to further optimize the denoising effect, especially for the details retained in the original signal; multi-level analysis and fusion: finally, through multi-level processing, analyze the different frequency components and select the optimal denoising method, even for targeted processing of certain specific frequency bands or noise types.

[0085] Furthermore, the motion estimation unit includes:

[0086] Data acquisition subunit, matching subunit, calculation subunit;

[0087] The data acquisition subunit is used to acquire image frames of the denoised images of the plurality of digital block signals and n frames of images of the historical denoised completed images; the matching subunit is used to determine different block sizes and corresponding historical denoised completed images according to an adaptive block size selection mechanism and perform matching calculations to obtain initial motion estimation values; and the calculation subunit is used to weight each initial motion estimation value according to the background of each digital block signal to obtain a final motion estimation value.

[0088] Specifically, the pre-filtered image and the saved n frames preceding the current frame that have undergone denoising are divided into several blocks of equal size. An adaptive block size selection mechanism is implemented to dynamically adjust the block size based on motion and image characteristics. Edge strength or the complexity of the motion region can be used to determine the block size, thereby improving motion estimation accuracy. Matching calculations are then performed on each block, resulting in n initial motion estimates for that block. These n initial motion estimates are weighted, using a weighting mechanism to process the estimates for different regions. Static background motion estimates can be assigned lower weights, while dynamic regions are assigned higher weights, ensuring that the final motion estimate more accurately reflects actual motion. The resulting motion estimate for the block in the image frame being processed is then obtained. The motion estimate for each pixel in the image frame being processed is the motion estimate for the block in which that pixel resides.

[0089] Furthermore, the filtering unit includes:

[0090] A spatial filtering subunit, a temporal filtering subunit and a weighting subunit;

[0091] The temporal filtering subunit is used to filter and reduce noise on the block signal using the Kalman filtering method in the time domain to obtain a denoised image after Kalman filtering. The spatial filtering subunit is used to perform bilateral filtering and denoising on the block signal using a bilateral filter to obtain a bilaterally filtered image. The weighting subunit is used to obtain a first processed signal based on the final motion estimation value of the block signal, the denoised image after Kalman filtering and the image after bilateral filtering.

[0092] This embodiment also provides a method for monitoring and hierarchical control of odor pollution in a garbage disposal site, including:

[0093] Perform graded dehumidification and dust removal on the monitored gas to obtain pre-treated gas;

[0094] Obtaining an electrical signal of odor component concentration of the pretreated gas;

[0095] Converting the odor component concentration electrical signal to obtain a chemical concentration monitoring value;

[0096] Converting the chemical concentration monitoring value into an odor concentration monitoring value;

[0097] The degree of olfactory stimulation to humans is graded according to the monitored odor concentration values, resulting in different levels of odor pollution that are directly related to human olfactory perception;

[0098] Different deodorization implementation plans are generated according to the odor concentration level.

[0099] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0100] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A device for monitoring and controlling odor pollution in a garbage disposal site, characterized in that: include: A power module and a multi-stage pre-processing module, an integrated monitoring module, a first data processing module, a second data processing module, a grading module and a deodorization module, all of which are connected to the power module; The power supply module is used to power the multi-stage preprocessing module, the integrated monitoring module, the first data processing module, and the second data processing module. The multi-stage preprocessing module is used to perform graded dehumidification and dust removal on the monitored gas to obtain pretreated gas. The integrated monitoring module is used to obtain an electrical signal of the odor component concentration of the pretreated gas. The first data processing module is used to convert the electrical signal of the odor component concentration to obtain a chemical concentration monitoring value. The second data processing module is used to convert the chemical concentration monitoring value to obtain an odor concentration monitoring value. The grading module is used to grade the degree of human olfactory stimulation based on the odor concentration monitoring value to obtain different levels of odor pollution directly related to human olfactory perception. The deodorization module is used to generate different deodorization implementation plans based on the odor concentration level. The multi-stage pre-processing module includes: A primary dust removal device, a primary dehumidification device, a final dust removal device and a final dehumidification device connected in sequence; The primary dust removal device is used to perform primary dust removal on the gas to be monitored using a filter to obtain a first-processed gas, the primary dehumidification device is used to perform primary dehumidification on the first-processed gas using a condenser to obtain a second-processed gas, the final dust removal device is used to perform final dust removal on the second-processed gas using a gas purifier to obtain a third-processed gas, and the final dehumidification device is used to perform final drying on the third-processed gas using a desiccant to obtain a pre-processed gas; The integrated monitoring module includes: A hydrogen sulfide sensor, an ammonia sensor, a sulfur dioxide sensor, a trimethylamine sensor, and a volatile organic compound sensor, all connected to the multi-stage pretreatment module; The hydrogen sulfide sensor is used to obtain the hydrogen sulfide concentration of the pretreated gas, the ammonia sensor is used to obtain the ammonia concentration of the pretreated gas, the sulfur dioxide sensor is used to obtain the sulfur dioxide concentration of the pretreated gas, the trimethylamine sensor is used to obtain the trimethylamine concentration of the pretreated gas, and the volatile organic compound sensor is used to obtain the volatile organic compound concentration of the pretreated gas.

2. The device for monitoring and controlling odor pollution in a garbage disposal site according to claim 1, characterized in that: The first data processing module includes: Signal conversion submodule, denoising submodule, normalization submodule, modeling submodule and monitoring submodule; The signal conversion submodule is used to perform data conversion on the electrical signal of the odor component concentration of the pretreated gas to obtain a digital signal. The denoising submodule is used to perform noise reduction on the digital signal to obtain a noise-reduced signal. The normalization submodule is used to perform normalization on the noise-reduced signal to obtain a normalized signal. The modeling submodule is used to construct a multivariate linear regression model and calculate the chemical concentration monitoring value based on the normalized signal. The monitoring submodule is used to obtain the chemical concentration monitoring value in real time and store it.

3. The device for monitoring and controlling odor pollution in a garbage disposal site according to claim 2, characterized in that: The second data processing module includes: A CNN conversion submodule and a threshold management submodule, a dynamic model update submodule and an output submodule, all of which are connected to the CNN conversion submodule; The threshold management submodule is used to store the odor thresholds of various pollutants and update the thresholds in the CNN conversion submodule according to the current temperature, humidity and air pressure to obtain updated thresholds. The dynamic model update submodule is used to update the model parameters of the CNN conversion submodule to obtain the optimal CNN model. The CNN conversion submodule is used to use the optimal CNN model and the updated threshold to convert the chemical concentration monitoring value to obtain the odor concentration monitoring value. The output submodule is used to output the odor concentration monitoring value to the classification module.

4. The device for monitoring and controlling odor pollution in a garbage disposal site according to claim 3 is characterized in that: The CNN conversion submodule includes: Input build subunits, convolutional layers, activation layers, pooling layers, and fully connected layers; The input construction subunit is used to construct a high-dimensional input tensor based on the chemical concentration monitoring value, the update threshold and the current environmental parameters; the convolution layer is used to extract the coupling features of the high-dimensional input tensor; the activation layer applies a nonlinear activation function; the pooling layer is used to reduce the dimension of the coupling features to obtain reduced-dimensional features; and the fully connected layer is used to obtain the odor concentration monitoring value based on the reduced-dimensional features.

5. The device for monitoring and controlling odor pollution in a garbage disposal site according to claim 2, characterized in that: The denoising submodule includes: Signal division unit, motion estimation unit, filtering unit, recognition unit, multi-level processing unit and feedback unit; The signal division unit is used to perform block processing on the digital signal to obtain a plurality of digital block signals, the motion estimation unit is used to obtain motion estimation values ​​of the plurality of digital block signals, the filtering unit is used to perform a first noise reduction processing on the block signal according to the Kalman filtering method to obtain a first processed signal, the identification unit is used to construct a convolutional neural network model to perform a second noise reduction processing on the first processed signal to obtain a second processed signal, the multi-level processing unit is used to identify the frequency band of the second processed signal and perform denoising according to different frequency bands to obtain a denoised signal, and the feedback unit is used to evaluate the denoising effect of the denoised signal and adjust the parameters of the filtering unit, the identification unit, and the multi-level processing unit in real time according to the obtained evaluation results.

6. The device for monitoring and controlling odor pollution in a garbage disposal site according to claim 5, characterized in that: The motion estimation unit comprises: Data acquisition subunit, matching subunit, and calculation subunit; The data acquisition subunit is used to acquire image frames of the denoised images of the plurality of digital block signals and n frames of images of the historical denoised completed images; the matching subunit is used to determine different block sizes and corresponding historical denoised completed images according to an adaptive block size selection mechanism and perform matching calculations to obtain initial motion estimation values; and the calculation subunit is used to weight each initial motion estimation value according to the background of each digital block signal to obtain a final motion estimation value.

7. The device for monitoring and controlling odor pollution in a garbage disposal site according to claim 6, characterized in that: The filtering unit comprises: A spatial filtering subunit, a temporal filtering subunit and a weighting subunit; The temporal filtering subunit is used to filter and reduce noise on the block signal using the Kalman filtering method in the time domain to obtain a denoised image after Kalman filtering. The spatial filtering subunit is used to perform bilateral filtering and denoising on the block signal using a bilateral filter to obtain a bilaterally filtered image. The weighting subunit is used to obtain a first processed signal based on the final motion estimation value of the block signal, the denoised image after Kalman filtering and the image after bilateral filtering.

8. A method for monitoring and hierarchical control of odor pollution in a garbage disposal site, applied to the device according to any one of claims 1 to 7, characterized in that: The method comprises: Perform graded dehumidification and dust removal on the monitored gas to obtain pre-treated gas; Obtaining an electrical signal of odor component concentration of the pretreated gas; Converting the odor component concentration electrical signal to obtain a chemical concentration monitoring value; Converting the chemical concentration monitoring value into an odor concentration monitoring value; The degree of olfactory stimulation to humans is graded according to the monitored odor concentration values, resulting in different levels of odor pollution that are directly related to human olfactory perception; Generate different deodorization implementation plans according to the odor concentration level.

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