A landfill excavation odor and dust synergistic treatment monitoring system and method

Abstract

The application discloses a landfill excavation odor and dust collaborative treatment monitoring system and method, relates to the dust separation field, and automatically adapts the optimal operation parameters of a washing tower through real-time collection of pipeline gas component data, so that the core odor load is reduced from the source; the interference of dust raising on deodorization is eliminated through targeted dust removal operation, operation parameters of a fog gun, meteorological data and initial odor concentration are synchronously collected, and the deodorization effect and the adhesion of the isolation layer medicament are accurately predicted with the aid of a mapping model; a hierarchical iteration optimization mechanism is constructed with a preset threshold as a benchmark, rapid standard reaching is realized by adjusting the fog gun parameters, and if the maximum number of repetitions is reached and the requirement is still not met, the operation parameters of the washing tower are reversely optimized, cross-device collaborative closed-loop adjustment is formed, odor treatment is stably up to standard, the adhesion amount of the medicament is strictly controlled to protect the isolation layer, membrane body aging and erosion are avoided, and manual intervention and energy consumption waste are reduced.

Description

Technical Field

[0001] This invention belongs to the field of dust separation, and specifically relates to a monitoring system and method for the synergistic treatment of odor and dust from landfill excavation. Background Technology

[0002] Excavation of existing landfills is a common but highly environmentally risky operation during urban renewal, land remediation, or resource recycling. This process simultaneously triggers three prominent problems: First, the disturbance causes the sudden release of malodorous and flammable gases such as hydrogen sulfide, ammonia, and methane, posing risks of poisoning, explosion, and environmental pollution. Second, the excavation machinery raises large amounts of waste dust carrying pathogens and heavy metals, seriously endangering the respiratory health of workers and causing secondary pollution. Finally, the accumulation of gases and dust in the excavation pit creates a harsh microclimate, directly threatening worker safety. Existing technologies often address single problems; for example, they only focus on odor control and early warning without considering the entire landfill environment. Furthermore, in terms of ventilation, the conventional approach is to use high-volume fans for overall air exchange, which is energy-intensive and prone to causing unorganized dispersion of pollutants, failing to create a clean air zone in the workers' breathing zone. For dust control, fixed water spraying or all-weather dust suppressant spraying is commonly used, which is inefficient and wasteful of resources. Moreover, relying solely on traditional fixed ground monitoring points makes it difficult to capture the three-dimensional spatial distribution of pollutants. When using drones equipped with ordinary electronic noses for inspection, the sensor response speed is slow and easily affected by rotor airflow, failing to provide accurate real-time feedback for the intelligent control system. Therefore, existing technologies lack a systematic solution that can uniformly perceive, intelligently predict, and collaboratively handle the multiple risks of "hazardous gases, dust, and personnel exposure" during landfill excavation. Summary of the Invention

[0003] In response to the problems in related technologies, this invention proposes a monitoring system and method for the synergistic control of odor and dust from landfill excavation, in order to overcome the aforementioned technical problems existing in the existing related technologies.

[0004] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: This invention relates to a monitoring method for the synergistic control of odor and dust from landfill excavation, comprising the following steps: S1. Lay an isolation layer and install gas extraction and injection pipelines; then collect historical operating parameters of the biological scrubbing tower and corresponding treatment gas composition data based on this. S2. Construct the final biological scrubbing tower operation parameter mapping model based on the data collected in S1; S3. Collect historical data on spray parameters, wind speed and wind direction of the deodorizing fog cannon unit, as well as data on odor concentration and isolation layer adhesion before and after spraying, and construct a final data mapping model of odor and isolation layer after spraying based on this data. S4. Collect real-time data on the gas component type, content, and concentration of the gas injection pipeline; input this data into the mapping model in S2; and configure the current biological scrubbing tower based on the mapping results. S5. Select a dust removal agent for dust removal operation, then collect the spraying parameters of each deodorizing fog cannon, as well as the on-site wind direction, wind speed, and odor component concentration before spraying, and input them into the mapping model in S3 for mapping, and output the current real-time odor component concentration and isolation layer adhesion data after spraying. S6. Set thresholds for odor component concentration and isolation layer adhesion amount. If the mapping data in S5 does not meet the thresholds, repeatedly adjust the fog cannon spray parameters and input them into the mapping model in S3. If the number of repetitions reaches the maximum number of repetitions and the mapping result still does not meet the standard, optimize the current operating parameters of the biological scrubbing tower in S4 and repeat S5 and S6 until the mapping result meets the standard; otherwise, the adjustment is complete.

[0005] Preferably, step S1 includes the following steps: S11. Select the current landfill to be monitored; lay an HDPE sealing film in the non-excavation area of ​​the current landfill to be monitored to form an isolation layer; and lay an air injection pipeline under the isolation layer and lay a vertical air extraction well or a horizontal air extraction blind trench in the waste pile to obtain the current air source extraction pipeline and the current air source injection pipeline. S12. Set the current biological scrubbing tower and several corresponding operating parameter types to obtain a set of biological scrubbing tower operating parameter types; the set of biological scrubbing tower operating parameter types includes circulating spray water volume, spray pressure, nutrient solution replenishment flow rate, scrubbing liquid level, and empty tower gas velocity, etc. S13. Based on the set of operating parameters of the biological scrubbing tower, obtain the operating parameter data of various biological scrubbing towers and the corresponding component types, contents and concentration data of the treated gas during multiple historical processes of treating gas in landfills, and obtain the historical biological scrubbing tower operating parameter dataset and the historical treated gas component dataset. By relying on historical operating parameters and waste gas composition data, data support is provided for the intelligent control of subsequent scrubbing tower operation, thereby comprehensively enhancing the overall effectiveness of odor control at the landfill source and harmless treatment of waste gas.

[0006] Preferably, step S2 includes the following steps: S21. Based on the historical biological scrubbing tower operating parameter dataset and the historical treated gas component dataset, construct a mapping model with the input of various components of the treated gas and the output of various operating parameter data of the biological scrubbing tower, and obtain the final biological scrubbing tower operating parameter mapping model. By constructing a final biological scrubbing tower operating parameter mapping model, the composition of different gas components can be accurately captured. This model can fully explore the inherent correlation between gas component characteristics and scrubbing tower operating parameters, so that the setting of operating parameters no longer relies on experience judgment, but is based on real operating data to form a scientific adaptation logic, which greatly improves the accuracy and rationality of biological scrubbing tower operating parameter control.

[0007] Preferably, step S3 includes the following steps: S31. Install air circulation and purification devices in areas with frequent personnel activity and around the excavation pit; and deploy deodorizing fog cannon units at the highest point and around the excavation area of ​​the current landfill to be monitored. S32. Based on the air circulation and purification device filtering the polluted air at the site, collect the spray flow rate, atomization particle size, spray elevation angle, wind direction angle, wind speed, concentration data of each odor component in the air before and after each spray of deodorizing agent, and the amount of deodorizing agent adhering to the isolation layer of each set of deodorizing fog cannons that are currently being monitored at the landfill. This will result in the following datasets: historical spray flow rate dataset, historical spray atomization particle size dataset, historical spray elevation angle dataset, historical wind direction angle dataset, historical wind speed dataset, historical odor component concentration dataset before spraying, historical odor component concentration dataset after spraying, and historical isolation layer adhesion amount dataset. S33. Based on the historical jet flow rate dataset, historical jet atomization particle size dataset, historical jet elevation angle dataset, historical wind direction angle dataset, historical wind speed dataset, historical pre-jet odor component concentration dataset, historical post-jet odor component concentration dataset, and historical isolation layer adhesion amount dataset, construct a mapping model with the inputs of jet flow rate data, jet atomization particle size data, jet elevation angle data, wind direction angle data, wind speed data, pre-jet odor component concentration data, and the outputs of post-jet odor component concentration data and the adhesion amount of deodorizing agent on the isolation layer, to obtain the final post-jet odor and isolation layer data mapping model. Based on the integrated historical datasets, a mapping model can be built to deeply explore the intrinsic relationship between spraying parameters, environmental conditions, deodorization effect, and the amount of agent adhering to the isolation layer. This model can not only accurately predict the odor concentration after spraying, but also effectively control the adhesion of the agent to the isolation layer, providing a scientific basis for optimizing fog cannon spraying parameters.

[0008] Preferably, step S4 includes the following steps: S41. Real-time acquisition of data on the type, content and concentration of each component of the gas under the isolation layer before it enters the current gas source extraction pipeline, to obtain the current real-time processing gas component dataset. S42. Input the current real-time processed gas component dataset into the final biological scrubbing tower operation parameter mapping model for mapping to obtain the current real-time operation parameter dataset; automatically set various operation parameters of the current biological scrubbing tower according to the current real-time operation parameter dataset; By inputting real-time gas component data into a pre-built mapping model, a real-time operating parameter dataset adapted to the current operating conditions can be quickly output, enabling the automated and precise setting of various core operating parameters of the scrubbing tower. This allows parameters such as circulating spray water volume and spray pressure to be flexibly adjusted according to changes in gaseous pollutant concentration and component ratio, effectively avoiding potential risks such as energy waste, biofilm damage, eutrophication of water bodies, or substandard treatment caused by improper parameter settings.

[0009] Preferably, step S5 includes the following steps: S51. Based on the completion of setting various operating parameters of the current biological scrubbing tower in S42, select the current dust removal agent; use the current dust removal agent to perform real-time agglomeration and dust removal on the dust generated by the landfill excavation machinery during the excavation process; S52. After the real-time dust removal operation in S51 is completed, the spray flow rate, atomization particle size, spray elevation angle, wind direction angle, wind speed, and concentration data of each odor component in the air before each spray of the deodorizing agent are collected in real time for each deodorizing mist cannon unit of the landfill to be monitored. The real-time spray flow rate, real-time atomization particle size, real-time spray elevation angle, real-time wind direction angle, real-time wind speed, and the real-time odor component concentration dataset before spraying are obtained. The current real-time injection flow rate data, current real-time atomization particle size data, current real-time injection pitch angle data, current real-time wind direction angle data, current real-time wind speed data, and current real-time pre-injection odor component concentration dataset are input into the final post-injection odor and isolation layer data mapping model for mapping, to obtain the current real-time post-injection odor component concentration dataset and the current real-time isolation layer adhesion amount data. The system first stabilizes the on-site air environment and eliminates the interference of dust on deodorization through intelligent dust suppression. Then, through mapping, it provides data support for the subsequent precise application of deodorizing agents based on data. While ensuring that both dust and odor control meet the standards, it minimizes the erosion of the isolation layer by excessive agent adhesion, thus balancing treatment efficiency, equipment operation stability, and the long-term protection requirements of the isolation layer.

[0010] Preferably, step S6 includes the following steps: S61. Set the concentration thresholds of various odor components in the air after the current deodorizing agent is sprayed and the adhesion threshold of the deodorizing agent on the isolation layer to obtain the current set of odor component concentration thresholds and the current adhesion threshold of the isolation layer; then set the first maximum number of repetitions. S62. Based on the current post-injection odor component concentration threshold set, if there is a concentration data in the current real-time post-injection odor component concentration dataset that is greater than or equal to the corresponding concentration threshold, or if the current real-time isolation layer adhesion data is greater than or equal to the current isolation layer adhesion threshold, the current real-time injection flow rate data, the current real-time atomized particle size data, and the current real-time injection pitch angle data are repeatedly adjusted to obtain the currently adjusted injection flow rate data, the currently adjusted atomized particle size data, and the currently adjusted injection pitch angle data; otherwise, no adjustment is required. The adjusted injection flow rate data, the adjusted atomization particle size data, the adjusted injection pitch angle data, the real-time wind direction angle data, the real-time wind speed data, and the real-time pre-injection odor component concentration dataset are input into the final post-injection odor and isolation layer data mapping model for mapping, to obtain the post-injection odor component concentration dataset and the post-injection isolation layer adhesion amount data. S63. If the number of repetitions is less than or equal to the first maximum number of repetitions, there is no concentration data in the current adjusted post-spray odor component concentration data that is greater than or equal to the corresponding concentration threshold, and the current real-time isolation layer adhesion data is less than the current isolation layer adhesion threshold, obtain the current first final spray flow rate data, the current first final atomized particle size data, and the current first final spray elevation angle data, and the adjustment is complete; then set the deodorizing fog cannon unit according to the current first final spray flow rate data, the current first final atomized particle size data, and the current first final spray elevation angle data; Otherwise, the spray water volume, spray pressure, nutrient solution replenishment flow rate, and washing liquid level in the current real-time operating parameter dataset are repeatedly adjusted to obtain the current adjusted operating parameter dataset; and S52 and S62 are executed until there is no concentration data in the current adjusted spray odor component concentration dataset that is greater than or equal to the corresponding concentration threshold and the current real-time isolation layer adhesion data is less than the current isolation layer adhesion threshold, to obtain the current final operating parameter dataset, the current second final spray flow rate data, the current second final atomization particle size data, and the current second final spray pitch angle data; In addition, the system should be checked for faults and warning information should be generated. The warning information should include: fault type, location, severity level, and suggested handling measures. It should also include human-machine interaction function. The warning information in the human-machine interaction function includes warnings for gas injection and gas extraction system faults, gas gathering well faults, and continuous odor concentration exceeding the standard. At the same time as the warning, suggested handling measures are given. After logging in through the platform, operation and maintenance personnel can view, confirm, and handle such operations. In addition, the above-mentioned manual operations can be recorded to provide data support for the entire system to learn from human experience and thus optimize control strategies. Then, based on the current final operating parameter dataset, the current second final spray flow rate data, the current second final atomization particle size data, and the current second final spray elevation angle data, the current biological scrubbing tower and the deodorizing fog cannon unit are set respectively; By setting adaptive odor concentration thresholds and isolation layer agent adhesion thresholds, coupled with a reasonable maximum number of repeated adjustments, and first using the preset thresholds as a benchmark, the odor concentration and isolation layer adhesion obtained in real-time mapping are judged. If there are any exceedances, the core parameters such as the spray flow rate, atomization particle size, and elevation angle of the mist cannon are adjusted in a targeted manner. Then, the adjustment effect is verified through the mapping model to ensure that the deodorization compliance rate is improved without exceeding the upper limit of the isolation layer protection. When the parameter adjustment reaches the maximum number of times and still does not meet the requirements, the operating parameters of the biological scrubbing tower, such as the spray water volume and spray pressure, are further optimized in reverse. By strengthening the source odor degradation capacity, the burden on subsequent deodorization operations is reduced. The data collection and parameter adjustment process is then repeated until both indicators meet the standards. Through layered and progressive adjustments, the stable odor treatment effect is ensured to meet the requirements, while the amount of agent adhering to the isolation layer is strictly controlled to avoid membrane erosion and aging.

[0011] A monitoring system for the coordinated control of odor and dust from landfill excavation includes an operational data acquisition module, a scrubbing tower mapping model construction module, an isolation layer mapping model construction module, an operational parameter mapping module, a jetting data mapping module, and a parameter adjustment module. The data acquisition module is used to collect historical operating parameters of the biological scrubbing tower and the corresponding processed gas composition data. The scrubbing tower mapping model building module is used to build the final biological scrubbing tower operating parameter mapping model; The isolation layer mapping model building module is used to build a data mapping model of odor and isolation layer after final injection; The operating parameter mapping module is used to map the operating parameters of the biological scrubbing tower; The injection data mapping module is used to map the current real-time concentration of odor components and the amount of insulating layer adhesion data after injection. The parameter adjustment module is used to repeatedly adjust the spray parameters of the deodorizing mist cannon and the operating parameters of the biological scrubbing tower.

[0012] The present invention has the following beneficial effects: 1. This invention first constructs a sealed gas collection foundation based on an isolation layer and an injection gas pipeline. Then, through historical data mining, it establishes a dual mapping model between the operating parameters of the biological scrubbing tower and gas composition, and between the mist cannon injection parameters and deodorization effect and the amount of adhesive on the isolation layer. This provides a scientific basis for subsequent intelligent control. Real-time collection of pipeline gas composition data is used, and the optimal operating parameters of the scrubbing tower are automatically adapted through model mapping, reducing the core odor load at the source. Furthermore, targeted dust removal operations eliminate the interference of dust on deodorization, while simultaneously collecting mist cannon operating parameters, meteorological data, and initial odor concentration, utilizing the mapping model. Accurately predict the deodorization effect and the adhesion of the isolation layer agent; based on the preset threshold, construct a layered iterative optimization mechanism. First, quickly achieve the standard by adjusting the fog cannon parameters. If the requirements are still not met after reaching the maximum number of repetitions, then optimize the operating parameters of the scrubbing tower in reverse, forming a cross-equipment collaborative closed-loop adjustment. This ensures stable compliance of odor treatment, strictly controls the amount of agent adhesion to protect the isolation layer, avoids membrane aging and erosion, and reduces manual intervention and energy waste. It comprehensively improves the intelligence, long-term effectiveness and economy of landfill pollution treatment, and achieves the dual goals of pollution control and facility protection.

[0013] 2. This invention utilizes visual processing technology to identify and track the operating status of excavating machinery in real time, enabling predictive and precise spraying targeting dust sources. The high-polymer composite coagulant dust suppressant effectively overcomes strong wind interference, quickly agglomerating and settling dust, preventing dust from encapsulating odor molecules and affecting subsequent deodorization, while also ensuring the accuracy of the intelligent equipment's sensing system. 3. In this invention, if parameter adjustments reach the maximum number of iterations and still fail to meet requirements, the operating parameters of the biological scrubbing tower, such as spray water volume and spray pressure, are further optimized in reverse. By enhancing the source odor degradation capacity, the burden on subsequent deodorization operations is reduced, and the data collection and parameter adjustment process is repeated until both indicators meet the standards.

[0014] 4. In this invention, real-time diagnosis of oxygen utilization efficiency and three-dimensional monitoring of odor enable proactive early warning of system malfunctions. Combined with a visualization platform and human-computer interaction functions, an enhanced closed loop is formed from automatic control to manual confirmation and intervention, which greatly improves the reliability and on-site adaptability of the system.

[0015] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the collaborative decision-making and control process in this invention; Figure 2 This is a schematic diagram of the architecture of the intelligent flight system of the present invention; Figure 3 This is a schematic diagram illustrating the process of mapping the adjusted parameters of the deodorizing fog cannon and determining the threshold in this invention. Figure 4 This is a flowchart illustrating the three-dimensional monitoring process of the present invention. Figure 5 This is a schematic diagram of the data and control process of the present invention; Figure 6 This is a scatter plot showing the distribution of odor monitoring points in landfills according to the present invention. Figure 7 This is a line graph showing the trend of landfill exhaust gas component concentration and biological scrubbing tower treatment. Figure 8 The present invention provides a real-time control volcano diagram for the biological scrubbing tower, wherein the control response intensity of the scrubbing tower operating parameters is a dimensionless value; Figure 9 This is a heatmap showing the differential control analysis of the biological scrubbing tower in this invention; Figure 10 This is a bar chart comparing the gas component concentration with the control parameters of the scrubbing tower in this invention; wherein, the control response value of the operating parameters of the biological scrubbing tower is a dimensionless value, and the larger the value, the greater the adjustment of the scrubbing tower parameters by the model according to the change of gas concentration. Detailed Implementation

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0020] Example 1 Please see Figure 1 This embodiment is a monitoring method for the synergistic control of odor and dust from landfill excavation, comprising the following steps: S1. Lay an isolation layer and install gas extraction and injection pipelines; then collect historical operating parameters of the biological scrubbing tower and corresponding treatment gas composition data based on this. Please see Figures 2-6 S1 includes the following steps: S11. Select the current landfill to be monitored; lay an HDPE sealing membrane in the non-excavation area of ​​the current landfill to be monitored to form an isolation layer; and lay an air injection pipe under the isolation layer and lay a vertical air extraction well or a horizontal air extraction blind trench in the waste pile to obtain the current air source extraction pipe and the current air source injection pipe; wherein, the current air source extraction pipe and the current air source injection pipe maintain a vertical distance to ensure that air passes through multiple layers of waste; S12. Set the current biological scrubbing tower (this biological scrubbing tower utilizes the oxidation of microorganisms to effectively remove malodorous components in the gas, such as H2S, NH3, VOCs, etc., to achieve the harmless and clean emission of the extracted gas; wherein, an adsorption tank equipped with modified sugarcane bagasse carbon adsorbent (AC-SNP) is added at the end of the biological scrubbing tower) and several corresponding operating parameter types to obtain a set of biological scrubbing tower operating parameter types; the set of biological scrubbing tower operating parameter types includes circulating spray water volume (referring to the total flow rate of scrubbing liquid delivered to the packing layer inside the tower by the liquid phase circulation pump per unit time; used to determine the degree of wet coverage on the packing surface. Sufficient water volume allows the odor to fully contact and react with the microbial liquid film, while insufficient water volume results in insufficient contact and low deodorization efficiency, while excessive water volume leads to high energy consumption and a surge in pressure drop inside the tower), spray pressure (referring to the liquid working pressure at the end of the spray pipeline or the inlet of the nozzle; used to determine the atomization effect and spray uniformity of the nozzle. When the pressure is stable, it can ensure that the scrubbing liquid is evenly distributed throughout the entire packing cross section; while too low a pressure results in poor atomization and There are dead zones in the spraying process; excessive pressure can easily erode the biofilm and damage the nozzles and pipes. Nutrient solution replenishment flow rate (refers to the daily or hourly quantitative replenishment rate of nitrogen, phosphorus, and trace element nutrient solution to maintain microbial activity; used to provide nutrients for microbial degradation of H2S and NH3 to maintain metabolism and reproduction; insufficient flow rate will lead to bacterial inactivation and degradation failure, while excessive replenishment will cause eutrophication and sludge accumulation). Washing liquid level (refers to the height of the washing liquid level inside the bottom circulating water tank; used to ensure circulation). The pump must not cavitate when drawing water and must maintain a stable liquid phase circulation in the system. If the liquid level is too low, the pump is prone to cavitation and dry running, which can cause damage. If the liquid level is too high, the pump is prone to overflow and will increase the risk of dead zones and odor accumulation. The empty tower gas velocity (refers to the apparent flow velocity of the odor gas through the cross-section of the scrubbing tower without considering the volume occupied by the packing material; it is used to determine the residence time of the odor gas in the tower. If the gas velocity is too high, the residence time is short, and the pollutants are discharged before they can be degraded, which will lead to substandard treatment. If the gas velocity is too low, it will result in a large footprint and high energy consumption, as well as easy liquid accumulation and flooding) etc. S13. Based on the set of operating parameters of the biological scrubbing tower, obtain the operating parameter data of various biological scrubbing towers and the corresponding component types, contents and concentration data of the treated gas during multiple historical processes of treating gas in landfills, and obtain the historical biological scrubbing tower operating parameter dataset and the historical treated gas component dataset. Specifically, for underground gases, sensors are deployed at key nodes of the injection and extraction pipeline network to monitor the concentration, content, and flow rate of gases such as H2S, NH3, O2, and CO2 through underground and aerial monitoring. Oxygen utilization efficiency is calculated in real time using O2 and CO2 sensor data from the injection and extraction pipelines and compared with historical baselines. If the deviation exceeds a threshold, early warning information is generated indicating efficiency degradation or leakage in the injection and extraction system. Atmospheric diffusion odor monitors and laser dust monitors are deployed at key locations such as the excavation face, site boundary, and upwind and downwind directions of the prevailing wind, forming a dust monitoring network and ground gas monitoring points. Mobile inspections and three-dimensional spatial monitoring are conducted using multi-parameter high-precision gas monitoring units mounted on UAVs. These multi-parameter high-precision gas monitoring units include at least: a methane monitoring module based on tunable semiconductor laser absorption spectroscopy technology, and an electrochemical or photoionization sensor array for monitoring hydrogen sulfide, ammonia, and volatile organic compounds. Multi-parameter gas sensors, flow meters, and pressure sensors are deployed at key nodes of the gas gathering well to form a pipeline monitoring unit. In order to eliminate the interference of the downwash airflow of the UAV rotor on the sampled gas, the multi-parameter high-precision gas monitoring unit adopts a top-mounted installation structure, that is, the sensor air inlet is located above the UAV body and maintains a certain vertical distance from the rotor rotation plane, so as to ensure that the collected gas sample can represent the true concentration of the ambient atmosphere. In addition, the data acquisition frequency of the corresponding area sensors can be dynamically adjusted according to the real-time monitoring of the dust concentration change rate or the specific gas concentration change rate, and dynamic acquisition instructions can be generated. Based on the dynamic acquisition instructions, the sensor network is controlled to acquire data, and the acquired real-time environmental and equipment data stream is transmitted to the intelligent collaborative control center through a hybrid transmission network composed of LoRa protocol, industrial Ethernet or optical fiber, and MQTT protocol. The hybrid transmission network operates as follows: mobile governance devices and portable monitoring points form a self-organizing network and transmit data to the local gateway via the LoRa protocol; fixed monitoring points are directly connected to the control center via industrial Ethernet or fiber optics; and the local gateway and the control center on the cloud or local server (Alibaba Cloud IoT platform) transmit data uplink and distribute control commands downlink via the MQTT protocol. A high-precision time synchronization module and a data cleaning and fusion module are used to add a unified timestamp to all real-time data streams from the sensors and perform time-series alignment. Kalman filtering is applied to monitoring data susceptible to mechanical vibration interference to eliminate noise. Based on a normal distribution model or isolated forest algorithm using historical data, outliers in the real-time environmental and equipment data streams are identified and removed to obtain a three-dimensional odor concentration field. For this three-dimensional odor concentration field, the Cesium-based intelligent operation and decision-making platform calculates the oxygen utilization efficiency of each extraction unit in real time and compares it with historical baselines. Simultaneously, the flow rate, pressure, and gas composition time-series data of each monitoring point are analyzed. Through a collaborative governance unit and human-machine interface, blockage, leakage, or efficiency degradation faults are automatically identified, and visual early warnings and diagnostic reports are generated. The response spectrum data collected by the electronic nose is processed using a pattern recognition algorithm to output gas category identification results, which are then fused with concentration monitoring data for auxiliary tracing and comprehensive judgment of pollution sources.

[0021] By deploying and constructing extraction and injection pipelines in the early stages of odor control at landfills, the closed collection and orderly discharge of landfill waste gas can be achieved at the source. Specifically, by laying a sealing membrane to form a complete isolation and protection structure based on the stable site conditions of the non-excavation area, the unorganized escape path of waste gas from the bottom layer can be effectively blocked. Combined with the vertical staggered layout of the under-membrane injection pipeline and the extraction facilities in the waste, the airflow can be guided to fully penetrate the multi-layer landfill structure, greatly improving the comprehensiveness of the extraction and collection of odorous gases inside and reducing gas collection blind spots and escape losses. In addition, by using a biological scrubbing tower simultaneously and defining the categories of various core operating parameters, the uniform wet state of the packing layer is maintained by reasonably controlling the spray water volume and pressure, ensuring sufficient gas-liquid contact reaction and avoiding operational risks such as excessive energy consumption and biofilm damage. By relying on the nutrient solution replenishment rhythm to match the metabolic needs of microorganisms, the activity of the bacterial community is continuously maintained to ensure the degradation efficiency of odorous pollutants such as hydrogen sulfide and ammonia, while avoiding water pollution caused by nutrient excess. Liquid level control ensures the stable operation of liquid circulation equipment, avoiding the risks of pump damage and odor accumulation. The standardized setting of empty tower gas velocity precisely matches the residence time of waste gas, ensuring the purification effect meets standards and optimizing equipment energy consumption and operating conditions. Furthermore, relying on historical operating parameters and waste gas composition data, it provides data support for the intelligent control of subsequent scrubbing tower conditions, thereby comprehensively enhancing the overall effectiveness of odor control at the source and harmless treatment of waste gas in landfills. S2. Construct the final biological scrubbing tower operation parameter mapping model based on the data collected in S1; S2 includes the following steps: S21. Based on the historical biological scrubbing tower operating parameter dataset and the historical treated gas component dataset, construct a mapping model with the input of various components of the treated gas and the output of various operating parameter data of the biological scrubbing tower, and obtain the final biological scrubbing tower operating parameter mapping model. S21 includes the following steps: S211. Construct an initial biological scrubbing tower operation parameter mapping model and set a first training data ratio (e.g., 8:2 or 7:3, which can be adjusted adaptively according to the actual training situation); divide the historical treated gas component dataset and the historical biological scrubbing tower operation parameter dataset according to the first training data ratio to obtain the first training dataset and the first test dataset. S212. Set a first training error threshold (10%~15%, which can be adjusted adaptively according to the actual training situation); input the first training dataset into the initial biological scrubbing tower operation parameter mapping model for training; during the training process, if the training error is less than the first training error threshold, stop training and obtain the trained biological scrubbing tower operation parameter mapping model; otherwise, continue training until the training error is less than the first training error threshold. S213. Set a first test accuracy threshold (90%~95%, which can be adjusted adaptively according to the actual test situation); input the first test dataset into the trained biological scrubbing tower operation parameter mapping model for testing; after the test is completed, obtain the first test accuracy data; if the first test accuracy data is greater than or equal to the first test accuracy threshold, use the trained biological scrubbing tower operation parameter mapping model as the final biological scrubbing tower operation parameter mapping model; otherwise, return to S212 to continue training the trained biological scrubbing tower operation parameter mapping model and repeat S213 until the first test accuracy data is greater than or equal to the first test accuracy threshold. The initial biological scrubbing tower operating parameter mapping model can adopt a multilayer perceptron neural network model, which can adapt to the nonlinear mapping fitting requirements of gas components and operating parameters. It includes an input layer, three fully connected layers, and an output layer. The number of nodes in the input layer corresponds to the dimension of the processed gas component data, used to receive the concentration and content characteristics of pollutants such as hydrogen sulfide and ammonia. The first fully connected layer has 128 neurons and uses the ReLU linearly corrected activation function to complete the nonlinear transformation and initial extraction of input features. The second fully connected layer has 64 neurons and uses the ReLU linearly corrected activation function to further fuse gas component correlation features and strengthen the expression of the mapping relationship. The third fully connected layer has 32 neurons and uses the ReLU linearly corrected activation function to compress high-dimensional features while retaining core correlation information. The number of nodes in the output layer corresponds to the dimension of the biological scrubbing tower operating parameters and uses a linear activation function to directly output continuous parameter results such as circulating spray water volume, spray pressure, nutrient solution replenishment flow rate, scrubbing liquid level, and empty tower gas velocity. By constructing a final biological scrubbing tower operation parameter mapping model, it can fully explore the inherent correlation between gas component characteristics and scrubbing tower operation parameters. This allows the setting of operation parameters to no longer rely on experience-based judgment, but rather to form a scientific adaptation logic based on real operating data. Moreover, this model can accurately capture the differentiated needs of scrubbing tower operation caused by changes in the composition, content, and concentration of different gas components, achieving targeted matching of operation parameters. For example, based on the proportion and concentration of odorous pollutants, it automatically adjusts the circulating spray water volume and spray pressure to optimize the gas-liquid contact effect, adapts the nutrient solution replenishment flow rate to accurately meet the metabolic needs of microorganisms, and regulates the empty tower gas velocity and scrubbing liquid level to ensure reaction efficiency and equipment stability. This avoids problems such as energy waste, biofilm damage, and eutrophication caused by excessively high parameter settings, as well as the hidden dangers of low deodorization efficiency and substandard treatment caused by insufficient parameters. It significantly improves the accuracy and rationality of biological scrubbing tower operation parameter control, allowing the scrubbing tower to quickly respond to dynamic changes in gas components and always maintain optimal operating conditions. S3. Collect historical data on spray parameters, wind speed and wind direction of the deodorizing fog cannon unit, as well as data on odor concentration and isolation layer adhesion before and after spraying, and construct a final data mapping model of odor and isolation layer after spraying based on this data. S3 includes the following steps: S31. Install air circulation and purification devices in areas with frequent personnel activity and around the excavation pit (the device has dual air ducts; one air duct is responsible for drawing in polluted on-site air, which passes through a primary filter and a chemical adsorption filter layer for high-efficiency filtration and purification of H2S, NH3, VOCs, and HEPA; then the other air duct delivers the purified clean air out at a certain angle and speed); and deploy deodorizing fog cannon units at and around the highest point of the excavation area of ​​the current landfill to be monitored (the deodorizing fog cannon is equipped with a variable spraying system that can adjust the spray flow rate, atomization particle size, and spray elevation angle of the deodorizing agent in real time). S32. Based on the air circulation and purification device filtering the polluted air at the site, collect the spray flow rate, atomization particle size, spray elevation angle, wind direction angle, wind speed, concentration data of each odor component (such as H2S, NH3, VOCs) in the air before and after each spray of the deodorizing agent from multiple sets of deodorizing fog cannons at the current landfill to be monitored, as well as the amount of deodorizing agent adhering to the isolation layer (i.e., the interval between the laying of the isolation layer and the aging of the isolation layer), to obtain the historical spray flow rate dataset, historical spray atomization particle size dataset, historical spray elevation angle dataset, historical wind direction angle dataset, historical wind speed dataset, historical odor component concentration dataset before spraying, historical odor component concentration dataset after spraying, and historical isolation layer adhesion dataset. Specifically, for the ground environment, fixed atmospheric diffusion monitors and laser dust monitors are deployed at site boundaries, work surfaces, and upwind and downwind directions to create real-time two-dimensional concentration fields. At the same time, a mobile monitoring platform using drones equipped with multi-parameter gas sensors and electronic nose sensor arrays is introduced. This platform can perform pre-set route inspections and use electronic noses to quickly collect fingerprint profiles and identify patterns of odors in the air, assisting in the identification of complex odor components and pollution sources. It is also fused with ground-based fixed network data to achieve three-dimensional spatial positioning and qualitative analysis of pollutants. S33. Based on the historical jet flow rate dataset, historical jet atomization particle size dataset, historical jet elevation angle dataset, historical wind direction angle dataset, historical wind speed dataset, historical pre-jet odor component concentration dataset, historical post-jet odor component concentration dataset, and historical isolation layer adhesion amount dataset, construct a mapping model with the inputs of jet flow rate data, jet atomization particle size data, jet elevation angle data, wind direction angle data, wind speed data, pre-jet odor component concentration data, and the outputs of post-jet odor component concentration data and the adhesion amount of deodorizing agent on the isolation layer, to obtain the final post-jet odor and isolation layer data mapping model. S33 includes the following steps: S331. Construct an initial post-spray odor and isolation layer data mapping model and set a second training data ratio (e.g., 8:2 or 7:3, which can be adjusted adaptively according to the actual training situation); divide the historical spray flow rate dataset, historical spray atomization particle size dataset, historical spray elevation angle dataset, historical wind direction angle dataset, historical wind speed dataset, historical pre-spray odor component concentration dataset, historical post-spray odor component concentration dataset, and historical isolation layer adhesion amount dataset according to the second training data ratio to obtain the second training dataset and the second test dataset; S332. Set a second training error threshold (10%~15%, which can be adjusted adaptively according to the actual training situation); input the second training dataset into the initial post-spray odor and isolation layer data mapping model for training; during the training process, if the training error is less than the second training error threshold, stop training and obtain the trained post-spray odor and isolation layer data mapping model; otherwise, continue training until the training error is less than the second training error threshold. S333: Set a second test accuracy threshold (90%~95%, which can be adjusted adaptively according to the actual test situation); input the second test dataset into the trained post-spray odor and isolation layer data mapping model for testing; after the test is completed, obtain the second test accuracy data; if the second test accuracy data is greater than or equal to the second test accuracy threshold, use the trained post-spray odor and isolation layer data mapping model as the final post-spray odor and isolation layer data mapping model; otherwise, return to S332 to continue training the trained post-spray odor and isolation layer data mapping model and repeat S333 until the second test accuracy data is greater than or equal to the second test accuracy threshold; The initial post-spray odor and isolation layer data mapping model can employ a deep fully connected multi-output regression neural network model. This model is adaptable to mapping scenarios where multiple dimensions are input simultaneously predicting two sets of continuous outputs. It includes an input layer, four fully connected layers, and an output layer. The number of nodes in the input layer is consistent with the number of all feature dimensions of the spraying parameters, meteorological parameters, and initial odor components. The first fully connected layer has 256 neurons and uses a linearly modified activation function to perform initial nonlinear transformation of high-dimensional features. The second fully connected layer has 128 neurons and continues to use a linearly modified activation function to deepen feature association mining. The third fully connected layer... With 64 neurons, the core associated features are still compressed and refined using a linear modified activation function. The fourth fully connected layer has 32 neurons to simplify redundant information and integrate the correlation between operating conditions and protection. The output layer has a dual-branch output structure, with both output branches using linear activation functions. One branch outputs the predicted concentration of each odor component after treatment, and the other branch outputs the predicted effective working time of the isolation layer. The entire network of this model can accurately capture the complex nonlinear relationship between spray adjustment parameters, environmental meteorological conditions, initial odor concentration and purification effect, and the aging life of the seepage prevention layer, meeting the engineering modeling requirements of multi-input, dual-objective synchronous accurate prediction. By installing a dual-duct air circulation and purification device in the core personnel area and around the excavation pit, near-field, multi-layered, precise filtration of polluted air is achieved, specifically removing hydrogen sulfide, ammonia, and volatile organic odors, creating a safe breathing environment for workers. Simultaneously, odor-reducing fog cannon units with adjustable parameters are deployed at the highest point in the excavation area, flexibly adapting to on-site conditions to control the spraying status and achieve large-scale odor suppression. Relying on a collaborative monitoring system combining ground-based fixed monitoring equipment and a mobile drone monitoring platform, it can not only map a two-dimensional concentration field in real time but also collect odor fingerprints and identify complex odors through drone inspections. By integrating fixed and mobile monitoring data, the system achieves three-dimensional localization and qualitative analysis of pollutants, accurately capturing multi-dimensional operating condition data and information on the amount of pesticide adhering to the isolation layer. Based on integrated historical datasets, a mapping model is constructed to deeply explore the intrinsic relationship between spraying parameters, environmental conditions, deodorization effect, and the amount of pesticide adhering to the isolation layer. This not only accurately predicts the odor concentration after spraying but also effectively controls the pesticide adhesion to the isolation layer, providing a scientific basis for optimizing fog cannon spraying parameters. While ensuring that deodorization meets standards across the entire area and fulfilling the health protection needs of personnel, it minimizes the erosion of the HDPE isolation layer by excessive pesticide adhesion, extends the service life of the isolation layer, and improves the accuracy, stability, and long-term effectiveness of the entire deodorization system. S4. Collect real-time data on the gas component type, content, and concentration of the gas injection pipeline; input this data into the mapping model in S2; and configure the current biological scrubbing tower based on the mapping results. Please see Figure 7 , Figure 8 S4 includes the following steps: S41. Real-time acquisition of data on the type, content and concentration of each component of the gas under the isolation layer before it enters the current gas source extraction pipeline, to obtain the current real-time processing gas component dataset. S42. Input the current real-time processed gas component dataset into the final biological scrubbing tower operation parameter mapping model for mapping to obtain the current real-time operation parameter dataset; automatically set various operation parameters of the current biological scrubbing tower according to the current real-time operation parameter dataset; By collecting real-time data on the type, content, and concentration of gas components before entering the extraction pipeline below the isolation layer, the dynamic changes in landfill exhaust emissions can be accurately captured, providing immediate and comprehensive operational data for the operation and control of the bio-scrubber. This avoids treatment mismatch issues caused by lagging gas source parameters. Real-time gas component data is input into a pre-built mapping model, which quickly outputs a dataset of real-time operating parameters adapted to the current operating conditions, enabling automated and precise setting of various core operating parameters of the scrubber. Furthermore, parameters such as circulating spray water volume and spray pressure can be flexibly adjusted according to changes in gas pollutant concentration and component ratio. This ensures uniform wetting of the packing layer and sufficient gas-liquid contact, while precisely matching the nutrient solution replenishment rhythm required for microbial metabolism. Simultaneously, it rationally controls the empty tower gas velocity and scrubbing liquid level. This improves the degradation efficiency of odorous pollutants such as hydrogen sulfide and ammonia while effectively avoiding potential risks such as energy waste, biofilm damage, eutrophication of water bodies, or substandard treatment caused by improper parameter settings. S5. Select the current dust removal agent for dust removal operation, then collect the spraying parameters of each deodorizing fog cannon and the on-site wind direction, wind speed, and odor component concentration before spraying, and input them into the mapping model in S3 for mapping, and output the current real-time odor component concentration and isolation layer adhesion data after spraying. Please see Figure 9 S5 includes the following steps: S51. Based on the completion of setting various operating parameters of the current biological scrubbing tower in S42, select the current dust removal agent; use the current dust removal agent to perform real-time agglomeration and dust removal on the dust generated by the landfill excavation machinery during the excavation process; Specifically, high-definition cameras and lidar can be installed on dust removal equipment, combined with visual processing technology to identify and track the position, posture and operation of excavating machinery in real time. Based on the identification results, the dust suppressant carried by the equipment can be sprayed in a tracking and predictive manner. The dust suppressant can be a foam type or a water mist type, and a high-molecular composite coagulating dust suppressant is also required. It can quickly agglomerate dust into large particles to overcome the impact of strong winds on the dust suppression effect. S52. After the real-time dust removal operation in S51 is completed, the spray flow rate, atomization particle size, spray elevation angle, wind direction angle, wind speed, and concentration data of each odor component in the air before each spray of the deodorizing agent are collected in real time for each deodorizing mist cannon unit of the landfill to be monitored. The real-time spray flow rate, real-time atomization particle size, real-time spray elevation angle, real-time wind direction angle, real-time wind speed, and the real-time odor component concentration dataset before spraying are obtained. The current real-time injection flow rate data, current real-time atomization particle size data, current real-time injection pitch angle data, current real-time wind direction angle data, current real-time wind speed data, and current real-time pre-injection odor component concentration dataset are input into the final post-injection odor and isolation layer data mapping model for mapping, to obtain the current real-time post-injection odor component concentration dataset and the current real-time isolation layer adhesion amount data. Based on the pretreatment foundation optimized by the bio-scrubber tower parameters, and through the orderly integration of intelligent tracking dust removal and precise mapping deodorization, efficient and coordinated management of dust and odor is achieved. After the scrubber tower operates stably and reduces the core odor load, multi-element dust suppressants adapted to different operating conditions are selected. Relying on intelligent equipment equipped with high-definition cameras and lidar, visual processing technology is used to identify and track the operating status of the excavating machinery in real time, and to perform predictive and tracking precision spraying on dust sources. Among them, the high-polymer composite coagulant dust suppressant can effectively overcome the interference of strong winds and quickly agglomerate and settle dust, thus preventing dust from encapsulating odor molecules and affecting subsequent deodorization. This approach achieves both effectiveness and accuracy of the intelligent equipment's sensing system. After dust suppression operations are completed, key parameters of the fog cannon spraying, on-site meteorological data, and initial odor concentration data are collected in real time and input into a preset mapping model. This model accurately outputs the predicted results of odor concentration and agent adhesion in the isolation layer after spraying, providing a scientific basis for fog cannon spraying. Therefore, the overall approach first stabilizes the on-site air environment and eliminates the interference of dust on deodorization through intelligent dust suppression, and then uses mapping to provide data support for the subsequent data-driven precise application of deodorizing agents. While ensuring that both dust and odor control meet the standards, it minimizes the erosion of the HDPE isolation layer by excessive agent adhesion, balancing treatment efficiency, equipment operational stability, and the long-term protection requirements of the isolation layer. S6. Set thresholds for odor component concentration and isolation layer adhesion amount; if the mapping data in S5 does not meet the thresholds, repeatedly adjust the fog cannon spray parameters and input them into the mapping model in S3. If the number of repetitions reaches the maximum number of repetitions and the mapping result still does not meet the standard, optimize the current operating parameters of the biological scrubbing tower in S4, and repeat S5 and S6 until the mapping result meets the standard; otherwise, the adjustment is complete. Please see Figure 10 S6 includes the following steps: S61. Set the concentration thresholds of various odor components in the air after the current deodorizing agent is sprayed and the adhesion threshold of the deodorizing agent on the isolation layer (which can be adaptively set according to the current landfill excavation deodorization requirements) to obtain the current set of odor component concentration thresholds and the current isolation layer adhesion threshold; then set the first maximum number of repetitions (which can be adaptively set according to the actual situation). S62. Based on the current post-injection odor component concentration threshold set, if there is a concentration data in the current real-time post-injection odor component concentration dataset that is greater than or equal to the corresponding concentration threshold, or if the current real-time isolation layer adhesion data is greater than or equal to the current isolation layer adhesion threshold, the current real-time injection flow rate data, the current real-time atomized particle size data, and the current real-time injection pitch angle data are repeatedly adjusted to obtain the currently adjusted injection flow rate data, the currently adjusted atomized particle size data, and the currently adjusted injection pitch angle data; otherwise, no adjustment is required. The adjusted injection flow rate data, the adjusted atomization particle size data, the adjusted injection pitch angle data, the real-time wind direction angle data, the real-time wind speed data, and the real-time pre-injection odor component concentration dataset are input into the final post-injection odor and isolation layer data mapping model for mapping, to obtain the post-injection odor component concentration dataset and the post-injection isolation layer adhesion amount data. S63. If the number of repetitions is less than or equal to the first maximum number of repetitions, there is no concentration data in the current adjusted post-spray odor component concentration data that is greater than or equal to the corresponding concentration threshold, and the current real-time isolation layer adhesion data is less than the current isolation layer adhesion threshold, obtain the current first final spray flow rate data, the current first final atomized particle size data, and the current first final spray elevation angle data; adjustment complete; then set the deodorizing fog cannon unit according to the current first final spray flow rate data, the current first final atomized particle size data, and the current first final spray elevation angle data; Otherwise, the spray water volume, spray pressure, nutrient solution replenishment flow rate, and washing liquid level in the current real-time operating parameter dataset are repeatedly adjusted to obtain the current adjusted operating parameter dataset; and S52 and S62 are executed until there is no concentration data in the current adjusted spray odor component concentration dataset that is greater than or equal to the corresponding concentration threshold and the current real-time isolation layer adhesion data is less than the current isolation layer adhesion threshold, to obtain the current final operating parameter dataset, the current second final spray flow rate data, the current second final atomization particle size data, and the current second final spray pitch angle data; In addition, the system should be checked for faults and warning information should be generated. The warning information should include: fault type, location, severity level, and suggested handling measures, and should also include human-computer interaction functions. After logging in through the platform, maintenance personnel can view, confirm, and handle such operations. Then, based on the current final operating parameter dataset, the current second final spray flow rate data, the current second final atomization particle size data, and the current second final spray elevation angle data, the current biological scrubbing tower and the deodorizing fog cannon unit are set respectively; For example, consider the excavation of a municipal solid waste landfill in a certain city; as follows: The non-excavation area is covered with a 3.0mm thick HDPE sealing membrane, while the excavation area is equipped with one biological scrubbing tower, three AI mobile dust suppression devices, and four intelligent deodorizing fog cannon units. The gas sensor array deployed under the isolation layer collects real-time data on gas composition before it enters the extraction pipe. The concentrations are: H2S 85 mg / m³, NH3 60 mg / m³, and VOCs 45 mg / m³. At the same time, the gas humidity is 75% and the temperature is 28℃. The dataset was input into the final biological scrubbing tower operation parameter mapping model, and the current real-time operation parameter dataset was output: circulating spray water volume 12 m³ / h, spray pressure 0.3 MPa, nutrient solution replenishment flow rate 5 L / h, scrubbing liquid level 1.2 m, and empty tower gas velocity 0.8 m / s. The biological scrubbing tower was automatically set according to this parameter set, and the pH value of the water tank inside the tower was stabilized at 7.2 (the optimal range for microorganisms), and the dissolved oxygen was maintained at 2.5 mg / L to ensure the activity of the microbial community. After the parameters of the scrubbing tower are set, a polymer composite cohesive dust suppressant is selected (with a foam dust suppressant as a backup); three AI mobile dust suppression devices are equipped with high-definition cameras and lidar to identify and track the working trajectories of two excavators and one loader in real time. When the dust intensity generated by the excavator bucket operation is detected to reach 1.2 mg / m³, tracking precision spraying is started. The dust suppressant spraying flow rate is 8 L / min and the atomization particle size is 100 μm. Predictive spraying is carried out on the core area of ​​the dust source, and the dust concentration on the working surface is reduced to below 0.3 mg / m³ within 30 minutes. After dust suppression is completed, real-time data is collected through sensors mounted on the fog cannon units and the on-site weather station: the current real-time spray flow rates of the four fog cannons are 15L / min, 15L / min, 12L / min, and 12L / min, respectively; the atomized particle size is 80μm; the spray pitch angle is 45°; and the horizontal rotation angle is locked within the excavation area (avoiding the HDPE membrane area); the on-site wind direction is 35° northeast, and the wind speed is 2.8m / s; the concentration of H2S in the air before spraying is 30mg / m³, the concentration of NH3 is 25mg / m³, and the concentration of VOCs is 18mg / m³, forming a real-time dataset of odor component concentrations before spraying; Inputting the above data into the final post-spray odor and isolation layer data mapping model yields the current real-time post-spray odor component concentration dataset: H2S concentration 7 mg / m³, NH3 concentration 5 mg / m³, VOCs concentration 4 mg / m³; the current real-time isolation layer adhesion amount is 0.8 g / m². The adaptive thresholds were then set as follows: the current threshold set for odor component concentrations after spraying is H2S≤5mg / m³, NH3≤4mg / m³, and VOCs≤3mg / m³; the current threshold for the amount of isolation layer adhesion is 1.0g / m²; the first maximum number of repetitions is set to 3. It was determined that the current real-time H2S concentration after spraying is 7mg / m³≥5mg / m³ and the NH3 concentration is 5mg / m³≥4mg / m³, which does not meet the threshold requirements. Therefore, the first parameter adjustment was initiated: the spray flow rates of the four fog cannons were adjusted to 18L / min, 18L / min, 15L / min, and 15L / min, respectively; the atomization particle size was optimized to 60μm; and the spray elevation angle was adjusted to 50°. The adjusted parameters, along with real-time wind direction, wind speed, and initial odor concentration data, were input into the model again, resulting in the following adjusted results: H2S concentration 6mg / m³, NH3 concentration 4.5mg / m³, VOCs concentration 3mg / m³, and isolation layer adhesion 0.9g / m². There were still items exceeding the standard, so a second adjustment was initiated: the injection flow rate was maintained at 18 L / min, the atomized particle size was adjusted to 50 μm, and the pitch angle was fine-tuned to 45°. The mapping results showed that the H2S concentration was 5.5 mg / m³ and the NH3 concentration was 4.2 mg / m³, which still did not meet the standard. After the third adjustment, the injection flow rate was increased to 20 L / min and the atomized particle size was 40 μm. The mapping results showed that the H2S concentration was 5.2 mg / m³ and the NH3 concentration was 4.1 mg / m³, which still did not meet the threshold, and the number of repetitions had reached 3 (the first maximum number of repetitions). A secondary adjustment was initiated to optimize the operating parameters of the biological scrubbing tower: the circulating spray water volume was adjusted to 14 m³ / h, the spray pressure was increased to 0.35 MPa, the nutrient solution replenishment flow rate was increased to 7 L / h, and the scrubbing liquid level was maintained at 1.2 m. Subsequently, the data was collected again from S52, and the fog cannon sprayed according to the adjusted parameters. The data was then input into the model again, resulting in: H2S concentration of 4.8 mg / m³, NH3 concentration of 3.8 mg / m³, VOCs concentration of 2.5 mg / m³, and isolation layer adhesion of 0.7 g / m². All indicators met the threshold requirements (odor concentration below the threshold, adhesion below 1.0 g / m²). The final operating parameter dataset was determined as follows: circulating spray water volume 14 m³ / h, spray pressure 0.35 MPa, nutrient solution replenishment flow rate 7 L / h, and washing liquid level 1.2 m; the current second final spray flow rate data is 20 L / min, atomized particle size 40 μm, and spray pitch angle 45°. The biological scrubbing tower and deodorizing fog cannon unit were set according to these parameters. After the system was running stably, the on-site odor concentration met the standard, and no excessive agent adhesion was observed on the HDPE isolation layer. By setting adaptive odor concentration thresholds and isolation layer agent adhesion thresholds, coupled with a reasonable maximum number of repeated adjustments, and first using the preset thresholds as a benchmark, the system judges the odor concentration and isolation layer adhesion amount obtained from real-time mapping after spraying. If there are any exceedances, the system first adjusts the core parameters such as the spray flow rate, atomization particle size, and elevation angle of the fog cannon, and then verifies the adjustment effect through the mapping model to ensure that the deodorization compliance rate is improved without exceeding the upper limit of the isolation layer protection. When the parameter adjustment reaches the maximum number of times and still does not meet the requirements, the system further optimizes the operating parameters of the biological scrubbing tower, such as the spray water volume and spray pressure, to enhance the source odor degradation capacity and reduce the burden on subsequent deodorization operations. The data collection and parameter adjustment process is then repeated until both indicators meet the standards. Through layered and progressive adjustments, the system ensures that the odor treatment effect is stable and meets the requirements, while strictly controlling the amount of agent adhesion on the isolation layer to avoid membrane erosion and aging. At the same time, relying on the adaptive threshold and repeated iteration mechanism, the system flexibly adapts to the dynamic changes in working conditions during landfill excavation, reduces human intervention errors, and achieves coordinated and precise operation of the biological scrubbing tower and the deodorizing fog cannon unit.

[0022] Example 2 This embodiment discloses a monitoring system for the synergistic treatment of odor and dust from landfill excavation. The system can implement the methods of the above embodiment and includes an operation data acquisition module, a scrubbing tower mapping model construction module, an isolation layer mapping model construction module, an operation parameter mapping module, a spray data mapping module, and a parameter adjustment module. The data acquisition module is used to collect historical operating parameters of the biological scrubbing tower and the corresponding processed gas composition data. The scrubbing tower mapping model building module is used to build the final biological scrubbing tower operating parameter mapping model; The isolation layer mapping model building module is used to build a data mapping model of odor and isolation layer after final injection; The operating parameter mapping module is used to map the operating parameters of the biological scrubbing tower; The injection data mapping module is used to map the current real-time concentration of odor components and the amount of insulating layer adhesion data after injection. The parameter adjustment module is used to repeatedly adjust the spray parameters of the deodorizing mist cannon and the operating parameters of the biological scrubbing tower.

[0023] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0024] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present invention.

Claims

1. A monitoring method for the synergistic control of odor and dust from landfill excavation, characterized in that, Includes the following steps: S1. Lay an isolation layer and install gas extraction and injection pipelines, and then collect historical operating parameters of the biological scrubbing tower and corresponding treatment gas composition data based on this. S2. Construct the final biological scrubbing tower operation parameter mapping model based on the data collected in S1; The input of the final biological scrubbing tower operating parameter mapping model is the data of various components of the processed gas, and the output is the data of various operating parameters of the biological scrubbing tower. S3. Collect historical data on spray parameters, wind speed and wind direction of the deodorizing fog cannon unit, as well as data on odor concentration and isolation layer adhesion before and after spraying, and construct a final data mapping model of odor and isolation layer after spraying based on this data. Specifically, it includes: S31. Install air circulation and purification devices and deploy deodorizing fog cannon units; S32. Collect the spray flow rate, atomization particle size, spray elevation angle, wind direction angle, wind speed, concentration of each odor component in the air before and after each spray of deodorizing agent, and the amount of deodorizing agent adhering to the isolation layer of multiple historical deodorizing fog cannon units at the current landfill to be monitored. Based on this, construct a data mapping model of odor and isolation layer after final spray. S4. Collect real-time data on the gas component type, content, and concentration of the gas injection pipeline; input this data into the mapping model in S2; and configure the current biological scrubbing tower based on the mapping results. S5. Select the current dust removal agent for dust removal operation, then collect the spraying parameters of each deodorizing fog cannon and the on-site wind direction, wind speed, and odor component concentration before spraying, and input them into the mapping model in S3 for mapping, and output the current real-time odor component concentration and isolation layer adhesion data after spraying. Specifically, it includes: S51. Select the current dust removal agent and perform real-time agglomeration and dust removal on the dust generated by the landfill excavation machinery during the excavation process; S52. Real-time collection of the spray flow rate, atomization particle size, spray elevation angle, wind direction angle, wind speed, and concentration data of each odor component in the air before each spray of the deodorizing agent for each deodorizing mist cannon unit of the landfill to be monitored, and input into the final spray odor and isolation layer data mapping model for mapping. S6. Set thresholds for odor component concentration and isolation layer adhesion amount. If the mapping data in S5 does not meet the thresholds, repeatedly adjust the fog cannon spray parameters and input them into the mapping model in S3. If the number of repetitions reaches the maximum number of repetitions and the mapping result still does not meet the standard, optimize the current operating parameters of the biological scrubbing tower in S4 and repeat S5 and S6 until the mapping result meets the standard; otherwise, the adjustment is complete.

2. The monitoring method for synergistic control of odor and dust from landfill excavation according to claim 1, characterized in that: The inputs to the final post-spray odor and isolation layer data mapping model in S3 are spray flow rate data, spray atomization particle size data, spray pitch angle data, wind direction angle data, wind speed data, and pre-spray odor component concentration data. The outputs are post-spray odor component concentration data and the amount of deodorizing agent adhering to the isolation layer.

3. The monitoring method for synergistic control of odor and dust from landfill excavation as described in claim 1, characterized in that, S4 includes the following steps: S41. Real-time acquisition of data on the type, content, and concentration of each component of the gas under the isolation layer before it enters the current gas source extraction pipeline, and input of this data into the final biological scrubbing tower operation parameter mapping model for mapping. Based on the mapping results, the various operation parameters of the current biological scrubbing tower are automatically set.

4. The monitoring method for synergistic control of odor and dust from landfill excavation according to claim 3, characterized in that, S6 includes the following steps: S61. If the concentration data in the mapping result of S52 is greater than or equal to the corresponding concentration threshold, or if the adhesion data is greater than or equal to the current isolation layer adhesion threshold, the real-time injection flow rate data, atomized particle size data and injection pitch angle data collected in S52 shall be repeatedly adjusted and then input into the final injection odor and isolation layer data mapping model for mapping; otherwise, no adjustment is required.

5. The monitoring method for synergistic control of odor and dust from landfill excavation according to claim 4, characterized in that, S6 further includes the following steps: S62. If, after a finite number of repeated adjustments, there is no concentration data greater than or equal to the corresponding concentration threshold and no adhesion data less than the current isolation layer adhesion threshold in the mapping result of S61, the adjustment is complete. Otherwise, repeatedly adjust the spray water volume, spray pressure, nutrient solution replenishment flow rate and washing liquid level in the mapping results of S41, and execute S52 and S61 until there is no concentration data greater than or equal to the corresponding concentration threshold and the adhesion amount data is less than the current isolation layer adhesion amount threshold in the mapping results of S61.

6. A system for implementing a monitoring method for the synergistic control of odor and dust from landfill excavation as described in any one of claims 1-5, characterized in that: It includes a data acquisition module, a scrubbing tower mapping model construction module, an isolation layer mapping model construction module, a parameter mapping module, a jet data mapping module, and a parameter adjustment module; The data acquisition module is used to collect historical operating parameters of the biological scrubbing tower and the corresponding processed gas composition data. The scrubbing tower mapping model building module is used to build the final biological scrubbing tower operating parameter mapping model; The isolation layer mapping model building module is used to build a data mapping model of odor and isolation layer after final injection; The operating parameter mapping module is used to map the operating parameters of the biological scrubbing tower; The injection data mapping module is used to map the current real-time concentration of odor components and the amount of insulating layer adhesion data after injection. The parameter adjustment module is used to repeatedly adjust the spray parameters of the deodorizing mist cannon and the operating parameters of the biological scrubbing tower.

Images