Farm waste gas purification system and related equipment

Abstract

The present application provides a farm waste gas purification system and related equipment, which includes a multi-source heterogeneous perception module, a data processing module, a main control module, a chemical decomposition system, a biological filtration system, a waste liquid treatment system and a fault diagnosis system; wherein the multi-source heterogeneous perception module is responsible for sensing various state parameters of the target farm and transmitting them to the data processing module; the data processing module is responsible for receiving various state parameters of the target farm transmitted by the multi-source heterogeneous perception module and pre-processing them, and transmitting the obtained target multivariate data to the main control module; so that it can perform fusion processing on the target multivariate data, and generate control decisions for the chemical decomposition system, the biological filtration system, and the waste liquid treatment system, and transmit each control decision to the chemical decomposition system, the biological filtration system, and the waste liquid treatment system respectively to control the chemical decomposition system, the biological filtration system, and the waste liquid treatment system to purify the waste gas discharged from the target farm.

Description

Technical Field

[0001] The present application relates to the technical field of aquaculture waste gas management, and in particular to a farm waste gas purification system and related equipment. Background Art

[0002] As the livestock and poultry farming industry continues to move toward scale and intensification, intensive farms, with their efficient use of vertical space, high land utilization rates, excellent biosafety, and high levels of intelligence, have gradually become a key method of intensive farming. However, while intensive farms offer excellent ventilation, they also cause odors to spread more widely and rapidly, leading to increasingly serious odor pollution. The harmful gases emitted by intensive farms not only negatively impact the surrounding environment and climate but also pose a serious threat to the health of humans and animals. Therefore, purifying and treating the waste gas generated by intensive farms is urgent.

[0003] The exhaust gas produced by intensive livestock farms is complex in composition, with different pollutants exhibiting varying physical and chemical properties and relatively low concentrations. This has limited the development of exhaust gas purification technologies. Commonly used exhaust gas purification technologies for intensive livestock farms include "source control," "process management," and "end-of-pipe purification." End-of-pipe purification, as it is easier to integrate with mechanical control, offers advantages such as high purification efficiency, reliable operation, and ease of use. It also reduces the risk of airborne disease transmission in intensive livestock farms and is considered a key measure for addressing exhaust gas pollution.

[0004] In practice, the "end-of-pipe purification" technology for intensive pig farms uses chemical or biological methods to treat exhaust gases emitted from piggeries at the end of the farm. While biological methods have a long reaction time and are easy to maintain, they require a large footprint, have low purification efficiency, and are prone to clogging sewage pipes with biomolecules and proteins. Chemical methods have a short reaction time, are highly effective and controllable in removing odors and ammonia, are stable, and require a small footprint. However, chemical methods consume a lot of electricity and water, rely primarily on manual control, and are difficult to operate. Given the high density of piggeries in floor-to-ceiling buildings and the high ammonia concentration within them, the purification system places high demands on its operation. Therefore, while ensuring proper ventilation in intensive pig farms, ensuring the stability and efficiency of the exhaust gas purification system and reducing its energy consumption has always been a concern. Summary of the Invention

[0005] The present application aims to solve at least one of the above-mentioned technical defects. In view of this, the present application provides a farm waste gas purification system and related equipment to solve the technical defects of the existing technology in the difficulty of farm waste gas purification.

[0006] A farm waste gas purification system, the system comprising: a multi-source heterogeneous perception module, a data processing module, a main control module, a chemical decomposition system, a biological filtration system, a waste liquid treatment system and a fault diagnosis system; wherein the multi-source heterogeneous perception module is responsible for sensing various state parameters of a target farm and transmitting them to the data processing module; the data processing module is responsible for receiving various state parameters of the target farm transmitted by the multi-source heterogeneous perception module, pre-processing them, and transmitting the obtained target multivariate data to the main control module; the main control module is responsible for fusing the target multivariate data, generating control decisions for the chemical decomposition system, the biological filtration system, and the waste liquid treatment system, and transmitting the control decisions of the chemical decomposition system, the biological filtration system, and the waste liquid treatment system to the chemical decomposition system, the biological filtration system, and the waste liquid treatment system respectively to control the chemical decomposition system, the biological filtration system, and the waste liquid treatment system to purify the waste gas discharged from the target farm.

[0007] Preferably, the farm waste gas purification system also includes a fault diagnosis system; the fault diagnosis system is used to receive and process the fault information fed back by the main control module when a fault occurs in the farm waste gas purification system, and provide the fault information and fault solution of the farm waste gas purification system.

[0008] Preferably, the target farm includes a waste gas purification platform, which includes several water spray valves, a pH sensor, a water pump, an acid solution pool, and a packing structure; the chemical decomposition system is responsible for controlling the water spray valve corresponding to the acid solution pool of the waste gas purification platform, spraying the acid solution in the acid solution pool into the packing structure of the waste gas purification platform, so that the packing structure of the waste gas purification platform removes ammonia in the waste gas discharged from the target farm through a chemical reaction; when the pH sensor detects that the pH value of the acid solution in the acid solution pool is higher than a preset first threshold value, the water pump corresponding to the acid solution pool is started, and concentrated acid of a preset first concentration is added to the acid solution pool to reduce the pH value of the acid solution in the acid solution pool and maintain the ability of the acid solution in the acid solution pool to neutralize ammonia.

[0009] Preferably, the target farm includes a waste gas purification platform, which includes several water spray valves, a biological solution pool and a filler structure. The biological filtration system is responsible for controlling the water spray valves corresponding to the biological solution pool, spraying the biological solution in the biological solution pool into the filler structure corresponding to the waste gas purification platform, so as to utilize the metabolic action of the microorganisms in the biological solution to decompose and remove organic pollutants in the waste gas discharged from the target farm.

[0010] Preferably, the target farm includes a waste gas purification platform, and the waste liquid treatment system is responsible for removing pollutants in the wastewater generated during the process of treating the waste gas discharged from the target farm by the waste gas purification platform of the target farm to ensure that it meets the emission standards.

[0011] Preferably, the process of the multi-source heterogeneous perception module perceiving various state parameters of the target farm and transmitting them to the data processing module comprises: the multi-source heterogeneous perception module receiving state data collected by various types of sensors deployed at the target farm, and analyzing the state data collected by each sensor to determine various state parameters of the target farm;

[0012] The determined status parameters of the target farm are classified and then transmitted to the data processing module.

[0013] Preferably, the data processing module receives the various status parameters of the target farm transmitted by the multi-source heterogeneous perception module and pre-processes them to obtain target multivariate data, which includes: receiving the various status parameters of the target farm transmitted by the multi-source heterogeneous perception module; performing data cleaning on the received various status parameters to eliminate erroneous and duplicate data to obtain first data; performing data conversion on the first data to obtain second data; and performing classification processing, data integration processing and feature extraction processing on the second data to obtain the target multivariate data.

[0014] Preferably, the main control module performs fusion processing on the target multivariate data to generate a control decision process for the chemical decomposition system, the biological filtration system, and the waste liquid treatment system, including: receiving the target multivariate data; analyzing the target multivariate data, and determining the weight coefficient of each state parameter of the target farm based on the influence factor of each state data in the target multivariate data on the waste gas purification work of the target farm; determining the relative weight of each state parameter to the chemical decomposition system, the biological filtration system, and the waste liquid treatment system based on the weight coefficient of each state parameter of the target farm; determining the control decision for the chemical decomposition system, the biological filtration system, and the waste liquid treatment system according to the relative weights of the chemical decomposition system, the biological filtration system, and the waste liquid treatment system and each state parameter.

[0015] A farm waste gas purification device comprises: one or more processors, and a memory; the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the implementation process of the farm waste gas purification system as described in any of the above introductions is realized.

[0016] A readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors implement the implementation process of the farm waste gas purification system as described in any of the above introductions.

[0017] From the above introduction, it can be seen that when it is necessary to purify the waste gas discharged from the farm with low energy consumption and high efficiency, the present application can provide a farm waste gas purification system, which includes a multi-source heterogeneous perception module, a data processing module, a main control module, a chemical decomposition system, a biological filtration system, a waste liquid treatment system and a fault diagnosis system; wherein the multi-source heterogeneous perception module can be responsible for sensing the various state parameters of the target farm and transmitting them to the data processing module; through the multi-source heterogeneous perception module, the various states of the target farm can be effectively collected, and the waste gas state and concentration of the target farm can be effectively understood through the various state parameters so as to better determine the target farm. The waste gas purification scheme of the farm is designed, and the data processing module can be responsible for receiving the various state parameters of the target farm transmitted by the multi-source heterogeneous perception module, pre-processing them, and transmitting the obtained target multivariate data to the main control module; the main control module is responsible for fusing the target multivariate data, and generating control decisions for the chemical decomposition system, the biological filtration system, and the waste liquid treatment system, and transmitting the control decisions of the chemical decomposition system, the biological filtration system, and the waste liquid treatment system to the chemical decomposition system, the biological filtration system, and the waste liquid treatment system respectively to control the chemical decomposition system, the biological filtration system, and the waste liquid treatment system to purify the waste gas discharged from the target farm.

[0018] From the above introduction, it can be seen that this application can combine the structural characteristics and waste gas emission characteristics of the centralized ventilation type of the farm, and on the basis of achieving efficient purification of the waste gas of the farm, take into account the initial investment cost and operation and maintenance costs of the waste gas treatment of the farm. It has the advantages of high efficiency purification and low energy consumption, and is of great significance to the optimal design of the livestock and poultry breeding waste gas purification spray system and the selection of supporting equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 A schematic diagram of the system architecture for achieving farm waste gas purification provided in this application;

[0021] Figure 2A schematic diagram of the structure of a pig house exhaust gas purification test platform provided in this application;

[0022] Figure 3 A schematic diagram of the structure of an intelligent pig house exhaust gas purification system provided in this application;

[0023] Figure 4 A flow chart of a method for treating waste gas from livestock farms provided in this application;

[0024] Figure 5 A schematic diagram of the research framework of a pig house exhaust gas purification system provided in this application;

[0025] Figure 6 A flow chart of an adaptive decision-making mechanism for farm waste gas treatment provided in this application;

[0026] Figure 7 This is a schematic diagram of the pig house exhaust gas purification control system architecture provided in this application;

[0027] Figure 8 This is a schematic diagram of the structure of a farm waste gas purification device exemplified in this application;

[0028] Figure 9 This is a hardware structure block diagram of a farm waste gas purification device disclosed in this application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0030] Currently, intensive piggeries, targeting the "end-of-pipe purification" approach, often use fillers such as shade nets, paper water curtains, and polypropylene hollow balls. Exhaust gas treatment is performed through spraying and washing. This approach lacks dynamic sensing of air resistance, fan pressure drop, and exhaust gas composition within the farm, resulting in relatively extensive control. Research has revealed that after operating this method for a period of time, fillers such as shade nets and paper water curtains tend to clog, affecting farm ventilation. The lack of a water recycling system allows atomized water vapor to drift outside the house during ventilation, resulting in significant water waste. Furthermore, this exhaust gas treatment method requires manual replacement of fillers, which compromises biosafety.

[0031] Given that most of the current farm waste gas purification solutions are difficult to adapt to complex and changing business needs, the applicant has studied a farm waste gas purification solution. The farm waste gas purification system can combine the centralized ventilation structural characteristics and waste gas emission characteristics of the farm. On the basis of achieving efficient purification of the farm's waste gas, it also takes into account the initial investment cost and operation and maintenance costs of the farm's waste gas treatment. It has the advantages of high-efficiency purification and low energy consumption, and is of great significance to the optimal design of livestock and poultry breeding waste gas purification spray systems and the selection of supporting equipment.

[0032] The methods provided in the embodiments of the present application can be used in a variety of general-purpose or specialized computing device environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multi-processor devices, and distributed computing environments including any of the above.

[0033] An embodiment of the present application provides an intelligent management system for fattening pig farming. The method can be applied to various waste gas purification and treatment systems, and can also be applied to various computer terminals or smart terminals. The execution entity can be the processor or server of the computer terminal or smart terminal.

[0034] The following combination Figure 1 , introduces an optional system architecture that can achieve efficient and low energy consumption exhaust gas purification treatment given in the embodiment of this application. Figure 1 As shown, the system architecture may include: a multi-source heterogeneous perception module, a data processing module, a main control module, a chemical decomposition system, a biological filtration system, a waste liquid treatment system, and a fault diagnosis system.

[0035] In the actual application process, the current livestock and poultry farming industry is constantly developing in the direction of scale and intensification. In order to improve land utilization, many farms choose to carry out intensive farming.

[0036] For example, in pig farming, some farms use multi-story piggeries. In practice, biological methods have long reaction times and are easy to maintain, but they occupy a large area, have low purification efficiency, and can easily clog pipes with biomolecules and proteins. Chemical methods have a shorter reaction time, offer efficient and controllable deodorization and ammonia removal, are more stable, and require less space. However, they consume significant amounts of electricity and water, and rely primarily on manual control. Furthermore, multi-story piggeries have high stocking densities and high ammonia concentrations within them, placing high demands on the purification system. Therefore, while ensuring proper ventilation in multi-story piggeries, the stability, efficiency, and energy consumption of the exhaust gas purification system must be considered.

[0037] Therefore, in order to build a more efficient and energy-saving end-of-pipe exhaust gas purification solution for farms, a comprehensive understanding of the farm environment is necessary. Different types of farms have different environments.

[0038] In actual application, the waste gas composition of farms is generally complex. For example, the waste gas composition of farms may contain a variety of pollutants such as ammonia, hydrogen sulfide, and methane. In view of the waste gas purification treatment requirements of the target farm, in order to better understand the waste gas emissions of the target farm, a multi-source heterogeneous perception module can be created in the waste gas purification system of the target farm, so that various types of sensors can be integrated in the target farm, such as ammonia sensors, hydrogen sulfide sensors, volatile organic compound (VOCs) sensors, etc., which can monitor different types of waste gas components in real time and accurately, obtain comprehensive waste gas parameter information and environmental information of the target farm, and thus more accurately understand the pollution status of the waste gas discharged from the target farm.

[0039] Furthermore, the environment of livestock farms is generally complex, characterized by high humidity, wide temperature fluctuations, and abundant dust. A single type of sensor may not be able to operate stably or accurately measure in such an environment. Deploying a multi-source heterogeneous sensing module allows the use of a variety of sensors with different principles and characteristics. This can fully leverage the advantages of each sensor type, complementing and verifying each other, and improving the reliability and stability of monitoring data in complex environments. For example, in high-humidity environments, some optical sensors may be significantly affected by water vapor, while electrochemical sensors are relatively stable. By combining them, accurate detection of exhaust gas components can be ensured under various humidity conditions.

[0040] Furthermore, farms in different areas, such as breeding areas, manure processing areas, and feed storage areas, experience varying levels of waste gas generation and emissions. Deploying multi-source heterogeneous sensing modules allows for flexible sensor deployment based on the characteristics and needs of each region, enabling refined monitoring of each area. By placing targeted sensors in different locations, the extent and patterns of waste gas pollution in different areas can be accurately determined, providing a basis for precise control and optimized operation of the waste gas purification system, helping to improve waste gas purification efficiency and reduce treatment costs.

[0041] For example, in actual applications, large-scale farms have large pig stocks, large pig houses, and large numbers of pigs. This generates high volumes of waste gas with complex composition, necessitating the deployment of numerous sensors at key locations to comprehensively sense the concentration, temperature, humidity, and other information about the waste gas emitted by the farms. This allows for precise monitoring and control of waste gas conditions across the entire farming area. However, small farms, due to their smaller scale and relatively low waste gas production, can simplify the layout of the sensing network. Simply placing sensors at key locations can meet the basic monitoring needs of the farm's waste gas purification system.

[0042] Furthermore, in intensive farming models, farms have high livestock densities, generating more waste gas per unit area and requiring stricter environmental control. In this case, densely deployed sensors are needed to monitor environmental parameters within the farm in real time, enabling timely adjustment of the operating parameters of waste gas purification equipment. In contrast, in ecological farming models, farms have ample room for livestock to move around, and waste gas emissions are relatively dispersed. The layout of the sensing network should consider factors such as the farm's topography and vegetation distribution, with a focus on monitoring areas surrounding the farm and waste gas outlets to ensure that waste gas emissions meet environmental requirements while protecting the ecological environment. For example, different livestock breeds vary in their physiological characteristics, growth rates, and feed conversion rates, resulting in varying adaptability to the environment and waste gas production. For example, in pig farms, some lean pigs grow quickly and have a high metabolism, potentially producing waste gas with high levels of harmful gases such as ammonia and hydrogen sulfide. This requires more precise environmental information sensing to optimize the waste gas purification system. However, local breeds of pigs may have strong adaptability to the local environment and different characteristics of waste gas generation. The setting of the environmental information perception network should be adjusted according to their specific circumstances to achieve efficient waste gas treatment.

[0043] For example, breeding pig farms have more stringent environmental requirements. To ensure the health and reproductive performance of breeding pigs, precise control of environmental parameters such as temperature, humidity, and air quality is required. Therefore, a distributed environmental information perception network must be more dense and precise, monitoring not only exhaust gas indicators but also other environmental factors related to breeding. In contrast, commercial pig farms focus more on growth rate and breeding efficiency. Their environmental information perception focuses on meeting the basic growth needs of pigs and complying with environmental emission standards, and the perception network configuration will be different.

[0044] For example, in actual application, after determining the distributed environmental information perception network of the target farm, different types of sensors can be deployed at each node based on the distributed environmental information perception network of the target farm, so as to collect different environmental information of the target farm. For example, the following types of sensors can be deployed at the target farm:

[0045] Temperature and humidity sensors can be deployed to monitor the temperature and humidity within the target farm. In practice, excessively high or low temperatures can affect the growth, feed conversion rate, and immunity of farmed animals, while inappropriate humidity can lead to disease infection and stress reactions. For example, a DHT11 digital temperature and humidity sensor can be deployed to convert detected temperature and humidity data into digital signals that can be easily read and processed by a microcontroller.

[0046] Air quality sensors can be deployed to monitor the concentrations of harmful gases such as ammonia, hydrogen sulfide, and carbon dioxide within target farms. Excessive concentrations of these gases can irritate the respiratory tracts of farmed animals, reduce their resistance, and cause respiratory illnesses. They can also affect the work environment and health of farm workers. For example, the MQ-135 gas sensor can be deployed, which has high sensitivity to harmful gases such as ammonia and hydrogen sulfide and can quickly and accurately detect changes in gas concentrations.

[0047] Light sensors can be deployed to monitor light intensity and duration within the target farm. Appropriate lighting significantly impacts the growth, reproduction, and behavior of farmed animals. For example, appropriate lighting can promote estrus in sows and improve the immunity and growth rate of piglets. For example, the BH1750FVI digital light sensor can be deployed. It offers high precision and low power consumption, accurately measuring ambient light intensity and transmitting this data to the control system.

[0048] Liquid level sensors can be deployed to monitor the water intake and urination of farmed animals. Water level sensors can also be deployed to monitor the water level of farmed animals, ensuring they always have sufficient clean drinking water. Water shortages can affect their feeding, digestion, and growth, and in severe cases, can be life-threatening. For example, a hydrostatic water level sensor can be deployed. This commonly used sensor calculates the water level by measuring water pressure, offering high accuracy and stability.

[0049] Feed weight sensors can be deployed to monitor feed levels in real time, allowing for timely replenishment and ensuring a stable feed supply for animals, preventing feed shortages from impacting their growth and development. Furthermore, assessing an animal's diet can be used to assess its excretion. For example, a common feed weight sensor can be deployed, using a strain gauge load cell. This sensor measures weight based on the principle that a strain gauge deforms under force, resulting in a change in resistance. It offers high accuracy and reliability.

[0050] Video surveillance sensors can be deployed to monitor the behavior and health of farm animals in real time, as well as the overall condition of the target farm. This allows for timely detection of abnormal behavior, such as illness or aggression, and helps managers better understand the operations of the target farm. For example, network HD cameras, a common video surveillance device in farms, can be deployed. They enable remote, real-time monitoring, include night vision and motion detection, and can transmit surveillance footage to devices such as mobile phones and computers.

[0051] pH sensors can also be deployed to measure the pH of various liquids within the target farm. Based on the Nernst equation, pH sensors typically consist of a sensitive membrane that selectively responds to hydrogen ions and a reference electrode. When the sensitive membrane comes into contact with the solution being measured, hydrogen ions in the solution exchange with the hydration layer on the membrane's surface, creating a potential difference across the membrane. This potential difference is related to the activity of hydrogen ions in the solution. Using the Nernst equation, this potential difference is converted to a corresponding pH value, enabling measurement of the solution's pH. For example, pH monitoring can be used in drinking water, wastewater, and pig urine. For example, monitoring the pH of drinking water is crucial in pig farming, as maintaining an appropriate pH range (typically 6.5-8.5) helps ensure water quality and pig health. Excessively acidic or alkaline water can affect the pig's digestive system, weakening its immune system and making it more susceptible to disease. Furthermore, monitoring the pH of pig urine can provide a glimpse into the pig's health, as certain diseases can cause abnormal urine pH. Common glass electrode pH sensors offer advantages such as high measurement accuracy and good stability, and are widely used to measure the pH value of various liquids. Antimony electrode pH sensors are also suitable for pH measurement in special environments, such as those with high temperatures and high salt concentrations.

[0052] EC sensors (EC, short for electrical conductivity) can also be deployed. EC sensors are primarily used to measure the conductivity of solutions, which reflects the electrolyte content in the solution. EC sensors use electrodes to measure the current in the solution. When current flows through the solution, ions in the solution carry their charge and move, generating conductivity. EC sensors measure the resistance between two electrodes and convert this resistance to conductivity based on factors such as the solution temperature and the electrode constant. To improve measurement accuracy, some EC sensors use a four-electrode structure to eliminate the effects of electrode polarization and solution resistance. For example, in pig farms, EC sensors can be used to monitor the conductivity of drinking water, feed solutions, and wastewater. By monitoring the conductivity of drinking water, we can determine the content of dissolved minerals, salts, and other electrolytes in the water, and assess its purity and quality. High conductivity may indicate excessive impurities or salts, making it unsuitable for pigs. Low conductivity may indicate a lack of essential minerals. For feed solutions, monitoring conductivity can help determine feed dissolution and nutrient content. In wastewater treatment, conductivity serves as a key indicator, reflecting the pollutant content and treatment effectiveness. Commonly used are inductive EC sensors, which employ a non-contact measurement method. Using an inductive coil to generate an alternating magnetic field, this induces a current in the solution, thereby measuring the conductivity of the solution. These sensors offer advantages such as pollution and corrosion resistance, making them suitable for conductivity measurements in a variety of complex environments. Alternatively, electrode-type EC sensors, which measure by directly inserting electrodes into the solution, offer high accuracy and fast response times, making them widely used for conductivity measurement in laboratories and industrial production.

[0053] Furthermore, the multiple sensors deployed in the multi-source heterogeneous perception module form a redundant system. If one sensor malfunctions or fails, other sensors continue to function, providing relevant data for the target farm, avoiding interruptions or loss of monitoring data. Furthermore, by comparing and analyzing data from multiple sensors, sensor failures can be promptly identified, facilitating timely repair or replacement, ensuring the continuity and reliability of exhaust gas monitoring.

[0054] To better collect various types of status data from the target farm and better leverage the capabilities of the multi-source heterogeneous perception module, a distributed environmental information perception network can be deployed within the target farm based on the specific functions of the multi-source heterogeneous perception module. The sensors in this distributed environmental information perception network collect data from the target farm. These sensors can monitor the concentration and composition of waste gases, such as ammonia, hydrogen sulfide, carbon dioxide, and volatile organic compounds, within the target farm and at locations such as exhaust outlets in real time. The concentration and composition of waste gases vary across different breeding stages, seasons, and feeding management methods. For example, in pig farms, ammonia concentrations can rise significantly in winter when piggeries are poorly ventilated. Accurately understanding this data helps determine the treatment capacity of waste gas purification equipment and tailor the purification process.

[0055] The process of the multi-source heterogeneous perception module perceiving various state parameters of the target farm and transmitting them to the data processing module may include the following:

[0056] The multi-source heterogeneous perception module receives status data collected by various types of sensors deployed in the target farm, and analyzes the status data collected by each sensor to determine the various status parameters of the target farm; finally, the various status parameters of the determined target farm are classified and then transmitted to the data processing module.

[0057] Since the multi-source heterogeneous perception module collects different types of data related to the target farm, and different types of data feedback information are different, and there may be some missing data or repeated or erroneous data, in order to better analyze various types of data to understand the environment of the target farm and the specific conditions of the exhaust gas discharged, the multi-source heterogeneous perception module of the present application can be responsible for sensing the various state parameters of the target farm and transmitting them to the data processing module; the data processing module is responsible for receiving the various state parameters of the target farm transmitted by the multi-source heterogeneous perception module and pre-processing them so as to eliminate the repeated, erroneous or missing data therein, so as to improve the analysis efficiency of the state parameters of the target farm, thereby obtaining the target multivariate data of the target farm.

[0058] Among them, the target multivariate data can provide feedback on the exhaust gas composition and environmental information of the target farm. The status parameters of the farm are important indicators reflecting the operation status of the farm and the animal growth environment, mainly including the following parameters:

[0059] 1. Environmental parameters: temperature, humidity, light, air quality parameters, and ventilation volume. Air quality parameters mainly involve the concentrations of harmful gases such as ammonia, hydrogen sulfide, and carbon dioxide. For example, the environmental parameters of a target farm may include temperature, relative humidity, ammonia concentration, carbon dioxide concentration, hydrogen sulfide concentration, formaldehyde concentration, and inhalable particulate matter (PM2.5 / PM10).

[0060] 2. Animal health parameters: body temperature, heart rate, feed and water intake, and feces and urine status of the animals.

[0061] 3. Breeding facility operating parameters: equipment temperature, equipment operating time, equipment fault alarm, and energy consumption data, including consumption data of electricity, gas, fuel oil and other energy sources.

[0062] 4. Breeding production parameters: inventory, reproductive performance, growth performance, and product quality parameters.

[0063] All of the above state parameters may affect the exhaust gas emitted by the target farm. Therefore, collecting and processing this data can better treat the exhaust gas emitted by the target farm.

[0064] The process in which the data processing module receives various state parameters of the target farm transmitted by the multi-source heterogeneous perception module and pre-processes them to obtain target multivariate data may include the following:

[0065] Receive various status parameters of the target farm transmitted by the multi-source heterogeneous perception module; perform data cleaning on the received various status parameters to eliminate erroneous and duplicate data to obtain first data; then perform data conversion on the first data to obtain second data; then perform classification processing, data integration processing and feature extraction processing on the second data to obtain target multivariate data.

[0066] Specifically, in a farm waste gas purification system, various sensors in the multi-source heterogeneous sensing module collect real-time information on farm environmental parameters, animal health parameters, and other status information. These sensors include, but are not limited to, temperature sensors, humidity sensors, ammonia sensors, and hydrogen sulfide sensors. During data collection, the frequency of data collection must meet system requirements to obtain sufficient detailed information while ensuring data accuracy and completeness. However, the collected data may contain noise, missing values, and outliers. Data cleaning removes these noise and outliers and handles missing values. For noisy data, filtering algorithms can be used for smoothing. For missing values, methods such as mean filling, median filling, and filling based on similar samples can be used depending on the specific situation. For example, if individual temperature data values ​​significantly deviate from the normal range, they can be identified as outliers and removed, then filled with the mean of adjacent time points. Furthermore, data collected by different sensors may have different formats, units, and dimensions. Data conversion involves converting this data into a standard format, units, and dimensions that the system can recognize and process. For example, temperature data collected by temperature sensors from different manufacturers can be uniformly converted to degrees Celsius, and gas concentration data can be converted from different ppm units to a unified standard unit. Furthermore, to facilitate subsequent analysis and processing, data may also be normalized or standardized, mapping the data to a specific interval or making the data have zero mean and unit variance.

[0067] Furthermore, the multi-source heterogeneous perception module collects data from different sensors, locations, times, and categories. After cleaning this data, it can be further classified and integrated, bringing these scattered data together to form a complete dataset. During the integration process, it's necessary to establish relationships between the data. For example, by correlating data collected by different sensors at the same time based on timestamps, or integrating sensor data from different regions based on spatial location, this ensures that subsequent analysis can comprehensively consider information from multiple dimensions.

[0068] After data cleaning, conversion, and integration, representative features are extracted from the cleaned, converted, and integrated data. These features can better reflect the status and patterns of farm waste gas. For example, features such as the average ammonia concentration, concentration change rate, and peak value over a period of time can be calculated. For temperature data, features such as the daily average temperature and the difference between day and night can be extracted. Feature extraction can reduce data dimensionality and data processing workload while highlighting key information, providing more effective input for subsequent data analysis and model building.

[0069] Furthermore, the target multivariate data obtained after preprocessing needs to be stored for subsequent querying, analysis, and visualization. This data is typically stored in a database or data warehouse, choosing an appropriate data storage structure and format to improve storage efficiency and access speed. For example, a relational database can be used to store current state data with high real-time requirements; for long-term storage and analysis of historical data, a data warehouse or distributed file system (such as HDFS) can be used.

[0070] After the above preprocessing process, multi-source heterogeneous raw data are converted into target multivariate data with consistency, accuracy and availability, providing a solid data foundation for subsequent data analysis, model prediction, decision support and other functions of the farm waste gas purification system.

[0071] As can be seen from the above introduction, the multi-source heterogeneous perception module can collect data of different types and formats. To better analyze these different types of data, the collected data can be fused. Data fusion technology can integrate and analyze this multi-source data to uncover more valuable information. For example, by combining meteorological data (such as wind speed, direction, temperature, and humidity) with exhaust gas composition data, it is possible to analyze the impact of meteorological conditions on exhaust gas diffusion and emissions, providing a scientific basis for optimizing farm layout and exhaust gas emission management. Furthermore, data fusion can improve data accuracy and credibility, providing stronger data support for subsequent decision-making and environmental assessments.

[0072] Therefore, after the data processing module pre-processes the different types of data collected by the multi-source heterogeneous perception module to obtain the target multivariate data, it can transmit the obtained target multivariate data to the main control module for analysis and processing, so that the main control module can fuse the obtained target multivariate data and generate control decisions for the chemical decomposition system, biological filtration system, and waste liquid treatment system.

[0073] Specifically, the exhaust gas situation at a farm is affected by a variety of factors, such as stocking density, ventilation conditions, and waste disposal methods. To fully understand the situation at a target farm and better treat its exhaust, the main control module integrates and processes the resulting target multivariate data. By integrating and processing different types of multivariate data, including environmental parameters (temperature, humidity, gas concentration, etc.), equipment operating parameters (fan speed, purification equipment operating status, etc.), and farming activity data (stock number, feeding amount, etc.), the main control module can fully grasp the overall status of the farm, accurately determine the patterns of exhaust gas generation and emission, and provide a basis for precise control of the exhaust gas purification process.

[0074] Furthermore, although the target farm's status parameters have been preprocessed, data from a single sensor or data source may still contain errors, noise, or incompleteness. By fusing information from multiple sensors and data sources, we can leverage the complementarity and redundancy between the data, verifying and supplementing each other, thereby improving data accuracy and reliability. For example, data fusion from multiple ammonia sensors can mitigate the impact of individual sensor failures or measurement deviations, more accurately reflecting the true ammonia concentration within the farm.

[0075] The fused multivariate data more clearly illustrates the complex relationships and interactions within the exhaust gas purification process. Based on this comprehensive information, the main control module can conduct in-depth analysis of the dynamic changes in exhaust gas generation, propagation, and purification, and determine and optimize control strategies. For example, it can adjust the operating parameters of purification equipment in real time based on different farming activities and environmental conditions to achieve more efficient and energy-saving exhaust gas purification results.

[0076] Fusion of diverse data helps build more comprehensive and accurate farm waste gas models. The main control module uses these models to make intelligent decisions, such as predicting waste gas emission trends and taking proactive measures to address potential excess emissions. It also provides data support for long-term farm planning and management, such as optimizing the scale and layout of farms.

[0077] Therefore, the main control module can fuse the obtained target multivariate data, and analyze the target multivariate data to generate control decisions for the chemical decomposition system, biological filtration system, and waste liquid treatment system, and transmit the control decisions of the chemical decomposition system, biological filtration system, and waste liquid treatment system to the corresponding chemical decomposition system, biological filtration system, and waste liquid treatment system to control the chemical decomposition system, biological filtration system, and waste liquid treatment system to purify the waste gas discharged from the target farm.

[0078] The main control module integrates the target multivariate data and generates control decisions for the chemical decomposition system, biological filtration system, and waste liquid treatment system, which may include the following:

[0079] Receive target multivariate data; analyze the target multivariate data, and determine the weight coefficients of various state parameters of the target farm based on the influence factors of various state data in the target multivariate data on the waste gas purification work of the target farm; based on the weight coefficients of various state parameters of the target farm, determine the relative weights of various state parameters to the chemical decomposition system, the biological filtration system, and the waste liquid treatment system; determine the control decision of the chemical decomposition system, the biological filtration system, and the waste liquid treatment system according to the relative weights of the chemical decomposition system, the biological filtration system, and the waste liquid treatment system and various state parameters.

[0080] The target farm may include an exhaust gas purification platform, which may include a number of water spray valves, pH sensors, water pumps, acid solution tanks, and filler structures;

[0081] Based on this, the chemical decomposition system can be responsible for controlling the water spray valve corresponding to the acid solution pool of the waste gas purification platform, spraying the acid solution in the acid solution pool into the packing structure of the waste gas purification platform, so that the packing structure of the waste gas purification platform removes ammonia in the waste gas discharged from the target farm through chemical reaction;

[0082] When the pH sensor detects that the pH value of the acid solution in the acid solution pool is higher than a preset first threshold value, the water pump corresponding to the acid solution pool can be started to add concentrated acid of a preset first concentration to the acid solution pool to reduce the pH value of the acid solution in the acid solution pool so as to maintain the ability of the acid solution in the acid solution pool to neutralize ammonia.

[0083] The preset first threshold value may be set to 4.5, and the preset first concentration may be set to [30%, 80%].

[0084] For example, when the pH sensor detects that the pH value of the acid solution in the acid solution pool is higher than 4.5, the acid adding pump can be started to add [30%, 80%] concentrated acid solution to the acid solution pool to lower the pH value of the acid solution in the acid solution pool to ensure that the acid solution has the ability to neutralize ammonia. The waste gas purification platform can also include a biological solution pool. The biological filtration system can be responsible for controlling the spray valve corresponding to the biological solution pool and spraying the biological solution in the biological solution pool into the filler structure corresponding to the waste gas purification platform to utilize the metabolic action of the microorganisms in the biological solution to decompose and remove organic pollutants in the waste gas discharged from the target farm.

[0085] The waste liquid treatment system can be responsible for removing pollutants from the wastewater generated during the waste gas purification platform treatment of the target farm's waste gas to ensure that it meets the emission standards.

[0086] In actual use, the farm waste gas purification system consists of multiple components and equipment, such as fans, purifiers, sensors, etc. Failure of any component may affect the normal operation of the entire system. In order to monitor the operating status of each device in real time, timely detect potential faults, avoid sudden system shutdowns or reduced purification effects, ensure the continuous and stable operation of waste gas purification, and reduce the impact on the farm environment and production. The farm waste gas purification system also includes a fault diagnosis system; when a fault occurs in the farm waste gas purification system, the fault diagnosis system can be used to receive and process the fault information fed back by the main control module, and provide the fault information and fault solution of the farm waste gas purification system.

[0087] Fast and accurate fault diagnosis helps maintenance personnel quickly locate the fault point and reduce troubleshooting time. The fault diagnosis system analyzes sensor data and equipment operating parameters to provide detailed fault information, such as fault type and location. This allows maintenance personnel to prepare the necessary tools and spare parts in advance, allowing for faster repairs, shortening system downtime and reducing maintenance costs.

[0088] Some equipment in the exhaust gas purification system involves electrical and mechanical operations. If faults are not discovered and addressed promptly, they may lead to safety accidents, such as fires caused by motor short circuits or exhaust gas leaks caused by equipment failures. The fault diagnosis system can promptly warn of potential safety hazards and take appropriate measures, such as automatically cutting off the power supply and initiating emergency ventilation, to ensure the safety of farm personnel and equipment.

[0089] By analyzing system operating data, the fault diagnosis system can identify causes of system performance degradation, such as reduced purification efficiency due to purifier clogs or insufficient air volume due to fan aging. Based on the diagnostic results, targeted system optimization and adjustments can be made, such as regularly changing filters and repairing or replacing aging equipment, to maintain optimal system performance, improve exhaust gas purification, and reduce pollutant emissions.

[0090] For example, based on the scheme introduced above, the present application can construct a waste gas purification platform for a target farm, which may include: a pressure chamber, a chemical decomposition chamber, a first biological filtration chamber and a second biological filtration chamber; wherein the pressure chamber is connected to the waste gas outlet of the target farm and the chemical decomposition chamber, and is responsible for evenly mixing the waste gas pollutants from the target farm, and reducing the wind speed of the waste gas discharged from the target farm so that the waste gas discharged from the target farm can be fully mixed and then discharged into the chemical decomposition chamber.

[0091] The chemical decomposition chamber is responsible for performing acid washing and spraying treatment on the waste gas discharged from the pressure chamber, and after absorbing the alkaline pollutants in the waste gas discharged from the target farm, the waste gas that has undergone the first purification treatment is discharged to the first biological filter chamber again; wherein, the process of the chemical decomposition chamber performing acid washing and spraying treatment on the waste gas discharged from the pressure chamber may include the following: when the pressure chamber discharges the waste gas to be treated to the chemical decomposition chamber, the water spray valve is started to spray the acid solution in the acid solution pool into the filler of the waste gas purification platform of the target farm to remove ammonia in the waste gas discharged from the target farm; in the process of removing ammonia from the waste gas of the target farm, the pH value of the acid solution in the acid solution pool is monitored in real time, and when the pH value of the acid solution in the acid solution pool is higher than a preset first threshold value, the water pump is started to add concentrated acid of a preset concentration to the acid solution pool to neutralize the acid solution in the acid solution pool, so that the acid solution in the acid solution pool continues to remove ammonia from the waste gas discharged from the target farm.

[0092] In the chemical decomposition chamber, ammonia is absorbed in a dilute acidic solution and converted into reduced ammonium ions through a chemical reaction. The reaction equation involved is as follows:

[0093] (1)

[0094] (2)

[0095] Equation (1) represents the equilibrium reaction of ammonia solubility in acidic solution. This equation describes the solubility of ammonia in water, where H is the Henry's law constant, which is , which has a higher solubility than other gases. Carbon dioxide, methane and hydrogen sulfide are 、 and The H value of Equation (2) is the equilibrium constant Equal to forward and backward The ratio of the reaction rate constants.

[0096] (3)

[0097] Equation (3) better describes the relationship between the reaction rate and 、 and The equilibrium constant Can be used as The reciprocal of the acid dissociation constant was deduced, and the value at 298.15K (25°C) was 1.78×109, which was favorable for the backward reaction.

[0098] The concentration of the concentrated acid solution in the acid solution pool in the chemical decomposition chamber is mainly in the range of [30%, 80%]. The preset first threshold value can be set to 4.5. For example, when the pH sensor detects that the pH value of the acid solution in the acid solution pool is higher than 4.5, the acid adding pump can be started to add [30%, 80%] concentrated acid solution to the acid solution pool to lower the pH value of the acid solution in the acid solution pool to ensure that the acid solution has the ability to neutralize ammonia.

[0099] The first biological filter chamber is connected to the chemical decomposition chamber and the second biological filter chamber; wherein, the first biological filter chamber and the second biological filter chamber both include biological solutions; the first biological filter chamber is responsible for secondary purification of the waste gas discharged from the chemical decomposition chamber, using the microorganisms in the biological solution to perform the first decomposition of organic pollutants in the waste gas discharged from the chemical decomposition chamber and then discharge it to the second biological decomposition chamber; the second biological decomposition chamber then decomposes the organic pollutants in the waste gas discharged from the first biological decomposition chamber again and discharges the resulting gas to the outside.

[0100] Microbial degradation technology is used for washing. A large number of known advantageous bacterial species can be utilized, such as photosynthetic bacteria. These species have low requirements for the composition and content of pollutants in specific wastewater, waste gas, waste residue, etc., and possess a variety of high-purification microorganisms. They can effectively improve the biodegradability of pollutants and increase the removal rate of chemical oxygen demand (COD), ammonia nitrogen, total phosphorus, and toxic and hazardous substances. At the same time, they have the advantages of low sludge volume, short startup time, high operational stability and impact resistance, safety, harmlessness, and easy use and maintenance.

[0101] (4)

[0102] Equation (4) describes the process of hydrogen sulfide and carbon dioxide being broken down by photosynthetic sulfur bacteria (hv). There are many other dominant bacterial species similar to photosynthetic bacteria, including EM bacteria and nitrifying bacteria. Under controlled conditions for bacterial survival and reproduction, biological water washing can remove most of the waste gas components generated in livestock farms. The biological filtration process involves microbial degradation of waste gas. Dominant bacterial species can attach to wet curtains through slowly flowing water, degrading ammonia, hydrogen sulfide, and nitrite contained in the air at the end of livestock and poultry farming operations.

[0103] After testing, this application takes the exhaust gas purification treatment of pig houses as an example to introduce the process of building a pig house exhaust gas purification treatment platform. Figure 2 As shown in the figure, the key structural parameters of the exhaust gas purification system mainly include the wind speed passing through the curtain, the airflow structure, the fluid structure, and the packing material. Among them, the airflow structure can include the fan parameters and the airflow direction. It is necessary to comprehensively consider the purification efficiency and energy consumption to determine the position of the fan and the flow field distribution in the purification room. The design of the fluid structure mainly considers the uniform distribution of the washing liquid in the washing liquid flow configuration, and the precise control of the droplet diameter and the spraying range. In the actual application process, it is mainly based on the porosity of the packing structure, comprehensively considering the relationship between the packing structure size and the airflow velocity, minimum mass transfer effective contact time, and pressure drop in the packing to determine the material, structure and size of the packing.

[0104] In this experiment, the packing size (length, width and height) of the pig house exhaust gas purification test platform was set to 680mm*680mm*150mm, and the diameter of the hexagonal through hole was 25mm. At this time, the gas flow rate in the packing was 1m / s, and the pressure drop before and after was about 10Pa.

[0105] For example, Figure 2Taking the piggery exhaust gas purification process shown in the figure as an example, exhaust gas from the piggery enters a pressure chamber (area ②), where a fan promotes mixing of the exhaust gas and reduces the wind speed to facilitate entry into the subsequent purification stage. The exhaust gas then enters a chemical decomposition chamber (area ③), where an acid solution reacts with the ammonia in the exhaust gas, converting most of the ammonia into ammonium ions and solidifying them in a solution. The solution is then transferred to a waste liquid recovery tank. After treatment in the chemical decomposition chamber, the exhaust gas enters biological filtration chamber 1 (area ④) and biological filtration chamber 2 (area ⑤). Specific microorganisms in the biological solution decompose the organic pollutants in the exhaust gas. The resulting waste liquid is returned to the biological solution tank, further reducing the concentration of harmful components in the gas, and ultimately, the clean gas is discharged into the environment.

[0106] In actual application, the spatiotemporal accumulation model of piggery exhaust gas components can be combined with theoretical analysis and experimental testing to study the coupling correlation mechanism of factors such as the wind speed through the curtain, airflow organization, filler structure, and spraying method of the exhaust gas purification unit on the exhaust gas purification efficiency and ventilation resistance. A mathematical model of the exhaust gas purification unit can be established to determine the key structural parameters of the pig farm exhaust gas purification unit, thereby improving the exhaust gas purification efficiency while ensuring the ventilation requirements of the piggery. Among them, the coupling correlation mechanism can be expressed as a mathematical model relationship between various parameters. Using the self-optimizing piggery exhaust gas purification control model, the optimal solution is calculated, that is, the fan speed, filler size structure, and spray rate parameters are adjusted to maximize the exhaust gas purification efficiency while meeting the ventilation requirements of the piggery.

[0107] For example, based on the above-mentioned scheme, the present application can be constructed as follows Figure 3 The intelligent pig house exhaust gas purification system shown in Figure 3 The intelligent pig house exhaust gas purification system shown includes two working modes: energy-saving mode and high-efficiency mode. The system can realize intelligent treatment of exhaust gas discharged from the pig house in a more efficient and energy-saving manner, and can also provide early warning of failures in various components.

[0108] The deodorant tank stores the deodorant needed to treat exhaust gas, while the deodorant pump pumps it to the reservoir. In the reservoir, clean water and wastewater are mixed, and a scrubbing pump delivers the deodorant mixture to the scrubbing equipment to remove harmful components from the exhaust gas. The treated wastewater is then directed to a sedimentation tank, where solid impurities settle. The clean water is then returned to the system for reuse via a circulation pump, forming a closed-loop system. The reservoir not only adds clean water but also stores treated wastewater. The wastewater tank collects wastewater generated during the treatment process, which is then pumped to the reservoir via a wastewater pump. The sewage pump pumps wastewater from the sedimentation tank to the sewage tank, which collects any remaining wastewater for further treatment. During system operation, a stop valve controls the spraying of the deodorant, ensuring effective treatment at the appropriate time and under the appropriate conditions. In high-efficiency mode, the entire system ensures optimal treatment results through real-time monitoring and automatic adjustments. In energy-saving mode, the system uses pressure sensors, pH sensors and conductivity sensors to monitor the status of waste gas and wastewater in real time, automatically adjusting the supply of deodorant and the operating frequency of the pump, thereby reducing energy consumption and avoiding full operation when the pig house load is low.

[0109] In actual application, after determining the type of pigs, the number of pigs, the installation conditions of the purification platform in the pig farm, and the fan parameters, the solution of this application can be used to calculate the overall size of the purification space, the specifications of the control room, the layout of the air inlet, the layout of the exhaust port, the size of the pressure chamber, the size of the filter screens at each level, the gap size, the size of the water reservoir, the air diversion equipment, the nozzle selection, the nozzle layout, and the pump selection; the annual power consumption, annual water consumption, annual acid consumption, and exhaust gas purification efficiency of the pig farm's purification treatment can also be analyzed.

[0110] Based on the technical solutions introduced above, Figure 4 , introduces an implementation process of the farm waste gas purification solution given in the embodiment of this application, such as Figure 4 As shown, the process can include the following steps:

[0111] Step S101: Determine the distributed environmental information perception network of the target farm based on the type of the target farm.

[0112] Step S102: collecting environmental parameters of the target farm based on the distributed environmental information perception network of the target farm.

[0113] Step S103: establishing and starting an exhaust gas purification platform for the target farm based on the environmental parameters of the target farm and the sewage discharge requirements of the target farm.

[0114] Specifically, the waste gas emission pattern of the target farm can be determined based on the environmental parameters of the target farm; for example, the waste gas components emitted by the target farm, the concentration parameters of pollutants, the temperature changes of the target farm, and the wind speed through the curtain of the target farm can be determined by analyzing the environmental parameters of the target farm; then, based on the waste gas components and concentration parameters of pollutants emitted by the target farm, the temperature changes of the target farm, and the wind speed through the curtain of the target farm, a dynamic accumulation model of the target farm is established, wherein the dynamic accumulation model of the target farm can be trained using the waste gas components and concentration parameters of pollutants, the temperature changes of the training farm, and the wind speed through the curtain of the training farm as training samples, and the waste gas emission pattern of the training farm as sample labels.

[0115] In actual applications, farm ammonia concentration, ventilation speed, initial pH of the pickling solution, nozzle pressure, nozzle rated aperture, and nozzle atomization angle can also be selected as input values, with exhaust gas purification efficiency as the predicted output. System data is fused using a hybrid adaptive weighting algorithm and the Dempster Shafer (DS) evidence theory fusion algorithm. During model training, the collected data is first divided into 80% for training and 20% for testing. The model is trained on the training set, using an adaptive weighting strategy to dynamically adjust the weights of input features. The DS evidence theory fusion algorithm is then applied to integrate multi-source data to enhance model robustness. After training, model performance is evaluated on the test set, using mean squared error and mean absolute error metrics to ensure that prediction capabilities meet actual requirements. Based on the evaluation results, model parameters are adjusted and optimized, and multiple rounds of iterations are performed to achieve optimal results.

[0116] The dynamic accumulation model can be used to understand the waste gas emission pattern of the target farm. Therefore, after determining the dynamic accumulation model of the target farm, the dynamic accumulation model of the target farm can be used to analyze the environmental parameters of the target farm and extract the spatiotemporal characteristic information of the waste gas emission of the target farm; finally, based on the spatiotemporal characteristic information of the waste gas emission of the target farm, the waste gas emission pattern of the target farm can be determined.

[0117] For example, as can be seen from the above introduction, various environmental parameter data of the target farm may include but are not limited to exhaust gas components, pollutant concentrations, temperature, wind speed over the curtain, etc. at different time points. If this data can be input into the dynamic accumulation model of the target farm for analysis. The dynamic accumulation model of the target farm will simulate the process of exhaust gas generation, diffusion, accumulation, and emission into the external environment within the farm based on the set algorithm and parameter relationships. During the simulation process, taking into account the changes in time factors, the dynamic accumulation model of the target farm will dynamically calculate the exhaust gas-related parameters at various locations within the farm at different times, thereby obtaining the change pattern of exhaust gas emissions over time.

[0118] Combined with farm structural parameters and geographic information, the dynamic accumulation model for a target farm can analyze the distribution of waste gas across different areas of the farm. For example, by simulating the distribution of waste gas concentrations at different locations, high- and low-concentration areas within the farm can be identified, as well as the primary paths and directions of waste gas diffusion. This helps understand the spatial propagation characteristics of waste gas and provides a basis for the optimal placement of waste gas collection and treatment equipment.

[0119] After determining the waste gas emission pattern of the target farm based on the environmental parameters of the target farm, the waste gas purification platform model of the target farm can be determined based on the waste gas emission pattern of the target farm, the pollution discharge requirements of the target farm, and the structural parameters of the target farm.

[0120] For example, based on the pollution discharge needs of the target farm, the waste gas emission patterns and structural parameters of the target farm can be analyzed to determine the treatment strategy for purifying the waste gas of the target farm; then, based on the environmental parameters of the target farm, the structural parameters of the target farm and the waste gas treatment strategy of the target farm, the airflow structure parameters, fluid structure parameters, and filler structure parameters of the waste gas purification platform model of the target farm can be determined; finally, based on the airflow structure parameters, fluid structure parameters, and filler structure parameters of the waste gas purification platform model of the target farm, the waste gas purification platform model of the target farm can be determined.

[0121] In practical application, the exhaust gas purification platform model provides comprehensive guidance and a basis for the construction and operation of the actual platform. The target farm's exhaust gas purification platform model is constructed based on in-depth research on the target farm's exhaust gas emission patterns. It defines key design parameters for the target farm's exhaust gas purification platform, such as treatment capacity and efficiency. For example, based on the type, concentration, and emission flow rate of pollutants in the target farm's exhaust gas, the target farm's exhaust gas purification platform model determines the scale and treatment process of the target farm's exhaust gas purification equipment, such as the selection of an appropriate activated carbon adsorption device or biofilter, to ensure that the target farm's exhaust gas purification platform can effectively treat the target farm's exhaust gas. The target farm's exhaust gas purification platform model also determines the design requirements of the target farm's exhaust gas collection system, including the shape, size, and location of the gas collection hood, as well as the layout and diameter of the ventilation ducts, to ensure that exhaust gas is efficiently collected from the breeding area and treated at the target farm's exhaust gas purification platform. Based on the target farm's exhaust gas purification platform model, the appropriate purification equipment and supporting facilities can be accurately selected for the target farm.

[0122] In actual application, different treatment processes and equipment are suitable for different exhaust gas characteristics. The exhaust gas purification platform model of the target farm can help determine which equipment can best meet the specific needs of the target farm. For example, if the exhaust gas purification platform model of the target farm shows that the exhaust gas contains a large amount of volatile organic compounds, catalytic combustion equipment may be selected for treatment. At the same time, the exhaust gas purification platform model of the target farm will also guide the reasonable layout of the exhaust gas purification equipment of the target farm within the target farm. Taking into account factors such as the spatial structure of the target farm, the direction of airflow, and the convenience of operation and maintenance, the exhaust gas purification platform model of the target farm will plan the optimal installation location of each device, so that the exhaust gas purification system of the entire target farm is compact and efficient, and does not affect the normal production activities of the target farm.

[0123] The target farm's waste gas purification platform model can simulate its operation under different operating conditions, thereby formulating corresponding operation and control strategies. For example, based on the cyclical or seasonal changes in the target farm's waste gas emissions, the target farm's waste gas purification platform model can provide equipment operating parameter adjustment plans for different time periods. For example, the model can increase the operating power of the treatment equipment during peak waste gas emission periods and appropriately reduce energy consumption during low emission periods to achieve energy-saving operation.

[0124] The target farm's waste gas purification platform model also provides a basis for the design of an automated control system, enabling real-time monitoring and automatic control of the target farm's waste gas purification platform. Sensors monitor waste gas emission parameters and the operating status of the purification equipment in real time. Based on the control logic preset in the target farm's waste gas purification platform model, the equipment's operating parameters are automatically adjusted to ensure that the target farm's waste gas purification platform is always in optimal operating condition, guaranteeing the stability and reliability of the target farm's waste gas treatment results.

[0125] Before establishing and launching a target farm's waste gas purification platform, the target farm's waste gas purification platform model can be used to evaluate and optimize its performance. By simulating different operating conditions and parameter settings, the target farm's waste gas purification platform's treatment effectiveness, energy consumption, equipment lifespan, and other indicators can be predicted. This allows for early identification of potential problems and optimization of the target farm's waste gas purification platform model.

[0126] For example, if the exhaust gas purification platform model for a target farm predicts that exhaust gas treatment may not meet standards in certain situations, timely adjustments can be made to the treatment process or equipment parameters. If energy consumption is found to be excessively high, operational strategies or equipment selection can be optimized to reduce energy consumption. This allows the exhaust gas purification platform for a target farm to be continuously refined before actual construction and operation, enhancing its performance and economic efficiency.

[0127] Therefore, after determining the waste gas purification platform model of the target farm based on the waste gas emission patterns of the target farm, the pollution discharge requirements of the target farm, and the structural parameters of the target farm, the waste gas purification platform of the target farm is further established and started based on the waste gas purification platform model of the target farm to purify the waste gas discharged from the target farm.

[0128] For example, in actual experiments, we can take the piggery of a certain scale pig farm as an example. The study found that when fresh air enters the piggery breeding environment and transfers heat and mass to become exhaust gas, there is obvious accumulation of ambient temperature, such as Figure 5 Pollutants such as ammonia, hydrogen sulfide, and air particulate matter in pig houses also have certain accumulation patterns. By establishing a temporal and spatial series accumulation model for pig house exhaust gas components, we can reveal the cumulative change patterns between exhaust gas components and the breeding environment. This can provide a basis for the subsequent design of exhaust gas purification units and a reference for the optimization design of pig house ventilation structures to ensure animal welfare.

[0129] Secondly, if Figure 5As shown in part c, how to design the exhaust gas purification unit, establish the mathematical model of the exhaust gas purification unit, and determine the key structural parameters to ensure the ventilation needs of the breeding environment in the house and efficiently purify the exhaust gas are scientific problems that need to be solved in the application of the pig house exhaust gas purification industry.

[0130] like Figure 5 The exhaust gas purification system in the shed shown here dynamically adjusts based on feedback from internal and external detectors. The system is equipped with various sensors, such as ammonia, temperature and humidity, and carbon dioxide, to monitor air quality in the sheds in real time and transmit this data to a central control system via Zigbee wireless transmission technology. The system uses a built-in algorithm to analyze current environmental conditions and compare them with preset standards, automatically determining whether to initiate or adjust the exhaust gas treatment process. When exhaust gas concentration exceeds a set threshold, the system increases the deodorant spray rate, and decreases it when concentration decreases, thereby conserving resources. The operating frequency of the scrubber pump and circulating water pump also adjusts based on exhaust gas conditions, dynamically adjusting the number and operating mode of the deodorizing equipment, optimizing treatment efficiency and saving deodorant, water, and electricity.

[0131] Therefore, after determining the waste gas purification platform model of the target farm based on the waste gas emission patterns of the target farm, the pollution discharge requirements of the target farm, and the structural parameters of the target farm, the waste gas purification platform of the target farm can be established and started based on the waste gas purification platform model of the target farm.

[0132] For example, after determining the exhaust gas purification platform of the target farm, the fan installation position of the target farm and the flow field distribution structure of the airflow in the fan purification room can be determined based on the airflow structure parameters of the exhaust gas purification platform model of the target farm and the structural parameters of the target farm; then, the detergent flow configuration strategy of the exhaust gas purification platform of the target farm can be determined based on the fluid structure parameters of the exhaust gas purification platform model of the target farm and the structural parameters of the target farm; and the material filling the exhaust gas purification platform of the target farm and the structure and size of the exhaust gas purification platform of the target farm can be determined based on the filler structure parameters of the exhaust gas purification platform model of the target farm and the structural parameters of the target farm; finally, the exhaust gas purification platform of the target farm can be established based on the material filling the exhaust gas purification platform of the target farm and the structure and size of the exhaust gas purification platform of the target farm.

[0133] Among them, airflow structure parameters, such as wind speed, wind direction, and airflow distribution, determine the flow path and residence time of the exhaust gas within the purification platform. A reasonable airflow structure can evenly distribute the exhaust gas within the purification platform, avoiding airflow short-circuits or dead zones, ensuring sufficient contact between the exhaust gas and the purification medium, and providing good conditions for subsequent purification processes. Appropriate airflow velocity helps promote the mass transfer process between pollutants in the exhaust gas and the purification medium. For example, during the adsorption process, a suitable airflow velocity can quickly diffuse pollutant molecules to the adsorbent surface, improving adsorption efficiency; during chemical reactions, good airflow distribution can ensure sufficient mixing of reactants, accelerate the reaction rate, and thus affect the purification effect.

[0134] Fluid structure parameters primarily relate to the flow characteristics of the liquid during the purification process, such as liquid velocity, flow rate, and spray pattern. For waste gas purification platforms that utilize wet purification processes, such as spray towers and wet scrubbers, fluid structure parameters determine the uniformity of liquid distribution within the equipment and the contact area with the waste gas. Uniform liquid distribution allows pollutants in the waste gas to fully absorb and neutralize the liquid, improving purification efficiency. Reasonable fluid structure parameters can enhance the mass transfer between the gas and liquid phases. By adjusting parameters such as the liquid spray angle and flow rate, gas-liquid contact can be more complete, increasing the mass transfer rate of pollutants at the gas-liquid interface and thus improving the ability to remove pollutants from the waste gas.

[0135] Packing structural parameters can include type, shape, size, porosity, and specific surface area. As a key component in exhaust gas purification platforms, packing provides a vast surface area for reactions such as gas-liquid mass transfer, adsorption, and catalysis. Different types of packing have varying structural characteristics and performance. Selecting the appropriate packing structural parameters can increase the contact area between exhaust gas and the purification medium, improving reaction efficiency.

[0136] Step S104: Real-time perception of the state parameters of the waste gas purification platform of the target farm and the real-time environmental parameters of the target farm.

[0137] Step S105 , based on the state parameters of the waste gas purification platform of the target farm and the real-time environmental parameters of the target farm, a preset control mechanism is used to control the operation of the waste gas purification platform of the target farm to treat the waste gas generated by the target farm.

[0138] In actual application, when the waste gas purification platform is started to treat the waste gas from the farm, in order to ensure the treatment effect of the waste gas and meet the emission standards and maintain stable treatment efficiency, it is necessary to timely adjust the operation control strategy of the waste gas purification platform according to the actual situation. Therefore, after starting the waste gas purification platform of the target breeding, it is necessary to perceive the state parameters of the waste gas purification platform and the real-time environmental parameters of the target breeding farm in real time, and based on the state parameters of the waste gas purification platform of the target breeding farm and the real-time environmental parameters of the target breeding farm, use the preset control mechanism to control the operation of the waste gas purification platform of the target breeding farm to treat the waste gas generated by the target breeding farm.

[0139] A pre-set control mechanism automatically adjusts the purification platform's operating parameters based on real-time environmental parameters, such as the concentration, type, and flow rate of pollutants in the exhaust gas, as well as the platform's operating parameters, such as equipment operating temperature, pressure, and catalyst activity. When pollutant concentrations in the exhaust gas rise, the control mechanism can increase the amount of treatment agent, raise the reaction temperature, or extend the treatment time to maintain stable treatment efficiency and ensure that the treated exhaust gas meets emission standards.

[0140] In practice, farm production activities and environmental conditions are constantly changing, leading to changes in the amount and composition of waste gas. For example, expanding farm scale can increase waste gas emissions, while seasonal changes can affect the concentration of certain components in waste gas. By sensing environmental parameters in real time and utilizing pre-set control mechanisms, the waste gas purification platform can quickly adapt to these changes, preventing degradation of treatment effectiveness due to fluctuations in environmental parameters.

[0141] The control mechanism rationally regulates the equipment based on the exhaust gas purification platform's status parameters. For example, if the equipment's operating temperature is too high, the control mechanism activates the cooling system to prevent damage from overheating, extending the equipment's service life and ensuring stable performance. By monitoring equipment parameters such as pressure and vibration in real time, the control mechanism can promptly identify potential equipment anomalies. Once an anomaly is detected, the control mechanism can take appropriate measures, such as reducing the equipment's operating load and issuing an alarm, to prevent equipment failure and reduce downtime and economic losses caused by equipment maintenance.

[0142] A preset control mechanism based on real-time parameters enables precise resource allocation. Based on the actual exhaust gas conditions, the consumption of treatment agents, energy, and other resources can be precisely controlled to avoid resource waste. For example, when the exhaust gas pollutant concentration is low, appropriately reducing the amount of agent added can both ensure treatment effectiveness and reduce agent costs.

[0143] The control mechanism optimizes the equipment's operating mode and reduces energy consumption based on changes in exhaust gas flow and composition. For example, when exhaust gas flow is low, the equipment's operating power can be appropriately reduced to achieve energy conservation and emission reduction goals, thereby improving the farm's overall economic and environmental benefits. The pre-set control mechanism records the exhaust gas purification platform's operating parameters and relevant data from the treatment process. This data not only helps the farm manage and optimize its own exhaust gas treatment, but also facilitates supervision and inspection by environmental regulators, enabling data traceability and improving the farm's environmental management capabilities.

[0144] For example, in actual application, we can select four indicators, namely resources, economic costs, environmental impact and technical factors, based on the theory of hierarchical classification models, combine the waste gas emission model and the requirements of waste gas purification guidelines, take chemical decomposition and biological filtration as decision-making objects, and establish a hierarchical classification model for waste gas purification technology decision-making. Build a waste gas purification test platform, carry out purification parameter operation test tests on reaction materials such as concentrated sulfuric acid, citric acid and beneficial bacteria metabolites, and explore the physical and chemical reaction characteristics of waste gas components during the purification process. To ensure the reliable operation of the purification system, establish a reasonable process control strategy to control the drip density, conductivity and pH value of the washing liquid. Based on the relationship between the waste gas purification efficiency and the liquid-to-gas ratio, the appropriate drip density can be determined to achieve a suitable liquid-to-gas ratio.

[0145] Conductivity typically measures the total amount of ammonia, nitrite, and nitrate in a liquid. By controlling the conductivity of the scrubbing liquid to keep it below the maximum solubility of ammonium sulfate, the scrubbing liquid's absorption efficiency for pollutants like ammonia can be ensured. The acidity of the scrubbing liquid, or its pH, not only affects ammonia absorption but also the composition of other pollutants during the purification process. Therefore, the pH range of the scrubbing liquid must be appropriately controlled based on the characteristics of the purification method.

[0146] A hierarchical structure for piggery waste gas purification decision-making was established. The importance of factors influencing waste gas purification technology decisions was qualitatively analyzed. An attribute judgment matrix was constructed to calculate their relative attribute weights. Using the top layer as the criteria, an attribute judgment matrix was constructed at the criterion level, and relative weights were calculated. Using the intermediate layers as the criteria, an attribute judgment matrix was constructed at the solution level, and relative weights were calculated. Finally, the combined weight of the waste gas purification solution relative to the objective was calculated, and a decision was made based on the given conditions.

[0147] For example, in the experiment, an adaptive control system structure suitable for the target farm can be constructed as Figure 6As shown, a sensor network can be used to collect environmental data through multi-sensor fusion. For example, a wireless multi-point, multi-source remote monitoring system for piggery environments can be used. Multiple slave nodes distributed across the piggery collect data in real time, and comprehensive analysis can be performed to determine the true environmental status. In this step, a wireless sensor network data fusion model can be designed to integrate and process the collected data.

[0148] System data can be fused using a hybrid approach combining an adaptive weighting algorithm and the DS (Dempster Shafer) evidence theory fusion algorithm. First, data from each wireless sensor network collection node is preprocessed and sent to the sensor network's coordinator node. The coordinator node then uses an adaptive weighting algorithm to perform data-level fusion on the data from each collection node. The fused data of different types is then sent to the control center in groups. DS evidence theory is then used for decision-level fusion, guiding control decisions through comprehensive analysis of various environmental parameters.

[0149] For example, in the experiment, a pig house exhaust gas purification system structure diagram can be constructed, such as Figure 7 As shown in the figure, the system is based on a sensor network and uses multi-sensor fusion to collect state parameters during the exhaust gas purification process. Based on the exhaust gas composition emission model at each stage, the system calculates the minimum effective mass transfer contact time. Through an automatic control system, optimal control parameter matching is achieved under different gas concentrations.

[0150] Combining the advantages of genetic algorithms (GAs)—fast, randomized, and globally convergent—with the parallel, positive feedback mechanism, and high solution efficiency of ant colony algorithms, an adaptive multi-objective ant colony genetic algorithm was designed. By monitoring multiple sources of information, including wind speed over the curtains and the pH and EC values ​​of the washing liquid, a self-optimizing control model for piggery exhaust gas purification was established with the goal of controlling water, electricity, and deodorant consumption and improving exhaust gas purification efficiency. Using an adaptive multi-objective ant colony genetic algorithm, this algorithm solves multi-constraint and multi-objective optimization problems, coordinates piggery environmental control and ventilation, eliminates system coupling effects, and optimizes exhaust gas purification.

[0151] It can be seen from the technical solutions introduced above that the method provided in the embodiment of the present application can be combined with the structural characteristics of the centralized ventilation type of the farm and the characteristics of the waste gas emission. On the basis of achieving efficient purification of the waste gas of the farm, it takes into account the initial investment cost and operation and maintenance costs of the waste gas treatment of the farm. It has the advantages of efficient purification and low energy consumption, and is of great significance to the optimal design of the livestock and poultry breeding waste gas purification spray system and the selection of supporting equipment.

[0152] The following describes the farm waste gas purification device provided in the embodiment of the present application. The farm waste gas purification device described below and the farm waste gas purification system described above can be referenced to each other.

[0153] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of a farm waste gas purification device disclosed in the embodiment of this application. Figure 8 As shown, the farm waste gas purification device may include:

[0154] The first determining unit 101 is configured to determine a distributed environmental information sensing network of a target farm based on the type of the target farm;

[0155] A collection unit 102 is configured to collect environmental parameters of the target farm based on the distributed environmental information sensing network of the target farm;

[0156] A construction unit 103 is configured to establish and start an exhaust gas purification platform for the target farm based on the environmental parameters of the target farm and the sewage discharge requirements of the target farm;

[0157] The sensing unit 104 is used to sense the state parameters of the exhaust gas purification platform of the target farm and the real-time environmental parameters of the target farm in real time;

[0158] The purification unit 105 is used to control the operation of the waste gas purification platform of the target farm based on the state parameters of the waste gas purification platform of the target farm and the real-time environmental parameters of the target farm, using a preset control mechanism to treat the waste gas generated by the target farm.

[0159] It can be seen from the technical solution introduced above that the device of the embodiment of the present application can be combined with the structural characteristics of the centralized ventilation type of the farm and the exhaust gas emission characteristics. On the basis of achieving efficient purification of the exhaust gas of the farm, it takes into account the initial investment cost and operation and maintenance costs of the exhaust gas treatment of the farm. It has the advantages of high efficiency purification and low energy consumption, and is of great significance to the optimal design of the livestock and poultry breeding exhaust gas purification spray system and the selection of supporting equipment.

[0160] Among them, the specific processing procedures of each unit included in the above-mentioned farm waste gas purification device can be referred to the relevant introduction of the fattening pig farming intelligent management system in the previous article, and will not be repeated here.

[0161] The farm waste gas purification device provided in the embodiment of the present application can be applied to farm waste gas purification equipment, such as terminals: mobile phones, computers, etc. Optionally, Figure 9 The hardware structure diagram of the farm exhaust equipment is shown. Figure 9 The hardware structure of the farm waste gas purification equipment may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4.

[0162] In the embodiment of the present application, there is at least one processor 1, communication interface 2, memory 3, and communication bus 4, and the processor 1, communication interface 2, and memory 3 communicate with each other via the communication bus 4. The processor 1 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present application; the memory 3 may include a high-speed RAM memory, or may also include a non-volatile memory, such as at least one disk storage; wherein the memory stores a program, and the processor can call the program stored in the memory, and the program is used to implement the various processing processes in the aforementioned terminal farm waste gas purification solution.

[0163] An embodiment of the present application also provides a readable storage medium, which can store a program suitable for execution by a processor, and the program is used to: implement various processing processes of the aforementioned terminal in the farm waste gas purification solution.

[0164] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0165] 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.

[0166] The above description of the disclosed embodiments is intended to enable those skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. The various embodiments may be combined with one another. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A farm waste gas purification system, characterized in that: The system includes: multi-source heterogeneous perception module, data processing module, main control module, chemical decomposition system, biological filtration system, waste liquid treatment system and fault diagnosis system; The multi-source heterogeneous perception module is responsible for sensing various status parameters of the target farm and transmitting them to the data processing module. The target farm includes an exhaust gas purification platform, which includes several water spray valves, pH sensors, water pumps, acid solution tanks, and filler structures. The data processing module is responsible for receiving the various state parameters of the target farm transmitted by the multi-source heterogeneous perception module, pre-processing them, and transmitting the obtained target multivariate data to the main control module; The main control module is responsible for receiving the target multivariate data; analyzing the target multivariate data, and determining the weight coefficients of various state parameters of the target farm based on the influence factors of various state data in the target multivariate data on the waste gas purification work of the target farm; determining the relative weights of various state parameters to the chemical decomposition system, the biological filtration system, and the waste liquid treatment system based on the weight coefficients of various state parameters of the target farm; determining the control decision of the chemical decomposition system, the biological filtration system, and the waste liquid treatment system according to the relative weights and various state parameters of the chemical decomposition system, the biological filtration system, and the waste liquid treatment system; and transmitting the control decision of the chemical decomposition system, the biological filtration system, and the waste liquid treatment system to the chemical decomposition system, the biological filtration system, and the waste liquid treatment system respectively. The biological filtration system and the waste liquid treatment system control the chemical decomposition system, the biological filtration system, and the waste liquid treatment system to purify the waste gas discharged from the target farm; wherein the chemical decomposition system is responsible for controlling the water spray valve corresponding to the acid solution pool of the waste gas purification platform to spray the acid solution in the acid solution pool into the packing structure of the waste gas purification platform, so that the packing structure of the waste gas purification platform removes ammonia from the waste gas discharged from the target farm through a chemical reaction; when the pH sensor detects that the pH value of the acid solution in the acid solution pool is higher than a preset first threshold value, the water pump corresponding to the acid solution pool is started, and concentrated acid of a preset first concentration is added to the acid solution pool to reduce the pH value of the acid solution in the acid solution pool and maintain the ability of the acid solution in the acid solution pool to neutralize ammonia.

2. The system according to claim 1, wherein: The farm waste gas purification system also includes a fault diagnosis system; The fault diagnosis system is used to receive and process the fault information fed back by the main control module when a fault occurs in the farm waste gas purification system, and provide the fault information and fault solution of the farm waste gas purification system.

3. The system according to claim 1, wherein: The target farm includes an exhaust gas purification platform, which includes a number of water spray valves, a biological solution pool, and a filler structure; The biological filtration system is responsible for controlling the water spray valve corresponding to the biological solution pool, spraying the biological solution in the biological solution pool into the filler structure corresponding to the waste gas purification platform, so as to utilize the metabolic effect of the microorganisms in the biological solution to decompose and remove organic pollutants in the waste gas discharged from the target farm.

4. The system according to claim 1, wherein: The target farm includes an exhaust gas purification platform; The waste liquid treatment system is responsible for removing pollutants in the wastewater generated during the waste gas purification platform of the target farm to treat the waste gas discharged from the target farm, so as to ensure that it meets the emission standards.

5. The system according to claim 1, wherein: The process of the multi-source heterogeneous perception module perceiving various status parameters of the target farm and transmitting them to the data processing module includes: The multi-source heterogeneous perception module receives status data collected by various types of sensors deployed at the target farm, and analyzes the status data collected by each sensor to determine various status parameters of the target farm; The determined status parameters of the target farm are classified and then transmitted to the data processing module.

6. The system according to claim 1, wherein: The data processing module receives the various state parameters of the target farm transmitted by the multi-source heterogeneous perception module and pre-processes them to obtain target multivariate data, including: Receiving various status parameters of the target farm transmitted by the multi-source heterogeneous perception module; Performing data cleaning on each received state parameter to eliminate erroneous and duplicate data to obtain first data; performing data conversion on the first data to obtain second data; The second data is subjected to classification processing, data integration processing and feature extraction processing to obtain the target multivariate data.

7. A farm waste gas purification device, characterized in that: include: One or more processors, and a memory; the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the implementation process of the farm waste gas purification system as described in any one of claims 1 to 6 is realized.

8. A readable storage medium, characterized in that: The readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors implement the implementation process of the farm waste gas purification system as described in any one of claims 1 to 6.

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