Organic waste gas purification treatment system and method

Abstract

The invention relates to the field of intelligent control, and particularly discloses an organic waste gas purification treatment system and method.The organic waste gas purification treatment system is characterized in that waste gas is preliminarily filtered through a filter screen, then ozone is catalyzed through an ozone catalyst, volatile organic compounds (VOCs) in the filtered waste gas are preliminarily oxidized, and then the waste gas is purified through a water purification system. According to the method, the VOCs waste gas subjected to preliminary oxidation is further oxidized by adopting an ultraviolet radiation nano photocatalyst, the VOCs waste gas is converted into micromolecular organic matters which are easier to biodegrade, the concentration of the VOCs in the waste gas is monitored in real time, and the rotating speed of a fan is dynamically adjusted according to monitoring data, so that the purification efficiency and the optimal running state of the system are ensured.

Description

Technical Field

[0001] The present application relates to the field of intelligent control, and more specifically, to a system and method for purifying and treating organic waste gas. Background Art

[0002] With the acceleration of industrialization, the emission of organic waste gas (VOCs) has become increasingly serious, posing a huge threat to the environment and human health. VOCs not only lead to deterioration of air quality, but also cause problems such as photochemical smog and ozone layer depletion. Therefore, the effective treatment of VOCs waste gas has become an important issue in the field of environmental protection. Traditional VOCs treatment methods such as adsorption, absorption, and combustion have problems such as high energy consumption, low treatment efficiency, and secondary pollution.

[0003] In this regard, patent CN113731168A discloses a volatile organic waste gas purification system and method, which includes five steps: air intake filtration, ozone catalytic oxidation, nano-photocatalyst enhanced oxidation, ozone decomposition and BAF biodegradation. This process uses physical filtration to remove particulate matter, chemical oxidation to decompose difficult-to-degrade VOCs into easily biodegradable small molecules, and finally further degrades the remaining organic matter through a biological filter to solve traditional treatment problems and achieve efficient purification.

[0004] When dealing with volatile organic compounds (VOCs), precise control of fan speed is crucial to improving purification efficiency and reducing energy consumption. In the above patent, the fan speed is adjusted through the controller configuration. However, the traditional control method uses a preset fan speed, which causes the system to react slowly to changes in VOCs exhaust gas concentration and is difficult to quickly adapt to actual working conditions. When the exhaust gas concentration suddenly increases, the fixed low speed cannot provide sufficient airflow speed to handle additional pollutants, thereby reducing the purification efficiency; and when the concentration decreases, the high speed will cause unnecessary energy consumption. In addition, due to the fixed parameters, the traditional system cannot be flexibly adjusted according to the real-time exhaust gas concentration fluctuations, making its performance unstable under different working conditions and difficult to always be in the optimal state.

[0005] Therefore, an optimized organic waste gas purification treatment solution is desired. Summary of the invention

[0006] In order to solve the above technical problems, this application is proposed.

[0007] According to one aspect of the present application, an organic waste gas purification treatment system is provided, which includes: a fan; a filter screen for filtering waste gas to obtain filtered waste gas; a preliminary oxidation module, using an ozone catalyst to catalyze ozone to perform preliminary oxidation of VOCs in the filtered waste gas to obtain preliminary oxidized VOCs waste gas; a secondary oxidation module, using ultraviolet radiation nano-photocatalysts to perform secondary oxidation on the preliminary oxidized VOCs waste gas to obtain biodegradable small molecular organic matter; a controller, used to monitor the VOCs waste gas concentration data in the waste gas in real time during the waste gas purification process and adjust the speed of the fan in real time; wherein the controller includes: A VOCs exhaust gas concentration collection and transmission unit, which is used to collect a time series data set of VOCs exhaust gas concentration data at a predetermined sampling frequency using a VOCs exhaust gas concentration detector, and transmit the time series data set of VOCs exhaust gas concentration data to a fan speed control center through a wireless communication module; A VOCs exhaust gas concentration time series analysis unit is used to perform phase space reconstruction and local time series extraction on the time series data set of the VOCs exhaust gas concentration data at the fan speed control center to obtain a set of VOCs exhaust gas concentration local time series implicit pattern features; The VOCs exhaust gas concentration time series propagation unit is used to perform multi-gated feature dynamic propagation on the set of local time series implicit pattern features of the VOCs exhaust gas concentration to obtain a VOCs exhaust gas concentration time series feature propagation representation, and based on the VOCs exhaust gas concentration time series feature propagation representation, obtain an optimization instruction, and the optimization instruction includes a recommended value of the fan speed at the next time point.

[0008] According to another aspect of the present application, a method for purifying organic waste gas is provided, which is used to execute the above-mentioned organic waste gas purification system, and the method for purifying organic waste gas comprises: Using a VOCs exhaust gas concentration detector to collect a time series data set of VOCs exhaust gas concentration data at a predetermined sampling frequency, and transmitting the time series data set of VOCs exhaust gas concentration data to a fan speed control center through a wireless communication module; At the fan speed control center, phase space reconstruction and local time series extraction are performed on the time series data set of the VOCs exhaust gas concentration data to obtain a set of local time series implicit pattern features of the VOCs exhaust gas concentration; The set of local time series implicit pattern features of the VOCs exhaust gas concentration is subjected to multi-gated feature dynamic propagation to obtain a VOCs exhaust gas concentration time series feature propagation representation, and based on the VOCs exhaust gas concentration time series feature propagation representation, an optimization instruction is obtained, the optimization instruction including a recommended value of the fan speed at the next time point.

[0009] Compared with the prior art, the present application provides an organic waste gas purification treatment system and method, which uses a VOCs waste gas concentration detector to collect a time series data set of VOCs waste gas concentration data at a predetermined sampling frequency, and transmits it to the fan speed control center through a wireless communication module, and uses a data analysis and encoding method based on deep learning to perform phase space reconstruction and local time series implicit extraction on the time series data set of VOCs waste gas concentration data in the fan speed control center, so as to intelligently obtain the recommended value of the fan speed at the next time point by performing multi-gated propagation according to the local time series implicit pattern characteristics of each VOCs waste gas concentration. In this way, the VOCs waste gas concentration can be monitored in real time, and the complex pattern of the VOCs waste gas concentration can be identified. The fan speed can be intelligently adjusted according to the current and predicted waste gas conditions, and the adaptability to different working conditions is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0011] Figure 1 It is a block diagram of an organic waste gas purification system according to an embodiment of the present application.

[0012] Figure 2 It is a block diagram of a controller in an organic waste gas purification system according to an embodiment of the present application.

[0013] Figure 3 It is a block diagram of a VOCs waste gas concentration time series propagation unit in an organic waste gas purification treatment system according to an embodiment of the present application.

[0014] Figure 4 It is a block diagram of a subunit for the time-series dynamic propagation of VOCs waste gas concentration in an organic waste gas purification treatment system according to an embodiment of the present application.

[0015] Figure 5 Flow chart of a method for purifying organic waste gas according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] The embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, but rather these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0017] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0018] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0019] With the acceleration of industrialization, the emission of organic waste gas (VOCs) has become increasingly serious, posing a huge threat to the environment and human health. VOCs not only lead to deterioration of air quality, but also cause problems such as photochemical smog and ozone layer depletion. Therefore, the effective treatment of VOCs waste gas has become an important issue in the field of environmental protection. Traditional VOCs treatment methods such as adsorption, absorption, and combustion have problems such as high energy consumption, low treatment efficiency, and secondary pollution.

[0020] In this regard, patent CN113731168A discloses a volatile organic waste gas purification system and method, which includes five steps: air intake filtration, ozone catalytic oxidation, nano-photocatalyst enhanced oxidation, ozone decomposition and BAF biodegradation. This process uses physical filtration to remove particulate matter, chemical oxidation to decompose difficult-to-degrade VOCs into easily biodegradable small molecules, and finally further degrades the remaining organic matter through a biological filter to solve traditional treatment problems and achieve efficient purification.

[0021] When dealing with volatile organic compounds (VOCs), precise control of fan speed is crucial to improving purification efficiency and reducing energy consumption. In the above patent, the fan speed is adjusted through the controller configuration. However, the traditional control method uses a preset fan speed, which causes the system to react slowly to changes in VOCs exhaust gas concentration and is difficult to quickly adapt to actual working conditions. When the exhaust gas concentration suddenly increases, the fixed low speed cannot provide sufficient airflow speed to handle additional pollutants, thereby reducing the purification efficiency; and when the concentration decreases, the high speed will cause unnecessary energy consumption. In addition, due to the fixed parameters, the traditional system cannot be flexibly adjusted according to the real-time exhaust gas concentration fluctuations, making its performance unstable under different working conditions and difficult to always be in the optimal state.

[0022] Based on this, the present application proposes an organic waste gas purification and treatment system. Figure 1 The block diagram of the organic waste gas purification treatment system according to the embodiment of the present application. Specifically, the organic waste gas purification treatment system 100 according to the embodiment of the present application includes: a fan 110; a filter 120, which is used to filter the waste gas to obtain the filtered waste gas; a preliminary oxidation module 130, which uses an ozone catalyst to catalyze ozone to perform preliminary oxidation of VOCs in the filtered waste gas to obtain preliminary oxidation VOCs waste gas; a secondary oxidation module 140, which uses ultraviolet radiation nano-photocatalysts to perform secondary oxidation on the preliminary oxidation VOCs waste gas to obtain biodegradable small molecular organic matter; a controller 150, which is used to monitor the VOCs waste gas concentration data in the waste gas in real time during the waste gas purification process and adjust the speed of the fan in real time.

[0023] Specifically, the fan 110. It should be understood that the fan plays a key role in the exhaust gas purification process, and it supports the effective implementation of the entire purification process by accurately controlling the gas flow rate of the VOCs exhaust gas.

[0024] Specifically, the filter screen 120 is used to filter the exhaust gas to obtain filtered exhaust gas.

[0025] That is, the filter is used to separate and filter particulate matter in VOCs waste gas, and its diameter is ≥ 0.3 μm. In particular, the filter can be one of a coarse filter, a HEPA filter or an activated carbon filter.

[0026] Specifically, the preliminary oxidation module 130 uses an ozone catalyst to catalyze ozone to preliminarily oxidize the VOCs in the filtered exhaust gas to obtain preliminarily oxidized VOCs exhaust gas. It should be understood that the preliminary oxidation module is a key component specially designed for the efficient oxidation of difficult-to-degrade VOCs exhaust gas. The module consists of a built-in ozone generator module and an external ozone source module. The ozone catalyst used is a microbial charcoal-supported multi-metal nanoparticle catalyst. These catalysts use microbial cells or bacterial residues as carriers and contain transition metal and rare earth metal components. This design can significantly improve the reaction efficiency between ozone and VOCs.

[0027] Specifically, first, the exhaust gas after preliminary filtration to remove particulate matter is introduced into the preliminary oxidation module, where the built-in or external ozone generator generates sufficient ozone, which then comes into contact with the VOCs in the exhaust gas and reacts under specific conditions.

[0028] In order to accelerate and optimize this oxidation process, the system uses a microbial charcoal-supported multi-metal nanoparticle catalyst. This catalyst uses microbial cells or bacterial residues as a carrier, contains transition metals and rare earth metal components, and can significantly improve the reaction efficiency between ozone and VOCs. When ozone meets VOCs, under the action of the catalyst, the ozone molecules become more active and can more effectively react with VOCs to oxidize. In this process, the carbon-hydrogen bonds in VOCs are broken to form a series of simpler intermediates, such as aldehydes and ketones. These intermediates are easier to be further decomposed by subsequent treatment steps than the original VOCs.

[0029] In the initial oxidation stage, the ozone catalyst not only promotes the oxidation reaction of VOCs, but also inhibits the generation of by-products to a certain extent, avoiding possible secondary pollution problems. For example, by controlling the reaction conditions, the generation of nitrogen oxides and other harmful gases can be reduced. In addition, since ozone is a strong oxidant, it can also effectively remove some other pollutants in the exhaust gas, such as sulfides and ammonia, further improving the purification effect. In short, the ozone catalyst is used to catalyze the initial oxidation of VOCs in the filtered exhaust gas, and the complex organic pollutants are converted into small molecules that are easy to handle through efficient chemical reactions, laying the foundation for subsequent deep purification.

[0030] Specifically, the secondary oxidation module 140 uses ultraviolet radiation nano-photocatalysts to perform secondary oxidation on the primary oxidation VOCs waste gas to obtain biodegradable small molecule organic matter. It should be understood that 1 to 6 ultraviolet lamps and nano-photocatalyst catalytic nets are installed inside the module, wherein the catalyst is titanium dioxide doped with one or more metals of iron, cobalt, manganese, and cerium. By irradiating the nano-photocatalyst with ultraviolet rays, the VOCs waste gas can be further oxidized, and its main function is to break or open the chain of typical pollutants such as aromatic compounds and heterocyclic compounds in the VOCs waste gas, and decompose them into biodegradable small molecule organic matter.

[0031] Specifically, first, after the ozone catalysis in the initial oxidation stage, most of the VOCs in the exhaust gas have been partially oxidized into relatively simple intermediates, such as aldehydes, ketones, etc. However, these intermediates still have a certain complexity and stability, and require further treatment to achieve higher purification standards. To this end, the system introduces ultraviolet radiation nanophotocatalyst technology for secondary oxidation. The core of this technology is to use ultraviolet light of a specific wavelength to irradiate a catalytic net containing nanoscale photocatalysts, thereby stimulating the photocatalyst to produce strongly oxidizing free radicals, thereby deeply oxidizing VOCs and their derivatives.

[0032] Specifically, 1 to 6 UV lamps and nano-photocatalyst catalytic nets are installed inside the secondary oxidation module. The photocatalyst here usually uses titanium dioxide doped with one or more metals of iron, cobalt, manganese, and cerium to form nanoparticles with highly active surfaces. When ultraviolet light is irradiated on these nano-photocatalysts, it will cause electrons to jump from the valence band to the conduction band, while leaving holes in the valence band. These excited electrons and holes can react with the surrounding oxygen and water molecules to generate hydroxyl radicals (·OH) and superoxide anion radicals (O2-) with strong oxidizing properties. These free radicals can quickly attack and destroy carbon-hydrogen bonds and other chemical bonds in the molecular structure of VOCs, and gradually decompose them into carbon dioxide, water and other harmless small molecules. Through this secondary oxidation process, not only can the concentration of residual VOCs in the exhaust gas be further reduced, but also its biodegradability can be significantly improved. This is because small-molecule organic matter is more easily decomposed by microorganisms than the original large-molecule VOCs, so that it can be more thoroughly removed in the subsequent biological treatment stage. In addition, since the photocatalyst used in the whole process has good stability and recyclability, it can maintain a high catalytic efficiency throughout its service life, reducing the cost of frequent catalyst replacement.

[0033] Specifically, the controller 150 is used to monitor the VOCs exhaust gas concentration data in the exhaust gas in real time during the exhaust gas purification process and adjust the speed of the fan in real time. It should be understood that during the exhaust gas purification process, when the VOCs concentration in the exhaust gas is high, if it is not handled in time, it may cause the purification system to be overloaded and unable to effectively remove all harmful substances, thereby affecting the final purification effect. If the VOCs concentration is low, the system may not need to run at full load, and energy can be saved by reducing the fan speed and other measures while still maintaining a good purification effect. Therefore, in order to ensure efficient, economical and environmentally friendly exhaust gas treatment, by continuously monitoring the VOCs concentration, the system can quickly respond to changes in pollutants in the exhaust gas and automatically adjust the fan speed to optimize the air flow speed, thereby improving the purification efficiency. When the VOCs concentration increases, increasing the fan speed can more effectively treat additional pollutants; when the concentration is low, the speed is reduced to save energy. This intelligent regulation not only improves the overall treatment effect, but also significantly reduces energy consumption and operating costs, while reducing the risk of secondary pollution caused by improper treatment, and achieving a cleaner and more efficient industrial production environment. Such a closed-loop control mechanism is an important technical means to achieve sustainable development.

[0034] Accordingly, in the control module, the technical concept of the present application is to use a VOCs exhaust gas concentration detector to collect a time series data set of VOCs exhaust gas concentration data at a predetermined sampling frequency, and transmit it to the fan speed control center through a wireless communication module, and use a deep learning-based data analysis and encoding method in the fan speed control center to perform phase space reconstruction and local time series implicit extraction on the time series data set of VOCs exhaust gas concentration data, so as to intelligently obtain the recommended value of the fan speed at the next time point by performing multi-gated propagation according to the local time series implicit pattern characteristics of each VOCs exhaust gas concentration. In this way, the VOCs exhaust gas concentration can be monitored in real time, and the complex pattern of the VOCs exhaust gas concentration can be identified. The fan speed can be intelligently adjusted according to the current and predicted exhaust gas conditions, thereby improving the adaptability to different working conditions.

[0035] Figure 2 is a block diagram of a controller in an organic waste gas purification system according to an embodiment of the present application. Figure 2As shown, the controller 150 includes: a VOCs exhaust gas concentration acquisition and transmission unit 151, which is used to use a VOCs exhaust gas concentration detector to collect a time series data set of VOCs exhaust gas concentration data at a predetermined sampling frequency, and transmit the time series data set of the VOCs exhaust gas concentration data to the fan speed control center through a wireless communication module; a VOCs exhaust gas concentration time series analysis unit 152, which is used to perform phase space reconstruction and local time series extraction on the time series data set of the VOCs exhaust gas concentration data at the fan speed control center to obtain a set of local time series implicit pattern features of the VOCs exhaust gas concentration; a VOCs exhaust gas concentration time series propagation unit 153, which is used to perform multi-gated feature dynamic propagation on the set of local time series implicit pattern features of the VOCs exhaust gas concentration to obtain a VOCs exhaust gas concentration time series feature propagation representation, and based on the VOCs exhaust gas concentration time series feature propagation representation, obtain an optimization instruction, and the optimization instruction includes a recommended value of the fan speed at the next time point.

[0036] In an embodiment of the present application, the VOCs waste gas concentration collection and transmission unit 151 is used to collect a time series data set of VOCs waste gas concentration data at a predetermined sampling frequency using a VOCs waste gas concentration detector, and transmit the time series data set of the VOCs waste gas concentration data to the fan speed control center through a wireless communication module. It should be understood that VOCs (volatile organic compounds) waste gas concentration data refers to the content of VOCs in the waste gas at a specific time and place. These data are usually expressed in mass concentration (such as mg / m³ or ppm), reflecting the amount of VOCs in the waste gas. VOCs are a class of organic compounds widely present in industrial waste gas, including but not limited to benzene, toluene, xylene, formaldehyde, etc., which are potentially harmful to the environment and human health. Therefore, in order to achieve real-time monitoring and effective management of VOCs waste gas concentration, in the technical solution of the present application, a time series data set of VOCs waste gas concentration data is collected using a VOCs waste gas concentration detector at a predetermined sampling frequency.

[0037] Highly sensitive VOCs exhaust gas concentration detectors are installed at exhaust gas outlets or key locations in the treatment process. These detectors are designed to continuously collect VOCs concentration data in exhaust gas at a predetermined sampling frequency. The choice of sampling frequency depends on the needs of the specific application scenario. For example, it can be set to every minute, every second, or even a shorter time interval. The time series data set formed in this way not only records in detail the changes in VOCs concentration over time, but also provides detailed basic information for subsequent data analysis.

[0038] VOCs exhaust gas concentration detectors usually use advanced sensing technologies, such as photoionization detectors (PID), flame ionization detectors (FID) or infrared absorption spectrometers, which can accurately measure the content of different types of VOCs in exhaust gas. Their working principles are based on different physical and chemical mechanisms, but they have the common feature of being able to respond quickly and provide accurate and reliable concentration readings. By setting the appropriate sampling frequency, the system can capture the subtle dynamics of VOCs concentration changes, which is crucial for identifying sudden pollution events and evaluating the purification effect.

[0039] Once the VOCs exhaust gas concentration data is collected, the next step is to transmit this data to the fan speed control center in real time. In this process, modern IoT technology plays a core role, ensuring that data transmission can be completed stably and reliably even in complex industrial environments. As a bridge between field sensors and the control center, the selection of wireless communication modules depends on the specific network conditions and requirements of the site, and may include Wi-Fi, 4G / 5G cellular networks or low-power wide area network technologies such as LoRa. Each technology has its advantages: Wi-Fi is suitable for high-speed data transmission over short distances; 4G / 5G cellular networks are suitable for application scenarios with wide coverage and high bandwidth requirements; and LPWAN (low-power wide area network) technologies such as LoRa are particularly suitable for long-distance, low-power environmental monitoring applications.

[0040] Wireless transmission greatly simplifies the deployment and maintenance of the system, avoiding the wiring complexity and high maintenance costs that may be caused by traditional wired connections. At the same time, it also ensures the real-time and security of data transmission. In actual operation, the wireless communication module regularly sends data packets containing the latest VOCs concentration information to the control center, which enables the control system to obtain the latest environmental conditions in a timely manner and make corresponding adjustments accordingly. In addition, in order to enhance the security of data transmission, the system can also use encryption technology and identity authentication mechanisms to protect data from unauthorized access.

[0041] Through such a design, the entire process from the collection of VOCs concentration data to transmission and then to the final analysis and decision-making forms a closed-loop feedback system, realizing the intelligent management of the exhaust gas purification process.

[0042] In an embodiment of the present application, the VOCs exhaust gas concentration time series analysis unit 152 is used to perform phase space reconstruction and local time series extraction on the time series data set of the VOCs exhaust gas concentration data at the fan speed control center to obtain a set of VOCs exhaust gas concentration local time series implicit pattern features. Specifically, the VOCs exhaust gas concentration time series analysis unit includes: a VOCs exhaust gas concentration phase space reconstruction subunit, which is used to perform phase space reconstruction on the time series data set of the VOCs exhaust gas concentration data at the fan speed control center to obtain a set of VOCs exhaust gas concentration data time series subsequences; a VOCs exhaust gas concentration local time series encoding subunit, which is used to input each VOCs exhaust gas concentration data time series subsequence in the set of VOCs exhaust gas concentration data time series subsequences into a sequence encoder based on a forward LSTM model to obtain a set of VOCs exhaust gas concentration local time series implicit pattern feature vectors as the set of VOCs exhaust gas concentration local time series implicit pattern features.

[0043] Specifically, the VOCs exhaust gas concentration phase space reconstruction subunit is used to perform phase space reconstruction on the time series data set of the VOCs exhaust gas concentration data in the fan speed control center to obtain a set of VOCs exhaust gas concentration data time series subsequences. Accordingly, considering that in VOCs exhaust gas treatment, VOCs exhaust gas concentration usually exhibits complex nonlinear dynamic behavior, that is, there are complex interactions between the variables, making its behavior difficult to describe with a simple linear model. In addition, some may exhibit chaotic behavior, that is, behavior that appears random but is actually driven by deterministic rules. In other words, in the VOCs treatment process, even a slight change in the initial conditions of the VOCs exhaust gas concentration may lead to significantly different results, and this sensitivity makes prediction and control difficult. Therefore, in the technical solution of the present application, phase space reconstruction is introduced to reconstruct the time series data set of the VOCs exhaust gas concentration data to convert the one-dimensional time series into a trajectory in a high-dimensional phase space, thereby better representing the dynamic characteristics of the VOCs exhaust gas concentration. It is worth mentioning that the phase space is a multidimensional space, in which each dimension represents a state variable of the system. For a complex system, a point in the phase space represents the state of the system at a certain moment. Takens' embedding theorem is the basis for phase space reconstruction. The theorem states that for an infinitely long, noiseless d-dimensional dynamical system, the one-dimensional time series can be reconstructed into a trajectory in an m-dimensional phase space by choosing a suitable time lag τ and embedding dimension m, thereby recovering the dynamic characteristics of the system.

[0044] Specifically, in a specific embodiment of the present application, the time series data set of the VOCs exhaust gas concentration data is reconstructed in phase space to obtain a set of time series subsequences of the VOCs exhaust gas concentration data, and the specific steps are as follows: Suppose there is a time series data of VOCs exhaust gas concentration x(t) = [x(1), x(2), ..., x(n)], where n is the number of data in the time series of VOCs exhaust gas concentration.

[0045] First, select a suitable time lag τ, which is usually determined by the autocorrelation function or mutual information method. For example, the first local minimum is found by calculating the autocorrelation function as the time lag τ. Next, select the embedding dimension m, which is usually determined by the false nearest neighbor method. Start with the embedding dimension m=2 and gradually increase m until the number of false nearest neighbors stabilizes.

[0046] Next, the original one-dimensional VOCs exhaust concentration time series data is reconstructed into a trajectory in a high-dimensional phase space according to the selected time lag τ and embedding dimension m. Each point in the reconstructed phase space consists of m consecutive data points with a time interval of τ between them. Specifically, for each starting time point i, the new data point Y(i) consists of x(i), x(i+τ), x(i+2τ) up to x(i+(m-1)τ).

[0047] Finally, the reconstructed phase space trajectory is divided into multiple subsequences, that is, a collection of VOCs exhaust gas concentration data time series subsequences, each of which represents the dynamic behavior of VOCs exhaust gas concentration data over a period of time. The number of subsequences depends on the length of the original time series and the embedding dimension m. For example, if the original time series has n data points and the embedding dimension is m, n - (m-1)τ subsequences can be generated. Through the above steps, the original one-dimensional VOCs exhaust gas concentration data set can be reconstructed into a trajectory in a high-dimensional phase space, and a collection of VOCs exhaust gas concentration data time series subsequences can be generated, laying the foundation for subsequent feature extraction and prediction.

[0048] Specifically, the VOCs exhaust gas concentration local time series encoding subunit is used to input each VOCs exhaust gas concentration data time series subsequence in the set of VOCs exhaust gas concentration data time series subsequences into a sequence encoder based on a forward LSTM model to obtain a set of VOCs exhaust gas concentration local time series implicit pattern feature vectors as a set of VOCs exhaust gas concentration local time series implicit pattern features. It should be understood that, considering that there are different degrees of temporal correlation relationships between different local time periods in each VOCs exhaust gas concentration data time series subsequence, in order to extract and represent the complex time series dynamic correlation patterns in each VOCs exhaust gas concentration data sequence data, in the technical solution of the present application, each VOCs exhaust gas concentration data time series subsequence in the set of the VOCs exhaust gas concentration data time series subsequences is respectively input into a sequence encoder based on a forward LSTM model to utilize the forward LSTM model to capture the long-term dependent correlation relationship in the time series through its internal memory unit, and obtain a set of VOCs exhaust gas concentration local time series implicit pattern feature vectors, so as to better reflect the inherent local structure and dynamic characteristics changes of the VOCs exhaust gas concentration data.

[0049] In an embodiment of the present application, the VOCs exhaust gas concentration time series propagation unit 153 is used to perform multi-gated feature dynamic propagation on the set of local time series implicit pattern features of the VOCs exhaust gas concentration to obtain a VOCs exhaust gas concentration time series feature propagation representation, and based on the VOCs exhaust gas concentration time series feature propagation representation, obtain an optimization instruction, and the optimization instruction includes a recommended value of the fan speed at the next time point.

[0050] Figure 3 : is a block diagram of a VOCs waste gas concentration time series propagation unit in an organic waste gas purification treatment system according to an embodiment of the present application. Specifically, Figure 3 As shown, the VOCs exhaust gas concentration time series propagation unit 153 includes: a VOCs exhaust gas concentration time series dynamic propagation subunit 1531, which is used to perform multi-gated feature dynamic propagation on the set of local time series implicit pattern feature vectors of the VOCs exhaust gas concentration to obtain a VOCs exhaust gas concentration time series feature propagation representation vector as the VOCs exhaust gas concentration time series feature propagation representation; an optimization instruction generation subunit 1532, which is used to obtain an optimization instruction based on the VOCs exhaust gas concentration time series feature propagation representation vector, and the optimization instruction includes a recommended value of the fan speed at the next time point.

[0051] Specifically, the VOCs exhaust gas concentration time series dynamic propagation subunit 1531 is used to perform multi-gated feature dynamic propagation on the set of the VOCs exhaust gas concentration local time series implicit pattern feature vectors to obtain the VOCs exhaust gas concentration time series feature propagation representation vector as the VOCs exhaust gas concentration time series feature propagation representation. Furthermore, considering that each VOCs exhaust gas concentration local time series implicit pattern feature captures the local time series change pattern of the VOCs exhaust gas concentration in different specific time periods, including short-term rise, fall or fluctuation. Considering that the change in VOCs exhaust gas concentration usually has multi-scale time dependencies, including short-term fluctuations and long-term trends, it may be difficult to capture these dependencies of different scales at the same time through a single LSTM model. Therefore, in order to further process the local time series implicit pattern features to generate a more comprehensive and richer feature representation, in the technical solution of the present application, the set of the VOCs exhaust gas concentration local time series implicit pattern feature vectors is subjected to multi-gated feature dynamic propagation to obtain the VOCs exhaust gas concentration time series feature propagation representation vector. In other words, multi-gated feature dynamic propagation can effectively transfer information between different time points, so that the characteristics of each time point not only contain the concentration information of the current time point, but also the historical and future concentration information, so as to better understand the global pattern and trend of VOCs concentration changes.

[0052] Figure 4 : is a block diagram of a VOCs waste gas concentration time series dynamic propagation subunit in an organic waste gas purification treatment system according to an embodiment of the present application. More specifically, Figure 4As shown, the VOCs exhaust gas concentration time series dynamic propagation subunit 1531 includes: a VOCs exhaust gas concentration feature mapping secondary subunit 15311, which is used to map each VOCs exhaust gas concentration local time series implicit pattern feature vector in the set of the VOCs exhaust gas concentration local time series implicit pattern feature vector to the Poincare space to obtain a set of VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vectors; a VOCs exhaust gas concentration feature propagation secondary subunit 15312, which is used to perform forward LSTM encoding on the set of VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vectors to obtain a VOCs exhaust gas concentration heterogeneous propagation representation vector ; The VOCs waste gas concentration modulation secondary subunit 15313 is used to calculate the weight value of the set of the VOCs waste gas concentration local time series heterodomain modulation coding feature vectors based on the VOCs waste gas concentration heterodomain propagation representation vector to obtain a set of VOCs waste gas concentration transfer modulation weights; The VOCs waste gas concentration time series propagation secondary subunit 15314 is used to perform residual processing and mapping on the set of the VOCs waste gas concentration local time series heterodomain modulation coding feature vectors and the VOCs waste gas concentration heterodomain propagation representation vector based on the set of VOCs waste gas concentration transfer modulation weights to obtain the VOCs waste gas concentration time series feature propagation representation vector.

[0053] Specifically, the VOCs exhaust gas concentration feature mapping secondary subunit 15311 is used to map each VOCs exhaust gas concentration local time series implicit pattern feature vector in the set of the VOCs exhaust gas concentration local time series implicit pattern feature vector to the Poincare space to obtain a set of VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vectors. That is, each VOCs exhaust gas concentration local time series implicit pattern feature vector is mapped to the Poincare space to use the unique geometric features of the Poincare space to capture the nonlinear interaction between the exhaust gas concentration features and their hierarchical structure, and obtain a set of VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vectors. That is, the Poincare space is a hyperbolic space that can project trajectories in a high-dimensional phase space into a low-dimensional space while retaining the semantic relevance between the features at each time point.

[0054] More specifically, the processing process of the above-mentioned VOCs exhaust gas concentration characteristic mapping secondary subunit 15311 can be expressed by the formula: ;in, is the set of characteristic vectors of the local time series implicit pattern of the VOCs exhaust gas concentration, and are the first, second, and third characteristic vectors of the local time series implicit pattern of the VOCs exhaust gas concentration. and The local time series implicit pattern feature vector of VOCs exhaust gas concentration, and are the first weight matrix and the second weight matrix respectively, and They are the first, second, and third in the set of VOCs exhaust gas concentration local temporal heterogeneous modulation coding feature vectors. and VOCs exhaust gas concentration local time series heterogeneous modulation encoding feature vector, It is a set of local temporal heterogeneous modulation coding feature vectors of the VOCs exhaust gas concentration.

[0055] Specifically, the VOCs exhaust gas concentration feature propagation secondary subunit 15312 is used to perform forward LSTM encoding on the set of VOCs exhaust gas concentration local temporal heterogeneous modulation coding feature vectors to obtain the VOCs exhaust gas concentration heterogeneous propagation representation vector. In this way, the set of heterogeneous modulation coding feature vectors is forward LSTM encoded to perform simulation transmission based on the Poincare semantic space, while capturing the long-term dependencies across multiple time points in the VOCs exhaust gas concentration data, and retaining the key temporal feature information through the synergy of the forget gate, input gate and output gate to obtain the VOCs exhaust gas concentration heterogeneous propagation representation vector.

[0056] More specifically, the processing process of the above-mentioned VOCs exhaust gas concentration characteristic propagation secondary subunit 15312 can be expressed by the formula: ;in, is the set of VOCs exhaust gas concentration local temporal heterogeneous modulation coding feature vectors, Encode the forward LSTM. It is the vector representing the heterogeneous propagation of VOCs exhaust gas concentration.

[0057] More specifically, in an embodiment of the present application, the VOCs exhaust gas concentration modulation secondary subunit 15313 is used to: calculate the semantic association between each VOCs exhaust gas concentration local temporal heterodomain modulation coding feature vector in the set of the VOCs exhaust gas concentration local temporal heterodomain modulation coding feature vector and the VOCs exhaust gas concentration heterodomain propagation representation vector to obtain a set of VOCs exhaust gas concentration semantic associations; calculate the semantic space jump value of each VOCs exhaust gas concentration local temporal heterodomain modulation coding feature vector in the set of the VOCs exhaust gas concentration local temporal heterodomain modulation coding feature vector relative to the VOCs exhaust gas concentration heterodomain propagation representation vector to obtain a set of VOCs exhaust gas concentration semantic space jump values; perform multiple gated information transfer modulation on the set of VOCs exhaust gas concentration semantic associations and the set of VOCs exhaust gas concentration semantic space jump values ​​to obtain a set of VOCs exhaust gas concentration transfer modulation weights. Accordingly, the semantic association between each foreign modulation coding feature vector and the foreign propagation representation vector is calculated to represent the semantic association between the transfer feature in the Poincare space and each foreign modulation coding feature, determine which local patterns have a significant impact on the overall trend and change, and obtain the set of semantic associations of VOCs exhaust gas concentration. Next, the semantic space jump value between each foreign modulation coding feature vector relative to the foreign propagation representation vector is calculated to evaluate the trend and speed of concentration feature expression over time by comparing the spatial distance difference between the concentration transfer feature and each foreign modulation coding feature in the Poincare space, and at the same time reveal the temporal dynamic information of the concentration. Subsequently, the semantic association and semantic space jump value of each foreign modulation coding feature vector are modulated by multiple gated information transfer to obtain the modulation weight of each foreign modulation coding feature vector. In this way, the weight of each foreign modulation coding feature vector can be dynamically adjusted according to the semantic association and semantic space jump value, which helps to highlight the important concentration temporal features and suppress unimportant or abnormal features, so that the focus can be on those times with high weights to achieve more refined and efficient control.

[0058] More specifically, the processing process of the above-mentioned VOCs waste gas concentration modulation secondary subunit 15313 can be expressed by the formula: ;in, To calculate the square of the one-norm of a vector, is the inverse hyperbolic cosine function, and They are the first, second, and third in the set of semantic associations of VOCs exhaust gas concentration. and The semantic correlation of VOCs exhaust gas concentration, is the set of semantic associations of the VOCs exhaust gas concentrations, and They are the first, second, and third values ​​in the set of semantic space jump values ​​of VOCs exhaust gas concentration. and VOCs exhaust gas concentration semantic space jump value, is the set of semantic space jump values ​​of the VOCs exhaust gas concentration, and are the semantic association weight matrix and the semantic space weight matrix respectively. is matrix multiplication, is the bias vector, yes function, It is a collection of VOCs exhaust gas concentration transfer modulation weights.

[0059] Specifically, the VOCs exhaust gas concentration time series propagation secondary subunit 15314 is used to: based on the set of VOCs exhaust gas concentration transfer modulation weights, calculate the position-weighted sum of the set of VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vectors to obtain the VOCs exhaust gas concentration heterogeneous feature significant propagation representation vector; input the VOCs exhaust gas concentration heterogeneous feature significant propagation representation vector and the VOCs exhaust gas concentration heterogeneous propagation representation vector into the residual unit to obtain the VOCs exhaust gas concentration feature significant propagation compensation representation vector; perform Euclidean space mapping on the VOCs exhaust gas concentration feature significant propagation compensation representation vector to obtain the VOCs exhaust gas concentration time series feature propagation representation vector. That is, based on the transfer modulation weights of each VOCs exhaust gas concentration, weighted fusion is performed on each VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vector to better characterize and depict the criticality and time series change trend of each feature in a more fine-grained manner, and obtain the VOCs exhaust gas concentration heterogeneous feature significant propagation representation vector. That is, the significance of the exhaust gas concentration characteristics and the change pattern in the time series are combined through the calculation method of position weighted summation, thereby ensuring that the generated feature vector can fully map the spatiotemporal properties of the data. Next, the generated significant propagation representation vector and the foreign propagation representation vector are input into the residual unit for residual connection, so as to utilize the jump connection in the residual unit to perform feature compensation on the significant propagation representation vector (emphasizing important features) and the foreign propagation representation vector (overall sequence features), while retaining the original features, so as to better capture the subtle changes and complex patterns in the concentration data, and obtain the VOCs exhaust gas concentration feature significant propagation compensation representation vector. Finally, the VOCs exhaust gas concentration feature significant propagation compensation representation vector is mapped to Euclidean space to obtain the VOCs exhaust gas concentration time series feature propagation representation vector. That is, by mapping the compensation representation vector to Euclidean space, the high-dimensional feature significant propagation compensation representation vector is reduced to a Euclidean space that is easier to handle and interpret, which is convenient for subsequent data analysis and interpretation.

[0060] More specifically, the processing process of the above-mentioned VOCs exhaust gas concentration time series propagation secondary subunit 15314 can be expressed by the formula: ;in, yes The corresponding VOCs exhaust gas concentration transfer modulation weight, is the number of vectors in the set of VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vectors, is the significant propagation vector of VOCs exhaust gas concentration heterogeneity characteristics, and are the third weight matrix and the fourth weight matrix respectively, is the Euclidean space mapping operation, is the VOCs exhaust gas concentration characteristic significant propagation compensation representation vector.

[0061] Specifically, the optimization instruction generation subunit 1532 is used to obtain the optimization instruction based on the VOCs exhaust gas concentration time series characteristic propagation representation vector, and the optimization instruction includes the recommended value of the fan speed at the next time point. Specifically, in an embodiment of the present application, the optimization instruction generation subunit is used to: input the VOCs exhaust gas concentration time series characteristic propagation representation vector into the fan speed dynamic optimization module based on the decoder to obtain the optimization instruction, and the optimization instruction includes the recommended value of the fan speed at the next time point.

[0062] More specifically, in an embodiment of the present application, the optimization instruction generation subunit is used to: multiply the decoding weight matrix of the decoder by the VOCs exhaust gas concentration time series feature propagation representation vector to obtain the decoded VOCs exhaust gas concentration time series feature propagation representation vector, and accumulate and sum all the eigenvalues ​​of the decoded VOCs exhaust gas concentration time series feature propagation representation vector to obtain the recommended fan speed value at the next time point. That is, the VOCs exhaust gas concentration time series feature propagation representation obtained by multi-gating the set of the VOCs exhaust gas concentration local time series implicit pattern feature vectors is used for decoding processing, so as to intelligently obtain the recommended fan speed value at the next time point. In this way, the VOCs exhaust gas concentration can be monitored in real time, and the complex pattern of the VOCs exhaust gas concentration can be identified. The fan speed can be intelligently adjusted according to the current and predicted exhaust gas conditions, thereby improving the adaptability to different working conditions.

[0063] It should be understood that here, when each VOCs exhaust gas concentration local temporal implicit pattern feature vector in the set of VOCs exhaust gas concentration local temporal implicit pattern feature vectors respectively represents the temporal correlation characteristics of the VOCs exhaust gas concentration data in the local phase space, when performing feature dynamic propagation based on a cross-domain multiple gate structure, the cross-domain gate structure differences caused by the differences in the temporal feature distribution in each local phase space are taken into account, and it is expected to improve the semantically consistent aggregation expression effect of the VOCs exhaust gas concentration temporal feature propagation representation vector obtained by feature dynamic propagation.

[0064] Preferably, inputting the VOCs exhaust gas concentration time series characteristic propagation representation vector into a fan speed dynamic optimization module based on a decoder to obtain an optimization instruction includes: The feature set of the VOCs exhaust gas concentration time series feature propagation representation vector is subjected to feature clustering to obtain a VOCs exhaust gas concentration time series feature propagation in-class feature set and a VOCs exhaust gas concentration time series feature propagation out-class feature set, namely: ;in, is the intra-class feature set of the VOCs exhaust gas concentration time series feature propagation, is the characteristic value of each position in the characteristic set within the VOCs exhaust gas concentration time series characteristic propagation class, is the characteristic value of each position in the out-of-class characteristic set of the VOCs exhaust gas concentration time series characteristic propagation; The ratio of the number of eigenvalues ​​in the feature set within the VOCs exhaust gas concentration time series feature propagation class to the number of eigenvalues ​​in the feature set of the VOCs exhaust gas concentration time series feature propagation representation vector is calculated, that is: ;in, is the number of eigenvalues ​​in the feature set within the VOCs exhaust gas concentration time series feature propagation class, is the number of eigenvalues ​​of the characteristic set of the VOCs exhaust gas concentration time series feature propagation representation vector, yes and The ratio of Calculate the sum of the absolute values ​​of all feature values ​​in the feature set within the VOCs exhaust gas concentration time series feature propagation class The power is the sum of the absolute values ​​of all eigenvalues ​​in the feature set of the VOCs exhaust gas concentration time series feature propagation representation vector. The ratio of the powers is used to obtain the modulation weight of the VOCs exhaust gas concentration time series characteristic propagation, that is: ;in, is the modulation weight of the VOCs exhaust gas concentration time series characteristic propagation; Calculate the sum of squares of all feature values ​​in the feature set of the VOCs exhaust gas concentration time series feature propagation class The power and the VOCs exhaust gas concentration time series feature propagation represent the sum of the squares of all eigenvalues ​​in the feature set of the vector The ratio of the powers is used to obtain the harmonic weight of the VOCs exhaust gas concentration time series characteristic propagation, that is: ;in, is the propagation harmonic weight of the VOCs exhaust gas concentration time series characteristic; For each feature value in the feature set within the VOCs exhaust gas concentration time series feature propagation class, the product of the feature value and the VOCs exhaust gas concentration time series feature propagation harmonic weight is calculated, and then the modulated weight of the VOCs exhaust gas concentration time series feature propagation is added to obtain the optimized feature value, that is: ;in, is the optimized characteristic value of each position of the characteristic set within the VOCs exhaust gas concentration time series characteristic propagation class; For each feature value in the out-of-class feature set of the VOCs exhaust gas concentration time series feature propagation, the product of the feature value and the VOCs exhaust gas concentration time series feature propagation adjustment weight is calculated to obtain the optimized feature value, that is: ;in, is the optimized characteristic value of each position of the VOCs exhaust gas concentration time series characteristic propagation outer feature set; The optimized VOCs exhaust gas concentration time series feature propagation representation vector composed of the optimized feature values ​​of the feature set within the VOCs exhaust gas concentration time series feature propagation class and the feature set outside the VOCs exhaust gas concentration time series feature propagation class is input into the fan speed dynamic optimization module based on the decoder to obtain the optimization instruction.

[0065] Therefore, while the VOCs exhaust gas concentration time series feature propagation representation vector is subjected to feature clustering, the interaction of the key feature information of the VOCs exhaust gas concentration time series feature propagation representation vector in the clustering process is described, and the geometric equivariant topology of the feature is constructed by low-rank harmonic modulation of the clustering features of the VOCs exhaust gas concentration time series feature propagation representation vector and the equivariance of the feature as a whole, so as to obtain the graphical distribution translation and rotation symmetry of the clustering features of the VOCs exhaust gas concentration time series feature propagation representation vector relative to the feature as a whole, thereby introducing geometric message passing into the feature expression of the VOCs exhaust gas concentration time series feature propagation representation vector, and realizing the clustering mapping symmetry of the VOCs exhaust gas concentration time series feature propagation representation vector through the manipulation of irreducible low-rank coefficients, thereby improving the consistency of the clustering-based feature representation of the VOCs exhaust gas concentration time series feature propagation representation vector, thereby improving the accuracy of the optimization instructions obtained by inputting the VOCs exhaust gas concentration time series feature propagation representation vector into the decoder-based fan speed dynamic optimization module.

[0066] In summary, the controller 150 is explained, which uses a VOCs exhaust gas concentration detector to collect a time series data set of VOCs exhaust gas concentration data at a predetermined sampling frequency, and transmits it to the fan speed control center through a wireless communication module, and the fan speed control center uses a data analysis and encoding method based on deep learning to perform phase space reconstruction and local time series implicit extraction on the time series data set of VOCs exhaust gas concentration data, so as to intelligently obtain the recommended value of the fan speed at the next time point by performing multi-gated propagation according to the local time series implicit pattern characteristics of each VOCs exhaust gas concentration. In this way, the VOCs exhaust gas concentration can be monitored in real time, and the complex pattern of the VOCs exhaust gas concentration can be identified. The fan speed can be intelligently adjusted according to the current and predicted exhaust gas conditions, thereby improving the adaptability to different working conditions.

[0067] In summary, the organic waste gas purification treatment system 100 based on the embodiment of the present application is explained, which uses a filter to perform preliminary filtration on the waste gas, and then uses an ozone catalyst to catalyze ozone to perform preliminary oxidation on the volatile organic compounds (VOCs) in the filtered waste gas. Subsequently, ultraviolet radiation nano-photocatalysts are used to further oxidize these preliminarily oxidized VOCs waste gases and convert them into small molecule organic matter that is more easily biodegradable. The concentration of VOCs in the waste gas is monitored in real time, and the speed of the fan is dynamically adjusted according to the monitoring data to ensure the purification efficiency and the optimal operating state of the system.

[0068] As described above, the organic waste gas purification treatment system 100 according to the embodiment of the present application can be implemented in various wireless terminals, such as a server with an organic waste gas purification treatment algorithm. In one possible implementation, the organic waste gas purification treatment system 100 according to the embodiment of the present application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the organic waste gas purification treatment system 100 can be a software module in the operating system of the wireless terminal, or it can be an application developed for the wireless terminal; of course, the organic waste gas purification treatment system 100 can also be one of the many hardware modules of the wireless terminal.

[0069] Alternatively, in another example, the organic waste gas purification treatment system 100 and the wireless terminal may also be separate devices, and the organic waste gas purification treatment system 100 may be connected to the wireless terminal via a wired and / or wireless network, and transmit interactive information in accordance with an agreed data format.

[0070] Figure 5 Flow chart of the organic waste gas purification method according to the embodiment of the present application. Figure 5As shown, according to the organic waste gas purification method of the embodiment of the present application, it is used to execute the above-mentioned organic waste gas purification system, and the organic waste gas purification method includes: S110, using a VOCs waste gas concentration detector to collect a time series data set of VOCs waste gas concentration data at a predetermined sampling frequency, and transmitting the time series data set of the VOCs waste gas concentration data to the fan speed control center through a wireless communication module; S120, at the fan speed control center, the time series data set of the VOCs waste gas concentration data is subjected to phase space reconstruction and local time series extraction to obtain a set of local time series implicit pattern features of the VOCs waste gas concentration; S130, the set of local time series implicit pattern features of the VOCs waste gas concentration is subjected to multi-gated feature dynamic propagation to obtain a VOCs waste gas concentration time series feature propagation representation, and based on the VOCs waste gas concentration time series feature propagation representation, an optimization instruction is obtained, and the optimization instruction includes a recommended value of the fan speed at the next time point.

[0071] Here, those skilled in the art will appreciate that the specific operations of each step in the above-mentioned organic waste gas purification method have been described in detail above. Figures 1 to 4 The description of the organic waste gas purification treatment system has been introduced in detail, and therefore, its repeated description will be omitted.

[0072] The above descriptions of various implementations of the present disclosure are exemplary and not exhaustive. The disclosure is not limited to the disclosed implementations, and many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The terms used herein are selected to best explain the principles of the implementations, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the various practical methods disclosed herein.

Claims

1. An organic waste gas purification and treatment system, comprising: A fan; a filter for filtering the exhaust gas to obtain filtered exhaust gas; a preliminary oxidation module, using an ozone catalyst to catalyze ozone to perform preliminary oxidation on the VOCs in the filtered exhaust gas to obtain preliminary oxidized VOCs exhaust gas; a secondary oxidation module, using ultraviolet radiation nano-photocatalysts to perform secondary oxidation on the preliminary oxidized VOCs exhaust gas to obtain biodegradable small molecular organic matter; characterized in that the organic waste gas purification and treatment system also includes: a controller, used to monitor the VOCs exhaust gas concentration data in the exhaust gas in real time during the exhaust gas purification process and adjust the speed of the fan in real time; wherein the controller includes: A VOCs exhaust gas concentration collection and transmission unit, which is used to collect a time series data set of VOCs exhaust gas concentration data at a predetermined sampling frequency using a VOCs exhaust gas concentration detector, and transmit the time series data set of VOCs exhaust gas concentration data to a fan speed control center through a wireless communication module; A VOCs exhaust gas concentration time series analysis unit is used to perform phase space reconstruction and local time series extraction on the time series data set of the VOCs exhaust gas concentration data at the fan speed control center to obtain a set of VOCs exhaust gas concentration local time series implicit pattern features; The VOCs exhaust gas concentration time series propagation unit is used to perform multi-gated feature dynamic propagation on the set of local time series implicit pattern features of the VOCs exhaust gas concentration to obtain a VOCs exhaust gas concentration time series feature propagation representation, and based on the VOCs exhaust gas concentration time series feature propagation representation, obtain an optimization instruction, and the optimization instruction includes a recommended value of the fan speed at the next time point.

2. The organic waste gas purification system according to claim 1, characterized in that: The VOCs waste gas concentration time series analysis unit comprises: A VOCs exhaust gas concentration phase space reconstruction subunit is used to perform phase space reconstruction on the time series data set of the VOCs exhaust gas concentration data at the fan speed control center to obtain a set of VOCs exhaust gas concentration data time series subsequences; The VOCs exhaust gas concentration local time series encoding subunit is used to input each VOCs exhaust gas concentration data time series subsequence in the set of the VOCs exhaust gas concentration data time series subsequences into a sequence encoder based on a forward LSTM model to obtain a set of VOCs exhaust gas concentration local time series implicit pattern feature vectors as the set of VOCs exhaust gas concentration local time series implicit pattern features.

3. The organic waste gas purification system according to claim 2, characterized in that: The VOCs waste gas concentration time series propagation unit comprises: A VOCs exhaust gas concentration time series dynamic propagation subunit, used for performing multi-gated feature dynamic propagation on the set of VOCs exhaust gas concentration local time series implicit pattern feature vectors to obtain a VOCs exhaust gas concentration time series feature propagation representation vector as the VOCs exhaust gas concentration time series feature propagation representation; The optimization instruction generation subunit is used to obtain the optimization instruction based on the VOCs exhaust gas concentration time series characteristic propagation representation vector, and the optimization instruction includes the recommended value of the fan speed at the next time point.

4. The organic waste gas purification system according to claim 3, characterized in that: The VOCs waste gas concentration time series dynamic propagation subunit includes: A VOCs exhaust gas concentration feature mapping secondary subunit is used to map each VOCs exhaust gas concentration local time series implicit pattern feature vector in the set of VOCs exhaust gas concentration local time series implicit pattern feature vectors to the Poincare space to obtain a set of VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vectors; A VOCs exhaust gas concentration feature propagation secondary subunit is used to perform forward LSTM encoding on the set of VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vectors to obtain a VOCs exhaust gas concentration heterogeneous propagation representation vector; A VOCs exhaust gas concentration modulation secondary subunit is used to calculate the weight value of the set of VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vectors based on the VOCs exhaust gas concentration heterogeneous propagation representation vector to obtain a set of VOCs exhaust gas concentration transfer modulation weights; The VOCs exhaust gas concentration time series propagation secondary subunit is used to perform residual processing and mapping on the set of VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vectors and the VOCs exhaust gas concentration heterogeneous propagation representation vector based on the set of VOCs exhaust gas concentration transfer modulation weights to obtain the VOCs exhaust gas concentration time series feature propagation representation vector.

5. The organic waste gas purification system according to claim 4, characterized in that: The VOCs waste gas concentration modulation secondary subunit is used to: Calculating the semantic association between each VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vector in the set of the VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vector and the VOCs exhaust gas concentration heterogeneous propagation representation vector to obtain a set of VOCs exhaust gas concentration semantic associations; Calculate the semantic space jump value of each VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vector in the set of the VOCs exhaust gas concentration local time series heterogeneous modulation coding feature vector relative to the VOCs exhaust gas concentration heterogeneous propagation representation vector to obtain a set of VOCs exhaust gas concentration semantic space jump values; The set of the VOCs exhaust gas concentration semantic associations and the set of the VOCs exhaust gas concentration semantic space jump values ​​are subjected to multiple gated information transfer modulation to obtain the set of the VOCs exhaust gas concentration transfer modulation weights.

6. The organic waste gas purification system according to claim 5, characterized in that: The VOCs exhaust gas concentration time series propagation secondary subunit is used for: Based on the set of VOCs exhaust gas concentration transfer modulation weights, a position-weighted sum of a set of VOCs exhaust gas concentration local temporal heterogeneous modulation coding feature vectors is calculated to obtain a VOCs exhaust gas concentration heterogeneous feature significant propagation representation vector; Inputting the VOCs exhaust gas concentration heterogeneous characteristic significant propagation representation vector and the VOCs exhaust gas concentration heterogeneous propagation representation vector into a residual unit to obtain a VOCs exhaust gas concentration characteristic significant propagation compensation representation vector; The VOCs waste gas concentration characteristic significant propagation compensation representation vector is mapped into Euclidean space to obtain the VOCs waste gas concentration time series characteristic propagation representation vector.

7. The organic waste gas purification system according to claim 6, characterized in that: The optimization instruction generation subunit is used to: input the VOCs exhaust gas concentration time series characteristic propagation representation vector into the decoder-based fan speed dynamic optimization module to obtain the optimization instruction, and the optimization instruction includes a recommended value of the fan speed at the next time point.

8. The organic waste gas purification system according to claim 7, characterized in that: The optimization instruction generation subunit is used to: multiply the decoding weight matrix of the decoder with the VOCs exhaust gas concentration timing characteristic propagation representation vector to obtain the decoded VOCs exhaust gas concentration timing characteristic propagation representation vector, and accumulate and sum all eigenvalues ​​of the decoded VOCs exhaust gas concentration timing characteristic propagation representation vector to obtain the recommended value of the fan speed at the next time point.

9. An organic waste gas purification method, used to implement the organic waste gas purification system according to claim 1, characterized in that: The organic waste gas purification method comprises: Using a VOCs exhaust gas concentration detector to collect a time series data set of VOCs exhaust gas concentration data at a predetermined sampling frequency, and transmitting the time series data set of VOCs exhaust gas concentration data to a fan speed control center through a wireless communication module; At the fan speed control center, phase space reconstruction and local time series extraction are performed on the time series data set of the VOCs exhaust gas concentration data to obtain a set of local time series implicit pattern features of the VOCs exhaust gas concentration; The set of local time series implicit pattern features of the VOCs exhaust gas concentration is subjected to multi-gated feature dynamic propagation to obtain a VOCs exhaust gas concentration time series feature propagation representation, and based on the VOCs exhaust gas concentration time series feature propagation representation, an optimization instruction is obtained, the optimization instruction including a recommended value of the fan speed at the next time point.

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