Intelligent control system of plasma air disinfection module

Abstract

An intelligent control system of a plasma air disinfection module comprises an air quality detection module used for detecting various index data related to air quality in real time; the data analysis module is electrically connected with the air quality detection module and is used for receiving various index data related to the air quality and classifying, sorting and analyzing the index data; and the module control module is electrically connected with the air quality prediction module, judges reasonable wind speed gears, discharge power and discharge time according to the air quality development trend model and a real-time matching strategy, can output limit discharge power on the premise of ensuring use safety, and greatly improves the air disinfection efficiency.

Description

Technical field:

[0001] The invention relates to the technical field of air purification, and more specifically to an intelligent control system of a plasma air disinfection module. Background technology:

[0002] As the current society's demand for air quality and public health increases, air disinfection technology is particularly important for people's healthy lives. Traditional air disinfection methods, such as chemical disinfectants, ozone, ultraviolet irradiation, etc., although these methods can effectively remove pathogens in the air to a certain extent, still have problems such as low efficiency, high cost, and possible secondary pollution. In addition, these methods may pose potential threats to human health and environmental safety during operation.

[0003] In recent years, plasma technology has gradually attracted attention due to its high efficiency and environmental protection characteristics. Plasma is a gas composed of charged particles (such as electrons and ions) and neutral particles (such as atoms and molecules). Active groups and high-energy electrons are generated during the release of plasma. Active groups are more easily oxidized with proteins and nucleic acids on bacteria and viruses, causing them to wither. High-energy electrons, including high-energy electrons and ions, have a breakdown and etching effect, destroying bacteria and virus particles. However, the widespread application of plasma air disinfection technology still faces some challenges, such as how to achieve high disinfection efficiency of plasma, how to ensure safety during the disinfection process, how to extend the service life of the plasma generator, and how to optimize the system design to improve disinfection efficiency. Summary of the invention:

[0004] In order to solve the above problems and overcome the deficiencies of the prior art, the present invention provides an intelligent control system for a plasma air disinfection module.

[0005] To achieve the above object, the present invention provides an intelligent control system for a plasma air disinfection module, comprising:

[0006] Air quality detection module, which is used to detect various indicators of air quality in real time;

[0007] A data analysis module, which is electrically connected to the air quality detection module and is used to receive various index data related to air quality and classify, organize and analyze them;

[0008] The air quality prediction module is electrically connected to the data analysis module and the air quality detection module, and predicts the development trend of air quality on the future time axis based on the classified, sorted and analyzed data, establishes an air quality development trend model, and receives real-time detection of various indicators of air quality, and fits and compares the data with the air quality development trend model to determine the accuracy of the air quality development trend model;

[0009] The module control module is electrically connected to the air quality prediction module, and determines the reasonable wind speed level, discharge power and discharge time according to the real-time matching strategy based on the air quality development trend model;

[0010] The data storage module is electrically connected to the air quality detection module, the data analysis module, the air quality prediction module, the module control module and the module control module, and is used to store and establish a database based on various index data related to air quality, air quality development trend model, wind speed gear, discharge power and discharge time, and establish a one-to-one mapping relationship between various data.

[0011] Furthermore, the air quality detection module includes an air quality sensor, which is used to detect various indicators of indoor air, including gas flow, microbial count, temperature, humidity, air pressure, light, PM2.5, PM10, TVOC, oxygen concentration, carbon dioxide concentration, carbon monoxide concentration, and formaldehyde concentration.

[0012] Furthermore, the real-time matching strategy is to adjust the discharge power according to the coefficient K based on the various indicators of air quality to obtain the discharge powers P1, P2, P3~P N , the actual discharge power is obtained by weighted calculation:

[0013] Among them, P is the actual discharge power and w is the weighting coefficient.

[0014]

[0015] Furthermore, the real-time matching strategy includes a gas flow and discharge power matching strategy, that is, the discharge power and the gas flow are proportional to each other with K as the coefficient:

[0016] P1=Qg / K

[0017] Among them, Qg is the gas flow rate, K is the flow coefficient, and P1 is the discharge power.

[0018] Furthermore, the construction process of the air quality development trend model is to first distribute the data of various indicators related to air quality according to the preset weights, then sum the weighted data to obtain the air quality score, and finally fit the air quality score with the time point to obtain the trend curve of the air quality evolution over time, and record the trend curve as the original data for machine learning;

[0019] By performing deep learning on historical machine learning raw data, we can predict the evolution of air quality over time and build an air quality development trend model.

[0020] Furthermore, the real-time air quality index data is obtained through the air quality detection module and distributed and calculated according to preset weights, and fitted into the air quality development trend model in the form of discrete points. When the discreteness is greater than the threshold, the model data of the air quality detection module at that time point is adjusted to improve the accuracy and adaptability of the air quality development trend model.

[0021] Furthermore, the module control module adjusts the power generation power and wind speed in advance according to the air quality development trend model, and the adjustment value is the minimum power based on the discrete point whose discreteness is not greater than the threshold.

[0022] The beneficial effects of the present invention are:

[0023] The intelligent control system of the plasma air disinfection module of the present invention obtains the best discharge power under the condition of balance and compliance with the harmful gas leakage through testing and analysis. Under the premise of ensuring safe use, the limit discharge power can be output, which greatly improves the air disinfection efficiency.

[0024] At the same time, a maximum 4-speed output program is set, combined with the data rules of the air disinfection experiment, to accurately control the optimal discharge power under different gas flow rates, to ensure the optimal discharge power under precise control of different gas flow rates, so as to achieve the best disinfection effect.

[0025] Record performance output time and parameters. The monitoring unit monitors the equipment status in real time to ensure continuous and efficient operation of the equipment.

[0026] After efficient air disinfection in the shortest time, it can automatically match different high and low discharge powers and work in a cycle, extending the maintenance time of the plasma generator and reducing maintenance costs.

[0027] Through advanced control systems and intelligent algorithms, the plasma generator can be adaptively optimized according to the signal from the main control system of the air disinfection machine, which improves the system's integration and intelligence level. This automated adjustment mechanism greatly reduces the need for manual operation, allowing users to set the air disinfection time requirements, and the system will automatically work to ensure that the indoor air is always kept in an ideal state, creating a healthy and fresh living environment for users.

[0028] The fault self-diagnosis function reduces the operation difficulty and maintenance cost.

[0029] In general, by solving the above-mentioned technical problems, the present invention not only improves the efficiency of air disinfection and extends the service life, but also optimizes the user experience of the equipment, and provides an efficient, reliable and economical disinfection solution for the design of air disinfectors. It has important practical application value for the product upgrade of plasma air disinfectors. Description of the drawings:

[0030] AttachedFigure 1 It is a flow chart of the full intelligent mode system of the present invention;

[0031] Attached Figure 2 It is a flow chart of the single air volume mode system of the present invention; Specific implementation method:

[0032] In order to make the purpose, technical scheme and advantages of the present invention more clear, the following will be combined with the appended claims of the present invention. Figure 1 and attached Figure 2 , the present invention is described in more detail.

[0033] The present invention provides an intelligent control system for a plasma air disinfection module, comprising:

[0034] Air quality detection module, which is used to detect various indicators of air quality in real time;

[0035] The data analysis module is electrically connected to the air quality detection module, and is used to receive various index data related to air quality and classify, organize and analyze them; the air quality prediction module is electrically connected to the data analysis module and the air quality detection module, and predicts the development trend of air quality on the future timeline based on the classified, organized and analyzed data, establishes an air quality development trend model, and receives various index data related to air quality detected in real time, and fits and compares the data with the air quality development trend model to determine the accuracy of the air quality development trend model; the module control module is electrically connected to the air quality prediction module, and determines the reasonable wind speed gear, discharge power and discharge time according to the real-time matching strategy based on the air quality development trend model; the data storage module is electrically connected to the air quality detection module, and is used to detect the air quality of the air quality on the future timeline based on the classified, organized and analyzed data, and establishes an air quality development trend model; the air quality prediction ... The quality detection module, the data analysis module, the air quality prediction module, the module control module and the module control module are electrically connected to store and establish a database according to various index data of air quality, air quality development trend model, wind speed gear, discharge power and discharge time, and establish a one-to-one mapping relationship between various data; the air quality detection module includes an air quality sensor, which is used to detect various index data of indoor air, including gas flow, microorganism quantity, temperature, humidity, air pressure, light, PM2.5, PM10, TVOC, oxygen concentration, carbon dioxide concentration, carbon monoxide concentration, and formaldehyde concentration; the real-time matching strategy is to adjust the discharge power according to the coefficient K according to various index data of air quality, and obtain discharge powers P1, P2, P3~P N , the actual discharge power is obtained by weighted calculation:

[0036]

[0037] Among them, P is the actual discharge power and w is the weighting coefficient.

[0038] The real-time matching strategy includes the gas flow and discharge power matching strategy, that is, the discharge power and the gas flow are proportional to K as the coefficient:

[0039] P1=Qg / K

[0040] Among them, Qg is the gas flow rate, K is the flow coefficient, and P1 is the discharge power.

[0041] The construction process of the air quality development trend model is to first distribute the various index data related to air quality according to the preset weights, then sum the weighted data to obtain the air quality score, and finally fit the air quality score to the time point to obtain the trend curve of the air quality evolution over time, and record the trend curve as the machine learning raw data; through deep learning of historical machine learning raw data, predict the law of air quality evolution over time and build an air quality development trend model; obtain real-time air quality index data in real time through the air quality detection module and distribute and calculate according to the preset weights, and fit it into the air quality development trend model in the form of discrete points. When the discreteness is greater than the threshold, adjust the model data of the air quality detection module at this time point to improve the accuracy and adaptability of the air quality development trend model; the module control module adjusts the power generation power and wind speed in advance according to the air quality development trend model, and the adjustment value is the minimum power based on the discreteness of the discrete point not greater than the threshold.

[0042] Embodiment 1 is as follows:

[0043] An intelligent control system for a plasma air disinfection module, comprising:

[0044] Air quality detection module, which is used to detect various indicators of air quality in real time, using air quality sensors, formaldehyde sensors, TVOC sensors, temperature sensors, humidity sensors and other equipment to detect various indicators of indoor air, including gas flow, microbial count, temperature, humidity, air pressure, light, PM2.5, PM10, TVOC, oxygen concentration, carbon dioxide concentration, carbon monoxide concentration, formaldehyde concentration;

[0045] A data analysis module, which is electrically connected to the air quality detection module, is used to receive various index data related to air quality and classify, sort and analyze them, classify and sort the data, and then perform weighted calculation;

[0046] The air quality prediction module is electrically connected to the data analysis module and the air quality detection module, and predicts the development trend of air quality on the future time axis based on the classified, sorted and analyzed data, establishes an air quality development trend model, and receives real-time detection of various indicators of air quality, and fits and compares the data with the air quality development trend model to determine the accuracy of the air quality development trend model;

[0047] The construction process of the air quality development trend model is to first distribute the various indicators of air quality according to the preset weights, then sum the weighted data to obtain the air quality score, and finally fit the air quality score with the time point to obtain the trend curve of the air quality evolution over time, and record the trend curve as the original data for machine learning;

[0048] By performing deep learning on historical machine learning raw data, we can predict the evolution of air quality over time and build an air quality development trend model.

[0049] The air quality detection module obtains real-time air quality index data in real time and distributes and calculates it according to preset weights, and fits it into the air quality development trend model in the form of discrete points. When the discreteness is greater than the threshold, the model data of the air quality detection module at that time point is adjusted to improve the accuracy and adaptability of the air quality development trend model.

[0050] The module control module is electrically connected to the air quality prediction module. According to the air quality development trend model, the reasonable wind speed gear, discharge power and discharge time are determined according to the real-time matching strategy. The module control module adjusts the power generation power of the plasma generating unit and the wind speed of the fan in advance according to the air quality development trend model. The adjustment value is the minimum power based on the discrete point whose discreteness is not greater than the threshold, thereby reducing the power of the whole machine while ensuring the purification efficiency.

[0051] The data storage module is electrically connected to the air quality detection module, the data analysis module, the air quality prediction module, the module control module and the module control module, and is used to store and establish a database based on various index data related to air quality, air quality development trend model, wind speed gear, discharge power and discharge time, establish a one-to-one mapping relationship between various data, and extract corresponding data from the database when constructing the air quality development trend model.

[0052] The real-time matching strategy is to adjust the discharge power according to the coefficient K based on the various indicators of air quality to obtain the discharge powers P1, P2, P3~P N , the actual discharge power is obtained by weighted calculation:

[0053]

[0054] Among them, P is the actual discharge power and w is the weighting coefficient.

[0055] All air index data that affect discharge efficiency are included in the calculation system, and different air index data are assigned different weights. For example, the impact of formaldehyde content on air quality increases, so its weight should be increased accordingly relative to other data. The specific weight distribution can be set according to industry specification documents.

[0056] In special cases, there may be an emergency situation of dangerous gas leakage. At this time, the calculation method of actual discharge power needs to include temporary discharge power P 临 , generally P 临 The maximum remaining power is fully output, but after the air quality sensor detects the air quality data, the phase data does not exceed the threshold, then the temporary discharge power is calculated by the following formula:

[0057] P=P 正 +P 临

[0058] P = Q / K 临

[0059] Where Q is the concentration of dangerous gas, K 临 is the calculation coefficient, where the calculation coefficient is set according to industry specification documents.

[0060] The real-time matching strategy includes the gas flow and discharge power matching strategy. The gas flow is related to the wind speed, that is, the discharge power is proportional to the gas flow with K as the coefficient:

[0061] P1=Qg / K

[0062] Among them, Qg is the gas flow rate, P1 is the discharge power, and K is the flow coefficient, which is measured by the Lissajous figure method to ensure that the best disinfection effect can be achieved under different operating conditions.

[0063] An intelligent control system for a plasma air disinfection module is designed to be installed in an air disinfector equipped with a plasma generator. It obtains feedback data through various external air quality sensors. After obtaining the level of indoor air quality based on the data, it infers the reasonable wind speed level to be used, and automatically matches key parameters such as the reactor discharge power and time program to achieve efficient disinfection of pathogens in the air.

[0064] The present invention matches the gas flow rate with the discharge power level by determining the reactor load. According to the formula, Qg = k*P gas flow rate and discharge power, where gas flow rate (Qg), discharge power (P), k is the flow coefficient. The coefficient is measured by the Lissajous figure method to ensure that the best disinfection effect can be achieved under different operating conditions.

[0065] By obtaining the optimal discharge power of the reactor and testing the air disinfection effect under different gas flow rates in the microbiology laboratory, different discharge powers and parameters of the reactor can be set according to the different gas flow rates and air disinfection times. The discharge power and parameters can be automatically adjusted multiple times after a certain period of time to ensure the optimal disinfection effect under high-power plasma. At the same time, the parameters can be automatically adjusted after the end. It can also be operated at a low parameter for a period of time, and then operate at a high discharge power for automatic circulation. There is no need to keep the plasma generation module running at full load, which eliminates the possibility of harmful gases, such as excessive ozone, and reduces energy consumption.

[0066] At the same time, it also combines a large amount of experimental data to achieve real-time monitoring of key indicator signals in the air, including positive and negative ion concentrations, temperature and humidity, through the monitoring unit inside the air disinfector. It can automatically adjust the discharge power and parameters of the reactor according to the impact of the indoor environment on the plasma generator to ensure the efficiency of the disinfection process.

[0067] Through the comprehensive application of the above technical points, the intelligent control system of the plasma air disinfection module can not only achieve rapid air disinfection in a short period of time, but also reduce energy consumption and module life, providing an efficient air purification solution. The intelligent design and automated operation process of the system enable users to easily manage the equipment and ensure that the air quality always meets the expected standards.

[0068] The traditional plasma air disinfection equipment uses a very weak plasma generator with a very low plasma concentration, which cannot achieve rapid disinfection. The present invention can output the maximum discharge power while ensuring the safety of use, greatly improving the air disinfection efficiency.

[0069] When air is used as the generating gas, the content of different substances generated under different discharge powers is analyzed by a spectrometer. After selecting the available output parameters, the concentration is tested by testing the tooling and experimental space to obtain the optimal discharge power under the condition of balance and compliance with the leakage of harmful gases.

[0070] The speed of air flow directly affects air circulation and the contact time between pathogens and plasma, which in turn affects the disinfection efficiency. According to the control principle of the air disinfector, the present invention sets the highest 4-speed output program, combines the data rules of air disinfection experiments, and accurately controls the optimal discharge power under different gas flow rates, ensuring the optimal discharge power under accurate control of different gas flow rates to achieve the best disinfection effect.

[0071] Considering the difference in temperature and humidity, which has an impact on the output of the plasma generator, the present invention has a temperature and humidity sensor, which combines experimental data to realize the automatic compensation function of temperature and humidity, greatly ensuring the consistency of the disinfection effect.

[0072] The present invention records the performance output time and parameters, and the monitoring unit monitors the device status in real time to ensure the continuous and efficient operation of the device.

[0073] Long-term high-load operation of plasma air disinfection equipment will lead to performance degradation. By using the optimal discharge power, after efficient air disinfection, different discharge powers can be matched, working in cycles, extending the maintenance time of the plasma generator and reducing maintenance costs.

[0074] The efficient operation of plasma air disinfection equipment requires the coordinated work of various units. Through advanced control systems and intelligent algorithms, the present invention can realize adaptive optimization of the plasma generator according to the signal of the main control system of the air disinfector, thereby improving the integration and intelligence level of the system. This automated adjustment mechanism greatly reduces the need for manual operation, so that users only need to set the volume of the space where the equipment is located, and the system will work automatically, ensuring that the indoor air is always kept in an ideal state, creating a healthy and fresh living environment for users.

[0075] In order to improve the portability of user experience, the present invention designs an automatic working mode and a fault self-diagnosis function to reduce the difficulty of operation and maintenance cost. By solving the above technical problems, the present invention not only improves the efficiency of air disinfection, but also optimizes the user experience of the equipment, providing a new direction for the development and application of air disinfection technology.

[0076] Through experimental observation and actual space disinfection test, the discharge spectrum, the leakage of harmful gases in the applicable minimum space of air flow (the national standard stipulates that the air volume of the equipment must reach more than 8 times the applicable space for the applicable space), and the air disinfection data of the microbial laboratory cabin are obtained to ensure the accurate setting of the most reasonable discharge power. Without setting the specific air volume, the adaptive optimization of the plasma generator is realized, and the integration and intelligence level of the system are improved. The system comprehensively monitors the key parameters in the disinfection process by integrating formaldehyde sensors, TVOC sensors, temperature sensors, humidity sensors and other equipment. Through advanced control systems and intelligent algorithms, the adaptive optimization of the plasma generator can be realized according to the signal of the main control system of the air disinfector, and the integration and intelligence level of the system are improved; under the condition of fixed gas flow, up to 4 gears of air volume anti-counterfeiting matching are reserved, and the temperature and humidity compensation function can also be provided while the intelligent program is working. After the optimal air disinfection time is completed, different discharge powers can be matched, and the work can be cyclically performed to extend the maintenance time of the plasma generator and reduce the maintenance cost. In order to improve the user experience and reduce user operations, the present invention designs an automatic working mode and a fault self-diagnosis function to reduce the difficulty of operation and maintenance cost.

[0077] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the system diagram of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. The optional embodiment is expected, wherein the order of various operations is changed and the sub-operation described as a part of a larger operation is performed independently.

[0078] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified to the contrary, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0079] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0080] The logic and / or steps represented in the system diagram or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0081] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the above-mentioned program is printed, since the above-mentioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in other suitable ways as necessary, and then stored in a computer memory.

[0082] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0083] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0084] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0085] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. An intelligent control system for a plasma air disinfection module, characterized in that: include Air quality detection module, which is used to detect various indicators of air quality in real time; A data analysis module, which is electrically connected to the air quality detection module and is used to receive various index data related to air quality and classify, organize and analyze them; The air quality prediction module is electrically connected to the data analysis module and the air quality detection module, and predicts the development trend of air quality on the future time axis based on the classified, sorted and analyzed data, establishes an air quality development trend model, and receives real-time detection of various indicators of air quality, and fits and compares the data with the air quality development trend model to determine the accuracy of the air quality development trend model; The module control module is electrically connected to the air quality prediction module, and determines the reasonable wind speed level, discharge power and discharge time according to the real-time matching strategy based on the air quality development trend model; The data storage module is electrically connected to the air quality detection module, the data analysis module, the air quality prediction module, the module control module and the module control module, and is used to store and establish a database based on various index data related to air quality, air quality development trend model, wind speed gear, discharge power and discharge time, and establish a one-to-one mapping relationship between various data.

2. The intelligent control system of the plasma air disinfection module according to claim 1 is characterized in that: The air quality detection module includes an air quality sensor, which is used to detect various index data of indoor air, including gas flow, microorganism quantity, temperature, humidity, air pressure, light, PM2.5, PM10, TVOC, oxygen concentration, carbon dioxide concentration, carbon monoxide concentration, and formaldehyde concentration.

3. The intelligent control system of the plasma air disinfection module according to claim 1 is characterized in that: The real-time matching strategy is to adjust the discharge power according to the coefficient K based on the various index data of air quality to obtain the discharge powers P1, P2, P3~P N , the actual discharge power is obtained by weighted calculation: Among them, P is the actual discharge power and w is the weighting coefficient.

4. The intelligent control system of the plasma air disinfection module according to claim 3 is characterized in that: The real-time matching strategy includes a gas flow and discharge power matching strategy, that is, the discharge power and the gas flow are proportional to each other with K as the coefficient: P1=Qg / K Among them, Qg is the gas flow rate, K is the flow coefficient, and P1 is the discharge power.

5. The intelligent control system of the plasma air disinfection module according to claim 1 is characterized in that: The construction process of the air quality development trend model is to first distribute the various index data related to air quality according to the preset weights, then sum the weighted data to obtain the air quality score, and finally fit the air quality score with the time point to obtain the trend curve of the air quality evolution over time, and record the trend curve as the original data for machine learning; By performing deep learning on historical machine learning raw data, we can predict the evolution of air quality over time and build an air quality development trend model.

6. The intelligent control system of the plasma air disinfection module according to claim 5 is characterized in that: The air quality detection module obtains real-time air quality index data in real time and distributes and calculates it according to preset weights, and fits it into the air quality development trend model in the form of discrete points. When the discreteness is greater than the threshold, the model data of the air quality detection module at that time point is adjusted to improve the accuracy and adaptability of the air quality development trend model.

7. The intelligent control system of the plasma air disinfection module according to claim 6 is characterized in that: The module control module adjusts the power generation power and wind speed in advance according to the air quality development trend model, and the adjustment value is the minimum power based on the discrete point whose discreteness is not greater than the threshold.

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