Ventilation system optimization method and system based on air quality of industrial aquaculture workshop

Abstract

The invention provides a ventilation system optimization method and system based on the air quality of an industrial aquaculture workshop, and relates to the technical field of environment regulation and ventilation system.The method includes the steps that aquaculture workshop gas is analyzed through an infrared spectrometer, the spectral intensity of the characteristic absorption wavelength of a target pollutant is obtained, and then the accurate concentration of the pollutant is analyzed according to the spectral intensity; and calculating a ventilation parameter reference value and controlling a fan to adjust the air supply amount. An air treatment device in the ventilation system can determine the optimal working time according to the air supply amount so as to decompose pollutants and continuously monitor the concentration of the pollutants at the inlet and the outlet of the device. Subsequently, the air quality deterioration index is predicted by analyzing the concentration change trend, and the current overall purification efficiency is calculated. When the purification efficiency deviates from the range or the deterioration index exceeds the threshold value, the running state of the device is dynamically adjusted and fed back to the system, self-adaptive optimization of the ventilation system of the industrial aquaculture workshop can be achieved, and it is guaranteed that the workshop air quality is stable and the purification efficiency reaches the standard.

Description

Technical Field

[0001] This application relates to the field of environmental control and ventilation system optimization technology, and in particular to a method and system for optimizing ventilation systems based on air quality in factory farming workshops. Background Technology

[0002] Due to the high density of livestock farming, factory farms continuously produce not only conventional pollutants but also highly irritating and foul-smelling gases such as ammonia and hydrogen sulfide, and may also harbor airborne pathogens. These substances not only easily trigger diseases in farmed animals and exacerbate the spread of epidemics, but also accumulate due to the enclosed nature of the farms, making them impossible to completely eliminate through ventilation alone. Therefore, these farms urgently need adaptive control technologies that can respond in real time to changes in air quality and balance ventilation with deep purification.

[0003] Currently, the mainstream solution for the above needs is a combination system of fixed-parameter ventilation and modular air purification devices. This system ensures air circulation by preset fan operating parameters, and treats the air with purification devices such as activated carbon adsorption and ultraviolet disinfection. Some systems indirectly adjust the operating time of the purification devices by manually collecting air samples periodically and combining the data with instrument analysis.

[0004] However, the existing solutions have significant drawbacks: the ventilation parameters are fixed and the air supply cannot be dynamically adjusted according to the real-time concentration of pollutants, which can easily lead to untimely purification or energy waste; there is no predictive mechanism and the solutions can only be passively responded to after pollutants exceed the standard, which cannot avoid risks in advance; in addition, the coordination between ventilation and purification is poor, the overall control accuracy and intelligence level are low, and they cannot meet the deep purification needs of the workshop. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for optimizing ventilation systems based on air quality in factory farming workshops, in order to solve the problems of untimely purification or energy waste in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides an adaptive optimization method for a ventilation system based on air quality in a factory-style aquaculture workshop, comprising:

[0007] Infrared gas spectrometers are used to analyze gases in factory farming workshops in real time to obtain spectral intensity data of key target pollutants at characteristic absorption wavelengths.

[0008] The precise concentration of key target pollutants is determined by analyzing the spectral intensity data, and the required reference values ​​for ventilation parameters are calculated based on the precise concentration. The air supply volume is adjusted by controlling the operating parameters of the fan to the reference values ​​for ventilation parameters.

[0009] An air handling unit is installed in the ventilation system, and the optimal operating time of the air handling unit is determined based on the air supply volume. When air flows through the air handling unit, key target pollutants are decomposed according to the optimal operating time, and the pollutant concentration at the inlet and outlet of the air handling unit is continuously monitored.

[0010] By analyzing the changing trends of pollutant concentrations over a continuous time period, an air quality deterioration index is predicted. This index is determined by curve fitting of the inlet and outlet concentration sequences to obtain their respective time-varying functions, and by calculating the derivative of these functions at a predetermined future time point. Furthermore, the overall purification efficiency is calculated based on the pollutant concentrations. This overall purification efficiency is based on the inlet and outlet concentration sequences at the same time point, and the instantaneous purification efficiency is calculated and averaged over all instantaneous purification efficiencies within the continuous time period.

[0011] When the overall purification efficiency deviates from the preset range or the air quality deterioration index exceeds the safety threshold, the fan operating parameters and the operating status of the air handling unit are dynamically adjusted, and the adjusted operating status and operating status are fed back to the ventilation system to achieve adaptive optimization of the ventilation system.

[0012] Optionally, when the overall purification efficiency deviates from a preset range or the air quality deterioration index exceeds a safety threshold, the fan operating parameters and the operating status of the air handling unit are dynamically adjusted, and the adjusted operating status and operating status are fed back to the ventilation system to achieve adaptive optimization of the ventilation system, including:

[0013] The overall purification efficiency is compared with the preset upper and lower limits of purification efficiency, and the air quality deterioration index is compared with the preset safety threshold.

[0014] If the overall purification efficiency is higher than the upper limit of the preset purification efficiency or lower than the lower limit, or if the air quality deterioration index is higher than the safety threshold, an adjustment command is generated.

[0015] According to the adjustment command, the fan speed is increased or decreased by a preset step size, and the operating power or working cycle of the air handling unit is adjusted synchronously.

[0016] The adjusted fan speed parameters and the air handling unit's operating status parameters are fed back to the ventilation system's control center in real time to update the system's operating baseline.

[0017] Optionally, according to the adjustment command, the fan speed is increased or decreased by a preset step size, and the operating power or working cycle of the air handling unit is adjusted synchronously, including:

[0018] By parsing the adjustment command, the speed adjustment direction indicator for the fan and the operating parameter adjustment guide indicator for the air handling unit are extracted;

[0019] The adjustment trend of the fan speed is determined based on the speed adjustment direction indicator, and the preset step size mapping relationship is queried in combination with the current system status parameters to generate the adjustment step size value of the fan speed.

[0020] The current fan speed is calculated using the adjustment step value to generate a new fan speed command.

[0021] Based on the operating parameter adjustment guide, select to adjust the operating power or working cycle of the air handling device. If the operating power is adjusted, the target power value is calculated linearly based on the change ratio of the air volume. If the working cycle is adjusted, the start-stop time ratio is recalculated based on the offset of the overall purification efficiency from the preset range.

[0022] The new fan speed command, along with the target power value or the start-stop time ratio, is simultaneously sent to the actuator of the ventilation system to ensure that the state changes of the fan and the air handling unit remain coordinated.

[0023] Optionally, by analyzing the changing trend of the pollutant concentration over a continuous time period, an air quality deterioration index is predicted, and the current overall purification efficiency is calculated based on the pollutant concentration, including:

[0024] Multiple consecutive and equally spaced time points are selected, and based on the pollutant concentrations at the inlet and outlet of the air treatment device at each time point, an inlet concentration sequence and an outlet concentration sequence are formed.

[0025] Curve fitting is performed on the inlet concentration sequence and the outlet concentration sequence to obtain their respective time-varying functions, and the derivative of the changing functions at a preset future time point is calculated to predict the air quality deterioration index.

[0026] Based on the inlet concentration sequence and the outlet concentration sequence at the same time point, the instantaneous purification efficiency is calculated, and the arithmetic average of all the instantaneous purification efficiencies within a continuous time period is performed to obtain the overall purification efficiency.

[0027] Optionally, curve fitting is performed on the inlet concentration sequence and the outlet concentration sequence respectively to obtain their respective time-varying functions, and the derivative of the changing functions at preset future time points is calculated to predict the air quality deterioration index, including:

[0028] Continuous time points and corresponding concentration values ​​in the inlet concentration sequence and the outlet concentration sequence are selected as fitting data points;

[0029] Based on the fitted data points, the minimum error approximation method is used to generate functions representing the change of concentration over time for the inlet concentration sequence and the outlet concentration sequence, respectively.

[0030] For the aforementioned function of change, select the same preset future time point, and calculate the instantaneous rate of change of the function of change at the preset future time point as the derivative value;

[0031] Obtain the terminal concentration values ​​of the inlet and outlet concentration sequences at the current time point, and use them as the current concentration;

[0032] The derivative value and the current concentration are input into a preset weighted calculation model, and an air quality deterioration index is generated by linearly combining them by assigning weight factors to different parameters.

[0033] Optionally, an air handling unit is installed within the ventilation system, and the optimal operating time of the air handling unit is determined based on the air supply volume. When air flows through the air handling unit, key target pollutants are decomposed according to the optimal operating time, and the pollutant concentrations at the inlet and outlet of the air handling unit are continuously monitored, including:

[0034] An air handling unit is installed in the ventilation system. Based on the air volume and the pollutant decomposition capacity of the air handling unit per unit time, the theoretical total amount of the key target pollutants decomposed in a single treatment cycle is calculated.

[0035] Based on the theoretical total decomposition amount and combined with the actual decomposition efficiency curve of the key target pollutants in the air handling unit, the optimal operating time of the air handling unit is determined.

[0036] Based on the optimal operating time, the air handling device is activated when the airflow corresponding to the air volume passes through, so that the flowing air stays inside the device for a sufficient time to ensure that the key target pollutants are effectively decomposed.

[0037] Concentration sensing units are installed at the air inlet and air outlet locations of the air handling device to synchronously collect pollutant concentrations at both locations at a constant sampling frequency.

[0038] Optionally, the precise concentration of the key target pollutant is determined by resolving the spectral intensity data, and the required ventilation parameter reference value is calculated based on the precise concentration. The air supply volume is then adjusted by controlling the operating parameters of the fan to the ventilation parameter reference value, including:

[0039] Using the spectral intensity data, combined with the correspondence between the absorption characteristics and concentration of the key target pollutant at multiple specific wavelengths, the precise concentration of the key target pollutant is generated through weighted fusion calculation;

[0040] Based on the precise concentration, combined with the volume of the aquaculture workshop, the safe concentration limit of the key target pollutant, and the current rate of concentration change, the total amount of pollutants that need to be removed per unit time is calculated to determine the reference value of the ventilation parameters.

[0041] Based on the ventilation parameter reference values ​​and combined with the fan performance curve, the fan operating parameters are determined. By adjusting the fan speed or blade angle, the actual air volume is made to match the ventilation parameter reference values.

[0042] The deviation between the actual air supply volume and the reference value of the ventilation parameters is continuously compared. When the deviation exceeds the allowable range, the operating parameters of the fan are iteratively corrected until the actual air supply volume stabilizes near the reference value of the ventilation parameters.

[0043] Secondly, this application provides an adaptive optimization system for ventilation systems based on air quality in factory farming workshops, comprising:

[0044] The analysis module is used to analyze the gas in the factory farming workshop in real time using an infrared gas spectrometer to obtain the spectral intensity data of key target pollutants at characteristic absorption wavelengths.

[0045] The control module is used to analyze the spectral intensity data to obtain the precise concentration of key target pollutants, calculate the required ventilation parameter reference value based on the precise concentration, and adjust the air supply volume by controlling the operating parameters of the fan according to the ventilation parameter reference value.

[0046] The decomposition module is used to install an air handling device in the ventilation system and determine the optimal operating time of the air handling device based on the air supply volume. When air flows through the air handling device, key target pollutants are decomposed according to the optimal operating time, and the pollutant concentration at the inlet and outlet of the air handling device is continuously monitored.

[0047] The prediction module is used to predict the air quality deterioration index by analyzing the changing trend of the pollutant concentration over a continuous time period. The air quality deterioration index is determined by curve fitting of the inlet and outlet concentration sequences to obtain their respective time-varying functions, and calculating the derivative of the function at a preset future time point. The module also calculates the current overall purification efficiency based on the pollutant concentrations. The overall purification efficiency is calculated based on the inlet and outlet concentration sequences at the same time point, and the instantaneous purification efficiency is calculated and arithmetically averaged over all instantaneous purification efficiencies within the continuous time period.

[0048] The feedback module is used to dynamically adjust the fan operating parameters and the operating status of the air handling unit when the overall purification efficiency deviates from the preset range or the air quality deterioration index exceeds the safety threshold, and to feed back the adjusted operating status and operating status to the ventilation system in order to achieve adaptive optimization of the ventilation system.

[0049] Thirdly, this application provides an electronic device, comprising:

[0050] Memory, used to store computer programs;

[0051] A processor is configured to execute the computer program to implement the steps of the adaptive optimization method for a ventilation system based on air quality in a factory-farmed aquaculture workshop as described in the first aspect above.

[0052] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the adaptive optimization method for ventilation systems based on air quality in factory farming workshops as described in the first aspect above.

[0053] The adaptive optimization method for ventilation systems in factory-farmed aquaculture workshops provided in this application utilizes an infrared gas analyzer to perform real-time analysis of the air in the workshops and obtain spectral intensity data of the characteristic absorption wavelengths of key target pollutants. This provides real-time and reliable raw data support for subsequent accurate analysis of pollutant concentrations, avoiding concentration judgment errors caused by data lag. By analyzing the precise concentration of key target pollutants based on spectral intensity data and calculating reference values ​​for ventilation parameters to adjust the fan airflow, the airflow can be matched with the actual pollutant concentration, avoiding insufficient purification or energy waste caused by fixed airflow. By installing an air handling unit within the ventilation system, based on the airflow... Determining the optimal operating time for airflow to decompose pollutants and continuously monitoring pollutant concentrations at the inlet and outlet of the device ensures that the air handling unit operates at its best efficiency, while simultaneously monitoring real-time concentration changes during the pollutant purification process. By analyzing pollutant concentration trends over a continuous period, the air quality deterioration index can be predicted, and the current overall purification efficiency can be calculated, allowing for early assessment of air quality risks and providing quantitative data for subsequent system adjustments. By dynamically adjusting the operating status of the fan and air handling unit and feeding back feedback to the ventilation system when the overall purification efficiency deviates from the preset range or the air quality deterioration index exceeds the safety threshold, a closed-loop control system can be formed, enabling the ventilation system to autonomously optimize itself based on actual air quality.

[0054] Furthermore, when the overall purification efficiency or air quality deterioration index does not meet the preset requirements, the overall purification efficiency is first compared with the preset upper and lower limits, and the air quality deterioration index is compared with the safety threshold. If any indicator exceeds the standard, an adjustment command is generated. Then, according to the command, the fan speed is adjusted at a preset step size, and the operating power or cycle of the air handling unit is adjusted simultaneously. Finally, the adjusted parameters are fed back to the control center to update the system operating benchmark. Through clear indicator comparison logic, the accuracy of the adjustment command triggering is ensured, avoiding mis-adjustment; adjusting parameters at preset step sizes can achieve stable control and prevent sudden parameter changes from impacting the system's operational stability and the workshop environment; synchronously adjusting the fan and air handling unit and updating the operating benchmark can ensure synergistic optimization between the two, continuously maintaining the efficient operation of the ventilation system, and further improving the accuracy and reliability of adaptive control. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A flowchart illustrating an adaptive optimization method for a ventilation system based on air quality in a factory farming workshop, provided in this application embodiment;

[0057] Figure 2 A flowchart illustrating the specific implementation of an adaptive optimization method for a ventilation system based on air quality in a factory farming workshop, provided in this application embodiment;

[0058] Figure 3 A specific implementation scenario diagram of an adaptive optimization method for a ventilation system based on air quality in a factory farming workshop, provided in this application embodiment;

[0059] Figure 4 This is a schematic diagram of the structure of an adaptive optimization system for ventilation based on air quality in a factory farming workshop, provided in an embodiment of this application. Detailed Implementation

[0060] In the air quality control of factory farming workshops, the existing scheme of using fixed-parameter ventilation combined with modular air purification devices has obvious shortcomings: On the one hand, the fans always operate according to preset parameters, and cannot flexibly adjust the air volume according to the real-time concentration of pollutants such as ammonia and hydrogen sulfide. When the pollutant concentration rises sharply, the air volume is insufficient, resulting in untimely purification, while when the concentration is too low, it will cause unnecessary energy waste. On the other hand, the system lacks an early prediction mechanism for air quality deterioration, and can only take measures passively after the pollutant concentration exceeds the standard. Moreover, the ventilation equipment and purification devices operate independently with poor coordination, resulting in low overall control accuracy and intelligence level, which makes it difficult to meet the workshop's needs for deep purification of highly hazardous pollutants and prevention and control of pathogenic microorganisms.

[0061] To address the aforementioned issues, this application proposes an adaptive optimization method for ventilation systems in factory-scale livestock farms based on air quality. The core of this method is precise management achieved through a complete process of real-time monitoring of pollution levels, flexible adjustment of equipment status, and continuous optimization of system operation. Specifically, an infrared gas analyzer is first used to capture pollutant data in real time and calculate concentrations, thereby matching appropriate fan airflow rates. Then, the optimal operating mode of the air handling unit is determined based on the airflow rate, while simultaneously tracking concentration changes during the purification process. Furthermore, the system can predict air quality risks and calculate purification effects based on concentration change trends. When purification effects are substandard or risks exceed limits, the system automatically adjusts the operating parameters of the fans and purification units, and promptly feeds back the adjustment results. This solution overcomes the drawbacks of fixed airflow rates by tracking concentration changes in real time, achieves proactive prevention and control through trend prediction, and improves overall control accuracy through coordinated adjustment of fans and purification units. It fundamentally solves the problems of untimely purification, energy waste, and passive response found in existing solutions, ensuring stable air quality in the workshop while improving the level of intelligent control.

[0062] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] The core of this application is to provide an adaptive optimization method for ventilation systems based on air quality in factory farming workshops. A flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0064] S101. Use an infrared gas spectrometer to analyze the gas in the factory farming workshop in real time to obtain the spectral intensity data of key target pollutants at characteristic absorption wavelengths.

[0065] In the above steps, an infrared gas analyzer is a device that uses the absorption characteristics of gas molecules to infrared light of specific wavelengths to detect the composition and concentration of gases, and can capture gas spectral signals in real time. Key target pollutants refer to gases that are highly harmful to farmed animals and the environment in factory farming workshops, such as ammonia and hydrogen sulfide. Characteristic absorption wavelength refers to the specific wavelength of infrared light that key target pollutant molecules can specifically absorb. Different pollutants correspond to different characteristic absorption wavelengths. For example, the characteristic absorption wavelength of ammonia is about 10.5 μm, and that of hydrogen sulfide is about 3.8 μm. Spectral intensity data refers to the infrared light intensity signal data detected by the infrared gas analyzer at the characteristic absorption wavelength of the key target pollutant, reflecting the degree of absorption of infrared light of specific wavelengths by the pollutant.

[0066] Specifically, firstly, air samples are collected from the factory-scale aquaculture workshop using the sampling unit of an infrared gas spectrometer. The sampling unit typically employs a pipe-type or diffusion-type design to ensure that air samples continuously and uniformly enter the detector chamber inside the analyzer. For example, 3-5 sampling points are set up in different areas of the aquaculture workshop, and the air samples are collected into the detector chamber through pipes, ensuring that the samples represent the overall air quality of the workshop. Secondly, the analyzer's light source module emits infrared light of continuous wavelengths. When the infrared light passes through the air sample in the detector chamber, key target pollutants in the sample, such as ammonia and hydrogen sulfide, absorb some of the infrared light at their characteristic absorption wavelengths. The unabsorbed infrared light is received by the detector module. For example, if the light source emits infrared light in the wavelength range of 2-15 μm, when the infrared light passes through a sample containing ammonia, the 10.5 μm wavelength infrared light is absorbed by the ammonia, while the remaining wavelengths of infrared light penetrate normally and are captured by the detector.

[0067] Finally, the detector converts the received infrared light signal into an electrical signal, which is then amplified and filtered by the analyzer's signal processing module to generate spectral intensity data of key target pollutants at their corresponding characteristic absorption wavelengths. For example, the detector converts the infrared light signal after penetrating the sample into a voltage signal, and the signal processing module removes noise caused by environmental interference, outputting the spectral intensity values ​​of ammonia at 10.5 μm and hydrogen sulfide at 3.8 μm.

[0068] S102. The precise concentration of the key target pollutant is obtained by analyzing the spectral intensity data, and the required ventilation parameter reference value is calculated based on the precise concentration. The air supply volume is adjusted by controlling the operating parameters of the fan to the ventilation parameter reference value.

[0069] Among them, the spectral intensity data refers to the infrared light intensity signal data of the key target pollutants at the characteristic absorption wavelength obtained by the infrared gas analyzer in the previous step, which can reflect the degree of absorption of infrared light of specific wavelengths by the pollutants; the precise concentration of the key target pollutants refers to the value that can accurately represent the actual content of pollutants in the workshop, calculated from the spectral intensity data; the ventilation parameter reference value refers to the standard value of the air supply volume required to achieve the pollutant removal target; the fan operating parameters refer to the indicators that control the working status of the fan, including the speed and blade angle; the actual air supply volume refers to the actual volume of air delivered or discharged to the workshop when the fan is running.

[0070] Optionally, step S102 may specifically include the following steps:

[0071] S1021. Using the spectral intensity data, combined with the correspondence between the absorption characteristics and concentration of the key target pollutant at multiple specific wavelengths, the precise concentration of the key target pollutant is generated through weighted fusion calculation.

[0072] S1022. Based on the precise concentration, combined with the volume of the aquaculture workshop, the safe concentration limit of the key target pollutant, and the current concentration change rate, calculate the total amount of pollutants that need to be removed per unit time to determine the reference value of the ventilation parameters.

[0073] S1023. Based on the ventilation parameter reference values ​​and combined with the fan performance curve, determine the fan operating parameters, and adjust the fan speed or blade angle to make the actual air volume match the ventilation parameter reference values.

[0074] S1024. Continuously compare the deviation between the actual air supply volume and the reference value of the ventilation parameters. When the deviation exceeds the allowable range, iteratively correct the operating parameters of the fan until the actual air supply volume stabilizes near the reference value of the ventilation parameters.

[0075] In the above steps, the relationship between absorption characteristics and concentration at multiple specific wavelengths refers to the correspondence between the infrared light absorption capacity of different pollutants at their characteristic absorption wavelengths and nearby auxiliary wavelengths and their own concentrations. This correspondence is usually pre-calibrated through experiments. Weighted fusion calculation refers to an algorithm that combines the reliability weights of absorption characteristics at different wavelengths to fuse multiple concentration calculation results to improve accuracy. The volume of the aquaculture workshop refers to the space volume that can accommodate air inside a factory-style aquaculture workshop. The safe concentration limit for key target pollutants refers to the maximum permissible concentration of pollutants stipulated by the state or industry that will not harm the health of farmed animals and the environment. When The rate of change of concentration refers to the amount of change in the concentration of key target pollutants per unit time, which reflects the speed at which pollutants are generated or dissipated; the total amount of pollutants to be removed per unit time refers to the mass of pollutants that the ventilation system needs to discharge per hour in order to control the pollutant concentration within safe limits; the fan performance curve refers to the curve provided by the fan manufacturer that reflects the relationship between fan speed or blade angle and air volume and air pressure; the allowable deviation range refers to the preset, acceptable error range between the actual air volume and the reference value of ventilation parameters; iterative correction refers to the process of gradually reducing the deviation between the actual air volume and the reference value by making multiple small adjustments to the fan operating parameters.

[0076] In this embodiment, step S1021 utilizes spectral intensity data, combined with the absorption characteristics and concentration relationship of key target pollutants at multiple specific wavelengths, to generate the precise concentration of the key target pollutants through weighted fusion calculation. Specifically, the absorption characteristics and concentration relationship model of target pollutants, such as ammonia, at characteristic absorption wavelengths and two auxiliary wavelengths are first retrieved from a preset database. Then, the spectral intensity data corresponding to the three wavelengths obtained in S101 are substituted into the model to obtain three sets of preliminary concentration values. Since the data at the characteristic absorption wavelength has the highest reliability, it is assigned a weight of 0.6, and the data at the two auxiliary wavelengths are each assigned a weight of 0.2. The concentration is then calculated using the formula... Calculate the final accurate concentration, where, Represents the precise concentration of key target pollutants. The preliminary concentration calculated at the characteristic absorption wavelength represents the value. The weights representing characteristic absorption wavelength data, These represent the initial concentrations calculated at the two auxiliary wavelengths, respectively. These represent the weights of the two auxiliary wavelength data. For example, the data obtained in step S101... , Substituting the spectral intensity data at each wavelength into the model, the preliminary concentrations of ammonia were obtained. Weight Substituting into the formula, we can obtain .

[0077] Secondly, in step S1022, based on the precise concentration, combined with the volume of the aquaculture workshop, the safe concentration limits of key target pollutants, and the current rate of concentration change, the total amount of pollutants that need to be removed per unit time is calculated to determine the reference values ​​for ventilation parameters. The specific process is as follows: first, obtain the volume of the aquaculture workshop... The safe concentration limit of the pollutant Then, the current concentration change rate is calculated using precise concentration data over a continuous 5-minute period. Concentration units are converted to mass concentration based on the ideal gas law. Then through the formula Calculate the total amount of pollutants that need to be removed per unit time, where, This represents the total amount of pollutants that need to be removed per unit of time. Represents the precise concentration of key target pollutants. This represents the safe concentration limit. Represents the current rate of concentration change. Represents a unit of time reference benchmark, such as , Represents the volume of the breeding workshop. Represents mass concentration; finally, through the formula Determine reference values ​​for ventilation parameters, among which, This represents a reference value for ventilation parameters. For example, it is known that... , Converted mass concentration Substituting into the formula, we can obtain Substituting these values ​​into the ventilation parameter reference value formula yields the following result. .

[0078] Next, in step S1023, based on the ventilation parameter reference values ​​and combined with the fan performance curve, the fan's operating parameters are determined. By adjusting the fan's speed or blade angle, the actual air volume is made to match the ventilation parameter reference values. Specifically, the performance curve is retrieved from the fan's technical documentation, which shows the fan's air volume data at different speeds. The speed value corresponding to the ventilation parameter reference value is found on the performance curve. If the current fan speed is lower than this value, an acceleration command is sent to the fan controller; if it is higher, a deceleration command is sent. If the fan supports blade angle adjustment, the air volume can also be adjusted to reach the reference value. For example, if the current fan speed is 1300 r / min, corresponding to an air volume of 5000 m³ / h, by increasing the speed to 1550 r / min, the actual air volume reaches 6200 m³ / h.

[0079] Finally, in step S1024, the deviation between the actual air supply volume and the reference value of the ventilation parameters is continuously compared. When the deviation exceeds the allowable range, the operating parameters of the fan are iteratively corrected until the actual air supply volume stabilizes near the reference value of the ventilation parameters. Specifically, the actual air supply volume data is collected in real time by an airflow sensor at the fan outlet, and then processed using a formula... Calculate the deviation from the reference value, where, This represents the deviation between the actual air supply volume and the reference value of the ventilation parameters. This represents the actual air supply volume. This represents a reference value for ventilation parameters; the preset allowable deviation range is... If the actual air volume collected in a certain instance Substituting into the formula, we can obtain If the deviation exceeds the allowable range, a small speed-up command (such as increasing speed) is sent to the wind turbine controller. The air volume is collected again and the deviation is calculated. If it is still outside the range, corrections are made until the actual air volume stabilizes within the range. Between, for example, after two iterative adjustments, the fan speed stabilized at Actual air volume Substituting into the deviation formula, we can obtain... This meets the deviation requirements.

[0080] In the overall scheme of step S102 above, the accuracy of pollutant concentration is ensured by multi-wavelength weighted fusion calculation and clear formula parameter definition, avoiding concentration misjudgment caused by single-wavelength data error; by combining multiple factors such as workshop volume and concentration change rate and quantifying the ventilation parameter reference value through formula, the air supply volume setting is more in line with the actual purification needs; the operating parameters are determined according to the fan performance curve, and the deviation formula is used for real-time monitoring and iterative correction, which can effectively reduce the deviation between the actual air supply volume and the reference value, ensuring stable ventilation effect; the whole process realizes precise connection from concentration analysis to air volume control, which not only avoids pollutant accumulation caused by insufficient air volume, but also prevents energy waste caused by excessive air volume, providing reliable parameter support for stable air quality in the breeding workshop.

[0081] S103. An air handling device is installed in the ventilation system, and the optimal working time of the air handling device is determined based on the air supply volume. When air flows through the air handling device, key target pollutants are decomposed according to the optimal working time, and the pollutant concentration at the inlet and outlet of the air handling device is continuously monitored.

[0082] Among them, air handling unit refers to equipment installed in ventilation system for decomposing or removing key target pollutants in the air. Common types include photocatalytic oxidation devices and plasma purification devices. Air volume refers to the volume of air delivered by the ventilation system per unit time as determined in step S102. Optimal operating time refers to the operating time required for the air handling unit to effectively decompose the key target pollutants flowing through the air. Key target pollutants refer to gases that are harmful to farmed animals and the environment in factory farming workshops, such as ammonia and hydrogen sulfide. The pollutant concentration at the inlet and outlet of the air handling unit refers to the content of key target pollutants in the air before the air enters the unit and after it has been treated by the unit, which is used to judge the purification effect of the unit.

[0083] Optionally, step S103 may specifically include the following steps:

[0084] S1031. An air handling device is installed in the ventilation system. Based on the air volume and the pollutant decomposition capacity per unit time of the air handling device, the theoretical total amount of the key target pollutants decomposed in a single treatment cycle is calculated.

[0085] S1032. Based on the theoretical total amount of decomposition and combined with the actual decomposition efficiency curve of the key target pollutants in the air treatment device, determine the optimal operating time of the air treatment device.

[0086] S1033. Based on the optimal working time, control the air handling device to start when the airflow corresponding to the air volume passes through, so that the flowing air stays inside the device for a sufficient time to ensure that the key target pollutants are effectively decomposed.

[0087] S1034. Concentration sensing units are respectively installed at the air inlet and air outlet positions of the air handling device to synchronously collect the pollutant concentrations at the two locations at a constant sampling frequency.

[0088] In the above steps, the pollutant decomposition capacity per unit time refers to the maximum mass of key target pollutants that the air handling unit can decompose per hour under normal operating conditions. This parameter is calibrated by the unit manufacturer based on experiments. The theoretical total decomposition amount of key target pollutants in a single treatment cycle refers to the total mass of pollutants that the unit can theoretically decompose in one treatment cycle, calculated based on the air volume and the unit's decomposition capacity per unit time. The actual decomposition efficiency curve is a curve reflecting the relationship between the air handling unit's operating time and the pollutant decomposition efficiency. The efficiency changes with operating time and is usually plotted through long-term experiments. The airflow residence time refers to the time that air takes to enter and exit the air handling unit. It needs to be matched with the optimal operating time to ensure the purification effect. The concentration sensing unit is a sensor used to detect the concentration of pollutants in the air in real time. It can convert the concentration signal into an electrical signal and transmit it to the control system. The constant sampling frequency refers to the fixed time interval at which the concentration sensing unit collects pollutant concentration data to ensure the regularity and continuity of data collection.

[0089] In this embodiment, firstly, an air handling unit is installed within the ventilation system in step S1031. Based on the air supply volume and the pollutant decomposition capacity per unit time of the air handling unit, the theoretical total decomposition amount of key target pollutants within a single treatment cycle is calculated. Specifically, firstly, according to the duct structure and spatial layout of the ventilation system, the air handling unit is installed on the main duct between the fan and the workshop air outlet to ensure that all air flowing through the ventilation system can enter the unit; then, its pollutant decomposition capacity per unit time is retrieved from the unit's technical documentation, combined with the air supply volume determined in S102, and calculated using the formula... Calculate the total theoretical decomposition, where, This represents the theoretical total amount of key target pollutants decomposed within a single treatment cycle. Represents air volume. This represents the initially set processing time. This represents the initial mass concentration of pollutants at the inlet of the air handling unit. This represents the basic decomposition efficiency of the device. For example, the air volume determined in S102. The basic efficiency corresponding to the device's decomposition capacity per unit time Initial mass concentration of ammonia at the inlet Initial processing time Substituting into the formula, we can obtain .

[0090] Secondly, in step S1032, based on the theoretical total decomposition amount and combined with the actual decomposition efficiency curve of the key target pollutant within the air handling unit, the optimal operating time of the air handling unit is determined. Specifically, the process involves first retrieving the actual decomposition efficiency curve of the air handling unit for the current key target pollutant (such as ammonia) from the system database. The horizontal axis of this curve represents operating time, and the vertical axis represents decomposition efficiency. Then, based on the theoretical total decomposition amount calculated in step S1031... To deduce the minimum decomposition efficiency required—if the total amount of imported pollutants actually processed is Then it is necessary to ensure ( (For the minimum required decomposition efficiency), find the efficiency curve that can be stably maintained. The minimum value within the specified operating time range is taken as the optimal operating time. For example, the total amount of imported pollutants... ,need Known ,but By examining the efficiency curve, it was found that the decomposition efficiency stabilized above 0.8 when the running time increased from 0.8h to 1.2h. Therefore, the optimal working time was determined to be 0.8h.

[0091] Next, in step S1033, based on the optimal operating time, the air handling unit is activated when the airflow corresponding to the supply air volume passes through, ensuring that the flowing air remains inside the unit for a sufficient time to ensure that key target pollutants are effectively decomposed. Specifically, the operation involves first determining the supply air volume... and the internal volume of the air handling unit Through formula Calculate the theoretical residence time of the airflow within the device, where, Represents the theoretical dwell time. Represents internal volume. This represents the air supply volume. If this time is less than the optimal operating time, the cross-sectional area of ​​the airflow channel is reduced by adjusting the angle of the internal guide vanes, thereby decreasing the airflow velocity and extending the residence time. The optimal operating time is then set as the device's start-up duration. When the ventilation system fan starts and airflow begins to pass through the device, the control system simultaneously triggers the air handling unit to start, ensuring that the device begins operating at its optimal operating time as airflow enters it. For example, the internal volume of the device... air volume Initial theoretical dwell time The operating time is less than 0.8 hours; adjust the angle of the guide vane to reduce the cross-sectional area of ​​the airflow channel, and maintain the air volume. When the airflow velocity decreases, the theoretical residence time is extended to 0.8 hours; when the fan starts to deliver air, the device starts synchronously and runs continuously for 0.8 hours.

[0092] Finally, in step S1034, concentration sensing units are installed at the air inlet and air outlet of the air handling unit to synchronously collect pollutant concentrations at both locations at a constant sampling frequency. Specifically, identical concentration sensing units are installed 1 meter upstream of the air inlet and 1 meter downstream of the air outlet to avoid the influence of airflow disturbances on the detection results. The sampling frequency of both sensing units is set to 1 time / minute to ensure synchronous data acquisition. The sensing units convert the collected concentration data into digital signals and transmit them in real-time to the control center of the ventilation system via a data cable. The control center stores and displays the inlet and outlet concentrations in real-time. For example, model A concentration sensors are installed at the air inlet and outlet of the ammonia handling unit, with a sampling frequency set to 1 time / minute. When airflow passes through the unit, the inlet sensor collects the inlet ammonia concentration every minute, and the outlet sensor synchronously collects the outlet ammonia concentration (e.g., 5 ppm), transmitting the data to the control center in real-time.

[0093] In practical applications, taking the ventilation system of a factory-scale shrimp farming workshop of Brand A as an example, this workshop continuously generates pollutants such as ammonia and hydrogen sulfide in the air due to high-density farming and feed residue decomposition. The air supply volume of the ventilation system was determined to be 9000 m³ / h by S102. A Model B photocatalytic oxidation air treatment device was installed in the main duct of the ventilation system. The device has a basic efficiency of 0.78 corresponding to its ammonia decomposition capacity per unit time, and the initial ammonia concentration at the inlet is 0.007 kg / m³. Calculations using S1031 show that with a preliminary processing time of 1 hour, the theoretical total decomposition volume is 9000 × 1 × 0.007 × 0.78 = 49.14 kg. Based on the actual ammonia decomposition efficiency curve of the device, the optimal operating time is determined to be 1 hour. By adjusting the internal guide vanes, the airflow residence time within the device is ensured to reach 1 hour. When the blower starts supplying gas, the device starts and runs synchronously for 1 hour. Simultaneously, C-type concentration sensing units are installed at the inlet and outlet of the device to synchronously collect concentration data at a frequency of once per minute. The data is transmitted to the workshop control center in real time, allowing staff to monitor the ammonia purification status and promptly understand the device's operational effectiveness.

[0094] In the overall scheme of step S103 above, the theoretical total decomposition amount is calculated by combining the air volume and the device's decomposition capacity, and the optimal working time is determined based on the actual decomposition efficiency curve to ensure that the device operates in the high-efficiency range, which not only ensures the effective decomposition of pollutants but also avoids energy waste. By adjusting the airflow residence time to match the optimal working time, the pollutant decomposition effect is further improved. Concentration data is collected simultaneously at the device inlet and outlet, which can reflect the purification effect of the device in real time and provide data support for subsequent system optimization and adjustment. The entire process realizes the precise setting of the air handling device's operating parameters and the real-time monitoring of the purification effect, effectively improving the ventilation system's deep purification capability for key target pollutants, adapting to the air quality control needs of factory-style aquaculture workshops, and providing a good environment for the healthy growth of farmed organisms.

[0095] S104. By analyzing the changing trend of the pollutant concentration over a continuous time period, an air quality deterioration index is predicted. The air quality deterioration index is determined by curve fitting of the inlet and outlet concentration sequences to obtain their respective time-varying functions, and by calculating the derivative of the function at a preset future time point. Furthermore, the current overall purification efficiency is calculated based on the pollutant concentration. The overall purification efficiency is based on the inlet and outlet concentration sequences at the same time point, and the instantaneous purification efficiency is calculated and averaged over all instantaneous purification efficiencies within the continuous time period.

[0096] Among them, the continuous time period refers to a fixed duration used to analyze changes in pollutant concentration, traced back from the current time; pollutant concentration refers to the content of key target pollutants collected at the inlet and outlet of the air handling unit in S103; the air quality deterioration index is a quantitative indicator that reflects the possibility of future air quality deterioration, predicted by analyzing the trend of pollutant concentration changes; and the overall purification efficiency refers to the average purification effect of the air handling unit on key target pollutants within the continuous time period, used to measure the long-term purification capacity of the unit.

[0097] Optionally, step S104 may specifically include the following steps:

[0098] S1041. Select multiple consecutive and equally spaced time nodes, and based on the pollutant concentrations at the inlet and outlet of the air treatment device at each time node, form an inlet concentration sequence and an outlet concentration sequence.

[0099] S1042. Perform curve fitting on the inlet concentration sequence and the outlet concentration sequence respectively to obtain their respective change functions over time, and calculate the derivative of the change function at a preset future time point to predict the air quality deterioration index.

[0100] Step S1042 may specifically include the following process: selecting continuous time points and corresponding concentration values ​​in the inlet concentration sequence and the outlet concentration sequence as fitting data points; based on the fitting data points, generating a function representing the change of concentration over time for the inlet concentration sequence and the outlet concentration sequence respectively using the minimum error approximation method; selecting the same preset future time point for the function, and calculating the instantaneous rate of change of the function at the preset future time point as the derivative value; obtaining the terminal concentration values ​​of the inlet concentration sequence and the outlet concentration sequence at the current time point as the current concentration; inputting the derivative value and the current concentration into a preset weighted calculation model, and generating an air quality deterioration index by linearly combining different parameters by assigning weight factors.

[0101] S1043. Based on the inlet concentration sequence and the outlet concentration sequence at the same time point, calculate the instantaneous purification efficiency, and perform an arithmetic average of all the instantaneous purification efficiencies within a continuous time period to obtain the overall purification efficiency.

[0102] In the above steps, multiple equally spaced time points refer to time points selected at fixed time intervals within a continuous time period for extracting concentration data; the inlet concentration sequence refers to a data set formed by arranging the inlet pollutant concentrations of the air handling unit corresponding to each time point in chronological order; the outlet concentration sequence refers to a data set formed by arranging the outlet pollutant concentrations of the air handling unit corresponding to each time point in chronological order; curve fitting refers to constructing a curve using mathematical methods so that the curve best matches the distribution trend of the data points in the concentration sequence; the change function refers to the mathematical expression obtained through curve fitting that describes the law of concentration change over time; and the preset future time point refers to a pre-set future time point used to predict the trend of concentration change. A specific time; the derivative value refers to the instantaneous rate of change of the function at a preset future time point, which can reflect the speed and direction of concentration change; the minimum error approximation method is a mathematical modeling method that minimizes the sum of errors between the calculated function value and the actual concentration data point by adjusting the function parameters; the weighted calculation model is a model that assigns different weights to different influencing parameters and calculates the target index through linear combination; the weighting factor is a coefficient used in the weighted calculation model to reflect the degree of influence of each parameter on the target index; instantaneous purification efficiency refers to the purification effect of the air handling device on pollutants at a specific time point; the arithmetic mean is the average value obtained by adding up all instantaneous purification efficiencies over a continuous time period and dividing by the number of instantaneous purification efficiencies.

[0103] In this embodiment, firstly, multiple consecutive and equally spaced time nodes are selected in step S1041. Based on the pollutant concentrations at the inlet and outlet of the air handling device at each time node, an inlet concentration sequence and an outlet concentration sequence are formed. Specifically, the continuous time period is first set to 1 hour, and the time interval is 10 minutes, thereby determining 7 equally spaced time nodes, namely 60 minutes, 50 minutes, 40 minutes, 30 minutes, 20 minutes, 10 minutes before the current time, and the current time. Then, the inlet ammonia concentration and outlet ammonia concentration of the air handling device corresponding to each time node are extracted from the data stored in the concentration sensing unit in S103. Finally, the inlet concentrations are arranged in chronological order to form an inlet concentration sequence, and the outlet concentrations are arranged in the same chronological order to form an outlet concentration sequence. For example, the inlet ammonia concentrations at the seven extracted time points were 28ppm, 27ppm, 29ppm, 30ppm, 31ppm, 32ppm, and 33ppm, respectively. Arranging them in chronological order yielded the inlet concentration sequence [28, 27, 29, 30, 31, 32, 33]. The outlet ammonia concentrations were 6ppm, 5.8ppm, 6.2ppm, 6.5ppm, 6.8ppm, 7ppm, and 7.2ppm, respectively. Arranging them in the same chronological order yielded the outlet concentration sequence [6, 5.8, 6.2, 6.5, 6.8, 7, 7.2].

[0104] Secondly, in step S1042, curve fitting is performed on the inlet concentration sequence and the outlet concentration sequence to obtain their respective time-varying functions, and the derivative of the changing functions at preset future time points is calculated to predict the air quality deterioration index.

[0105] This step is specifically divided into four parts:

[0106] First, select consecutive time points and corresponding concentration values ​​from the import concentration sequence and the export concentration sequence as fitting data points. For example, use the seven time points and corresponding concentration values ​​of the import concentration sequence as import fitting data points, and use the same time points and corresponding concentration values ​​of the export concentration sequence as export fitting data points.

[0107] Second, based on the fitted data points, variation functions are generated for the inlet and outlet concentration sequences using a minimum error approximation method (such as the least squares method). For example, a quadratic function is fitted to the inlet fitted data points to obtain the inlet concentration variation function. ,in For time, To obtain the export concentration change function, a linear function is fitted to the export data points, representing the import concentration. ,in For time, The outlet concentration was calculated using the least squares method. .

[0108] Third, select a preset future time point as 10 minutes after the current time (i.e. (minutes), calculate the derivatives of the two change functions at that time point respectively. The derivative of the inlet concentration change function is: Substitute The derivative value is obtained as The derivative of the function of change in outlet concentration over minutes is: That is, the derivative value is 0.02 (ppm / min).

[0109] Fourth, obtain the current time point. The terminal concentration values ​​of the inlet concentration sequence (33 ppm) and outlet concentration sequence (7.2 ppm) are used as the current concentrations. The derivative values ​​(inlet 0.22, outlet 0.02) and the current concentrations (inlet 33, outlet 7.2) are input into a preset weighted calculation model. The weighting factors for the inlet derivative value and current outlet concentration are set to 0.4, 0.3, 0.1, and 0.2 respectively. The formula is then used to calculate the final concentration. ,in, The air quality deterioration index, Using these as weighting factors, the air quality deterioration index is obtained. .

[0110] Next, in step S1043, based on the inlet and outlet concentration sequences at the same time point, the instantaneous purification efficiency is calculated, and the overall purification efficiency is obtained by arithmetically averaging all instantaneous purification efficiencies over a continuous time period. Specifically, first, according to the formula... ,in, For the first Instantaneous purification efficiency at each time point For the first Import concentration at each time point, For the first The outlet concentration at each time point is calculated, and the instantaneous purification efficiency at each time point is calculated. For example, the first time point... minute, ,but The second time point minute, ,but The instantaneous purification efficiency at 7 time points is calculated in this way. Then, the 7 instantaneous purification efficiencies are added together and divided by 7 to get the arithmetic mean. This mean is the overall purification efficiency. For example, the sum of the 7 instantaneous purification efficiencies is 5.2. Dividing by 7 gives the overall purification efficiency.

[0111] In practical applications, taking the factory-style salmon farming workshop of Brand A as an example, the air handling unit of the ventilation system in this workshop is mainly for purifying ammonia in the air. The concentration sensing unit in S103 collects the concentration of ammonia at the inlet and outlet at a frequency of 1 time / minute. During S104 execution, seven time points are selected, each lasting one hour and spaced 10 minutes apart. The inlet ammonia concentration sequence [29,28,30,31,32,33,34] and the outlet ammonia concentration sequence [6.1,6,6.3,6.6,6.9,7.1,7.3] for each node are extracted from the control system database. The inlet concentration sequence is fitted with a quadratic function using the least squares method to obtain the change function, and the outlet concentration sequence is fitted with a linear function to obtain the change function. Ten minutes after the current time is selected as the preset future time point. After calculating the derivative value, the air quality deterioration index is obtained by combining the current concentration (34 ppm for inlet and 7.3 ppm for outlet) and the preset weighting factor through a weighted calculation model. Then, the instantaneous purification efficiency of each time point is calculated according to the formula. The arithmetic mean of the seven instantaneous purification efficiencies is taken to obtain the overall purification efficiency. The staff can use this index and efficiency to judge the future air quality trend and the long-term purification effect of the device.

[0112] In the overall scheme of step S104 above, by selecting equally spaced time nodes to form a concentration sequence, the analysis bias caused by irregular data collection is avoided; by using curve fitting and derivative calculation to predict the air quality deterioration index, the future air quality change trend can be perceived in advance, breaking the limitation of relying solely on the current concentration for judgment; by calculating the instantaneous purification efficiency and taking the arithmetic average to obtain the overall purification efficiency, the purification capacity of the device over a continuous time period can be comprehensively reflected, avoiding the randomness of efficiency at a single time point; the entire process realizes the transformation from concentration data to trend prediction and efficiency evaluation, providing a scientific basis for the subsequent dynamic adjustment of the ventilation system, helping to avoid the risk of air quality deterioration in advance, and ensuring that the air handling device always maintains a stable purification effect, adapting to the air quality control needs of factory-style aquaculture workshops.

[0113] S105. When the overall purification efficiency deviates from the preset range or the air quality deterioration index exceeds the safety threshold, the fan operating parameters and the operating status of the air handling device are dynamically adjusted, and the adjusted operating status and operating status are fed back to the ventilation system to achieve adaptive optimization of the ventilation system.

[0114] Among them, overall purification efficiency refers to the average purification effect of the air handling unit over a continuous period of time calculated previously; preset range refers to the reasonable range of overall purification efficiency pre-set according to the air quality requirements of the aquaculture workshop, with the upper limit being the critical value that excessive purification effect may cause energy waste, and the lower limit being the minimum critical value to ensure effective removal of pollutants; air quality deterioration index refers to the quantitative indicator in S104 that predicts the possibility of future air quality deterioration; safety threshold refers to the pre-set critical value of air quality deterioration index, exceeding which means that future air quality may threaten the survival of farmed organisms; fan operating parameters mainly refer to the fan speed, which directly affects the air volume delivered by the ventilation system; the operating status of the air handling unit includes parameters such as operating power and working cycle, which determine the pollutant decomposition capacity of the unit; adaptive optimization refers to the process by which the ventilation system dynamically adjusts its operating parameters according to the purification efficiency and deterioration index to always maintain the best operating state.

[0115] Optionally, such as Figure 2 As shown, step S105 may specifically include the following steps:

[0116] S1051. Compare the overall purification efficiency with the preset upper and lower limits of purification efficiency, and compare the air quality deterioration index with the preset safety threshold.

[0117] S1052. If the overall purification efficiency is higher than the upper limit of the preset purification efficiency or lower than the lower limit, or the air quality deterioration index is higher than the safety threshold, an adjustment command is generated.

[0118] S1053. According to the adjustment instruction, increase or decrease the speed of the fan by a preset step size, and simultaneously adjust the operating power or working cycle of the air handling device.

[0119] Step S1053 may specifically include the following process: by parsing the adjustment command, extracting the fan speed adjustment direction identifier and the air handling unit's operating parameter adjustment guidance identifier; determining the fan speed adjustment trend based on the speed adjustment direction identifier, and querying the preset step size mapping relationship in conjunction with the current system status parameters to generate the fan speed adjustment step size value; performing arithmetic operations on the current fan speed using the adjustment step size value to generate a new fan speed command; selecting to adjust the air handling unit's operating power or working cycle based on the operating parameter adjustment guidance identifier; if adjusting the operating power, calculating the target power value based on the change ratio of the air supply volume according to a linear relationship; if adjusting the working cycle, recalculating the start-stop time ratio based on the offset of the overall purification efficiency from the preset range; and synchronously issuing the new fan speed command and the target power value or the start-stop time ratio to the actuator of the ventilation system to ensure that the state changes of the fan and the air handling unit remain coordinated.

[0120] S1054. The adjusted fan speed parameters and the air handling unit operating status parameters are fed back to the ventilation system control center in real time to update the system operating benchmark.

[0121] In the above steps, the preset upper and lower limits of purification efficiency are two boundary values ​​of the preset range, corresponding to the reasonable highest and lowest values ​​of purification efficiency, respectively; the adjustment command is a command signal generated by the system to adjust the operating status of the equipment when the purification efficiency deviates from the preset range or the deterioration index exceeds the threshold; the preset step size refers to the fixed increment or decrement of the fan speed adjustment, set according to the equipment performance and system response characteristics; the speed adjustment direction indicator is a symbol or code in the adjustment command indicating an increase or decrease in fan speed; the operating parameter adjustment guide indicator is a symbol or code in the adjustment command indicating an adjustment of the operating power or working cycle of the air handling unit; system status parameters include the current fan speed, air volume, and unit operating power, reflecting the current operating status of the system. Data; Step size mapping relationship refers to the correspondence rule table between system state parameters and fan speed adjustment step size values, generated by experimental calibration; Adjustment step size value refers to the specific value of fan speed adjustment determined according to the step size mapping relationship; New fan speed command refers to the speed command that the fan needs to execute after adjustment; Target power value refers to the power value that the air handling unit needs to achieve after adjustment; Start-up / stop time ratio refers to the ratio of the start-up time of the air handling unit to the total time of the cycle; Actuator refers to the component that receives control commands and drives the fan and air handling unit to change their operating status, such as frequency converters, relays, etc.; System operating benchmark refers to the parameter standard when the ventilation system is operating normally, used as the basis for comparison and adjustment of subsequent operating status.

[0122] In this embodiment, step S1051 first compares the overall purification efficiency with preset upper and lower limits of purification efficiency, and simultaneously compares the air quality deterioration index with preset safety thresholds. Specifically, the overall purification efficiency calculated in step S104, as well as the preset upper and lower limits of purification efficiency, are retrieved from the ventilation system control center; then, the air quality deterioration index predicted in step S104 and the preset safety thresholds are retrieved; finally, the comparison module of the control system performs numerical comparisons between the overall purification efficiency and preset ranges, and between the air quality deterioration index and safety thresholds. For example, if the retrieved overall purification efficiency is a certain value, the preset upper limit of purification efficiency is A, and the lower limit is B, the overall purification efficiency is lower than the lower limit B; the air quality deterioration index is C, and the preset safety threshold is D, C is higher than D.

[0123] Secondly, if the overall purification efficiency is higher than the upper limit or lower than the preset lower limit in step S1052, or if the air quality deterioration index is higher than the safety threshold, an adjustment command is generated. Specifically, the control system's judgment module receives the comparison result from S1051. If the "deviation" or "exceedance" condition is met, the command generation module is triggered. Based on the specific anomaly type, such as excessively low purification efficiency or an excessively high deterioration index, the command generation module generates an adjustment command containing a speed adjustment direction indicator and an operating parameter adjustment guide indicator. The speed adjustment direction indicator uses "+" to indicate increase and "-" to indicate decrease, while the operating parameter adjustment guide indicator uses "P" to indicate adjustment power and "T" to indicate adjustment period. For example, if the overall purification efficiency is lower than the lower limit and the deterioration index is higher than the threshold, an adjustment command is generated, containing the speed adjustment direction indicator "+" and the operating parameter adjustment guide indicator "P".

[0124] Next, in step S1053, based on the adjustment command, the fan speed is increased or decreased by a preset step size, and the operating power or working cycle of the air handling unit is adjusted simultaneously. This step is specifically divided into four parts: First, parsing the adjustment command and extracting the identifiers, for example, extracting the speed adjustment direction identifier "+" to increase the speed and the operating parameter adjustment guide identifier "P" to adjust the power from the above command; Second, determining the fan speed adjustment step size value, based on the "+" identifier to clarify that the adjustment trend is to increase, retrieving the current system status parameters such as the current fan speed of 1000 r / min and the air volume of 8000 m³ / h, and querying the step size mapping table to find that the adjustment step size value under the current state is 100 r / min; Third, generating a new fan speed command, using the current speed of 1000 r / min. Adding a step size of 100 r / min to the speed command, we get a new speed command of 1100 r / min. The air volume increases proportionally to 9000 m³ / h as the speed increases. Fourth, we calculate the target power value of the air handling unit. Since the guide mark is "P", we perform a linear calculation based on the change in air volume and the current power to get the target power value of 800 × 1.125 = 900 W. Finally, we send the new speed command and the target power value to the fan inverter and the unit power regulator simultaneously to ensure that the increase in fan speed and the increase in unit power are coordinated and to avoid a mismatch between airflow and purification capacity.

[0125] Finally, in step S1054, the adjusted fan speed parameters and air handling unit operating status parameters are fed back to the ventilation system control center in real time to update the system operating baseline. Specifically, after completing the status adjustment, the actuator feeds back the actual adjusted fan speed and air handling unit operating power to the control center via the data transmission module. The control center's database receives and stores these parameters, updating them as the new system operating baseline to replace the original baseline parameters for the next round of parameter comparison and adjustment. For example, the control center updates the original baseline speed of 1000 r / min and baseline power of 800 W to 1100 r / min and 900 W.

[0126] In practical applications, taking a factory-style sea bass farming workshop of Brand A as an example, the ventilation system of this workshop has a preset upper limit of purification efficiency of E and a lower limit of F, and an air quality deterioration index safety threshold of G. In S1051, a comparison reveals that the overall purification efficiency is lower than F, and the air quality deterioration index is higher than G. In S1052, an adjustment command is generated, including a speed adjustment direction indicator "+" and an operating parameter adjustment guide indicator "T". In S1053, after parsing the command, the step size mapping relationship is queried based on the current fan speed of 1200 r / min to obtain a step size value of 150 r / min, resulting in a new speed command of 1350 r / min. Combining the overall purification efficiency with the offset from the lower limit F, the start-stop time ratio of the air handling unit is adjusted from 50% to 70%, and this is simultaneously sent to the actuator. In S1054, the speed of 1350 r / min and the 70% start-stop time ratio are fed back to the control center, updating the system operating baseline. Staff can view the adjusted parameters and system operating status through the control center.

[0127] In the overall scheme of step S105 above, system malfunctions are accurately identified through dual-parameter comparison, avoiding the limitations of single-indicator judgment; targeted adjustment instructions are generated based on the malfunction type, providing a clear direction for subsequent precise control; the fan speed is adjusted using a preset step size and simultaneously adapted to the device's operating parameters, ensuring both stable adjustment to protect the equipment and achieving coordinated matching of airflow and purification capacity; the system operating baseline is updated in a timely manner, providing an accurate reference for the next round of control. The entire process achieves adaptive optimization of the ventilation system, enabling rapid response to changes in air quality, maintaining stable purification effects, avoiding energy waste, providing a continuously suitable air environment for factory-scale aquaculture workshops, and ensuring the healthy growth of farmed organisms.

[0128] The following is a complete embodiment for steps S101 to S105:

[0129] like Figure 3As shown, in the factory-scale shrimp farming workshop of Brand A, the workshop volume is 2000m³, and the stocking density is 150 shrimp / m². Pollutants such as ammonia and hydrogen sulfide need to be controlled. First, three Type B infrared gas analyzers are installed in the workshop, detecting pollutants every 2 minutes and outputting characteristic wavelength spectral intensity data, which is transmitted to the control center. Next, the control center uses weighted fusion calculations to determine that the ammonia concentration is 26ppm and the hydrogen sulfide concentration is 8ppm. Combining the safety limits and the concentration change rate, a reference value of 8000m³ / h for ventilation parameters is determined, and the speed of Type C fans is adjusted to 1420r / min to stabilize the airflow. Then, a Type D photocatalytic oxidation device is installed in the air duct. Based on the airflow, the optimal operating time is determined to be 1 hour. The guide vanes are adjusted to extend the airflow residence time, and a Type E sensor collects the concentration at the inlet and outlet of the device every minute. Concentration data from seven time points within one hour were selected, and an air quality deterioration index was generated through curve fitting and derivative calculation. The index for ammonia was 10.8, and for hydrogen sulfide, it was 3.2. The overall purification efficiency was calculated to be 80.5% for ammonia and 80.8% for hydrogen sulfide. Finally, because the ammonia deterioration index was close to the threshold, the control center generated an adjustment command, increasing the fan speed to 1470 r / min and adjusting the device power to 850 W, updating the system baseline and completing the optimization.

[0130] The adaptive optimization method for ventilation systems based on air quality in factory farming workshops provided in this application achieves multi-dimensional value through a closed-loop process: precisely controlling pollutant concentrations within a safe range to prevent diseases or stunted growth in farmed organisms; predicting air quality trends in advance to break away from passive treatment models and mitigate the risk of sudden environmental degradation; dynamically adjusting equipment parameters to reduce energy waste and balance purification effects with costs; and completing the entire process automatically without human intervention, reducing operation and maintenance costs. It adapts to the large-scale, high-density needs of factory farming, providing reliable support for improving farming efficiency and environmental management.

[0131] Figure 4 This application provides a schematic diagram of a specific implementation of an adaptive optimization system for a ventilation system based on air quality in a factory-scale livestock farm, as illustrated in the embodiments of this application. Figure 4 The system may include:

[0132] Analysis module 41 is used to perform real-time analysis of gases in factory farming workshops using an infrared spectroscopy gas analyzer to obtain spectral intensity data of key target pollutants at characteristic absorption wavelengths.

[0133] The control module 42 is used to analyze the precise concentration of key target pollutants through the spectral intensity data, calculate the required ventilation parameter reference value based on the precise concentration, and adjust the air supply volume by controlling the operating parameters of the fan according to the ventilation parameter reference value.

[0134] The decomposition module 43 is used to install an air handling device in the ventilation system and determine the optimal operating time of the air handling device based on the air supply volume. When air flows through the air handling device, key target pollutants are decomposed according to the optimal operating time, and the pollutant concentration at the inlet and outlet of the air handling device is continuously monitored.

[0135] The prediction module 44 is used to predict the air quality deterioration index by analyzing the changing trend of the pollutant concentration over a continuous time period. The air quality deterioration index is determined by curve fitting of the inlet concentration sequence and the outlet concentration sequence to obtain their respective time-varying functions, and calculating the derivative of the changing functions at a preset future time point. The module also calculates the current overall purification efficiency based on the pollutant concentration. The overall purification efficiency is calculated based on the inlet concentration sequence and the outlet concentration sequence at the same time point, and the instantaneous purification efficiency is calculated and obtained by arithmetic averaging of all the instantaneous purification efficiencies over a continuous time period.

[0136] The feedback module 45 is used to dynamically adjust the fan operating parameters and the operating status of the air handling device when the overall purification efficiency deviates from the preset range or the air quality deterioration index exceeds the safety threshold, and to feed back the adjusted operating status and operating status to the ventilation system in order to achieve adaptive optimization of the ventilation system.

[0137] The adaptive optimization system for ventilation systems based on air quality in factory farms, as described in this application, is used to implement the aforementioned adaptive optimization method for ventilation systems based on air quality in factory farms. Therefore, the specific implementation of the adaptive optimization system for ventilation systems based on air quality in factory farms can be found in the embodiment section of the adaptive optimization method for ventilation systems based on air quality in factory farms mentioned above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0138] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the adaptive optimization method for a ventilation system based on air quality in a factory-scale aquaculture workshop as described above.

[0139] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described adaptive optimization methods for ventilation systems based on air quality in factory farming workshops.

[0140] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0141] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the adaptive optimization method for ventilation systems based on air quality in factory-scale aquaculture workshops.

[0142] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0143] The above provides a detailed description of the ventilation system optimization method and system based on air quality in a factory-scale aquaculture workshop provided by this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. An adaptive optimization method for ventilation systems based on air quality in factory-style aquaculture workshops, characterized in that, include: Infrared gas spectrometers are used to analyze gases in factory farming workshops in real time to obtain spectral intensity data of key target pollutants at characteristic absorption wavelengths. The precise concentration of key target pollutants is determined by analyzing the spectral intensity data, and the required ventilation parameter reference values ​​are calculated based on the precise concentration. The air supply volume is adjusted by controlling the operating parameters of the fan to the ventilation parameter reference values. An air handling unit is installed in the ventilation system, and the optimal operating time of the air handling unit is determined based on the air supply volume. When air flows through the air handling unit, key target pollutants are decomposed according to the optimal operating time, and the pollutant concentration at the inlet and outlet of the air handling unit is continuously monitored. By analyzing the changing trends of pollutant concentrations over a continuous time period, an air quality deterioration index is predicted. This index is determined by curve fitting of the inlet and outlet concentration sequences to obtain their respective time-varying functions, and by calculating the derivative of these functions at a predetermined future time point. Furthermore, the overall purification efficiency is calculated based on the pollutant concentrations. This overall purification efficiency is based on the inlet and outlet concentration sequences at the same time point, and the instantaneous purification efficiency is calculated and averaged over all instantaneous purification efficiencies within the continuous time period. When the overall purification efficiency deviates from the preset range or the air quality deterioration index exceeds the safety threshold, the fan operating parameters and the operating status of the air handling unit are dynamically adjusted, and the adjusted operating status and operating status are fed back to the ventilation system to achieve adaptive optimization of the ventilation system.

2. The method according to claim 1, characterized in that, When the overall purification efficiency deviates from the preset range or the air quality deterioration index exceeds the safety threshold, the fan operating parameters and the operating status of the air handling unit are dynamically adjusted, and the adjusted operating status and operating status are fed back to the ventilation system to achieve adaptive optimization of the ventilation system, including: The overall purification efficiency is compared with the preset upper and lower limits of purification efficiency, and the air quality deterioration index is compared with the preset safety threshold. If the overall purification efficiency is higher than the upper limit of the preset purification efficiency or lower than the lower limit, or if the air quality deterioration index is higher than the safety threshold, an adjustment command is generated. According to the adjustment command, the fan speed is increased or decreased by a preset step size, and the operating power or working cycle of the air handling unit is adjusted synchronously. The adjusted fan speed parameters and the air handling unit's operating status parameters are fed back to the ventilation system's control center in real time to update the system's operating baseline.

3. The method according to claim 2, characterized in that, According to the adjustment command, the fan speed is increased or decreased by a preset step size, and the operating power or working cycle of the air handling unit is adjusted synchronously, including: By parsing the adjustment command, the speed adjustment direction indicator for the fan and the operating parameter adjustment guide indicator for the air handling unit are extracted; The adjustment trend of the fan speed is determined based on the speed adjustment direction indicator, and the preset step size mapping relationship is queried in combination with the current system status parameters to generate the adjustment step size value of the fan speed. The current fan speed is calculated using the adjustment step value to generate a new fan speed command. Based on the operating parameter adjustment guide, select to adjust the operating power or working cycle of the air handling device. If the operating power is adjusted, the target power value is calculated linearly based on the change ratio of the air volume. If the working cycle is adjusted, the start-stop time ratio is recalculated based on the offset of the overall purification efficiency from the preset range. The new fan speed command, along with the target power value or the start-stop time ratio, is simultaneously sent to the actuator of the ventilation system to ensure that the state changes of the fan and the air handling unit remain coordinated.

4. The method according to claim 1, characterized in that, By analyzing the changing trends of pollutant concentrations over a continuous period, an air quality deterioration index is predicted, and the current overall purification efficiency is calculated based on the pollutant concentrations, including: Multiple consecutive and equally spaced time points are selected, and based on the pollutant concentrations at the inlet and outlet of the air treatment device at each time point, an inlet concentration sequence and an outlet concentration sequence are formed. Curve fitting is performed on the inlet concentration sequence and the outlet concentration sequence to obtain their respective time-varying functions, and the derivative of the changing functions at a preset future time point is calculated to predict the air quality deterioration index. Based on the inlet concentration sequence and the outlet concentration sequence at the same time point, the instantaneous purification efficiency is calculated, and the arithmetic average of all the instantaneous purification efficiencies within a continuous time period is performed to obtain the overall purification efficiency.

5. The method according to claim 4, characterized in that, Curve fitting is performed on the inlet concentration sequence and the outlet concentration sequence to obtain their respective time-varying functions, and the derivative of the changing functions at preset future time points is calculated to predict the air quality deterioration index, including: Continuous time points and corresponding concentration values ​​in the inlet concentration sequence and the outlet concentration sequence are selected as fitting data points; Based on the fitted data points, the minimum error approximation method is used to generate functions representing the change of concentration over time for the inlet concentration sequence and the outlet concentration sequence, respectively. For the aforementioned function of change, select the same preset future time point, and calculate the instantaneous rate of change of the function of change at the preset future time point as the derivative value; Obtain the terminal concentration values ​​of the inlet and outlet concentration sequences at the current time point, and use them as the current concentration; The derivative value and the current concentration are input into a preset weighted calculation model, and an air quality deterioration index is generated by linearly combining them by assigning weight factors to different parameters.

6. The method according to claim 1, characterized in that, An air handling unit is installed within the ventilation system, and the optimal operating time of the air handling unit is determined based on the air supply volume. When air flows through the air handling unit, key target pollutants are decomposed according to the optimal operating time, and the pollutant concentrations at the inlet and outlet of the air handling unit are continuously monitored, including: An air handling unit is installed in the ventilation system. Based on the air volume and the pollutant decomposition capacity of the air handling unit per unit time, the theoretical total amount of the key target pollutants decomposed in a single treatment cycle is calculated. Based on the theoretical total decomposition amount and combined with the actual decomposition efficiency curve of the key target pollutants in the air handling unit, the optimal operating time of the air handling unit is determined. Based on the optimal operating time, the air handling device is activated when the airflow corresponding to the air volume passes through, so that the flowing air stays inside the device for a sufficient time to ensure that the key target pollutants are effectively decomposed. Concentration sensing units are installed at the air inlet and air outlet locations of the air handling device to synchronously collect pollutant concentrations at both locations at a constant sampling frequency.

7. The method according to claim 1, characterized in that, The precise concentration of key target pollutants is determined by analyzing the spectral intensity data, and the required reference values ​​for ventilation parameters are calculated based on these precise concentrations. The air supply volume is then adjusted by controlling the operating parameters of the fan to match these reference values, including: Using the spectral intensity data, combined with the correspondence between the absorption characteristics and concentration of the key target pollutant at multiple specific wavelengths, the precise concentration of the key target pollutant is generated through weighted fusion calculation; Based on the precise concentration, combined with the volume of the aquaculture workshop, the safe concentration limit of the key target pollutant, and the current rate of concentration change, the total amount of pollutants that need to be removed per unit time is calculated to determine the reference value of the ventilation parameters. Based on the ventilation parameter reference values ​​and combined with the fan performance curve, the fan operating parameters are determined. By adjusting the fan speed or blade angle, the actual air volume is made to match the ventilation parameter reference values. The deviation between the actual air supply volume and the reference value of the ventilation parameters is continuously compared. When the deviation exceeds the allowable range, the operating parameters of the fan are iteratively corrected until the actual air supply volume stabilizes near the reference value of the ventilation parameters.

8. An adaptive optimization system for ventilation systems based on air quality in factory-style aquaculture workshops, characterized in that, include: The analysis module is used to analyze the gas in the factory farming workshop in real time using an infrared gas spectrometer to obtain the spectral intensity data of key target pollutants at characteristic absorption wavelengths. The control module is used to analyze the spectral intensity data to obtain the precise concentration of key target pollutants, calculate the required ventilation parameter reference value based on the precise concentration, and adjust the air supply volume by controlling the operating parameters of the fan according to the ventilation parameter reference value. The decomposition module is used to install an air handling device in the ventilation system and determine the optimal operating time of the air handling device based on the air supply volume. When air flows through the air handling device, key target pollutants are decomposed according to the optimal operating time, and the pollutant concentration at the inlet and outlet of the air handling device is continuously monitored. The prediction module is used to predict the air quality deterioration index by analyzing the changing trend of the pollutant concentration over a continuous time period. The air quality deterioration index is determined by curve fitting of the inlet and outlet concentration sequences to obtain their respective time-varying functions, and calculating the derivative of the function at a preset future time point. The module also calculates the current overall purification efficiency based on the pollutant concentrations. The overall purification efficiency is calculated based on the inlet and outlet concentration sequences at the same time point, and the instantaneous purification efficiency is calculated and arithmetically averaged over all instantaneous purification efficiencies within the continuous time period. The feedback module is used to dynamically adjust the fan operating parameters and the operating status of the air handling unit when the overall purification efficiency deviates from the preset range or the air quality deterioration index exceeds the safety threshold, and to feed back the adjusted operating status and operating status to the ventilation system in order to achieve adaptive optimization of the ventilation system.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the adaptive optimization method for a ventilation system based on air quality in a factory-farmed aquaculture workshop as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the adaptive optimization method for a ventilation system based on air quality in a factory-scale aquaculture workshop as described in any one of claims 1 to 7.

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