Ventilation cabinet variable air volume control method based on laboratory environment monitoring

Abstract

The invention discloses a ventilation cabinet variable air volume control method based on laboratory environment monitoring, and relates to the technical field of control regulation, and the ventilation cabinet variable air volume control method comprises the following steps: arranging a sensor and collecting laboratory data; establishing a basic fluid dynamic model of laboratory gas flow and diffusion; predicting a gas diffusion path to obtain an optimized gas diffusion path; adjusting the laboratory wind speed according to the optimized gas diffusion path to obtain a wind speed adjusting result; performing gas concentration control according to the wind speed regulation result to obtain a wind speed control result; an alarm and response mechanism is realized based on a wind speed control result in combination with a safety threshold, and alarm information and wind speed data are obtained; performing wind speed optimization and fault diagnosis based on the alarm information and the wind speed data to obtain optimized data; and generating an environment safety report and adjusting a wind speed control strategy based on the optimization data. The safety, efficiency and energy-saving performance of the laboratory fume hood are improved, and a more effective gas control solution is provided for the laboratory environment.

Description

Technical Field

[0001] The present invention relates to the technical field of control and regulation, and specifically provides a variable air volume control method for fume hoods based on laboratory environment monitoring. Background Art

[0002] The variable air volume control method for laboratory fume hoods is of great significance in ensuring the safety of laboratory personnel, optimizing energy consumption, and improving the stability of the experimental environment. Traditional fume hood systems usually rely on fixed airspeed control or only manually adjust the airspeed to cope with the change of harmful gas concentration in the laboratory. However, this traditional control method often cannot respond in real time to dynamic factors such as gas leakage, temperature and humidity changes in the experimental environment, resulting in too high or too low gas concentration, affecting experimental safety and energy efficiency. Therefore, it is of great significance to develop an intelligent airspeed regulation and control method based on laboratory environment monitoring.

[0003] Currently, some technologies have tried to control the airspeed of fume hoods through gas concentration sensors, but most of these methods rely on simple threshold setting and single feedback control, and do not fully consider the complex influence of airspeed on gas diffusion and emission, lacking accurate modeling of fluid dynamics and real-time data correction. The existing technologies generally have the following two problems: 1. In the existing technology, the basic fluid dynamics model has deficiencies: Although certain fluid dynamics models have been adopted in the existing technology to simulate the diffusion and flow of gas in the fume hood, most models are too simplified and do not consider complex factors in the experimental environment (such as temperature and humidity, gas types, airspeed changes, etc.), thus unable to accurately describe the influence of airspeed adjustment on gas concentration. Existing models often ignore the non-linear relationship between air flow and gas diffusion, resulting in low prediction accuracy of gas concentration and inability to achieve accurate and real-time control; 2. In the existing technology, the Kalman filter correction has deficiencies: In the existing technology, the Kalman filter is used to correct the relationship between gas concentration and airspeed in the experimental environment, but most methods only rely on simple linear assumptions and ignore the influence of non-linear dynamic changes on the system state. In addition, the existing Kalman filter methods do not combine the calculation results of the basic fluid dynamics model and the gas diffusion model, resulting in inaccurate airspeed adjustment after correction and inability to dynamically adjust the changes of fluid and gas states. Summary of the Invention

[0004] In view of the deficiencies of the existing technology, the present invention provides a variable air volume control method for fume hoods based on laboratory environment monitoring to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a variable air volume control method for a fume hood based on laboratory environment monitoring, including the following steps: S1. Arrange sensors and collect laboratory data; S2. Establish a basic fluid dynamics model for gas flow and diffusion in the laboratory; S3. Predict the gas diffusion path to obtain an optimized gas diffusion path; S4. Adjust the laboratory wind speed according to the optimized gas diffusion path to obtain a wind speed adjustment result; S5. Control the gas concentration according to the wind speed adjustment result to obtain a wind speed control result; S6. Implement an alarm and response mechanism based on the wind speed control result combined with a safety threshold to obtain alarm information and wind speed data; S7. Perform wind speed optimization and fault diagnosis based on the alarm information and wind speed data to obtain optimized data; S8. Generate an environmental safety report and adjust the wind speed control strategy based on the optimized data.

[0006] To further optimize this technical solution, the types of sensors arranged in S1 and the laboratory data collected include: Temperature sensor, humidity sensor, pressure sensor, flow meter, gas sensor; Airflow velocity, gas concentration, temperature, humidity, fluid density, gas type, air pressure, fluid viscosity, and diffusion coefficient.

[0007] To further optimize this technical solution, the steps for establishing the fluid dynamics model in S2 include: Navier-Stokes Equation: This equation describes the flow law of air in space and time and is used to simulate the gas flow process; ; Where: : Fluid density, with the unit of kg / m³, affected by the laboratory environment temperature and pressure, and calculated based on the data obtained in S1; : Airflow velocity vector, with the unit of m / s, measured by a flow meter, such as an upper-spin ball gas flow meter; : Time rate of change of velocity; : Gas pressure gradient, with the unit of Pa / m, representing the rate of change of pressure in space, measured by a pressure sensor; :The fluid viscosity, in Pa·s, is obtained by referring to data according to the type of gas measured by the gas sensor; : The second-order spatial derivative of the velocity field; It represents: the external force, obtained by measurement, used to represent gravity and surface friction; Solve the equation to obtain: The velocity field v(x, t) of the air flow in the laboratory, representing the magnitude and direction of the air flow velocity at each point x and each moment t; The pressure field p(x, t), representing the magnitude of the gas pressure at each point x and each moment t; By analyzing the magnitude and direction of the air flow velocity and the magnitude of the gas pressure, assist in judging the diffusion path of the gas, and thus simulate the gas flow process; Diffusion-convection equation: This equation is used to describe the propagation and diffusion behavior of gas in a fluid; ; Wherein: : The gas concentration, in mg / m³, representing the gas concentration at the spatial position (x, y, z) and time t, measured by the gas sensor; : The gas concentration gradient, representing the change of the gas concentration in space, and its magnitude is equal to the change rate of the gas concentration; : The gas diffusion coefficient, in m 2 / s, estimated according to the type of gas measured by the gas sensor in combination with environmental factors; : The Laplacian operator of the gas concentration, describing the second-order change rate of the gas concentration in space, reflecting how the gas diffuses from a high-concentration region to a low-concentration region; Solve the equation to obtain the time and space distribution of the gas concentration in the laboratory. By observing the change of the gas concentration, determine the diffusion path of the gas. The greater the gas concentration gradient, the faster the gas diffuses.

[0008] Further optimize this technical solution. The steps of predicting the gas diffusion path in S3 include: Input real-time environmental data; Use the hydrodynamic model established in step S2 to predict the diffusion path and concentration distribution of the gas in the laboratory; Adopt the Kalman filtering technology to correct the prediction result of the model.

[0009] Further optimize this technical solution. The steps of using the Kalman filtering technology to correct the prediction result include: System state model: The system state model describes how the current system state is calculated; ; Where: : System state, including gas concentration, wind speed, fluid velocity, diffusion coefficient; : State transition matrix, representing the dynamic change from the previous state to the current state, calculated by combining the Navier-Stokes equation and the diffusion-convection equation with the gas diffusion process in the laboratory, reflecting the influence of air flow, gas diffusion, temperature and humidity in the laboratory on gas diffusion; : Control matrix, representing the influence of control input on the system state; : Wind speed adjustment amount, which is adjusted in real time by the wind speed control system in the laboratory and is affected by temperature and humidity; : Process noise, representing the uncertainty of the model and external disturbances, set according to the results; Observation equation: Apply the data of temperature, humidity and gas sensors to the update process of the system state; ; Where: : Observed value, obtaining real-time measured data through sensors; : Observation matrix, describing the relationship between system states such as gas concentration and wind speed and the observed value, adjusting the predicted observed value of gas concentration according to temperature and humidity changes; : Observation noise, representing the measurement error of the sensor, adjusted according to the actual situation; Kalman filter prediction step: Through the state transition matrix and the control matrix Make a prediction and output the system state at the next moment; ; Where: : Predicted system state, including gas concentration, wind speed, diffusion coefficient; : State estimate at the previous moment; Kalman filter update step: Fuse the prediction result with the actual observed data to obtain a more accurate state estimate; ; : Updated system state estimation; : Kalman gain, which is used to measure the weight of the difference between the predicted value and the observed value; : Residual, which is set according to the gas diffusion path calculated by the diffusion-convection equation and the influence of temperature and humidity on the diffusion coefficient, representing the difference between the actual observed value and the predicted value; Covariance update: It reflects the uncertainty of the system state estimation, and the credibility obtained by updating the covariance matrix gradually improves the prediction accuracy. Whether to continue optimization is determined according to the size of the credibility.

[0010] To further optimize this technical solution, the steps of adjusting the laboratory wind speed in S4 include: Real-time monitoring of environmental data through sensors; Calculating and judging the gas concentration according to the automatic control algorithm; Automatically adjusting the wind speed through the wind speed adjustment system according to the judgment result.

[0011] To further optimize this technical solution, the steps of controlling the gas concentration in S'5 include: Relationship between gas concentration and wind speed: The change of wind speed directly affects the diffusion rate of gas. The greater the wind speed, the lower the gas concentration. If the wind speed is too low, the gas will stay and the gas concentration will rise; ; Among them: : Gas concentration, obtained through a gas sensor; : Air flow rate, obtained through an anemometer; : Diffusion coefficient of gas, estimated according to the gas type measured by the gas sensor combined with environmental factors; Changing the air flow rate by adjusting the wind speed , changing the diffusion rate of gas concentration and regulating the gas concentration; Goal of wind speed adjustment: Real-time adjustment: Adjust the wind speed in real time according to the data monitored by the gas concentration sensor; Feedback control: Combining the gas concentration data provided by the sensor, using the feedback control algorithm to optimize the gas concentration in the laboratory by real-time monitoring and adjusting the wind speed; Control process: Sensor monitoring: Continuously monitor the gas concentration inside the fume hood through a gas concentration sensor, and the fluctuation of the gas concentration is fed back to the control system through the sensor signal; Concentration comparison with the safety threshold: Compare the real-time monitored gas concentration with the set safety concentration threshold to determine whether it exceeds the standard; Air velocity adjustment strategy: The system dynamically adjusts the air velocity according to the degree of deviation of the gas concentration from the safety threshold; Adjustment strategy: Fuzzy control: Use fuzzy control rules to adjust the air velocity according to the deviation between the gas concentration and the safety threshold and the rate of change of the error.

[0012] Further optimize this technical solution. The method for implementing the alarm and response mechanism in S6 includes: Set the safety threshold; judge the gas concentration to trigger an alarm and take emergency measures.

[0013] Further optimize this technical solution. The method for wind speed optimization and fault diagnosis in S7 includes: Through machine learning algorithms, learn the relationship between different gas leakage situations and air velocity adjustment, and optimize future air velocity adjustment strategies.

[0014] Further optimize this technical solution. The environmental safety report in S8 includes: Generate a detailed laboratory gas environment safety report based on the optimized data, including the gas concentration change trend, the historical record of air velocity adjustment, alarm information and emergency response measures, so that laboratory managers can optimize the air velocity adjustment strategy according to the detailed environmental safety report and improve the intelligent level of the system.

[0015] Compared with the prior art, the present invention provides a variable air volume control method for a fume hood based on laboratory environment monitoring, having the following beneficial effects: This variable air volume control method for a fume hood based on laboratory environment monitoring can accurately predict and adjust the changes in gas concentration and air velocity in the laboratory environment through data acquisition and monitoring control, combined with advanced basic fluid dynamics models (such as the Navier-Stokes equation and the diffusion-convection equation) and Kalman filter correction technology. Based on the accurate modeling of air flow and gas diffusion, this method dynamically adjusts the air velocity by real-time monitoring of environmental parameters (such as gas concentration, temperature and humidity, etc.) to ensure that the gas concentration is always maintained within the safe range. At the same time, using the Kalman filter to correct the real-time data can timely adjust the system state, optimize the air velocity adjustment strategy, and improve the response speed and accuracy of the system. Through this intelligent adjustment mechanism, the safety, efficiency and energy saving of the laboratory fume hood can be greatly improved, providing a more stable and reliable gas control solution for the laboratory environment. Description of the Drawings

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0017] Figure 1 It is a schematic flowchart of a variable air volume control method for a fume hood based on laboratory environment monitoring proposed by the present invention; Figure 2 It is a schematic flowchart of the basic fluid dynamics model of a variable air volume control method for a fume hood based on laboratory environment monitoring proposed by the present invention; Figure 3 It is a schematic flowchart of the Kalman filter model of a variable air volume control method for a fume hood based on laboratory environment monitoring proposed by the present invention; Figure 4 It is a schematic flowchart of the machine learning algorithm of a variable air volume control method for a fume hood based on laboratory environment monitoring proposed by the present invention. Specific Embodiments

[0018] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification.

[0019] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0020] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is separate or selectively mutually exclusive with other embodiments.

[0021] Embodiment 1: Referring to Figures 1 to 4 , it is the first embodiment of the present invention. This embodiment provides a variable air volume control method for a fume hood based on laboratory environment monitoring, including the following steps: S1. Arrange sensors and collect laboratory data.

[0022] In this embodiment, sensor devices are first arranged in key areas of the laboratory, and these sensors will continuously collect environmental data, providing real-time environmental data for subsequent hydrodynamic modeling, gas diffusion path prediction, and wind speed regulation.

[0023] Furthermore, the types of sensors arranged include: Temperature sensor: used to monitor the laboratory temperature; Humidity sensor: used to monitor the laboratory humidity; Pressure sensor: used to monitor the laboratory air pressure; Anemometer: used to monitor the air flow velocity in the laboratory; Gas sensor: used to monitor the gas concentration and gas types in the laboratory.

[0024] Furthermore, the laboratory data collected includes: Air flow velocity, gas concentration, temperature, humidity, fluid density, gas types, air pressure, fluid viscosity, and diffusion coefficient (the gas types in the laboratory are analyzed through gas sensors and experimental content, and the data of fluid viscosity and diffusion coefficient can be obtained by referring to laboratory materials).

[0025] S2. Establish a basic hydrodynamic model for gas flow and diffusion in the laboratory.

[0026] In this embodiment, the steps for establishing the hydrodynamic model include: Navier-Stokes Equation: This equation describes the flow law of air in space and time and is used to simulate gas flow and fluid mechanics processes; ; Where: : Fluid density, with the unit of kg / m³, affected by the laboratory environmental temperature and pressure, and calculated based on the data obtained in S1; : Air flow velocity vector, with the unit of m / s, measured by an anemometer such as an upper-spin ball gas anemometer; : The time rate of change of velocity; : Gas pressure gradient, with the unit of Pa / m, representing the rate of change of pressure in space, measured by a pressure sensor; : Fluid viscosity, with the unit of Pa·s, obtained by referring to materials according to the gas types measured by gas sensors; : The second-order spatial derivative of the velocity field; Representation: external force, obtained by measurement, used to represent gravity and surface friction; Solve the equation to obtain: The velocity field v(x, t) of the airflow in the laboratory, representing the magnitude and direction of the airflow velocity at each point x and each moment t; The pressure field p(x, t), representing the magnitude of the gas pressure at each point x and each moment t; By analyzing the magnitude and direction of the airflow velocity and the magnitude of the gas pressure, assist in judging the diffusion path of the gas, thereby simulating the gas flow process; Diffusion - convection equation: This equation combines the two processes of gas diffusion and convection, and is used to describe the propagation and diffusion behavior of gas in a fluid. In a laboratory environment, the diffusion and convection effects of gas jointly affect the gas concentration distribution; ; Wherein: : Gas concentration, with the unit of mg / m³, representing the gas concentration at spatial position (x, y, z) and time t, measured by a gas sensor; : Gas concentration gradient, representing the change of gas concentration in space, and its magnitude is equal to the change rate of gas concentration; : Gas diffusion coefficient, with the unit of m 2 / s, estimated based on the gas type measured by the gas sensor and combined with environmental factors; : Laplace operator of gas concentration, describing the second - order change rate of gas concentration in space, reflecting how gas diffuses from high - concentration regions to low - concentration regions; Solve the equation to obtain the time and space distribution of the gas concentration in the laboratory. This equation simulates the propagation process of gas under the action of wind speed and diffusion. By observing the change of gas concentration, determine the diffusion path of the gas. The greater the gas concentration gradient, the faster the gas diffuses. Combining the results of the Navier - Stokes equation, it is possible to understand in detail the interaction between airflow and gas diffusion, simulate the flow characteristics and diffusion path of gas, predict the possible leakage areas of gas and their concentration changes, optimize the wind speed adjustment, and ensure that gas emissions do not pose a hazard to personnel in the laboratory.

[0027] S3. Conduct gas diffusion path prediction to obtain the optimized gas diffusion path.

[0028] In this embodiment, the steps of conducting gas diffusion path prediction include: Input real - time environmental data: such as gas concentration, wind speed, fluid velocity, diffusion coefficient, etc.; Using the hydrodynamic model established in step S2, predict the diffusion path and concentration distribution of gas in the laboratory: According to the input data, simulate the flow characteristics and diffusion path of the gas, and predict the possible leakage areas and concentration changes of the gas. Adopt Kalman filtering technology to correct the prediction results of the model: Perform real-time correction on the prediction results of the model, optimize the prediction of the gas diffusion path, make it more in line with the actual situation, and improve the accuracy of the laboratory environmental safety.

[0029] Further, the steps of using the Kalman filtering technology to correct the prediction results include: System state model: The core of the Kalman filtering model is to predict and update the system state, such as gas concentration and wind speed. The system state model describes the calculation method of the current system state. ; Where: : System state, including gas concentration, wind speed, fluid velocity, diffusion coefficient; : State transition matrix, representing the dynamic change from the previous state to the current state, calculated by combining the Navier-Stokes equation and the diffusion-convection equation with the gas diffusion process in the laboratory, reflecting the influence of laboratory air flow, gas diffusion, temperature and humidity on gas diffusion. : Control matrix, representing the influence of control input on the system state. : Wind speed adjustment amount, which is adjusted in real time by the wind speed control system in the laboratory and is affected by temperature and humidity. : Process noise, representing the uncertainty of the model and external disturbances, set according to the results. Influence of temperature and humidity on the system: ; : Temperature, obtained through a temperature sensor. : Humidity, obtained through a humidity sensor. : Reference wind speed, that is, the wind speed under reference conditions. , , : Adjustment coefficient, adjusted according to the results. Observation equation: The observation equation relates the system state to the observed data of the sensors. Through the observation equation of the Kalman filter, the temperature, humidity, and gas concentration data are applied to the update process of the system state; ; Where: : Observed value, which is the real-time measurement data obtained by the sensor; : Observation matrix, which describes the relationship between the system state, such as gas concentration and wind speed, and the observed value, and adjusts the predicted observed value of the gas concentration according to the temperature and humidity changes; : Observation noise, which represents the measurement error of the sensor and is adjusted according to the actual situation; Influence of temperature and humidity: Temperature and humidity directly affect the change of gas concentration by influencing the gas diffusion rate D and wind speed In the observation equation, the observation matrix will be dynamically adjusted according to the temperature and humidity to ensure that the predicted gas concentration matches the actual measured value; Kalman filter prediction step: Predict the system state at the next moment through the state transition matrix and control input matrix; ; Where: : Predicted system state, including gas concentration, wind speed, diffusion coefficient; : State estimate at the previous moment; Application of the diffusion-convection equation in the prediction stage: In the prediction stage, the solution of the diffusion-convection equation (the diffusion path and concentration distribution of the gas) affects the prediction of the system state. The change of temperature and humidity affects the gas diffusion model, and further affects the prediction of the concentration; Kalman filter update step: Fuse the prediction result with the actual observed data to obtain a more accurate state estimate; ; Where: : Updated system state estimate; : Kalman gain, which is used to measure the weight of the difference between the predicted value and the observed value; : Residual, which is set according to the gas diffusion path calculated by the diffusion-convection equation and the influence of temperature and humidity on the diffusion coefficient, and represents the difference between the actual observed value and the predicted value; Relationship between diffusion-convection equation and Kalman gain: The gas diffusion path calculated by the diffusion-convection equation and the influence of temperature and humidity on the diffusion coefficient determine the residuals in the Kalman filter. If the gas concentration is greatly affected by changes in temperature and humidity, the Kalman gain will be dynamically adjusted to optimize the concentration estimation; Covariance update: It reflects the uncertainty of the system state estimation. By updating the covariance matrix, the prediction accuracy is gradually improved, and it is decided whether to continue optimization according to the size of the credibility; ; : The updated covariance matrix represents the credibility of the state estimation at the current moment; : The covariance matrix in the prediction stage represents the credibility of the predicted value.

[0030] Relationship between temperature, humidity and covariance matrix: Temperature and humidity affect the diffusion coefficient and wind speed, thus affecting the diffusion path and flow characteristics of the gas concentration. During the covariance update process, the changes in temperature and humidity affect the estimation uncertainty through the dynamic adjustment of the diffusion coefficient and wind speed, optimize the accuracy of the state estimation, and improve the prediction accuracy.

[0031] S4. Adjust the laboratory wind speed according to the optimized gas diffusion path to obtain the wind speed adjustment result.

[0032] In this embodiment, the steps of adjusting the laboratory wind speed include: Real-time monitoring of environmental data through sensors: Based on the gas diffusion path optimized in step S3, adjust the wind speed in the laboratory according to the real-time monitoring data; Calculate and judge the gas concentration according to the automatic control algorithm: Calculate the gas concentration in the laboratory according to the real-time monitored environmental data and judge whether the concentration exceeds the standard; Automatically adjust the wind speed through the wind speed adjustment system according to the judgment result: When the gas concentration exceeds the standard in some areas, the system automatically adjusts the wind speed to increase the ventilation volume, thereby accelerating the gas emission.

[0033] Through the wind speed adjustment, ensure that the gas concentration in the laboratory is kept within the safe range and avoid the accumulation of gas in local areas.

[0034] S5. Control the gas concentration according to the wind speed adjustment result to obtain the wind speed control result.

[0035] In this embodiment, the steps of controlling the gas concentration include: Relationship between gas concentration and wind speed: The change in wind speed directly affects the diffusion rate of the gas. The greater the wind speed, the lower the gas concentration. If the wind speed is too low, the gas will stagnate and the gas concentration will rise; ; where: : the gas concentration, obtained through a gas sensor; : the air flow rate, obtained through an anemometer; : the diffusion coefficient of the gas, estimated based on the type of gas measured by the gas sensor combined with environmental factors; Changing the air flow rate by adjusting the wind speed , changing the diffusion rate of the gas concentration, and adjusting the gas concentration; Temperature and humidity affect the diffusion coefficient and wind speed, thus affecting the diffusion path and flow characteristics of the gas concentration. Through the dynamic adjustment of the parameters therein by Kalman filtering, the prediction of the diffusion path and concentration distribution is optimized; The goal of wind speed regulation: Real-time regulation: According to the data monitored by the gas concentration sensor, the wind speed is adjusted in real time; Feedback control: Combining the gas concentration data provided by the sensor, using the feedback control algorithm, the gas concentration in the laboratory is optimized by real-time monitoring and adjusting the wind speed; Control process: Sensor monitoring: Continuously monitor the gas concentration in the fume hood through the gas concentration sensor, and the fluctuation of the gas concentration is fed back to the control system through the sensor signal; Comparison of concentration with safety threshold: Compare the real-time monitored gas concentration with the set safety concentration threshold to determine whether it exceeds the standard; Wind speed adjustment strategy: According to the degree of deviation of the gas concentration from the safety threshold, the system dynamically adjusts the wind speed; Adjustment strategy: Fuzzy control: Use fuzzy control rules to adjust the wind speed according to the deviation between the gas concentration and the safety threshold and the error change rate. For example, when the gas concentration is high, the wind speed increases, and when the gas concentration is low and stable, the wind speed decreases.

[0036] Through the gas concentration control method, it is ensured that when the gas concentration changes, the wind speed of the fume hood can be automatically adjusted to maintain a safe laboratory environment.

[0037] S6. Based on the wind speed control result and combined with the safety threshold, an alarm and response mechanism is implemented to obtain alarm information and wind speed data.

[0038] In this embodiment, the method for implementing the alarm and response mechanism includes: Set a safety threshold; judge whether the gas concentration triggers an alarm and take emergency measures.

[0039] The safety threshold represents the acceptable upper limit of various harmful gases in the laboratory environment. After the wind speed is adjusted, the system will judge whether the preset safety threshold is exceeded according to the gas concentration data. If the gas concentration exceeds the safety threshold, the system will trigger a safety alarm and immediately take emergency measures, such as further increasing the wind speed or shutting down some experimental equipment, to avoid safety accidents.

[0040] During the wind speed adjustment process, the system automatically adjusts according to the deviation between the gas concentration and the safety threshold, and triggers the alarm mechanism through an algorithm. Combining the relationship between the wind speed adjustment and the gas concentration change in step S5, optimize the system's response to the excessive gas concentration in the laboratory.

[0041] The alarm mechanism formula is as follows: ; Where: C(t) is the gas concentration; C threshold is the preset safety threshold.

[0042] S7. Based on the alarm information and wind speed data, perform wind speed optimization and fault diagnosis to obtain optimized data.

[0043] In this embodiment, the methods for wind speed optimization and fault diagnosis include: Through machine learning algorithms, learn the relationship between different gas leakage situations and wind speed adjustment, and optimize future wind speed adjustment strategies.

[0044] Through data analysis and fault diagnosis, improve the system's response ability to laboratory gas diffusion and wind speed adjustment, and optimize the system performance.

[0045] The machine learning steps are as follows: Regression analysis: Establish the relationship between gas concentration and wind speed adjustment, train a regression model based on historical data to predict the wind speed adjustment values required under different gas leakage situations; Support Vector Machine Regression (SVR): Model the non-linear relationship, optimize the wind speed adjustment, input factors such as gas concentration, temperature and humidity into the SVR model, and output the optimal wind speed adjustment amount.

[0046] Cluster analysis (K-means): Identify different gas leakage patterns and optimize the wind speed adjustment strategy. Through the clustering algorithm, automatically discover the distribution patterns of gas concentration and wind speed under different gas leakage scenarios. It can be used to identify common gas leakage types and design different wind speed adjustment strategies for these types.

[0047] Principal Component Analysis (PCA): Dimension reduction, extraction of main influencing factors, improvement of adjustment efficiency. By performing PCA on variables such as gas concentration, temperature and humidity, and wind speed, the main components affecting gas diffusion are identified, thereby streamlining the model input and improving the wind speed adjustment efficiency.

[0048] Anomaly detection: Real-time fault diagnosis to ensure the normal operation of the system. By training the model to identify the "normal" wind speed adjustment patterns in historical data, once abnormal data (such as a sudden drop in wind speed or a sharp increase in gas concentration) is detected, an alarm will be triggered.

[0049] S8. Generate an environmental safety report based on the optimized data and adjust the wind speed control strategy.

[0050] In this embodiment, based on the optimized data in step S7, a detailed laboratory gas environment safety report is generated. The report includes the gas concentration change trend, the historical record of wind speed adjustment, alarm information, and emergency response measures. In addition, based on the historical data and optimization results, the wind speed control strategy is adjusted to further improve the safety and response speed of the system.

[0051] Embodiment 2: This embodiment also provides a computer device applicable to a variable air volume control method for a fume hood based on laboratory environment monitoring, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a variable air volume control method for a fume hood based on laboratory environment monitoring as proposed in the above embodiment.

[0052] This embodiment also provides a storage medium with a computer program stored thereon, and when the program is executed by a processor, it implements a variable air volume control method for a fume hood based on laboratory environment monitoring as proposed in the above embodiment.

[0053] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0054] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., which are all media that can store program codes.

[0055] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0056] More specific examples (nonexhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0057] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0058] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A variable air volume control method for a fume hood based on laboratory environment monitoring, characterized in that, It includes the following steps: S1. Arrange sensors and collect laboratory data; S2. Establish a basic fluid dynamics model for gas flow and diffusion in the laboratory; S3. Conduct gas diffusion path prediction to obtain an optimized gas diffusion path; S4. Adjust the laboratory wind speed according to the optimized gas diffusion path to obtain a wind speed adjustment result; S5. Control the gas concentration according to the wind speed adjustment result to obtain a wind speed control result; S6. Based on the wind speed control result, combine with the safety threshold to implement an alarm and response mechanism to obtain alarm information and wind speed data; S7. Conduct wind speed optimization and fault diagnosis based on the alarm information and wind speed data to obtain optimized data; S8. Generate an environmental safety report based on the optimized data and adjust the wind speed control strategy.

2. The variable air volume control method for a fume hood based on laboratory environment monitoring according to claim 1, wherein The types of sensors arranged in S1 and the laboratory data collected include: Temperature sensor, humidity sensor, pressure sensor, flow meter, gas sensor; Airflow velocity, gas concentration, temperature, humidity, fluid density, gas type, gas pressure, fluid viscosity, and diffusion coefficient.

3. The variable air volume control method for a fume hood based on laboratory environment monitoring according to claim 1, wherein, The fluid dynamics model in S2 includes: Navier-Stokes Equation: This equation describes the flow law of air in space and time and is used to simulate the gas flow process; ； Where: : The fluid density, in kg / m³, is affected by the laboratory environmental temperature and pressure and is calculated based on the data obtained in S1; : The air velocity vector, with the unit of m / s, is measured by the topspin ball gas flowmeter; : The time rate of change of velocity; : Gas pressure gradient, with the unit of Pa / m, representing the rate of change of pressure in space, measured by a pressure sensor; : The fluid viscosity, in units of Pa·s, is obtained by referring to data according to the type of gas measured by the gas sensor; : The second-order spatial derivative of the velocity field; Denote: external force, obtained by measurement, used to represent gravity and surface friction force; Solve the equation to obtain: The velocity field v(x,t) of the airflow in the laboratory, which represents the magnitude and direction of the airflow velocity at each point x and each moment t; The pressure field p(x,t), which represents the magnitude of the gas pressure at each point x and each moment t; Auxiliary judgment of the gas diffusion path by analyzing the magnitude and direction of the airflow velocity and the magnitude of the gas pressure, thereby simulating the gas flow process; Diffusion-convection equation: This equation is used to describe the propagation and diffusion behavior of gas in a fluid; ； Where: : Gas concentration, with the unit of mg / m³, representing the gas concentration at the spatial position (x, y, z) and time t, measured by a gas sensor; : Gas concentration gradient, indicating the variation of gas concentration in space, with a magnitude equal to the rate of change of gas concentration; : Gas diffusion coefficient, unit: m 2 / s, estimated based on the gas type measured by the gas sensor combined with environmental factors; : The Laplacian operator of gas concentration, which describes the second-order change rate of gas concentration in space and reflects how gas flows from high-concentration regions to low-concentration regions through diffusion; Solve the equation to obtain the time and space distribution of the gas concentration in the laboratory. By observing the change of the gas concentration, determine the gas diffusion path. The greater the gas concentration gradient, the faster the gas diffuses.

4. A variable air volume control method for a fume hood based on laboratory environment monitoring according to claim 1, characterized in that, The steps for gas diffusion path prediction in S3 include: Input real-time environmental data; Use the fluid dynamics model established in step S2 to predict the gas diffusion path and concentration distribution in the laboratory; Adopt Kalman filtering technology to correct the prediction result of the model.

5. A variable air volume control method for a fume hood based on laboratory environment monitoring according to claim 4, characterized in that, The Kalman filtering technology includes: System state model: The system state model describes the calculation method of the current system state; ； Where: : System status, including gas concentration, wind speed, fluid velocity, diffusion coefficient; : The state transition matrix represents the dynamic change from the previous state to the current state, which is calculated by combining the Navier-Stokes equation and the advection-diffusion equation with the gas diffusion process in the laboratory, reflecting the influence of air flow, gas diffusion, temperature, and humidity in the laboratory on gas diffusion; : Control matrix, representing the influence of control inputs on the system state; : The wind speed adjustment amount is adjusted in real time by the wind speed control system in the laboratory and is affected by temperature and humidity; : Process noise, representing the uncertainty of the model and external disturbances, is set according to the results; Observation equation: Apply the data of temperature, humidity, and gas sensors to the update process of the system state; ； Where: : Observed value, which is the real-time measurement data obtained by the sensor; : An observation matrix that describes the relationship between the system state, including gas concentration and wind speed, and the observed values, and adjusts the predicted observed values of gas concentration according to temperature and humidity changes; : Observation noise, representing the measurement error of the sensor, which is adjusted according to the actual situation; Kalman filter prediction step: Through the state transition matrix and the control matrix make a prediction and output the system state at the next moment; ； Where: : Predicted system states, including gas concentration, wind speed, and diffusion coefficient; : State estimation at the previous moment; Kalman filter update step: Fuse the prediction result with the actual observation data to obtain a more accurate state estimate; ； Where: : Updated system state estimation; : The Kalman gain, which is used to measure the weight of the difference between the predicted value and the observed value; : Residual, which is set according to the gas diffusion path calculated by the diffusion-convection equation and the influence of temperature and humidity on the diffusion coefficient, representing the difference between the actual observed value and the predicted value; Covariance update: It reflects the uncertainty of the system state estimate. The credibility obtained by updating the covariance matrix gradually improves the prediction accuracy. Determine whether to continue optimization according to the size of the credibility.

6. The variable air volume control method for a fume hood based on laboratory environment monitoring according to claim 1, wherein, The steps for adjusting the laboratory wind speed in S4 include: Real-time monitor environmental data through sensors; Calculate the gas concentration according to the automatic control algorithm and make a judgment; Automatically adjust the wind speed through the wind speed adjustment system according to the judgment result.

7. A variable air volume control method for a fume hood based on laboratory environment monitoring according to claim 1, characterized in that The gas concentration control in S5 includes: The relationship between gas concentration and wind speed: The change in wind speed directly affects the gas diffusion rate. The greater the wind speed, the lower the gas concentration. If the wind speed is too low, the gas will stagnate and the gas concentration will rise; ； Among them: : Gas concentration, obtained by a gas sensor; : Air flow velocity, obtained by a flow meter; : The diffusion coefficient of the gas, which is estimated by combining the type of gas measured by the gas sensor with environmental factors; Changing the air flow rate by adjusting the wind speed to change the diffusion rate of the gas concentration and adjust the gas concentration; The goal of wind speed adjustment: Real-time adjustment: Adjust the wind speed in real time according to the data monitored by the gas concentration sensor; Feedback control: Combine the gas concentration data provided by the sensor, use the feedback control algorithm, and optimize the gas concentration in the laboratory by monitoring and adjusting the wind speed in real time; Control process: Sensor monitoring: Continuously monitor the gas concentration in the fume hood through the gas concentration sensor, and the fluctuations in the gas concentration are fed back to the control system through the sensor signal; Comparison of concentration with safety threshold: Compare the real-time monitored gas concentration with the set safety concentration threshold to determine whether it exceeds the standard; Wind speed adjustment strategy: The system dynamically adjusts the wind speed according to the degree of deviation of the gas concentration from the safety threshold; Adjustment strategy: Fuzzy control: Use fuzzy control rules to adjust the wind speed according to the deviation between the gas concentration and the safety threshold and the error change rate.

8. A variable air volume control method for a fume hood based on laboratory environment monitoring according to claim 1, characterized in that, The method for implementing the alarm and response mechanism in S6 includes: Set the safety threshold; judge the gas concentration to trigger an alarm and take emergency measures.

9. A variable air volume control method for a fume hood based on laboratory environment monitoring according to claim 1, characterized in that The method for wind speed optimization and fault diagnosis in S7 includes: Through machine learning algorithms, learn the relationship between different gas leakage situations and wind speed adjustment, and optimize future wind speed adjustment strategies.

10. A variable air volume control method for a fume hood based on laboratory environment monitoring according to claim 1, characterized in that, The environmental safety report in S8 includes: Generate a detailed laboratory gas environmental safety report based on the optimized data, including the gas concentration change trend, wind speed adjustment historical records, alarm information and emergency response measures, so that laboratory managers can optimize the wind speed adjustment strategy according to the detailed environmental safety report.

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