Laboratory intelligent ventilation system, method of use, and laboratory

Abstract

The application provides a laboratory intelligent ventilation system, comprising: a data acquisition module that acquires current environmental parameters and current personnel activity data of the laboratory; a control module that starts a sterilization function of a ventilation device when the concentration of harmful gas exceeds a preset safety standard, wherein the ventilation device has multiple ventilation inlets, each of which is configured to be movably arranged, and is used to be arranged and controlled in the laboratory according to the concentration distribution of the harmful gas, and the wind pressure, the wind speed and the wind volume of the ventilation inlet are greater when the position tends to be a position with a high concentration of harmful gas; when the concentration of harmful gas does not exceed the preset safety standard, a first training model is constructed according to human heat and movement trajectories, the correlation between personnel activity modes and ventilation requirements is determined, the global ventilation function or the local ventilation function of the ventilation device is started according to the change in the monitored personnel activity condition or the laboratory air quality condition, and the ventilation inlet tends to a position with minimum air flow disturbance.

Description

Technical Field

[0001] This invention belongs to the field of air conditioning technology, specifically relating to a laboratory intelligent ventilation system, its usage method, and the laboratory. Background Technology

[0002] A laboratory is a place where experiments are conducted. It is the cradle of science, the base of scientific research, and the source of technological development, playing a vital role in improving living standards and advancing science and technology. With the development of the times, the role of laboratories has become increasingly prominent. Many laboratories have significantly increased in size compared to the past, and the number of large, sophisticated, and expensive experimental instruments and equipment has also increased, posing greater challenges to laboratory safety management.

[0003] Currently, existing laboratory ventilation systems all require manual control. For example, the ventilation system is only turned on manually when people in the laboratory feel short of breath, which is not conducive to the normal work of laboratory personnel, or it is kept on all the time, wasting resources. Therefore, existing laboratory ventilation systems cannot achieve the effect of intelligent and adaptive control. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a laboratory intelligent ventilation system to solve the problem that existing laboratory ventilation systems cannot achieve intelligent and adaptive control.

[0005] One embodiment of the present invention provides a laboratory intelligent ventilation system, comprising: The data acquisition module is used to collect current environmental parameters and current personnel activity data in the laboratory; the current environmental parameters include the concentration of harmful gases; the current personnel activity data includes body heat and movement trajectory. The control module is used to execute a ventilation control scheme based on the current environmental parameters and the current personnel activity data, including: When the concentration of harmful gases exceeds the preset safety standard, the disinfection function of the ventilation equipment is activated. Air in the laboratory enters the gas diffusion chamber through the ventilation inlet. The aerosol sprayer sprays disinfectant into the gas diffusion chamber, where it mixes with the air to form a mesh-like aerosol mist. This mist fills the gas diffusion chamber and slowly flows towards the ventilation outlet, where it is discharged through the exhaust pipe. There are multiple ventilation inlets, all of which are configured to be movable and distributed throughout the laboratory according to the concentration distribution of harmful gases. The higher the concentration of harmful gases, the greater the wind pressure, wind speed, and air volume of the ventilation inlet. When the concentration of harmful gases does not exceed the preset safety standard, a first training model is constructed based on the human body heat and the movement trajectory to determine the correlation between personnel activity patterns and ventilation needs. Based on changes in the monitored personnel activity status or laboratory air quality, the global ventilation function or local ventilation function of the ventilation equipment is activated, and the ventilation inlet is controlled to tend to the position with the least disturbance to the airflow.

[0006] In this embodiment, intelligent and adaptive control of environmental parameters in the laboratory can be achieved, ensuring timely, accurate and rapid adjustment of the current ambient air without the need for manual adjustment. At the same time, it avoids unexpected situations, such as excessively high concentrations of harmful gases causing harm to laboratory personnel, or fires, thus preventing dangers from occurring.

[0007] In one embodiment, the current environmental parameters also include temperature and humidity; The control module is also used to construct a second training model based on the isolated forest algorithm according to the current environmental parameters, in order to determine whether the current ambient air quality is abnormal.

[0008] In one embodiment, the construction of the second training model based on the isolated forest algorithm according to the current environmental parameters includes: Historical environmental parameters are obtained, including one or more of the following: concentration of harmful gases, temperature, and humidity for each time period. Feature extraction is performed on the historical environmental parameters to generate the first dataset; The isolated forest algorithm is trained using the first dataset. When the prediction accuracy of the isolated forest algorithm does not meet the preset value, the parameters of the isolated forest algorithm are tuned until the prediction accuracy meets the preset value, and then a second training model is generated.

[0009] In this embodiment, feature extraction is performed on historical environmental parameters, and the Isolation Forest algorithm is trained based on the generated first dataset to ensure the accuracy of current environmental parameter detection. Furthermore, parameter tuning of the Isolation Forest algorithm further improves the accuracy and efficiency of the detection results.

[0010] In one embodiment, constructing the first training model based on the body heat and the movement trajectory includes: Based on the K-means clustering algorithm, a first training model is constructed according to the human body heat and the movement trajectory, and the first training model is used to determine whether the current activity status of the person has changed.

[0011] In one embodiment, a first training model is constructed based on the K-means clustering algorithm, according to the human body heat and the movement trajectory, including: Acquire historical personnel activity data, which includes one or more of the following: personnel density distribution, activity hotspots, and movement frequency for each time period; Feature extraction is performed on the historical personnel activity data to generate a second dataset; The K-means clustering algorithm is trained using the second dataset. When the prediction accuracy of the K-means clustering algorithm does not meet the preset value, the parameters of the K-means clustering algorithm are tuned until the prediction accuracy meets the preset value, and then the first training model is generated.

[0012] In this embodiment, the K-means clustering algorithm is used to analyze the current personnel activity data, which enables rapid detection of the density of personnel activity. It can process data in real time, quickly detect and monitor the density of personnel activity, and the clustering results are intuitive and easy to interpret. This is conducive to discovering the basic laws and patterns of personnel activity from the data. It can run without labeled training data, is suitable for unsupervised learning scenarios, and is beneficial for handling situations where there is a lack of labeled information in the detection of personnel activity density.

[0013] By extracting features from historical personnel activity data and training a K-means clustering algorithm based on the generated second dataset, the accuracy of detecting current environmental parameters is ensured. Furthermore, by optimizing the parameters of the K-means clustering algorithm, the accuracy and efficiency of the detection results are further improved.

[0014] In one embodiment, the step of executing the ventilation control scheme based on the current environmental parameters and the current personnel activity data further includes: When the temperature difference between the inside and outside of the laboratory exceeds the preset temperature difference, the fresh air preheating and recovery function of the ventilation equipment is activated to capture the waste heat in the laboratory and preheat the incoming outside air through a heat exchanger. The heat exchanger consists of two independent channels, namely the air supply channel and the air exhaust channel. When the exhaust air is discharged from the laboratory, its heat energy is transferred to the heat exchanger. At the same time, fresh outside air enters the laboratory through the air supply channel and exchanges heat with the exhaust air when passing through the heat exchanger, so that the fresh air is preheated.

[0015] In this embodiment, In one embodiment, the exhaust duct is equipped with a HEPA filter for capturing and filtering particulate matter larger than 0.3 micrometers. The HEPA filter consists of a series of fiber mesh layers, which form a tortuous channel between the fiber mesh layers to intercept particulate matter between the mesh layers.

[0016] In one embodiment, the ventilation equipment includes one or more of a fume hood, an exhaust fan, an air conditioner, a humidifier, a condenser, and a heater.

[0017] Under the same inventive concept, the present invention also provides a method of using a laboratory intelligent ventilation system, comprising: Collect current environmental parameters and current personnel activity data in the laboratory; the current environmental parameters include the concentration of harmful gases; the current personnel activity data includes body heat and movement trajectory. Based on the current environmental parameters and the current personnel activity data, a ventilation control plan is executed, including: When the concentration of harmful gases exceeds the preset safety standard, the disinfection function of the ventilation equipment is activated. Air in the laboratory enters the gas diffusion chamber through the ventilation inlet. The aerosol sprayer sprays disinfectant into the gas diffusion chamber, where it mixes with the air to form a mesh-like aerosol mist. This mist fills the gas diffusion chamber and slowly flows towards the ventilation outlet, where it is discharged through the exhaust pipe. There are multiple ventilation inlets, all of which are configured to be movable and distributed throughout the laboratory according to the concentration distribution of harmful gases. The higher the concentration of harmful gases, the greater the wind pressure, wind speed, and air volume of the ventilation inlet. When the concentration of harmful gases does not exceed the preset safety standard, a first training model is constructed based on the human body heat and the movement trajectory to determine the correlation between personnel activity patterns and ventilation needs. Based on changes in the monitored personnel activity status or laboratory air quality, the global ventilation function or local ventilation function of the ventilation equipment is activated, and the ventilation inlet is controlled to tend to the position with the least disturbance to the airflow.

[0018] Under the same inventive concept, the present invention also provides a laboratory, comprising: a laboratory intelligent ventilation system, wherein the laboratory intelligent ventilation system is any one of the above-described laboratory intelligent ventilation systems, and performs the above-described method of using the laboratory intelligent ventilation system. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the structure of a laboratory intelligent ventilation system provided in one embodiment of the present invention; Figure 2 A flowchart illustrating the usage method of a laboratory intelligent ventilation system according to one embodiment of the present invention; Figure 3 This is a schematic diagram of the hardware structure of an electronic device for loading a laboratory intelligent ventilation system, provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly.

[0023] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text includes three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0024] Please see Figure 1 One embodiment of the present invention provides a laboratory intelligent ventilation system.

[0025] The data acquisition module is used to collect current environmental parameters and current personnel activity data in the laboratory; the current environmental parameters include the concentration of harmful gases; the current personnel activity data includes human body heat and movement trajectory.

[0026] Collect hazardous gas concentrations: Install hazardous gas sensors and collect the concentrations of hazardous gases in the laboratory regularly; Temperature and humidity data collection: Install temperature and humidity sensors to collect temperature and humidity information in the laboratory, and provide data on temperature and humidity changes in the laboratory's internal and external environments; Collecting human body heat: Using body temperature sensors or motion monitoring equipment, monitor and collect changes in the body temperature and activity level of people in the laboratory. This data can be used to determine the heat generated by human activities, and to assess ventilation needs. Collect movement trajectories: By installing location tracking devices or cameras to monitor people's movement trajectories in real time, and by analyzing people's positions and activities, areas where people gather can be predicted in advance, so as to make corresponding ventilation adjustments.

[0027] In this embodiment, it is understood that the sensors include a gas sensor, a temperature sensor, and a humidity sensor. The gas sensor is used to collect the gas in the laboratory; the temperature sensor is used to collect the temperature of the laboratory; and the humidity sensor is used to collect the humidity of the laboratory. The gas sensor includes one or more of the following: a TVOC sensor, a combustible gas detector, an electrical fire detector, a carbon dioxide sensor, a toxic gas detector, a smoke sensor, and a flame detector.

[0028] Since the gas sensors include multiple types of sensors such as TVOC sensors, combustible gas detectors, electrical fire detectors, carbon dioxide sensors, toxic gas detectors, infrared detection sensors, smoke sensors, and flame detectors, they can effectively monitor various aspects of the laboratory environment, enabling intelligent monitoring and management of laboratory safety. Depending on the needs, the data acquisition device may also include an imaging device (such as a camera).

[0029] The control module is used to execute a ventilation control scheme based on the current environmental parameters and the current personnel activity data, including: When the concentration of harmful gases exceeds the preset safety standard, the disinfection function of the ventilation equipment is activated. Air in the laboratory enters the gas diffusion chamber through the ventilation inlet. The aerosol sprayer sprays disinfectant into the gas diffusion chamber and mixes with the air to form a mesh-like aerosol mist. This mist fills the gas diffusion chamber and slowly flows towards the ventilation outlet, where it is discharged through the exhaust pipe. There are multiple ventilation inlets, all of which are configured to be movable and distributed throughout the laboratory according to the concentration distribution of harmful gases. The higher the concentration of harmful gases, the greater the wind pressure, wind speed, and air volume of the ventilation inlet.

[0030] Air from the laboratory enters the gas diffusion chamber through ventilation inlets. Inside the gas diffusion chamber, the gas velocity gradually slows down, and airborne particles and harmful substances are initially diluted and dispersed.

[0031] Next, the aerosol sprayer sprays disinfectant into the gas diffusion chamber, where it mixes with airborne particles and harmful substances. The solution particles produced by the aerosol sprayer are extremely small, allowing them to be evenly distributed within the gas diffusion chamber, forming an aerosol mist. These tiny solution particles combine with airborne particles and harmful substances, further reducing the concentration of pollutants in the air.

[0032] As the gas mixture slowly flows towards the ventilation outlet, the aerosol mist gradually thins, but it still plays a role in purifying the air. Finally, guided by the exhaust pipe, the gas mixture is discharged from the laboratory, effectively purifying the air inside.

[0033] In addition, the ventilation equipment can be equipped with filters and other purification devices as needed to further improve the air purification effect. For example, a high-efficiency filter can be added to the exhaust pipe to further filter and purify the exhaust gas to prevent the emission of fine particulate matter and harmful gases.

[0034] In summary, the ventilation equipment, through proper design and configuration, can effectively improve air quality in the laboratory, ensuring the health of laboratory personnel and the smooth conduct of experiments. Furthermore, it can be customized and optimized according to pre-set experimental plans and actual needs to meet the requirements of different laboratories and different types of experiments.

[0035] When the concentration of harmful gases does not exceed the preset safety standard, a first training model is constructed based on the human body heat and the movement trajectory to determine the correlation between personnel activity patterns and ventilation needs. Based on changes in the monitored personnel activity status or laboratory air quality, the global ventilation function or local ventilation function of the ventilation equipment is activated, and the ventilation inlet is controlled to tend to the position with the least disturbance to the airflow.

[0036] In this embodiment, intelligent and adaptive control of environmental parameters in the laboratory can be achieved, ensuring timely, accurate and rapid adjustment of the current ambient air without the need for manual adjustment. At the same time, it avoids unexpected situations, such as excessively high concentrations of harmful gases causing harm to laboratory personnel, or fires, thus preventing dangers from occurring.

[0037] In one embodiment, the current environmental parameters also include temperature and humidity; The control module is also used to construct a second training model based on the isolated forest algorithm according to the current environmental parameters, in order to determine whether the current ambient air quality is abnormal.

[0038] In one embodiment, the construction of the second training model based on the isolated forest algorithm according to the current environmental parameters includes: Historical environmental parameters are obtained, including one or more of the following: concentration of harmful gases, temperature, and humidity for each time period. Feature extraction is performed on the historical environmental parameters to generate the first dataset; The isolated forest algorithm is trained using the first dataset. When the prediction accuracy of the isolated forest algorithm does not meet the preset value, the parameters of the isolated forest algorithm are tuned until the prediction accuracy meets the preset value, and then a second training model is generated.

[0039] In this embodiment, feature extraction is performed on historical environmental parameters, and the Isolation Forest algorithm is trained based on the generated first dataset to ensure the accuracy of current environmental parameter detection. Furthermore, parameter tuning of the Isolation Forest algorithm further improves the accuracy and efficiency of the detection results.

[0040] The advantages of using the isolated forest algorithm for ambient air quality detection are: High efficiency: The Isolation Forest algorithm can quickly identify outliers when processing large-scale data, making it suitable for large-scale data analysis in ambient air monitoring.

[0041] Sensitive to outliers: The algorithm performs well in identifying outliers, which is helpful in discovering potential sources of pollution or abnormal situations.

[0042] No need for large amounts of labeled data: It can run effectively without a large amount of labeled training data, which is especially advantageous when it is often difficult to obtain labeled information for environmental monitoring data.

[0043] Suitable for multidimensional data: It can process multidimensional data and consider the relationship between multiple features at the same time, which helps to comprehensively analyze various influencing factors in ambient air monitoring.

[0044] In one embodiment, constructing the first training model based on the body heat and the movement trajectory includes: Based on the K-means clustering algorithm, a first training model is constructed according to the human body heat and the movement trajectory, and the first training model is used to determine whether the current activity status of the person has changed.

[0045] In one embodiment, a first training model is constructed based on the K-means clustering algorithm, according to the human body heat and the movement trajectory, including: Acquire historical personnel activity data, which includes one or more of the following: personnel density distribution, activity hotspots, and movement frequency for each time period; Feature extraction is performed on the historical personnel activity data to generate a second dataset; The K-means clustering algorithm is trained using the second dataset. When the prediction accuracy of the K-means clustering algorithm does not meet the preset value, the parameters of the K-means clustering algorithm are tuned until the prediction accuracy meets the preset value, and then the first training model is generated.

[0046] In this embodiment, the K-means clustering algorithm is used to analyze the current personnel activity data, which enables rapid detection of the density of personnel activity. It can process data in real time, quickly detect and monitor the density of personnel activity, and the clustering results are intuitive and easy to interpret. This is conducive to discovering the basic laws and patterns of personnel activity from the data. It can run without labeled training data, is suitable for unsupervised learning scenarios, and is beneficial for handling situations where there is a lack of labeled information in the detection of personnel activity density.

[0047] By extracting features from historical personnel activity data and training a K-means clustering algorithm based on the generated second dataset, the accuracy of detecting current environmental parameters is ensured. Furthermore, by optimizing the parameters of the K-means clustering algorithm, the accuracy and efficiency of the detection results are further improved.

[0048] The advantages of using the K-means clustering algorithm to detect personnel activity data include: Simple and efficient: The K-means clustering algorithm is simple to understand and relatively efficient to implement, making it suitable for the rapid detection of the density of human activity.

[0049] Real-time performance: It can process data in real time, and quickly detect and monitor the density of personnel activities.

[0050] High interpretability: The clustering results are intuitive and easy to interpret, which is helpful in discovering the basic patterns and rules of human activities from the data.

[0051] Unsupervised learning: It can run without labeled training data, and is suitable for unsupervised learning scenarios. It is beneficial for handling situations where there is a lack of labeled information in the detection of the density of human activity.

[0052] In one embodiment, the step of executing the ventilation control scheme based on the current environmental parameters and the current personnel activity data further includes: When the temperature difference between the inside and outside of the laboratory exceeds the preset temperature difference, the fresh air preheating and recovery function of the ventilation equipment is activated to capture the waste heat in the laboratory and preheat the incoming outside air through a heat exchanger. The heat exchanger consists of two independent channels, namely the air supply channel and the air exhaust channel. When the exhaust air is discharged from the laboratory, its heat energy is transferred to the heat exchanger. At the same time, fresh outside air enters the laboratory through the air supply channel and exchanges heat with the exhaust air when passing through the heat exchanger, so that the fresh air is preheated.

[0053] In this embodiment, In one embodiment, the exhaust duct is equipped with a HEPA filter for capturing and filtering particulate matter larger than 0.3 micrometers. The HEPA filter consists of a series of fiber mesh layers, which form a tortuous channel between the fiber mesh layers to intercept particulate matter between the mesh layers.

[0054] In one embodiment, the ventilation equipment includes one or more of a fume hood, an exhaust fan, an air conditioner, a humidifier, a condenser, and a heater.

[0055] Please see Figure 2 Under the same inventive concept, the present invention also provides a method for using a laboratory intelligent ventilation system.

[0056] Step 1: Collect current environmental parameters and personnel activity data in the laboratory; the current environmental parameters include the concentration of harmful gases; the current personnel activity data includes body heat and movement trajectory. Step 2: Based on the current environmental parameters and the current personnel activity data, execute the ventilation control plan, including: Step 2-1: When the concentration of harmful gas exceeds the preset safety standard, the disinfection function of the ventilation equipment is activated. The air in the laboratory enters the gas diffusion chamber through the ventilation inlet. The aerosol sprayer sprays disinfectant into the gas diffusion chamber and mixes with the air to form a mesh-like aerosol mist. This mist fills the gas diffusion chamber and slowly flows towards the ventilation outlet, where it is discharged through the exhaust pipe. There are multiple ventilation inlets, all of which are configured to be movable and distributed in the laboratory according to the concentration distribution of harmful gas. The higher the concentration of harmful gas, the greater the wind pressure, wind speed, and air volume of the ventilation inlet. Step 2-2: When the concentration of harmful gases does not exceed the preset safety standard, a first training model is constructed based on the human body heat and the movement trajectory to determine the correlation between personnel activity patterns and ventilation needs. Based on changes in the monitored personnel activity status or laboratory air quality, the global ventilation function or local ventilation function of the ventilation equipment is activated, and the ventilation inlet is controlled to tend to the position with the least disturbance to the airflow.

[0057] Under the same inventive concept, the present invention also provides a laboratory, comprising: a laboratory intelligent ventilation system, wherein the laboratory intelligent ventilation system is any one of the above-described laboratory intelligent ventilation systems, and the above-described method of using the laboratory intelligent ventilation system is performed.

[0058] In one embodiment, the present invention also provides an electronic device for loading a laboratory intelligent ventilation system, comprising: a processor, a transmitting device, an input device, an output device, and a memory, wherein the memory is used to store computer program code, the computer program code including computer instructions, and when the processor executes the computer instructions, the electronic device performs a method as described in any of the above possible implementations.

[0059] Please see Figure 3 , Figure 3 This is a schematic diagram of the hardware structure of an electronic device for loading a laboratory intelligent ventilation system, provided in an embodiment of the present invention.

[0060] The electronic device 2 includes a processor 21, a memory 22, an input device 23, and an output device 24. The processor 21, memory 22, input device 23, and output device 24 are coupled together via connectors, which may include various interfaces, transmission lines, or buses, etc., and are not limited in this embodiment of the invention. It should be understood that in various embodiments of the invention, coupling refers to mutual connection through a specific method, including direct connection or indirect connection through other devices, such as through various interfaces, transmission lines, buses, etc.

[0061] The processor 21 can be one or more graphics processing units (GPUs). If the processor 21 is a GPU, the GPU can be a single-core GPU or a multi-core GPU. Optionally, the processor 21 can be a processor group composed of multiple GPUs, with the multiple processors coupled to each other via one or more buses. Optionally, the processor can also be other types of processors, etc., and this embodiment of the invention is not limited thereto.

[0062] The memory 22 can be used to store computer program instructions, as well as various types of computer program code, including program code for executing the present invention. Optionally, the memory includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM), which is used for related instructions and data.

[0063] Input device 23 is used to input data and / or signals, and output device 24 is used to output data and / or signals. Output device 23 and input device 24 can be independent devices or an integrated device.

[0064] It is understood that in this embodiment of the invention, the memory 22 can be used not only to store related instructions, but also the specific data stored in the memory is not limited.

[0065] Understandable, Figure 3 This is merely a simplified design of an electronic device. In practical applications, the electronic device may also include other necessary components, including, but not limited to, any number of input / output devices, processors, memory, etc., and all video analysis devices that can implement embodiments of the present invention are within the protection scope of the present invention.

[0066] Those skilled in the art will 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, or a combination of computer software and electronic hardware. 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.

[0067] Those skilled in the art will readily understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. Those skilled in the art will also readily understand that the various embodiments of the present invention have different focuses, and for the sake of convenience and brevity, the same or similar parts may not be repeated in different embodiments. Therefore, parts not described or not described in detail in one embodiment can be referred to in other embodiments.

[0068] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0069] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0071] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital versatile discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0072] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A laboratory intelligent ventilation system, characterized in that, include: The data acquisition module is used to collect current environmental parameters and current personnel activity data in the laboratory; the current environmental parameters include the concentration of harmful gases; the current personnel activity data includes body heat and movement trajectory. The control module is used to execute a ventilation control scheme based on the current environmental parameters and the current personnel activity data, including: When the concentration of harmful gases exceeds the preset safety standard, the disinfection function of the ventilation equipment is activated. Air in the laboratory enters the gas diffusion chamber through the ventilation inlet. The aerosol sprayer sprays disinfectant into the gas diffusion chamber, where it mixes with the air to form a mesh-like aerosol mist. This mist fills the gas diffusion chamber and slowly flows towards the ventilation outlet, where it is discharged through the exhaust pipe. There are multiple ventilation inlets, all of which are configured to be movable and distributed throughout the laboratory according to the concentration distribution of harmful gases. The higher the concentration of harmful gases, the greater the wind pressure, wind speed, and air volume of the ventilation inlet. When the concentration of harmful gases does not exceed the preset safety standard, a first training model is constructed based on the human body heat and the movement trajectory to determine the correlation between personnel activity patterns and ventilation needs. Based on changes in the monitored personnel activity status or laboratory air quality, the global ventilation function or local ventilation function of the ventilation equipment is activated, and the ventilation inlet is controlled to tend to the position with the least disturbance to the airflow.

2. The intelligent laboratory ventilation system as described in claim 1, characterized in that, The current environmental parameters also include temperature and humidity; The control module is also used to construct a second training model based on the isolated forest algorithm according to the current environmental parameters, in order to determine whether the current ambient air quality is abnormal.

3. The intelligent laboratory ventilation system as described in claim 2, characterized in that, The construction of the second training model based on the isolated forest algorithm according to the current environmental parameters includes: Historical environmental parameters are obtained, including one or more of the following: concentration of harmful gases, temperature, and humidity for each time period. Feature extraction is performed on the historical environmental parameters to generate the first dataset; The isolated forest algorithm is trained using the first dataset. When the prediction accuracy of the isolated forest algorithm does not meet the preset value, the parameters of the isolated forest algorithm are tuned until the prediction accuracy meets the preset value, and then a second training model is generated.

4. The intelligent laboratory ventilation system as described in claim 1, characterized in that, The step of constructing a first training model based on the human body heat and the movement trajectory includes: Based on the K-means clustering algorithm, a first training model is constructed according to the human body heat and the movement trajectory, and the first training model is used to determine whether the current activity status of the person has changed.

5. The intelligent laboratory ventilation system as described in claim 4, characterized in that, Based on the K-means clustering algorithm, a first training model is constructed according to the human body heat and the movement trajectory, including: Acquire historical personnel activity data, which includes one or more of the following: personnel density distribution, activity hotspots, and movement frequency for each time period; Feature extraction is performed on the historical personnel activity data to generate a second dataset; The K-means clustering algorithm is trained using the second dataset. When the prediction accuracy of the K-means clustering algorithm does not meet the preset value, the parameters of the K-means clustering algorithm are tuned until the prediction accuracy meets the preset value, and then the first training model is generated.

6. The intelligent laboratory ventilation system as described in claim 1, characterized in that, The step of executing the ventilation control scheme based on the current environmental parameters and the current personnel activity data further includes: When the temperature difference between the inside and outside of the laboratory exceeds the preset temperature difference, the fresh air preheating and recovery function of the ventilation equipment is activated to capture the waste heat in the laboratory and preheat the incoming outside air through a heat exchanger. The heat exchanger consists of two independent channels, namely the air supply channel and the air exhaust channel. When the exhaust air is discharged from the laboratory, its heat energy is transferred to the heat exchanger. At the same time, fresh outside air enters the laboratory through the air supply channel and exchanges heat with the exhaust air when passing through the heat exchanger, so that the fresh air is preheated.

7. The intelligent laboratory ventilation system as described in claim 6, characterized in that, The exhaust duct is equipped with a HEPA filter for capturing and filtering particulate matter larger than 0.3 micrometers. The HEPA filter consists of a series of fiber mesh layers, which form a tortuous channel between the fiber mesh layers to intercept particulate matter between the mesh layers.

8. The intelligent laboratory ventilation system as described in any one of claims 1-7, characterized in that, The ventilation equipment includes one or more of the following: fume hood, exhaust fan, air conditioner, humidifier, condenser, and heater.

9. A method of using a laboratory intelligent ventilation system, characterized in that, include: Collect current environmental parameters and current personnel activity data in the laboratory; the current environmental parameters include the concentration of harmful gases; the current personnel activity data includes body heat and movement trajectory. Based on the current environmental parameters and the current personnel activity data, a ventilation control plan is executed, including: When the concentration of harmful gases exceeds the preset safety standard, the disinfection function of the ventilation equipment is activated. Air in the laboratory enters the gas diffusion chamber through the ventilation inlet. The aerosol sprayer sprays disinfectant into the gas diffusion chamber, where it mixes with the air to form a mesh-like aerosol mist. This mist fills the gas diffusion chamber and slowly flows towards the ventilation outlet, where it is discharged through the exhaust pipe. There are multiple ventilation inlets, all of which are configured to be movable and distributed throughout the laboratory according to the concentration distribution of harmful gases. The higher the concentration of harmful gases, the greater the wind pressure, wind speed, and air volume of the ventilation inlet. When the concentration of harmful gases does not exceed the preset safety standard, a first training model is constructed based on the human body heat and the movement trajectory to determine the correlation between personnel activity patterns and ventilation needs. Based on changes in the monitored personnel activity status or laboratory air quality, the global ventilation function or local ventilation function of the ventilation equipment is activated, and the ventilation inlet is controlled to tend to the position with the least disturbance to the airflow.

10. A laboratory, characterized in that, include: A laboratory intelligent ventilation system, wherein the laboratory intelligent ventilation system is the laboratory intelligent ventilation system according to any one of claims 1-8, and the method of using the laboratory intelligent ventilation system according to claim 9 is performed.

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