Laboratory air safety purification method and device based on Internet

Abstract

The invention discloses a laboratory air safety purification method and device based on the Internet, and relates to the technical field of laboratory air safety purification, and the method comprises the steps: deploying an environment sensor and an air purification device in a laboratory, configuring a control center, and connecting the environment sensor and the air purification device to the control center through a wired network; setting initial parameters; the method comprises the following steps: acquiring environment data by using an environment sensor, encrypting and uploading the environment data to a control center, and preprocessing the environment data through the control center; the preprocessed environment data is constructed into an environment data set, and the future air quality index of the laboratory is calculated through a long-short-term memory network algorithm; pollution grades are divided according to future air quality indexes of the laboratory, and a purification strategy is made based on the pollution grades. According to the invention, the future air quality index is predicted by using the LSTM algorithm, accurate prediction of the change trend of complex pollutants in the laboratory environment is realized, the prediction accuracy is improved, and the real-time response capability is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of laboratory air safety purification, and particularly to an Internet-based laboratory air safety purification method and device. Background Art

[0002] In recent years, the problem of volatile organic compound (VOCs) pollution caused by the volatilization of laboratory hazardous chemicals (such as chloroform, toluene, butyronitrile, etc.) has become increasingly prominent, and air quality management has become a research hotspot. Traditional air purification methods rely on the operation of equipment at fixed cycles and manual intervention, lacking the ability of real-time monitoring and dynamic adjustment. Some technologies collect data through sensors and use simple threshold judgment for control, but it is difficult to cope with complex pollution change trends. Traditional methods are insufficient in predicting future air quality and cannot formulate effective purification strategies in advance, resulting in a lag in response and affecting the purification effect.

[0003] Although there have been attempts to combine machine learning algorithms for air quality prediction, these methods mostly use shallow learning models, and their prediction accuracy and generalization ability are limited, making it difficult to meet the requirements of high precision and real-time. Traditional methods have defects in task allocation and monitoring and cannot dynamically adjust the working intensity of equipment according to real-time load, resulting in waste of resources or poor purification effect. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an Internet-based laboratory air safety purification method to solve the problems of low prediction accuracy, lag in response, and inability to dynamically adjust the working intensity of the purification equipment according to real-time load.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an Internet-based laboratory air safety purification method, which includes

[0008] Deploying environmental sensors and air purification equipment in the laboratory, configuring a control center, and connecting the environmental sensors and air purification equipment to the control center through a wired network, and setting initial parameters;

[0009] Using the environmental sensors to collect environmental data, encrypting and uploading it to the control center, and preprocessing the environmental data through the control center;

[0010] Constructing the preprocessed environmental data into an environmental data set, and calculating the future air quality index of the laboratory through a long short-term memory network algorithm;

[0011] Dividing the pollution level according to the future air quality index of the laboratory, and formulating a purification strategy based on the pollution level;

[0012] Evaluate the effectiveness of the purification strategy through simulation tests to obtain the optimal strategy;

[0013] Determine the priority of each air purification device, allocate purification tasks to each air purification device according to the optimal strategy, monitor the workload of each air purification device, and adjust the operation intensity of each air purification device.

[0014] As a preferred embodiment of the Internet-based laboratory air safety purification method described in the present invention, wherein: deploy environmental sensors and air purification devices in the laboratory, configure a control center, and connect the environmental sensors and air purification devices to the control center through a wired network, and set initial parameters. The specific steps are as follows.

[0015] The environmental sensors refer to PM2.5 sensors, CO2 sensors, volatile organic compound sensors, and temperature and humidity sensors;

[0016] Install the control center on the server in the laboratory, connect the control center to the local area network of the laboratory, and connect the environmental sensors and air purification devices to the control center through a wired network;

[0017] The initial parameters refer to the acquisition frequency and data upload interval of the environmental sensors.

[0018] As a preferred embodiment of the Internet-based laboratory air safety purification method described in the present invention, wherein: use the environmental sensors to collect environmental data and encrypt and upload it to the control center, and preprocess the environmental data through the control center. The specific steps are as follows.

[0019] The environmental data refers to PM2.5 concentration, CO2 concentration, volatile organic compounds, temperature, and humidity;

[0020] Use the AES-256 encryption algorithm to encrypt the data, and upload the encrypted data to the control center through a secure channel;

[0021] Preprocess the environmental data through the control center to remove outliers and normalize it.

[0022] As a preferred embodiment of the Internet-based laboratory air safety purification method described in the present invention, wherein: construct the preprocessed environmental data into an environmental data set, and calculate the future air quality index of the laboratory through the long short-term memory network algorithm. The specific steps are as follows.

[0023] Construct the preprocessed environmental data into an environmental data set in chronological order, and use the environmental data set to calculate the future air quality index of the laboratory through the long short-term memory network algorithm. The expression is:

[0024] Y = L(X t-n , X t-n+1 , …, X t-1 );

[0025] Wherein, Y is the future air quality index of the laboratory, and X t-n is the environmental data set at the t - nth time step, X t-n+1 is the environmental data set at the t - n + 1th time step, X t-1 is the environmental data set at the t - 1th time step, t is the current time step index, L is the long short - term memory network algorithm, and n is the number of time steps for backtracking.

[0026] As a preferred solution of the Internet - based laboratory air safety purification method described in the present invention, wherein: the pollution level is divided according to the future air quality index of the laboratory, and a purification strategy is formulated based on the pollution level. The specific steps are as follows.

[0027] The pollution level is divided into a low - pollution level, a medium - pollution level, and a high - pollution level according to the future air quality index of the laboratory;

[0028] The purification strategy refers to the working mode and operation intensity of the air purification equipment;

[0029] The working mode of the air purification equipment is selected according to the pollution level, and the mode coefficient of the working mode is set. By calculating the product of the future air quality index of the laboratory and the mode coefficient of the working mode, the operation intensity is obtained.

[0030] As a preferred solution of the Internet - based laboratory air safety purification method described in the present invention, wherein: through simulation tests, the effect of the purification strategy is evaluated to obtain the best strategy. The specific steps are as follows.

[0031] A simulation environment is constructed according to the future air quality index of the laboratory;

[0032] The purification strategy is used for operation simulation, and the purification efficiency and operation time are recorded to evaluate the effect of the purification strategy. The expression is:

[0033]

[0034] Wherein, S k is the comprehensive effect score of the kth purification strategy, E k is the purification efficiency of the kth purification strategy, α is the weight coefficient of the future air quality index of the laboratory, Y is the future air quality index of the laboratory, β is the weight coefficient of the operation time of the kth purification strategy, and T k is the operation time of the kth purification strategy;

[0035] The purification strategy combination with the highest comprehensive effect score of the purification strategy is selected as the best strategy.

[0036] As a preferred solution of the Internet-based laboratory air safety purification method described in the present invention, wherein: determining the priority of each air purification device, allocating purification tasks to each air purification device according to the optimal strategy, monitoring the workload of each air purification device, and adjusting the operation intensity of each air purification device, the specific steps are as follows:

[0037] Setting a priority rule based on the pollution level to determine the priority of each air purification device;

[0038] The purification task refers to the start time and wind speed mode of the air purification device;

[0039] Based on the purification task allocation result, real-time monitoring the workload of each air purification device, and adjusting the task allocation according to the workload status;

[0040] Setting an operation intensity rule according to the workload status, and sending an instruction to adjust the operation intensity to each air purification device.

[0041] In a second aspect, the present invention provides an Internet-based laboratory air safety purification device, including:

[0042] A device deployment module, deploying environmental sensors and air purification devices in the laboratory, configuring a control center, connecting the environmental sensors and air purification devices to the control center through a wired network, and setting initial parameters;

[0043] A preprocessing module, using environmental sensors to collect environmental data, encrypting and uploading it to the control center, and preprocessing the environmental data through the control center;

[0044] A prediction module, constructing the preprocessed environmental data into an environmental data set, and calculating the future air quality index of the laboratory through a long short-term memory network algorithm;

[0045] A strategy formulation module, dividing the pollution level according to the future air quality index of the laboratory, and formulating a purification strategy based on the pollution level;

[0046] A strategy evaluation module, evaluating the effect of the purification strategy through simulation tests, selecting the best combination of purification strategies, and setting operation parameters for each air purification device;

[0047] An adjustment module, determining the priority of each air purification device, allocating purification tasks to each air purification device, monitoring the workload of each air purification device, and adjusting the operation intensity of each air purification device.

[0048] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the Internet-based laboratory air safety purification method described in the first aspect of the present invention is implemented.

[0049] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the Internet-based laboratory air safety purification method described in the first aspect of the present invention is implemented.

[0050] The beneficial effects of the present invention are as follows: By using the LSTM algorithm to predict future air quality indicators, accurate prediction of the changing trends of complex pollutants in the laboratory environment is achieved, the accuracy of prediction is improved, and the real-time response ability is enhanced. By real-time monitoring the working loads of various air purification devices and dynamically adjusting their operation intensities according to the working load status, optimal allocation of resources and maximization of the purification effect are realized, and the response speed and flexibility are improved. Description of the Drawings

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the 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 drawings can be obtained based on these drawings.

[0052] Figure 1 It is a flowchart of the Internet-based laboratory air safety purification method in Embodiment 1.

[0053] Figure 2 It is a schematic diagram of the Internet-based laboratory air safety purification system in Embodiment 1. Detailed Embodiments

[0054] To make the above objects, features, and advantages of the present invention more obvious and understandable, the detailed embodiments of the present invention will be described in detail below with reference to the drawings of the specification.

[0055] Many specific details are set forth in the following description to facilitate a thorough understanding of the present invention, but the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0056] Secondly, the so-called "one embodiment" or "embodiment" herein refers to specific features, structures or characteristics that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that are mutually exclusive with other embodiments.

[0057] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides an Internet-based laboratory air safety purification method, including the following steps:

[0058] S1: Deploy environmental sensors and air purification equipment in the laboratory, configure a control center, and connect the environmental sensors and air purification equipment to the control center through a wired network, and set initial parameters.

[0059] The specific steps are as follows.

[0060] S1.1: The environmental sensors refer to PM2.5 sensors, CO2 sensors, volatile organic compound sensors (such as MR516), and temperature and humidity sensors. The PM2.5 sensor is installed at a height of 1.5 meters from the ground, the CO2 sensor is installed in areas with frequent human activities, and the temperature and humidity sensor is avoided from being directly exposed to sunlight or ventilation outlets;

[0061] Use brackets or wall fixing devices to fix the sensors in predetermined positions, ensuring that the sensors are in close contact with the wall or brackets to avoid shaking.

[0062] S1.2: Install the control center on the laboratory server, ensure that the software version is compatible with the hardware device, connect the control center to the laboratory local area network, connect the environmental sensors and air purification equipment to the control center through a wired network, and add all the deployed sensors and air purification equipment to the control center;

[0063] S1.3: The initial parameters refer to the acquisition frequency and data upload interval of the environmental sensors.

[0064] Set the acquisition frequency of the PM2.5 sensor to once per minute, the acquisition frequency of the CO2 sensor to once every 5 minutes, the acquisition frequency of the volatile organic compound sensor to once per minute, and the acquisition frequency of the temperature and humidity sensor to once every 10 minutes.

[0065] Set the data upload interval of all sensors to once every 15 minutes to ensure that environmental data can be uploaded to the control center in a timely manner.

[0066] S2: Use the environmental sensors to collect environmental data, encrypt and upload it to the control center, and preprocess the environmental data through the control center.

[0067] The specific steps are as follows:

[0068] S2.1. Environmental data refers to PM2.5 concentration, CO2 concentration, volatile organic compounds, temperature, and humidity;

[0069] Start all environmental sensors to collect data at the set frequency.

[0070] S2.2. Encrypt the data using the AES-256 encryption algorithm. AES-256 is a symmetric encryption algorithm with high security and efficiency. Establish a secure communication channel between the sensor and the control center using TLS. The encrypted data is uploaded to the control center through the secure channel;

[0071] S2.3. Decrypt the encrypted data. Use Z-score in the control center to remove outliers from the environmental data, and use min-max normalization to normalize the environmental data.

[0072] Store the preprocessed environmental data in the database of the control center and perform regular backups.

[0073] S3: Construct the preprocessed environmental data into an environmental dataset, and calculate the future air quality index of the laboratory through the long short-term memory network algorithm.

[0074] The specific steps are as follows:

[0075] S3.1. Arrange the preprocessed environmental data in chronological order to form a time series dataset. Set the number of time steps for backtracking as n. Use a sliding window to construct the time series data into an environmental dataset, and use the environmental dataset to calculate the future air quality index of the laboratory through the long short-term memory network algorithm. The expression is:

[0076] Y = L(X t-n , X t-n+1 , …, X t-1 );

[0077] where Y is the future air quality index of the laboratory, X t-n is the environmental dataset at the t - nth time step, X t-n+1 is the environmental dataset at the t - n + 1th time step, X t-1 is the environmental dataset at the t - 1th time step, t is the current time step index, L is the long short-term memory network algorithm, and n is the number of time steps for backtracking.

[0078] For example: Arrange the normalized PM2.5 data in chronological order to form a time series dataset. Set the number of backtracking time steps n = 6 (i.e., predict the future using the data of the past 1.5 hours). If the PM2.5 data for the past 6 time steps are 50, 60, 70, 85, 100, 120 μg / m 3 , and the future air quality index Y of the laboratory is 0.72.

[0079] It should also be noted that: Using the long short-term memory network algorithm to predict the future air quality index of the laboratory not only improves the accuracy and real-time performance of the prediction, but also provides a scientific basis for the subsequent formulation of purification strategies, thus realizing efficient and accurate air purification management.

[0080] S4: Divide the pollution levels according to the future air quality index of the laboratory, and formulate purification strategies based on the pollution levels.

[0081] The specific steps are as follows.

[0082] S4.1: Divide the pollution levels into low pollution level, medium pollution level, and high pollution level according to the future air quality index of the laboratory;

[0083] When the future air quality index of the laboratory is less than or equal to 0.3, it is a low pollution level. When the future air quality index of the laboratory is greater than 0.3 and less than or equal to 0.7, it is a medium pollution level. When the future air quality index of the laboratory is greater than 0.7, it is a high pollution level.

[0084] S4.2: The purification strategy refers to the working mode and operation intensity of the air purification equipment. The working modes are divided into three types: energy-saving mode, standard mode, and strong mode;

[0085] In the energy-saving mode, the equipment operates with the lowest energy consumption and has a moderate purification efficiency.

[0086] In the standard mode, the equipment operates with medium energy consumption and has a relatively high purification efficiency.

[0087] In the strong mode, the equipment operates with the highest energy consumption and has the highest purification efficiency.

[0088] S4.3: Select the working mode of the air purification equipment according to the pollution level. The energy-saving mode is applicable to the low pollution level, the standard mode is applicable to the medium pollution level, and the strong mode is applicable to the high pollution level. Set the mode coefficient of the working mode. The mode coefficient of the energy-saving mode is 0.5, the mode coefficient of the standard mode is 1.0, and the mode coefficient of the strong mode is 1.5. By calculating the product of the future air quality index of the laboratory and the mode coefficient of the working mode, the operation intensity is obtained.

[0089] For example: The future air quality index Y of the laboratory is 0.72, which belongs to the high pollution level. Start the strong mode (mode coefficient 1.5), and calculate the operation intensity = 1.5 × 0.72 = 1.08.

[0090] It should also be noted that by ensuring the scientificity and effectiveness of the purification strategy through the above steps, different pollution levels can be effectively dealt with, and the air quality in the laboratory can be guaranteed.

[0091] S5: Through simulation tests, evaluate the effect of the purification strategy to obtain the best strategy.

[0092] The specific steps are as follows.

[0093] S5.1: Construct a simulation environment by simulating the future air quality indicators of the laboratory and the operation of air purification equipment.

[0094] S5.2: Set the working mode and operation intensity of the purification equipment according to the purification strategy, then conduct the simulation, and record the purification efficiency and operation time during the simulation to evaluate the effect of the purification strategy. The expression is:

[0095]

[0096] where S k is the comprehensive effect score of the k-th purification strategy, E k is the purification efficiency of the k-th purification strategy, α is the weight coefficient of the future air quality indicators of the laboratory, Y is the future air quality indicators of the laboratory, β is the weight coefficient of the operation time of the k-th purification strategy, and T k is the operation time of the k-th purification strategy.

[0097] It should also be noted that the setting of the weight coefficient needs to be adjusted according to the actual situation to balance the influence of purification efficiency and operation time on the comprehensive effect. For example, in the case of a high pollution level, the value of α can be appropriately increased to highlight the importance of purification efficiency. In the case of limited resources, the value of β can be increased to reduce the operation time.

[0098] S5.3: Summarize and compare the results of all simulation runs, and select the combination of purification strategies with the highest comprehensive effect score of the purification strategy as the best strategy.

[0099] For example: The future air quality indicator of the laboratory Y = 0.72. Test two strategies, purification strategy A and purification strategy B. Among them, purification strategy A runs in strong mode for 20 minutes with a purification efficiency of 90%. The comprehensive effect score of purification strategy A is 1.49 (α is 0.7, β is 0.3). Purification strategy B runs in standard mode for 40 minutes with a purification efficiency of 85%. The comprehensive effect score of purification strategy B is 1.21. The comprehensive effect score of purification strategy A is higher and is selected as the best strategy.

[0100] It should also be noted that this strategy not only performs excellently in terms of purification efficiency, but also can complete the purification task in a relatively short time, thus achieving the optimal allocation of resources.

[0101] S6: Determine the priority of each air purification device, allocate purification tasks to each air purification device according to the optimal strategy, monitor the workload of each air purification device, and adjust the operation intensity of each air purification device.

[0102] The specific steps are as follows.

[0103] S6.1: Set the priority rules based on the pollution level. Allocate devices with a purification efficiency of 80% or more to high pollution levels, and further increase the priority if the device running time is less than or equal to 5h. Allocate devices with a running time of less than or equal to 5h to medium pollution levels, and further increase the priority if the device purification efficiency is greater than or equal to 50% and less than 80%. Allocate devices with a running time greater than 5h to low pollution levels, and determine the priority of each air purification device.

[0104] S6.2: The purification task refers to the start time and wind speed mode of the air purification device. The wind speed modes are divided into high speed mode, medium speed mode, and low speed mode.

[0105] The start time is determined according to the priority rules. The high pollution level uses the high speed mode, the medium pollution level uses the medium speed mode, and the low pollution level uses the low speed mode.

[0106] S6.3: Based on the purification task allocation result, the control center monitors the workload of each air purification device in real time and adjusts the task allocation according to the workload status.

[0107] Calculate the ratio of the current energy consumption of the air purification device to the maximum energy consumption as the workload of the air purification device.

[0108] The workload less than or equal to 0.3 is low load, the workload greater than 0.3 and less than or equal to 0.7 is medium load, and the workload is greater than 0.7.

[0109] If the air purification device is in a high load state, reduce the task volume. If the air purification device is in a low load state, increase the task volume. If the device is in a medium load state, keep the current task allocation unchanged.

[0110] S6.4: Set the operation intensity rules according to the workload status. The low load state represents low operation intensity, the medium load state represents medium operation intensity, and the high load state represents high operation intensity. Reduce the operation intensity for air purification devices with reduced task volume, increase the operation intensity for air purification devices with increased task volume, and send instructions to adjust the operation intensity to each air purification device.

[0111] If the workload of the air purification equipment is still unbalanced after adjustment, further optimize the task allocation and operation intensity.

[0112] For example, there are 3 air purification devices in the laboratory. Among them, the purification efficiency of P-01 is 85%, the operation time is 4 hours, the maximum energy consumption (kW) is 1.2, the current energy consumption (kW) is 0.9, and the current wind speed mode is the low-speed mode; the purification efficiency of P-02 is 78%, the operation time is 3 hours, the maximum energy consumption (kW) is 1.0, the current energy consumption (kW) is 0.4, and the current wind speed mode is the medium-speed mode; the purification efficiency of P-03 is 90%, the operation time is 6 hours, the maximum energy consumption (kW) is 1.5, the current energy consumption (kW) is 0.5, and the current wind speed mode is the low-speed mode.

[0113] The future air quality index Y of the laboratory is 0.72, belonging to the high pollution level. According to the priority rule, equipment with a purification efficiency ≥ 80% and an operation time ≤ 5h needs to be selected. P-01 is the first priority, P-02 has insufficient purification efficiency and does not participate in high pollution tasks. Although the operation time of P-03 is greater than 5 hours, its purification efficiency is higher than 90%, so it is the second priority.

[0114] Start P-01 and P-03, and the wind speed mode is the high-speed mode. At this time, the current energy consumption (kW) of P-01 is 1.2, the workload is 1.0, the current energy consumption (kW) of P-03 is 1.5, and the workload is 1.0. Both P-01 and P-03 are in a high load state. Reduce the task volume to reduce the operation intensity (reduce the wind speed to the medium-speed mode, energy consumption = 1.0kW). The workload of P-02 is 0.4, which is in a medium load state, and maintain the current task.

[0115] After adjusting the task allocation, the current energy consumption (kW) of P-01 and P-03 is 1.0. The workload of P-01 is 0.83 and it is still in a high load state, and the operation intensity needs to be further reduced. Reduce the wind speed mode to the low-speed mode. The workload of P-03 is 0.67 (medium load), and keep the current mode.

[0116] This embodiment also provides an Internet-based laboratory air safety purification device, including:

[0117] An equipment deployment module that deploys environmental sensors and air purification equipment in the laboratory, configures a control center, and connects the environmental sensors and air purification equipment to the control center through a wired network to set initial parameters;

[0118] A preprocessing module that uses environmental sensors to collect environmental data, encrypts and uploads it to the control center, and preprocesses the environmental data through the control center;

[0119] The prediction module constructs the preprocessed environmental data into an environmental dataset and calculates the future air quality indicators of the laboratory through the long short-term memory network algorithm;

[0120] The strategy formulation module divides the pollution levels according to the future air quality indicators of the laboratory and formulates purification strategies based on the pollution levels;

[0121] The strategy evaluation module evaluates the effects of the purification strategies through simulation tests, selects the best combination of purification strategies, and sets the operating parameters for each air purification device;

[0122] The adjustment module determines the priority of each air purification device, assigns purification tasks to each air purification device, monitors the workload of each air purification device, and adjusts the operation intensity of each air purification device.

[0123] This embodiment also provides a computer device applicable to the situation of the Internet-based laboratory air safety purification method, 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 the Internet-based laboratory air safety purification method proposed in the above embodiment.

[0124] The computer device can be a terminal. 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 implemented 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 covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0125] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for realizing Internet-based laboratory air safety purification proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0126] In summary, the present invention: uses the LSTM algorithm to predict future air quality indicators, realizes the accurate prediction of the change trend of complex pollutants in the laboratory environment, improves the accuracy of prediction, and enhances the real-time response ability. By real-time monitoring the workload of each air purification device and dynamically adjusting its operation intensity according to the workload status, the optimal allocation of resources and the maximization of the purification effect are realized, and the response speed and flexibility are improved.

[0127] 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 by the scope of the claims of the present invention.

Claims

1. An Internet-based method for purifying laboratory air safety, characterized in that: Including, Deploy environmental sensors and air purification equipment in the laboratory, configure the control center, connect the environmental sensors and air purification equipment to the control center through a wired network, and set initial parameters; Use environmental sensors to collect environmental data, encrypt and upload it to the control center, and preprocess the environmental data through the control center; Construct the preprocessed environmental data into an environmental dataset, and calculate the future air quality indicators of the laboratory through the long short-term memory network algorithm; Divide the pollution level according to the future air quality indicators of the laboratory, and formulate a purification strategy based on the pollution level; Through simulation tests, evaluate the effect of the purification strategy to obtain the best strategy; Determine the priority of each air purification equipment, assign purification tasks to each air purification equipment according to the best strategy, monitor the workload of each air purification equipment, and adjust the operation intensity of each air purification equipment.

2. The Internet-based laboratory air safety purification method according to claim 1, characterized in that: The steps of deploying environmental sensors and air purification equipment in the laboratory, configuring the control center, connecting the environmental sensors and air purification equipment to the control center through a wired network, and setting initial parameters are as follows. The environmental sensors refer to PM2.5 sensors, CO2 sensors, volatile organic compound sensors, and temperature and humidity sensors; Install the control center on the server in the laboratory, connect the control center to the local area network of the laboratory, and connect the environmental sensors and air purification equipment to the control center through a wired network; The initial parameters refer to the acquisition frequency and data upload interval of the environmental sensors.

3. The method for purifying laboratory air safety based on the Internet according to claim 2, wherein: The steps of using environmental sensors to collect environmental data, encrypt and upload it to the control center, and preprocess the environmental data through the control center are as follows. The environmental data refers to PM2.5 concentration, CO2 concentration, volatile organic compounds, temperature, and humidity; Use the AES-256 encryption algorithm to encrypt the data, and upload the encrypted data to the control center through a secure channel; Preprocess the environmental data through the control center to remove outliers and normalize it.

4. The internet-based laboratory air safety purification method according to claim 3, characterized in that: The steps of constructing the preprocessed environmental data into an environmental dataset and calculating the future air quality indicators of the laboratory through the long short-term memory network algorithm are as follows. Construct the preprocessed environmental data into an environmental dataset in chronological order, and use the environmental dataset to calculate the future air quality indicators of the laboratory through the long short-term memory network algorithm. The expression is: Y = L(X t-n , X t-n+1 , …, X t-1 ); Among them, Y is the future air quality index of the laboratory, and X t-n is the environmental data set at the (t - n)-th time step, and X t-n+1 is the environmental data set at the (t - n + 1)-th time step, and X t-1 is the environmental data set at the (t - 1)-th time step, t is the current time step index, L is the long short-term memory network algorithm, and n is the number of time steps for backtracking.

5. The method for purifying laboratory air safety based on the Internet according to claim 4, characterized in that: The steps of dividing the pollution level according to the future air quality indicators of the laboratory and formulating a purification strategy based on the pollution level are as follows. Divide the pollution level into low pollution level, medium pollution level, and high pollution level according to the future air quality indicators of the laboratory; The purification strategy refers to the working mode and operation intensity of the air purification equipment; Select the working mode of the air purification equipment according to the pollution level, set the mode coefficient of the working mode, and obtain the operation intensity by calculating the product of the future air quality indicators of the laboratory and the mode coefficient of the working mode.

6. The method for purifying laboratory air safety based on the Internet according to claim 5, characterized in that: The steps of evaluating the effect of the purification strategy through simulation tests to obtain the best strategy are as follows. Construct a simulation environment according to the future air quality indicators of the laboratory; Apply the purification strategy to run the simulation, record the purification efficiency and running time, and evaluate the effect of the purification strategy. The expression is as follows: Among them, S k is the comprehensive effect score of the k-th purification strategy, E k is the purification efficiency of the k-th purification strategy, α is the weight coefficient of the future air quality index of the laboratory, Y is the future air quality index of the laboratory, β is the weight coefficient of the running time of the k-th purification strategy, T k is the running time of the k-th purification strategy; Select the purification strategy combination with the highest comprehensive effect score of the purification strategy as the best strategy.

7. The method for purifying laboratory air safety based on the Internet according to claim 6, wherein: Determine the priority of each air purification device, allocate purification tasks to each air purification device according to the best strategy, monitor the workload of each air purification device, and adjust the operation intensity of each air purification device. The specific steps are as follows: Set the priority rule based on the pollution level to determine the priority of each air purification device; The purification task refers to the startup time and wind speed mode of the air purification device; Based on the purification task allocation result, monitor the workload of each air purification device in real time, and adjust the task allocation according to the workload status; Set the operation intensity rule according to the workload status, and send an instruction to adjust the operation intensity to each air purification device.

8. An Internet-based laboratory air safety purification device, based on the Internet-based laboratory air safety purification method according to any one of claims 1 to 7, characterized in that: Including: Device deployment module: Deploy environmental sensors and air purification devices in the laboratory, configure the control center, connect the environmental sensors and air purification devices to the control center through a wired network, and set the initial parameters; Pretreatment module: Use environmental sensors to collect environmental data, encrypt and upload it to the control center, and perform pretreatment on the environmental data through the control center; Prediction module: Construct the environmental data set from the pretreated environmental data, and calculate the future air quality index of the laboratory through the long short-term memory network algorithm; Strategy formulation module: Divide the pollution level according to the future air quality index of the laboratory, and formulate a purification strategy based on the pollution level; Strategy evaluation module: Evaluate the effect of the purification strategy through simulation tests, select the best purification strategy combination, and set the operation parameters for each air purification device; Adjustment module: Determine the priority of each air purification device, allocate purification tasks to each air purification device, monitor the workload of each air purification device, and adjust the operation intensity of each air purification device.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the Internet-based laboratory air safety purification method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the Internet-based laboratory air safety purification method according to any one of claims 1 to 7.

Images