Isatis indigotica fog culture method and system

Abstract

The application discloses a kind of radix isati rhizome air mist culture planting method and system, it is related to negative pressure control technical field, including: the optimum position of determining planting area, establish system negative pressure area;For each planting area is equipped with adjustable fan, according to the real-time feedback of pressure difference sensor, automatically adjust fan speed, establish and maintain the negative pressure in planting area;Air flow direction in planting area is simulated and predicted using CFD software, dynamically adjust air flow direction;Real-time monitoring of air quality and pressure difference of each area, data analysis is carried out, when abnormal occurs, automatically trigger alarm.The radix isati rhizome air mist culture planting method and system provided by the application accurately carry out incubator area space analysis, determine the optimum position of planting area, real-time monitoring of air quality and pressure difference of each area, optimize air flow path in planting area, reduce the risk of infection spread, improve the overall performance and stability of system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of negative pressure control, in particular to a kind of radix isatidis aeroponics planting method and system. BACKGROUND

[0002] Radix isatidis is also known as Daqing, Caolan, Caocyan and Daqing, etc. It is a biennial herb of Brassicaceae. The root is used as medicine and called radix isatidis, and the leaf is used as medicine and called Daqin Ye. The root and leaf are bitter and cold in nature. They have the functions of clearing heat and resolving toxins, cooling blood and removing spots. They are widely used in various diseases such as influenza, encephalitis B, hepatitis, etc. Radix isatidis is distributed in Inner Mongolia, Shaanxi, Gansu, Hebei, Shandong, Jiangsu, Zhejiang, Anhui and Guizhou of China.

[0003] Due to different sources, the use of radix isatidis as a traditional Chinese medicine also has differences, which are manifested as differences in drug efficacy. There are many reports on high-yield planting technology of radix isatidis in the prior art, but the methods are different. In the northern region of Anhui, due to the characteristics of land and climate, most of the existing technologies use aeroponics to plant radix isatidis. However, there is no good control method and system for the aeroponics during planting. SUMMARY

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

[0005] To solve the above technical problems, the present application provides the following technical scheme: an aeroponics planting method of radix isatidis, comprising:

[0006] Using GIS and BIM to perform spatial analysis on the incubator area to determine the best position for planting;

[0007] Assembling an adjustable fan for each area, automatically adjusting the fan speed according to the real-time feedback of the pressure difference sensor, establishing and maintaining the negative pressure in the planting area, and transmitting the pressure difference data to the central control console in real time through the wireless communication module;

[0008] Setting HEPA and ULPA filter screens at the aeroponics outlet, and combining an electronic dust collector to assemble an RFID tag for the filter to track the service life and replacement cycle;

[0009] Using CFD software to simulate and predict the air flow direction in the planting area, combining an automatic damper adjustment system to dynamically adjust the air flow direction, thereby affecting the direction of the aeroponics flow;

[0010] Deploying IoT technology and sensor network to monitor the air quality and pressure difference of each area in real time, transmitting the data to the central database in real time for data analysis, automatically triggering an alarm when an abnormality occurs, and performing big data analysis and deep learning on the collected data to automatically optimize the control strategy of negative pressure and air flow direction.

[0011] As a preferred scheme of the Radix Isatidis air fog culture planting method, the optimal position of the planting area is determined by assigning an initial pheromone concentration to the raft position according to the collected incubator space data, and each ant updates the pheromone concentration τ ij (t) in the possible planting area position space according to the path selected by the ant in the current iteration, and updates the pheromone concentration τ ij (t+1) iteratively searches the ant colony until the ant colony converges to the optimal combination position, and outputs the final determined position of each type of planting area.

[0012] As a preferred scheme of the Radix Isatidis air fog culture planting method, the optimal position of the planting area is determined by assigning an initial pheromone concentration to the raft position according to the collected incubator space data, and each ant updates the pheromone concentration τ

[0013] h(i,j)=αh1(i,j)+βh2(i,j)+γh3(i,j)+δh4(i,j)+εh5(i,j)

[0014] wherein h(i,j) represents the total value of the heuristic function, i and j represent different spatial positions, α, β, γ, δ, and ε represent weight coefficients, h1(i,j) represents an inverse correlation degree sub-heuristic function of the flow path length, h2(i,j) represents a space utilization rate sub-heuristic function, h3(i,j) represents an evaluation value sub-heuristic function of infectious disease isolation effect, h4(i,j) represents a safety distance evaluation value sub-heuristic function, and h5(i,j) represents a relative position evaluation value sub-heuristic function between various planting areas.

[0015] The updated pheromone concentration τ ij is represented as

[0016]

[0017] wherein τ ij represents the pheromone concentration from position i to position j, represents the evaporation coefficient of the pheromone, and ∑ k∈ant represents the accumulation of all ants k, represents the pheromone increment left by ant k between position i and position j in the current iteration.

[0018] As a preferred scheme of the Radix Isatidis air fog culture planting method, the simulation and prediction of the air flow direction in the planting area includes collecting air flow rate, temperature, humidity, pressure difference, and harmful gas concentration parameters in the negative pressure planting area, obtaining the real-time working state and parameters of the fan and damper in the planting area, and cleaning the collected data to remove outliers and normalize the data.

[0019] The historical data is used to initialize the conditions of the air flow direction prediction algorithm, and the results of the model prediction are compared with real-time data;

[0020] The air flow direction prediction algorithm is represented as,

[0021]

[0022] wherein u represents the flow velocity vector, t represents time, p represents fluid pressure, and μ represents the viscosity of the fluid, represents the gradient operator, f(u, p, t) represents the correction term, g(u(s)) represents the function, τ represents the length of the time delay, s represents a variable at a certain time in the past τ, and ds represents the change in the integral variable s, represents the partial derivative of the flow velocity with respect to time, represents the convection term of the flow velocity, represents the change in velocity due to the pressure gradient, represents the change in velocity due to the viscosity of the fluid.

[0023] As a preferred scheme of the Isatis indigotica aeroponic planting method, the dynamic adjustment of the air flow direction comprises:

[0024] When and f(u, p, t) > 0.3, it is determined that the flow velocity is rapidly increasing, the standby cooling system is started, the fan speed is increased according to the first preset standard, the pressure data of the surrounding area is collected, it is verified whether there is an abnormal pressure difference, the required time delay τ is recalculated, the τ is considered to be shortened to reduce the shock, and the auxiliary flow regulator is started to assist in adjusting the flow velocity;

[0025] When and u is less than a preset threshold, it is determined that the flow velocity is stable for a long time, the equipment is initially checked to verify whether there is an object blocking or hindering the flow, the fan speed is increased according to the second preset standard, the diagnostic algorithm is run to detect whether there is other non-standard interference, the time delay τ of the system is reduced, and the supercharging device is started;

[0026] When and f(u, p, t) ≤ 0.3, it is determined that the flow velocity is rapidly decreasing, all the fans are immediately suspended and the system is checked, the emergency flow velocity recovery program is started, the flow velocity is gradually restored to the predetermined flow velocity, the data of all related sensors is collected to determine whether there is an abnormality, the time delay τ of the system is gradually increased, and if the system is still unstable, the auxiliary flow velocity adjustment system is started;

[0027] When the rate of change of the external environment exceeds the preset threshold, it is judged that the external environment is in a state of rapid change, the temperature and pressure stabilizing system is activated, the fan speed and output are adjusted according to the third preset standard, the time delay τ is adjusted, and if the external conditions continue to deteriorate, the emergency shutdown program is started and the alarm is triggered.

[0028] Another object of the present application is to provide a Radix Isatidis aeroponic planting system that can solve the problems of inaccurate spatial layout and untimely response adjustment through GIS and BIM technology, IoT technology and sensor network, CFD software and automated damper adjustment system.

[0029] To solve the above technical problems, the present application provides the following technical solutions: an Radix Isatidis aeroponic planting system, comprising: a sampling detection module, a negative pressure module, an optimization module and a control module; the negative pressure module is equipped with an adjustable fan for each planting area and is connected with a differential pressure sensor, and according to the real-time feedback of the differential pressure sensor, the fan speed is automatically adjusted to establish and maintain a negative pressure environment in the planting area, HEPA and ULPA filters are arranged at the air outlet, combined with an electronic dust collector, and RFID tags are arranged for these filters to track the service life and replacement cycle; the optimization module uses CFD software to simulate and predict the air flow direction in the planting area, dynamically adjusts the air flow direction according to the simulation data combined with an automated damper adjustment system, collects the air flow rate, temperature, humidity, differential pressure and harmful gas concentration parameters in the negative pressure planting area, cleans these data to remove outliers, uses historical data to predict the air flow direction, compares the prediction results with real-time data, and makes necessary adjustments; the control module is used to deploy IoT technology and sensor network to monitor the air quality and differential pressure of each area in real time, transmit the data to the central database in real time for data analysis, and if the data shows abnormal conditions, the system automatically triggers an alarm, deploys a cloud computing platform, and automatically optimizes the control strategy of negative pressure and air flow direction.

[0030] As a preferred scheme of the Radix Isatidis aeroponic planting system, the sampling detection module comprises a measurement assembly, the measurement assembly comprises a base, wheels arranged at the bottom of the base, an air mist pump arranged on the base, a to-be-measured pipeline connected with the air mist pump, a sensor arranged on the to-be-measured pipeline, and an information processing end connected with the sensor.

[0031] The reinforcing assembly is arranged at the bottom of the to-be-measured pipeline and comprises a mounting seat, clamps arranged in the mounting seat, springs connected with the clamps, rotating blocks arranged between the clamps, and handles connected with the rotating blocks.

[0032] As a preferred scheme of the Radix Isatidis aeroponic cultivation system, the side wall of the to-be-tested pipeline is provided with through holes, two through holes are oppositely arranged, two sensors are arranged, the sensors correspond to the through holes one by one, the sensors are inserted into the through holes, and the information processing end receives sensor data and outputs the amount of the aerosol flowing in the to-be-tested pipeline.

[0033] The Radix Isatidis aeroponic cultivation method and system provided by the application can accurately perform spatial analysis on the incubator area, determine the optimal position of the planting area, effectively isolate external factors, monitor the air quality and pressure difference of each area in real time by using the IoT technology and sensor network, quickly respond and adjust to ensure the stability of the negative pressure and the aerosol, optimize the airflow path in the planting area by using the CFD software and the automatic air door adjustment system, and realize automatic optimization of the system by using the ant colony algorithm and other optimization algorithms, thereby improving the overall performance and stability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0035] Figure 1 The overall structure diagram of the Radix Isatidis aeroponic cultivation system provided by an embodiment of the application is shown.

[0036] Figure 2 The structure schematic diagram of the Radix Isatidis aeroponic cultivation system provided by an embodiment of the application is shown.

[0037] Figure 3 The overall structure side view of the Radix Isatidis aeroponic cultivation system provided by an embodiment of the application is shown.

[0038] Figure 4 The overall structure cross-sectional view of the Radix Isatidis aeroponic cultivation system provided by an embodiment of the application is shown.

[0039] Figure 5 The reinforcing assembly schematic diagram of the Radix Isatidis aeroponic cultivation system provided by an embodiment of the application is shown.

[0040] Figure 6 The reinforcing assembly cross-sectional view of the Radix Isatidis aeroponic cultivation system provided by an embodiment of the application is shown.

[0041] Figure 7 The rotating block schematic diagram of the Radix Isatidis aeroponic cultivation system provided by an embodiment of the application is shown. DETAILED DESCRIPTION

[0042] In order to make the above objectives, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0043] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details that are set forth in the following description, and it is understood that persons having ordinary skill in the art, upon reading and understanding this description, can readily ascertain the presence of other embodiments of the present application that are not explicitly set forth or that are otherwise inherently determined. Accordingly, the present application is not intended to be limited by the embodiments described herein.

[0044] Embodiment 1

[0045] In an embodiment of the present application, a kind of planting method of Isatis root aeroponics is provided, comprising:

[0046] Carry out spatial analysis to incubator area using GIS and BIM, determine the best position of planting area, establish system negative pressure area.

[0047] Assemble adjustable fan for each planting area, automatically adjust fan speed according to real-time feedback of differential pressure sensor, establish and maintain negative pressure in planting area, and transmit differential pressure data to central control console in real time through wireless communication module.

[0048] Set HEPA and ULPA filter screen at the outlet of air mist, and combine with electronic dust collector, assemble RFID tag for filter, track service life and replacement cycle.

[0049] Simulate and predict air flow direction in planting area using CFD software, dynamically adjust air flow direction combined with automatic damper adjustment system.

[0050] Deploy IoT technology and sensor network to monitor air quality and differential pressure of each area in real time, transmit data to central database in real time, analyze data, automatically trigger alarm when abnormal, deploy cloud computing platform to analyze collected data and deep learning, automatically optimize control strategy of negative pressure and air flow direction.

[0051] Determine the best position of planting area, including assigning initial pheromone concentration to the position of account raft according to the collected incubator space data, each ant is based on heuristic function h (i,j) and current pheromone concentration τ ij(t) search in the possible planting area location space, update pheromone concentration τ ij (t+1), the search process of the ant colony is iterated until the ant colony converges to the optimal combination location, and the final determined location of each type of planting area is output.

[0052] The heuristic function is represented as,

[0053] h(i,j) = αh1(i,j) + βh2(i,j) + γh3(i,j) + δh4(i,j) + εh5(i,j)

[0054] Wherein, h(i,j) represents the total value of the heuristic function, i,j represent different spatial positions respectively, α, β, γ, δ, ε represent weight coefficients respectively, h1(i,j) represents the inverse correlation degree sub-heuristic function of the flow path length, h2(i,j) represents the space utilization rate sub-heuristic function, h3(i,j) represents the evaluation value sub-heuristic function of the infectious disease isolation effect, h4(i,j) represents the safety distance evaluation value sub-heuristic function, and h5(i,j) represents the relative position evaluation value sub-heuristic function between various planting areas.

[0055] The pheromone concentration τ ij is represented as,

[0056]

[0057] Wherein, τ ij represents the pheromone concentration from position i to position j, represents the evaporation coefficient of pheromone, and ∑ k∈ant represents the accumulation for all ants k, represents the pheromone increment left by ant k between position i and position j in the current iteration.

[0058] The simulation and prediction of the air flow direction in the planting area include collecting the air flow rate, temperature, humidity, pressure difference and harmful gas concentration parameters in the negative pressure planting area, obtaining the real-time working state and parameters of the fan and air door in the planting area, cleaning the collected data to remove outliers, and data normalization.

[0059] Use historical data to initialize the conditions of the air flow direction prediction algorithm, and compare the results of the model prediction with real-time data;

[0060] The air flow direction prediction algorithm is represented as,

[0061]

[0062] Wherein, u represents the flow rate vector, t represents time, p represents fluid pressure, and μ represents the viscosity of the fluid. Let f(u,p,t) represent the gradient operator, f(u,p,t) represent the correction term, g(u(s)) represent the function, τ represent the length of the time delay, s represent the variable at a specific moment in the past τ time interval, and ds represent the change in the integration variable s. This represents the partial derivative of the flow velocity with respect to time. The convection term represents the flow velocity. This indicates the velocity change caused by the pressure gradient. This indicates the change in velocity caused by the viscosity of the fluid.

[0063] Dynamically adjusting the direction of airflow includes:

[0064] when When f(u,p,t)>0.3, it is judged to be a state of rapid increase in flow rate. The backup cooling system is started, the fan speed is increased according to the first preset standard, pressure data of the nearby area is collected, the presence of abnormal pressure difference is verified, the required time delay τ is recalculated, and the oscillation is reduced by shortening τ. The auxiliary flow regulator is started to assist in regulating the flow rate.

[0065] when When u is less than the preset threshold, it is determined that the flow rate is in a stable state for a long time. The equipment is initially checked to verify whether there are any objects blocking or hindering the flow. The fan speed is increased according to the second preset standard, the diagnostic algorithm is run to detect whether there are other non-standard interferences, the system time delay τ is reduced, and the booster device is started.

[0066] when When f(u,p,t)≤0.3, it is determined that the flow rate is decreasing sharply. All fans are immediately stopped and the system is checked. An emergency flow rate recovery procedure is started to gradually restore the flow rate to the predetermined value. Data from all relevant sensors are collected to determine if there is any abnormality. The system time delay τ is gradually increased. If the system is still unstable, the auxiliary flow rate regulation system is started.

[0067] When the rate of change of the external environment exceeds the preset threshold, it is judged as a state of rapid change of the external environment. The temperature and pressure stabilization system is activated, and the fan speed and output are adjusted according to the third preset standard. The time delay τ is adjusted. If the external conditions continue to deteriorate, the emergency shutdown procedure is initiated and an alarm is triggered.

[0068] To verify the beneficial effects of this invention, scientific demonstration was conducted through economic benefit calculations and simulation experiments.

[0069] Select an existing environment and use the complete system based on the technical solution of our invention, combined with GIS and BIM software and CFD software, configure differential pressure sensors, humidity sensors, harmful gas concentration sensors and other related sensors, microprocessors, wireless communication modules and central control consoles, HEPA and ULPA filter screens and electronic dust collectors, RFID tag readers.

[0070] Use GIS and BIM software to determine the optimal location of the planting area based on incubator area data analysis. Record the exact location of each planting area. Compare with the prior art solution to determine the accuracy of the planting area positioning between the two.

[0071] Use differential pressure sensors to monitor the negative pressure state of each planting area under different time periods and conditions. Record the response speed and adjustment effect of the system. Compare with the prior art solution to determine the negative pressure stability between the two.

[0072] Use CFD software to simulate the air flow path in the planting area. Adjust the speed of the air door and fan according to the actual and simulated data. Compare with the prior art solution to determine the air flow optimization rate between the two.

[0073] In the case of sudden changes in external conditions, such as opening the incubation room, monitor the response and adjustment speed of the system. Compare with the prior art solution to determine the system response speed between the two.

[0074] Observe how the system automatically adjusts the fan speed, air door position and other parameters. Compare with the prior art solution to determine the system automatic optimization rate between the two.

[0075] By simulating the release of traceable harmless gas or particles, monitor their diffusion speed and range in the planting area. Compare with the prior art solution.

[0076] Monitor the total energy consumption of the system within a fixed time period. Compare with the prior art solution to determine the system energy consumption between the two. The experimental results are shown in Table 1.

[0077] Table 1 Comparison of experimental results

[0078] Test items Prior art solution Our invention solution Planting area positioning accuracy (%) 75.4 95.3 Negative pressure stability (%) 80.3 98.5 Air flow optimization rate (%) 70.5 96.3 System response speed (s) 10 3 System automatic optimization rate (%) 60.5 94.8 Infection spread risk reduction (%) 20.6 50.2 System energy consumption (kWh) 100 80

[0079] The scheme of the present application carries out accurate spatial analysis of the incubator area through GIS and BIM technology, so as to more accurately determine the location of the planting area, and the sensor network and IoT technology enable the system to monitor the air pressure difference in the planting area in real time and make rapid adjustments, so as to ensure the stability of the negative pressure, and the CFD software and the automatic air door adjustment system help to optimize the air flow path in the planting area, thereby reducing the risk of infection, and the present application uses advanced technology for real-time monitoring and automatic adjustment, greatly improving the response speed of the system, and the ant colony algorithm and other optimization algorithms are used for automatic optimization of the system, thereby improving the overall performance and stability of the system.

[0080] Embodiment 2

[0081] Reference Figures 1-7 For another embodiment of the present application, a Radix Isatidis aeroponics planting system is provided, which comprises a negative pressure module, an optimization module, a control module, and a sampling and detection module.

[0082] The negative pressure module is equipped with an adjustable fan for each incubator and is connected with a pressure difference sensor. According to the real-time feedback of the pressure difference sensor, the fan speed is automatically adjusted to establish and maintain the negative pressure environment in the incubator. HEPA and ULPA filters are set at the fresh air inlet, combined with an electronic dust collector, and RFID tags are installed for these filters to track the service life and replacement cycle.

[0083] The optimization module uses CFD software to simulate and predict the air flow direction in the incubator. According to the simulation data, the air flow direction is dynamically adjusted by an automatic air door adjustment system. The air flow rate, temperature, humidity, pressure difference, and harmful gas concentration parameters in the negative pressure incubator are collected, and these data are cleaned to remove outliers. Historical data are used for air flow direction prediction, and the prediction results are compared with real-time data and necessary adjustments are made.

[0084] The control module is used to deploy IoT technology and sensor networks to monitor the air quality and pressure difference of each area in real time. The data is transmitted to the central database in real time for data analysis. If the data shows abnormal conditions, the system automatically triggers an alarm. A cloud computing platform is deployed to automatically optimize the control strategy of negative pressure and air flow direction.

[0085] In the embodiment, the sampling detection module comprises a measuring assembly 100 and a reinforcing assembly 200, wherein the measuring assembly 100 comprises a base 101, wheels 102 arranged at the bottom of the base 101, an aerosol pump 103 arranged on the base 101, a pipeline to be detected 104 connected with the aerosol pump 103, sensors 105 arranged on the pipeline to be detected 104, and an information processing end 106 connected with the sensors 105; and the reinforcing assembly 200 is arranged at the bottom of the pipeline to be detected 104 and comprises a mounting seat 201, clamps 202 arranged in the mounting seat 201, springs 203 connected with the clamps 202, rotating blocks 204 arranged between the clamps 202, and handles 205 connected with the rotating blocks 204.

[0086] The wheels 102 are arranged at the bottom of the base 101, the wheels 102 are provided with four, the aerosol pump 103 introduces aerosol into the pipeline to be detected 104, the pipeline to be detected 104 is provided with through holes 104a on the side wall, two through holes 104a are arranged oppositely, the sensors 105 are provided with two, the sensors 105 correspond to the through holes 104a one by one, the sensors 105 are inserted into the through holes 104a, the information processing end 106 receives data of the sensors 105 and outputs the aerosol amount flowing in the pipeline to be detected 104, the mounting seat 201 is arranged directly below the pipeline to be detected 104, the clamps 202 are provided with two, the clamps 202 are symmetrically arranged on the mounting seat 201, the clamps 202 are rotationally connected with the mounting seat 201, and the end portions of the clamps 202 are located on both sides of the pipeline to be detected 104. One end of the spring 203 is connected with the mounting seat 201, the other end is connected with the bottom of the clamp 202, the handle 205 is rotationally connected with the mounting seat 201, the handle 205 is further provided with a protrusion 205a, the rotating block 204 is arranged between the two clamps 202, the rotating block 204 is provided with a groove 204a, and the protrusion 205a is embedded into the groove 204a.

[0087] In use, the device is moved to a measuring point by the wheels 102, the pipe 104 to be measured is connected to the aerosol pump 103, the aerosol is introduced into the pipe 104 to be measured by the aerosol pump 103, the sensor 105 is arranged on the pipe 104 to be measured, a pair of through holes 104a are formed on the pipe wall of the pipe 104 to be measured at opposite positions, the sensor 105 is arranged in and fixed to the through holes 104a, the information processing end 106 is connected to the sensor 105, the aerosol amount is measured from the pipe 104 to be measured through which the aerosol flows by detection of the sensor 105, the light signal is emitted from one sensor 105 to the other sensor 105, and the aerosol amount is determined by the received light amount unit; in order to ensure the connection stability of the pipe 104 to be measured and the aerosol pump 103, the reinforcing assembly 200 is arranged at the bottom of the pipe 104 to be measured, in use, the handle 205 is pressed down, the protrusion 205a on the handle 205 rotates synchronously in the rotating process of the handle 205, since the handle 205 is connected to the rotating block 204 and the protrusion 205a is embedded in the groove 204a, when the protrusion 205a rotates, the rotating block 204 is driven to rotate, before rotation, the thickness of the rotating block 204 is small and does not contact the clamps 202 on both sides, after rotation, the thickness of the rotating block 204 is increased to spread the clamps 202 on both sides, according to the lever principle, the bottom of the clamp 202 is spread, and the top clamp 202 is clamped, i.e. the pipe 104 to be measured is clamped and fixed, since the protrusion 205a can slide in the groove 204a, the rotating block 204 can automatically bear force and keep balance between the clamps 202, when the clamps 202 are spread, the rotating block 204 can slide in the direction of the protrusion 205a for fine adjustment, when it is needed to release the fixation, the handle 205 is lifted up, the spring 203 is reset to pop up, the clamps 202 on both sides of the rotating block 204 are contracted, and the top clamp 202 is spread to release the fixation of the pipe 104 to be measured.

[0088] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method for planting Isatis indigotica Fort. by air-planting, characterized in that, Comprise: Using GIS and BIM to analyze the space of incubator area and determine the best location for planting; Assembling adjustable fans for each area, automatically adjusting fan speed according to real-time feedback from pressure difference sensors, establishing and maintaining negative pressure in the planting area, and transmitting pressure difference data to the central control console in real time through a wireless communication module; Setting HEPA and ULPA filters at the aerosol outlet, and combining electronic dust collectors, and assembling RFID tags for the filters to track service life and replacement cycle; Using CFD software to simulate and predict air flow in the planting area, and dynamically adjusting air flow direction to affect aerosol flow direction by combining an automatic damper adjustment system; Deploying IoT technology and sensor networks to monitor air quality and pressure difference in each area in real time, transmitting data to a central database in real time for data analysis, automatically triggering an alarm when an anomaly occurs, and performing big data analysis and deep learning on collected data to automatically optimize negative pressure and air flow control strategies; The optimal position of the planting area is determined, which includes assigning an initial pheromone concentration to the planting position according to the collected incubator space data, and each ant is based on the heuristic function h(i,j) and the current pheromone concentration τ ij (t) search in the possible planting area position space, update the pheromone concentration τ ij (t+1) according to the path selected by the ant in the current iteration, iterate the search process of the ant colony, and output the finally determined position of each type of planting area until the ant colony converges to the optimal combination position; The heuristic function is represented as, h(i,j) = αh1(i,j) + βh2(i,j) + γh3(i,j) + δh4(i,j) + εh5(i,j) Where h(i,j) represents the total value of the heuristic function, i and j represent different spatial positions, α, β, γ, δ, and ε represent weight coefficients, h1(i,j) represents an inverse correlation degree sub-heuristic function with flow path length, h2(i,j) represents a space utilization rate sub-heuristic function, h3(i,j) represents an evaluation value sub-heuristic function of infectious disease isolation effect, h4(i,j) represents a safety distance evaluation value sub-heuristic function, and h5(i,j) represents a relative position evaluation value sub-heuristic function between various planting areas; The pheromone concentration τ ij is represented by, where τ ij denotes the pheromone concentration from position i to position j, p denotes the evaporation coefficient of pheromone, and k∈ant denotes the accumulation over all ants k, denotes the pheromone increment left by ant k between position i and position j in the current iteration.

2. The Isatidis Radix aerosol culture planting method according to claim 1, characterized in that: The simulation and prediction of air flow in the planting area include collecting air flow rate, temperature, humidity, pressure difference, and possible harmful gas concentration parameters in the negative pressure planting area, obtaining the real-time working status and parameters of the fans and dampers in the planting area, cleaning the collected data to remove outliers, and normalizing the data; Using historical data to initialize the conditions of the air flow prediction algorithm, and comparing the results of the model prediction with real-time data; The air flow prediction algorithm is represented as, where u denotes the flow velocity vector, t denotes time, p denotes fluid pressure, and μ denotes the viscosity of the fluid, denotes the gradient operator, f(u, p, t) denotes a correction term, g(u(s)) denotes a function, τ denotes the length of the time delay, s denotes a variable at a certain time in the past τ, and ds denotes the change in the integral variable s, denotes the partial derivative of the flow velocity with respect to time, denotes the convection term of the flow velocity, denotes the change in velocity due to the pressure gradient, denotes the change in velocity due to the viscosity of the fluid.

3. The Isatidis Radix aerosol culture planting method according to claim 2, characterized in that: The dynamic adjustment of air flow direction includes: When And f(u, p, t) > 0.3, it is judged as a rapid flow rate increasing state, the standby cooling system is started, the fan speed is increased according to the first preset standard, the pressure data of the nearby area are collected, it is verified whether there is an abnormal pressure difference, the required time delay τ is recalculated, the τ is considered to be shortened to reduce the shock, and the auxiliary flow regulator is started to assist in adjusting the flow rate; When and u is less than a preset threshold, it is judged that the flow rate is in a long-time stable state, the initial checking device is checked to verify whether there is an object blocking or hindering the flow, the fan rotating speed is increased according to a second preset standard, a diagnosis algorithm is run to detect whether there is other non-standard interference, the time delay τ of the system is reduced, and the supercharging device is started. When and f(u, p, t)≤0.3, it is judged as a sharp decrease in flow rate state, all the fans are immediately suspended and the system is checked, the emergency flow rate recovery program is started, gradually recovering to the predetermined flow rate, the data of all related sensors are collected, it is judged whether there is an abnormality, the time delay τ of the system is gradually increased, and if the system is still unstable, the auxiliary flow rate adjustment system is started. When the change rate of the external environment exceeds a preset threshold, it is determined that the external environment is in a state of rapid change, the temperature and pressure stabilization system is activated, the fan speed and output are adjusted according to the third preset standard, and the time delay τ is adjusted, and if the external conditions continue to deteriorate, the emergency shutdown program is started and an alarm is triggered.

4. A mist culture planting system of Isatidis radix using the mist culture planting method of Isatidis radix according to any one of claims 1 to 3, characterized by, Comprise: A negative pressure module, an optimization module, a control module, and a sampling and detection module; The negative pressure module is equipped with adjustable fans for each planting area and is connected with pressure difference sensors, automatically adjusts fan speed according to real-time feedback from pressure difference sensors to establish and maintain a negative pressure environment in the planting area, sets HEPA and ULPA filters at the aerosol outlet, and combines electronic dust collectors, and assembles RFID tags for these filters to track service life and replacement cycle; The optimization module simulates and predicts the air flow direction in the planting area by using CFD software, dynamically adjusts the air flow direction according to the simulation data combined with the automatic damper adjustment system, collects the air flow rate, temperature, humidity, pressure difference and harmful gas concentration parameters in the negative pressure planting area, cleans these data, removes outliers, uses historical data to predict the air flow direction, compares the prediction results with real-time data, and makes necessary adjustments. The control module is used to deploy IoT technology and sensor network, monitor the air quality and pressure difference of each area in real time, transmit the data to the central database in real time, analyze the data, automatically trigger the alarm if the data shows abnormal condition, deploy the cloud computing platform, and automatically optimize the control strategy of negative pressure and air flow direction.

5. The Isatidis Radix aerosol culture growing system according to claim 4, wherein: The sampling detection module comprises a measuring assembly (100), the measuring assembly (100) comprises a base (101), wheels (102) arranged at the bottom of the base (101), an aerosol pump (103) arranged on the base (101), a pipeline to be measured (104) connected with the aerosol pump (103), a sensor (105) arranged on the pipeline to be measured (104), and an information processing end (106) connected with the sensor (105). A reinforcing assembly (200) is arranged at the bottom of the pipeline to be measured (104) and comprises a mounting seat (201), clamps (202) arranged in the mounting seat (201), springs (203) connected with the clamps (202), rotating blocks (204) arranged between the clamps (202), and handles (205) connected with the rotating blocks (204).

6. The Isatidis Radix aerosol culture growing system according to claim 5, wherein: Holes (104a) are formed in the side wall of the pipeline to be measured (104), two holes (104a) are arranged oppositely, two sensors (105) are arranged, the sensors (105) correspond to the holes (104a) one by one, the sensors (105) are inserted into the holes (104a), and the information processing end (106) receives the data of the sensors (105) and outputs the aerosol amount flowing through the pipeline to be measured (104).

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