A biological control drone specially designed for live transport

Abstract

The present invention discloses a biological control type drone for special living body transportation, including a biological chamber, a rotor, a protective cover, a camera, a battery, a flight control board, an airborne computer and a main body connector. The rotor and the protective cover are four in number and fixedly arranged at the bottom of the main body connector. The biological chamber and the battery are detachably arranged in the middle of the main body connector. The camera and the airborne computer are fixedly arranged on both sides of the main body connector. The flight control board is fixedly arranged on the top of the main body connector. The rotor and the flight control board are configured to complete the flight function of the drone. The camera and the airborne computer are configured to complete the path planning and obstacle avoidance functions. The biological chamber is configured to complete the biological living body transportation and delivery functions. The present invention can perform autonomous obstacle avoidance and path planning, as well as a multi-drone collaborative operation mode. After demarcating a certain area, it can autonomously complete the specific biological delivery work in the area.

Description

Technical Field

[0001] The present invention relates to the technical field of biological control type unmanned aerial vehicles, and in particular to a biological control type unmanned aerial vehicle dedicated to the transportation of living organisms. Background Art

[0002] In current UAV research, with the rapid development of electronic information technology and the maturity of civil UAV hardware, the functional classification of UAVs is becoming more and more refined, and UAVs for special purposes are also entering a rapid development channel.

[0003] In the biological field, local pests and invasive alien pests have long caused serious damage or threats to my country's forest ecosystems and agricultural planting areas. Due to the long-term and uncontrolled use of traditional chemical pesticides, many pests have developed obvious resistance, and a large number of natural enemy insects have been killed, causing some pests to be rampant. Many chemical pesticides seriously pollute water, air and soil, and enter the human body through the food chain, seriously endangering human health. As an effective natural regulatory factor, natural enemy insects play an important role in suppressing the population of these pests, and can effectively avoid the above-mentioned shortcomings, so they have broad application and development prospects.

[0004] The traditional method of delivery is manual carrying and delivery, which is not only inflexible and inaccurate, but also costly. Emerging drones are very suitable for biological control tasks because of their more efficient and low-cost flight missions. However, the existing drones equipped with biological beehives will lead to the inflexibility of drone movement, the low success rate of biological delivery, and poor biological control effects in a region.

[0005] Therefore, technicians in this field are committed to developing a biological control drone dedicated to live transport, which can achieve autonomous flight, live transport, and fixed-point delivery. Summary of the invention

[0006] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is how to carry out quantitative and fixed-point delivery of a certain type of natural enemy insects.

[0007] To achieve the above-mentioned objectives, the present invention provides a biological control type UAV dedicated to the transportation of living organisms, comprising a biological compartment, a rotor, a protective cover, a camera, a battery, a flight control board, an airborne computer and a main body connector, wherein the rotor and the protective cover are four in number and are fixedly arranged at the bottom of the main body connector, the biological compartment and the battery are detachably arranged in the middle of the main body connector, the camera and the airborne computer are fixedly arranged on both sides of the main body connector, the flight control board is fixedly arranged on the top of the main body connector, the rotor and the flight control board are configured to complete the flight function of the UAV, the camera and the airborne computer are configured to complete the path planning and obstacle avoidance functions, and the biological compartment is configured to complete the transportation and delivery functions of living organisms.

[0008] Furthermore, the main connecting piece is made of polyester resin by photocuring 3D printing and is integrally formed.

[0009] Furthermore, the biological chamber is in the shape of a long cylinder, including side-opening release doors arranged on both sides, and patch-type temperature control and humidity control boxes.

[0010] Furthermore, the flight control board is a Pixhawk 4 flight control board.

[0011] Furthermore, the camera is an Intel RealSense D435i camera.

[0012] Furthermore, the onboard computer is an NVIDIA Jetson Xavier NX development board.

[0013] Furthermore, the protective cover serves as a tripod for the drone.

[0014] Furthermore, the camera and the onboard computer are also configured to collect image information of crops in the detection area through the camera, and aggregate the information to the onboard computer to perform image recognition and pest inspection.

[0015] Furthermore, the image recognition adopts MobileNet algorithm.

[0016] Furthermore, the drone is configured to adopt a group collaboration mode, and through the autonomous honeycomb, the drone cluster is communicated and integrated, tasks are arranged, and supplies are carried out, and the drone batteries and biological compartments are refilled autonomously.

[0017] The beneficial effects of the present invention are:

[0018] 1. It can perform autonomous obstacle avoidance and path planning, as well as multi-UAV collaborative operation mode. After demarcating a certain area, it can autonomously complete the specific biological delivery work in that area.

[0019] 2. Compared with large drones, the collaborative operation mode of small drones can control the location and quantity of release more accurately, greatly improve the survival rate of released organisms, and can accurately control the distribution density of released organisms in the area.

[0020] 3. With its compact size and autonomous obstacle avoidance function, the delivery device can perform operations in narrow locations, or accurately deliver to a specific plant or environment through graphic recognition.

[0021] 4. Through information sharing among multiple drones, a more in-depth survey of the deployed area can be conducted, and the deployment strategy can be corrected in real time. At the same time, targeted deployment can be carried out on hypothetical targets with certain characteristics, and the actual biological conditions in the area can be analyzed.

[0022] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of the structure of a biological delivery drone according to a preferred embodiment of the present invention;

[0024] Figure 2 This is a top view of a biological delivery drone according to a preferred embodiment of the present invention;

[0025] Figure 3 This is an exploded view of a biological delivery drone according to a preferred embodiment of the present invention;

[0026] Figure 4 It is a structural schematic diagram of the main connecting parts of a biological delivery drone according to a preferred embodiment of the present invention;

[0027] Figure 5 This is a schematic diagram of the placement of components of a biological delivery drone in a preferred embodiment of the present invention;

[0028] Figure 6 This is a schematic diagram of the structure of a biological warehouse of a biological delivery drone according to a preferred embodiment of the present invention;

[0029] Figure 7 It is a dynamic model of a UAV under crosswind interference in a preferred embodiment of the present invention;

[0030] Figure 8 It is a principle block diagram of a fuzzy controller of a preferred embodiment of the present invention;

[0031] Fig. 9 It is an obstacle avoidance strategy for a drone of a preferred embodiment of the present invention;

[0032] Fig.10is a schematic diagram of a MobileNet algorithm for an image recognition process of a preferred embodiment of the present invention;

[0033] Fig.11 is a flow chart of an image recognition process of a preferred embodiment of the present invention;

[0034] Fig.12 This is a schematic structural diagram of a drone beehive in a preferred embodiment of the present invention;

[0035] Fig.13 This is a front view of a drone beehive of a preferred embodiment of the present invention;

[0036] Fig.14 This is a schematic diagram of a complete mission link of a UAV according to a preferred embodiment of the present invention;

[0037] Fig.15a This is a predatory functional response of a caterpillar adult to a third-instar larva of a fall armyworm according to a preferred embodiment of the present invention;

[0038] Fig.15b This is a predatory functional response of a caterpillar adult to a fourth-instar larva of a fall armyworm according to a preferred embodiment of the present invention;

[0039] Fig.15c This is the predatory functional response of an adult caterpillar to a fifth instar larva of a fall armyworm according to a preferred embodiment of the present invention.

[0040] Among them, 10-biological delivery drone, 11-biological warehouse, 12-rotor, 13-protective cover, 14-camera, 15-battery, 16-flight control board, 17-onboard computer, 18-main body connector, 111-release door, 112-chip temperature control, 113-humidity control box. DETAILED DESCRIPTION

[0041] The following describes the preferred embodiments of the present invention with reference to the drawings in the specification, so that the technical content is clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0042] In the drawings, components with the same structure are indicated by the same numerical reference numerals, and components with similar structures or functions are indicated by similar numerical reference numerals. The size and thickness of each component shown in the drawings are arbitrarily shown, and the present invention does not limit the size and thickness of each component. In order to make the illustration clearer, the thickness of the components is appropriately exaggerated in some places in the drawings.

[0043] The flight mode of large drones is not flexible enough, and they are not as convenient and fast as small specialized drones when performing small-area tasks. The drones in this application take a simple and lightweight route, and work in a precise delivery mode, plus a multi-drone cluster cooperation mode.

[0044] The drone of the present application specifically realizes the biological control function of living transportation, and it is a precise delivery that large drones are not capable of. Therefore, the objective environment it faces will be more complex and changeable. The destination may include streams, plateaus, jungles and other environments with uncertain humidity, wind speed and air pressure. It is necessary to balance stability and survival rate of delivery as much as possible, and even consider the cost control of accidental losses of drones under objective conditions.

[0045] The overall structure of the UAV is very compact. It is a standard quad-rotor UAV with a wheelbase of 70×70 mm. It uses a 2-inch small motor. The overall size of the UAV is 130×130×90 mm, and the empty take-off mass is about 160 grams.

[0046] This design is aimed at being able to operate in various special terrains, not only in vast plains, but also in jungles. Combined with path planning and autonomous obstacle avoidance functions, it can even fly autonomously. The ultra-small size and ultra-low weight design also make it more stable in the face of complex aerodynamic environments, improving the success rate of missions. At the same time, objective conditions such as vibration and noise generated by small rotors can also improve the survival rate of living targets.

[0047] like Figures 1 to 3 As shown, the biological delivery drone 10 is mainly composed of a biological chamber 11, a rotor 12, a protective cover 13, a camera 14, a battery 15, a flight control board 16, an onboard computer 17 and a main body connector 18. Among them, the rotor 12 and the flight control board 16 complete the flight function of the drone and belong to the power module of the drone; the camera 14 and the onboard computer 17 complete the path planning and obstacle avoidance functions and belong to the autonomous flight module; the biological chamber 11 is a task execution module; and finally the battery 15 and the protective cover 13 belong to the flight support module.

[0048] The specific structural components are introduced as follows:

[0049] 1. Drone body:

[0050] The main connecting member 18 is as follows Figure 4 and Figure 5 As shown, it is made of one-piece light-curing 3D printing and the raw material is polyester resin. The structural strength and quality are guaranteed. The overall weight is only 18 grams, which greatly improves the flight time of the drone.

[0051] The drone adopts a downward rotor design, which has three major advantages: first, the downward rotor will not take up the upper space of the drone, making it easier to debug and disassemble the drone; second, the airflow interference generated by the rotor is directed downward, reducing the impact on the upper biological compartment, and it fits the insect's habit of taking off obliquely upward, which increases the success rate of biological release; finally, the protective cover of the drone's rotor directly acts as a tripod, further reducing the take-off mass.

[0052] Finally, the battery 15 and the biological chamber 11 are placed in the middle of the drone, and the main body connector 18 is used for protection. At the same time, a lateral quick-release structure is made to increase the continuous operation time of the drone and achieve the effect of continuous operation of the drone swarm.

[0053] 2. Biological warehouse:

[0054] like Figure 6 As shown, the biological chamber 11 is in the shape of a long cylinder, and the volume is maximized while fitting the size of the drone. The release adopts a double-sided side-opening release door 111, which fits the habit of insects to take off obliquely, so that the insect take-off channel is not disturbed; the patch-type temperature control 112 can simultaneously heat up and cool down, so that the temperature in the biological chamber 11 is always at a temperature suitable for the target insects; the humidity control box 113 can also play a humidification function. According to the different biological characteristics of the released insects and the different target environments, the biological chamber 11 can adjust the temperature and humidity to make the insects in the chamber more active, or hibernate in a more energy-saving state.

[0055] 3. Choice of control system:

[0056] First of all, the flight control system uses the more common Pixhawk 4 flight control board 16 of the PX4 system. The versatility and stability of this series of flight control boards 16 have been tested by the market, and the size is also relatively suitable. It can also be easily compatible with the onboard computer 17. This series of flight control boards 16 use the STM32F765 chip, which has a higher main frequency, larger memory, higher computing performance, and lower latency, which can best play the performance of the drone.

[0057] The performance comparison of Pixhawk control system is shown in Table 1.

[0058] Table 1 Performance comparison of several Pixhawk control systems

[0059]

[0060]

[0061] Next is the camera 14. This application uses the Intel RealSense D435i camera 14, which is the sensor of the external environment during the autonomous flight of the drone. The D435i camera 14 is currently a relatively outstanding binocular camera. It is a stereo tracking solution that can provide high-quality depth for various applications. Its wide field of view is very suitable for applications such as robots or augmented reality and virtual reality, in which it is crucial to maximize the scene perspective. This compact camera 14 has a shooting range of up to 10 meters, can be easily integrated into the solution, and is fully configured and uses Intel RealSense SDK 2.0 technology, making it the most ideal choice for this drone.

[0062] The onboard computer 17 uses the NVIDIA Jetson Xavier NX development board, which is a powerful artificial intelligence (AI) development board. With 384 Compute Unified Device Architecture (CUDA) cores, 48 ​​Tensor Cores, and 2 NVIDIA Deep Learning Accelerator (NVDLA) engines, it can run multiple modern neural networks in parallel and process high-resolution data from multiple sensors at the same time, which is enough to meet the needs of a complete AI system and supports all common AI frameworks. After being equipped with the Ubuntu system, image recognition and settlement can be performed directly on the aircraft, and autonomous flight tasks such as obstacle avoidance and path planning can be completed.

[0063] The software control design is introduced as follows:

[0064] 1. Control of quadrotor drone

[0065] The current quadcopter control solution already has a very mature control solution. What needs to be paid attention to is the balancing after the relevant equipment is installed, and the design of the stable controller of the drone under environmental disturbances.

[0066] Firstly, the dynamic model of the quadrotor UAV is modeled and analyzed.

[0067] The dynamic model of the UAV under crosswind interference is as follows: Figure 7 shown.

[0068] The following formulas (1) and (2) are the six-degree-of-freedom motion equation and the rotor output total force equation of the UAV, respectively.

[0069]

[0070] F z =Ft +F w =2ρAV d V t (2)

[0071] Among them, the three lines of the six-degree-of-freedom motion equation (1) are defined by the three coordinate axes of the drone body coordinate system extending from the drone's center of gravity, forming a pitch angle According to Newton's second law, the second derivatives of the roll angle θ and the yaw angle ψ are the rotational angular acceleration of the drone along the three axes, which is equal to the resultant force of all the external rotational torques of the drone. The same is true for the following three items. The second derivatives of the drone along the xyz axes, that is, the acceleration of the drone in three directions, are the sum of the external forces of the drone in the three directions. (The detailed decomposition of external forces and external torques needs to be defined according to the drone model, and the calculation of external torques needs to consider the calculation of precession torques in different directions)

[0072] The total rotor force equation (2) needs to be combined with Figure 7 The dynamic model of the UAV under crosswind interference, the lift generated by the rotor output is mainly determined by the effective area of ​​the UAV rotor and the air flow. When the UAV rotor is disturbed by external crosswind, the air flow is no longer V d , it is also necessary to consider the V caused by external crosswind t , so the output force of the rotor in a crosswind state is the sum of the two. (where ρ is the air density, A is the effective rotor area, V d V is the air flow velocity generated by the rotor rotation in static state. w is the ambient crosswind speed, V t is the combined air flow velocity of the two, Ω is the rotor speed)

[0073] For the above-mentioned models with interference, nonlinear controllers such as fuzzy control and sliding film control can be selected. They have good robustness and high adaptability to the environment, and can better improve the stability of the UAV.

[0074] The principle block diagram of the fuzzy controller is as follows: Figure 8 shown.

[0075] The flowchart shows the basic working principle of the second-order fuzzy controller. First, the task decides the current control quantity, minus the output quantity of the actual state of the controlled object. The difference between the two is the control quantity e required now. The second-order fuzzy controller reads the control quantity and the first-order derivative of the control quantity. After fuzzification, it defines the control quantity required now according to the fuzzy rules established in advance, and the pre-established logic rule module infers the required control quantity according to the fuzzified definition. After defuzzification, it can output the three control quantities of "proportional", "differential" and "integral" required for PID control on the aircraft, and output them to the controlled object through the actuator. Fuzzy control can achieve good control effects based on the possible situations set in advance without very detailed calculations and modeling, and has high robustness, which conforms to human control logic.

[0076] 2. Regarding the autonomous flight function, based on the NX development board carried on the drone and the image acquisition function of the binocular camera, the small drone can recognize obstacles through images, autonomously plan the route, and finally reach the target point.

[0077] The specific process is to first set the maximum distance of the optical binocular camera and initialize the maximum obstacle avoidance angle of the aircraft when an obstacle is detected; determine whether an obstacle is detected. If no obstacle is detected, directly send the expected angle converted from the remote control command to the attitude controller to adjust the aircraft; if an obstacle is detected, then continue to determine the direction of the obstacle, so that the corresponding direction obstacle distance can be converted into a deflection percentage within 0 to 1, and then calculate the deflection angle of the aircraft in the corresponding direction, and sum the obtained deflection angle with the expected angle, and finally obtain the roll angle and heading deviation angle required by the attitude controller.

[0078] The obstacle avoidance strategy of the drone is as follows: Fig. 9 shown.

[0079] This is a relatively simple planar obstacle avoidance strategy, which mainly consists of two loops: a large loop to determine whether an obstacle is detected, and a small loop to determine the obstacle avoidance strategy. When an obstacle is encountered on the path of travel, the system will stop and reset the pitch axis to zero based on the current flight status of the drone. Then, while monitoring, the drone will perform translational motion perpendicular to the direction of travel. The translational motion is synthesized by a small-angle rotation of the roll axis until no obstacles are detected, and then the flight will be resumed according to the predetermined path. The obstacle avoidance strategy of the drone is nested in the monitoring loop. The drone's planar obstacle avoidance is completed by controlling the drone's level, height, and attitude. The drone's direction does not change during the translation process, so the tracking flight after obstacle avoidance is smoother.

[0080] 3. Image recognition for pest inspection

[0081] When a drone performs a delivery mission in a designated area, it will also start an image comparison and recognition mission in the area. Based on the functions of the onboard camera, it will check samples with specific features and provide feedback on the biological characteristics of the area.

[0082] First, the camera collects image information of crops in the detection area and aggregates the information to the onboard computer to complete the detection; in the crop detection module, the MobileNet algorithm is used, such as Fig.10 As shown in the figure, the MobileNet algorithm is a lightweight convolutional neural network that divides the standard convolution into two parts, namely deep convolution and point-by-point convolution. It also divides the merging calculation and filtering into two steps. The first step is to use the filter to convolve each input channel, and then use point-by-point convolution to perform convolution kernel operations on the results calculated by deep convolution to obtain the output results. This method can effectively reduce the amount of calculation and model parameters, and finally feed back to the terminal network.

[0083] The image recognition process is as follows: Fig.11 shown.

[0084] As described above, the small biological delivery drone of the present application, with its compact size and take-off weight, as well as the multi-eye camera and onboard computer, can bring multiple benefits:

[0085] 1. It can perform autonomous obstacle avoidance and path planning, as well as multi-UAV collaborative operation mode. After demarcating a certain area, it can autonomously complete the specific biological delivery work in that area.

[0086] 2. Compared with large drones, the collaborative operation mode of small drones can control the location and quantity of release more accurately, greatly improve the survival rate of released organisms, and can accurately control the distribution density of released organisms in the area.

[0087] 3. With its compact size and autonomous obstacle avoidance function, the delivery device can perform operations in narrow locations, or accurately deliver to a specific plant or environment through graphic recognition.

[0088] 4. Through information sharing among multiple drones, a more in-depth survey of the deployed area can be conducted, and the deployment strategy can be corrected in real time. At the same time, targeted deployment can be carried out on hypothetical targets with certain characteristics, and the actual biological conditions in the area can be analyzed.

[0089] The above four points reflect the advantages of the intelligence and precise delivery of the small biological delivery drone of this application, which can greatly improve the efficiency and success rate of biological control in a region.

[0090] First of all, the drone adopts a group collaboration mode and has an autonomous beehive, such as Fig.12and Fig.13 As shown, the drone cluster can be used for communication integration, mission assignment and other tasks; and with supplies on board, the drone batteries and biological compartments can be refilled autonomously to achieve long-term and sustained missions.

[0091] The complete mission chain of the drone is as follows Fig.14 shown.

[0092] Finally, a practical simulation is conducted based on a case of existing biological control.

[0093] The fall armyworm has a wide range of habitats, strong migratory ability, and high reproduction rate. Since it was confirmed to be introduced into Jiangcheng County, Pu'er City, Yunnan Province, my country on January 11, 2019, it has now caused damage in 124 counties and districts in 16 prefectures and cities in Yunnan, and has spread to 19 provinces and regions across the country. The stink bug belongs to the order Hemiptera and is widely distributed in my country. Both its nymphs and adults can prey on a variety of pests such as Lepidoptera, Coleoptera, and Diptera, especially Lepidoptera pests, and have a strong predatory ability, making it an important predatory natural enemy.

[0094] The predatory functional response of adult caterpillars to the 3rd, 4th, and 5th instar larvae of Spodoptera frugiperda Fig.15a , 15b , as shown in 15c.

[0095] The biological warehouse carried by the drone of this embodiment has a volume of about 20 cubic centimeters and can carry 15 to 25 adult crickets at a time. According to density measurement, the amount of one delivery can control the number of fall armyworms within 100×100 meters. Including the replenishment time and information collection time, one mission takes about 20 minutes. Under the conditions of the replenishment of the drone beehive and the coordinated work of multiple drones, a cluster of 4 drones can autonomously complete the delivery of adult crickets within a diameter of ten kilometers within about 40 hours. And with the support of information collection and image analysis carried by drones, real-time situation judgment and drone path adjustment can be carried out.

[0096] The use of native natural enemy insects can effectively control the fall armyworm. The use of the small biological transport drone of this embodiment to carry out precise delivery can not only effectively and quickly reduce the spread of alien species, but also will not seriously disrupt the local ecological balance.

[0097] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A biological control drone specially designed for transporting living organisms, characterized in that: The invention comprises a biological chamber, a rotor, a protective cover, a camera, a battery, a flight control board, an airborne computer and a main connecting piece. The UAV adopts a bottom-mounted rotor design. The main connecting piece is made of polyester resin by light-curing 3D printing and is integrally formed. There are four rotors and a protective cover, which are fixedly arranged at the bottom of the main connecting piece. The biological chamber and the battery are detachably arranged in the middle of the main connecting piece. The biological chamber and the battery adopt a lateral quick-release structure. The camera and the airborne computer are fixedly arranged on both sides of the main connecting piece. The flight control board is fixedly arranged on the At the top of the main connecting part, the rotor and the flight control board are configured to complete the flight function of the UAV, the camera and the onboard computer are configured to complete the path planning and obstacle avoidance functions, the biological warehouse is configured to complete the biological living body transportation and delivery functions, the UAV is configured to adopt a group collaboration mode, and through the autonomous beehive, the UAV cluster is communicated and integrated, the task is arranged, and the supplies are carried on board to refill the UAV battery and the biological warehouse autonomously; the biological warehouse is in the shape of a long column, including a side-opening release door arranged on both sides, and a patch-type temperature control and humidity control box.

2. The biological control drone for transporting living organisms as claimed in claim 1, characterized in that: The flight control board is a Pixhawk 4 flight control board.

3. The biological control drone for special living organism transportation as claimed in claim 1, characterized in that: The camera is an Intel RealSense D435i camera.

4. The biological control drone for special living organism transportation as claimed in claim 1, characterized in that: The onboard computer is an NVIDIA Jetson Xavier NX development board.

5. The biological control drone for special living organism transportation as claimed in claim 1, characterized in that: The protective cover acts as a tripod for the drone.

6. The biological control drone for special living organism transportation as claimed in claim 1, characterized in that: The camera and the onboard computer are also configured to collect image information of crops in the detection area through the camera, and aggregate the information to the onboard computer to perform image recognition and pest inspection.

7. The biological control drone for special living organism transportation as claimed in claim 6, characterized in that: The image recognition adopts the MobileNet algorithm.

8. The biological control drone for special living organism transportation as claimed in claim 1, characterized in that: The drone is configured to adopt a group collaboration mode, and uses an autonomous honeycomb to integrate communications, arrange tasks, and carry out supply replenishment for the drone cluster, and autonomously refill the drone battery and biological compartment.

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