TWI820870B - Methods and devices for biological monitoring by automated systems based on artificial intelligence - Google Patents

Abstract

A method and device for monitoring organisms through an automated system based on artificial intelligence. This method places an autonomous mobile device with artificial intelligence and edge computing functions next to a biological breeding cage to facilitate detection and management of the biological breeding cage. The conditions of the experimental animals in the cages are analyzed, and the biological images captured are processed for data calculation or analysis, and are sent back to a central management platform to achieve the purpose of automatically monitoring, caring for and managing the animals, and then standardizing the conditions in the cages of these animals. Reduce the interference to the lives of experimental animals to improve the quality of experimental animal care. In addition to solving the problem of manual rounds, it can also improve the abnormal detection rate and pollution control level, and achieve the purpose of automated monitoring and care and management of organisms.

Description

Methods and devices for monitoring organisms using automated systems based on artificial intelligence

The present invention relates to a method and device for monitoring organisms, and in particular, to a method and device for monitoring organisms using an automated system based on artificial intelligence.

According to the current situation, experimental animal care facilities are facing three major problems:

1. The artificial care mode of experimental animals is time-consuming and the detection status is not good: the daily care of experimental animals is carried out manually to observe the animal status. Experimental animals need to undergo routine care and observation once a day in the morning and afternoon. The observation content includes counting animals. Abnormal conditions such as the number of animals, whether the animals are injured or dead, and whether the living environment of the animals is suitable. The current manual method of patrolling the rooms and observing cage by cage is not only time-consuming, but also has a low abnormality detection rate due to the large number of animal cages. It is often impossible to immediately reflect the conditions of the animals in the cages, resulting in animals being in a state of emergency and unable to implement animal welfare.

2. Animal room pollution control and light cycle considerations: Due to pollution control in the animal room, personnel entering the animal room need to dress before entering the animal room. Therefore, staff cannot continuously observe animal activities for a long time, even if they can observe the animals in the animal room. It is also very limited, and there has been a lack of long-term and continuous animal observation data. Another consideration is to simulate the animals' normal daily routine. The lighting in the animal house is automatically controlled for 12 hours of light and 12 hours of darkness. Entering the animal house at night will not affect the animal's light cycle, so special equipment and training are required to enter. As a result, night care cannot be carried out and data on the activity patterns of experimental animals at night cannot be accumulated.

3. The conditions in animal cages cannot be standardized: Because the inspection work is performed manually, everyone has different standards for judging the conditions in the cage, which often leads to no unified standards for the conditions in the same animal cage. Therefore, when changing cages, the same animal room is often used to complete the cage changing work at the same time. However, animals do not like to be disturbed by nature. Every cage changing action is an urgent stimulus for the animals. If the cage changing intervention can be reduced, both considerations can be taken into consideration. The quality of life of laboratory animals is the best model for laboratory animal care.

Therefore, after active thinking, prototype testing and continuous improvement, the inventor of this case finally developed a simple and practical improvement method, which can especially solve the above-mentioned shortcomings.

The present invention discloses a method for monitoring organisms by an automated system based on artificial intelligence, which includes the following steps: Step 1: Acquire multiple image data through an autonomous mobile device to form an image data set. The autonomous mobile device includes a host unit and a sensing unit, and the autonomous mobile device is placed next to a biological breeding cage; Step 2: For the image data of the image data set, capture the image data through a motion detection algorithm and a key frame capture algorithm. Get the required image frame and convert it into a static image; Step 3: Use a computer equipped with a graphical user interface software to mark the coordinates of the target object in the above static image through the graphical user interface software to form a mark data, and store the labeled data in the computer. It can also be assisted by graphics algorithms to quickly label the labeled data to form a labeled data set for training of a machine learning machine; Step 4: Convert the labeled data to The data of the group is input into the machine learning machine, and a recognition model is established through the machine learning algorithm; Step 5: Deploy the recognition model to the host unit; Step 6: Get the new data obtained by the sensing unit of the autonomous mobile device The image data is processed through steps 2 and 3 to form a new label data set, which is input to the machine learning machine and compared with the identification model to obtain identification of the organism and its feeding environment status. As a result, the above identification results are simultaneously transmitted to a central management platform as a source of information for monitoring and abnormal notification, thereby achieving the purpose of automatically monitoring and caring for and managing living creatures.

The present invention also discloses an automated system-based biological monitoring device based on artificial intelligence, which is installed on a biological breeding cage. The biological breeding cage is equipped with a plurality of biological breeding cages. The automated system-based artificial intelligence monitoring biological device is It includes: a track unit, which includes at least one track group, the track group has a first track, and the first track is provided on one side of the biological breeding cage; a drive unit, which is electrically connected to The track unit, the driving unit is provided with a driver corresponding to the track group, the driver can drive the track group to operate; a host unit, the host unit is provided with at least one host corresponding to the first track, and the bottom of the host is equipped with a A first set of connecting boards is provided with a second set of connecting boards located on the track group; a sensing unit is provided with a sensor corresponding to the host system; The sensor is electrically connected to the host and is located at one end of the host.

With the above method and structure, the main machine moves horizontally or axially back and forth on the first track, so that the sensor provided on the main machine can monitor the activity status of the experimental animals in the biological breeding cage for a long time. And continuous observation will further standardize the conditions in these biological breeding cages, and only replace the biological breeding cages that need to be replaced, thereby reducing interference to the lives of experimental animals and improving the quality of experimental animal care. In this way, in addition to solving the problem of artificial methods In addition to the issue of ward inspections, it can also improve the abnormal detection rate and pollution control level, achieving the purpose of automated monitoring and care and management of organisms.

Please refer to Figure 1 and Figure 6, which discloses a method of biological monitoring based on an automated system based on artificial intelligence, which includes the following steps:

Step 1S1: Acquire multiple image data through an autonomous mobile device to form an image data set, and the autonomous mobile device is placed next to a biological breeding cage 200.

Among them, the image data can be static images or dynamic videos.

The autonomous mobile device can be a linear track set, a self-propelled vehicle or a drone. The autonomous mobile device includes a host unit 40 and a sensing unit 50. The host unit 40 is an edge computing host. The edge computing The host includes a central processing unit (CPU), memory, graphics processing unit (GPU, Graphics processing unit), peripheral I/O interface, wireless transmission, data storage unit and power supply unit. The sensing unit 50 includes a camera module, Infrared detection, temperature meter, humidity meter, directional microphone, vibration meter and pressure meter or any combination of the above.

Among them, the image data set includes animal species (such as age, body color), environmental conditions (such as skewed cage frames, low feed stocks, wet bedding due to excrement or leaking drinking fountains) and animal behaviors (such as eating feed, Climbing, fighting, mating, abnormal mobility).

Step 2S2: For the image data of the image data set, use motion detection algorithms and key frames extraction algorithms to extract some frames with reference value and convert them into static Images, and the images generated by different models of camera modules can be further normalized through computer vision algorithms to enhance the consistency of the data and help improve the training speed of a machine learning machine. and accuracy.

Step 3S3: As shown in Figures 2 to 5, use a computer equipped with a graphical user interface software (GUI, graphic user interface) to mark the target object in the above static image through the GUI software. coordinates to form a mark data, and store the mark data in the computer. In the embodiment of the present invention, the target objects are experimental animals and objects in the environment that need to be marked. The computer can be a desktop computer or a The notebook computer can also be used with the aid of graphics algorithms to assist image annotators to complete the annotation of the tagged data in a faster manner to form a tagged data set for training of the machine learning machine. The tagged data set includes an object. Recognition module, an image segmentation module, an animal entity recognition module and an animal behavior recognition module, users can choose the modules they want to use based on different above-mentioned target objects or different monitoring purposes.

Among them, the object recognition module marks the location of the marking data one by one in the form of bounding box A, as shown in Figure 2, and stores the marking data in the object recognition module. The image The segmentation module marks out the pixels (image pixels) in the area of the object outline (boundary/contour) B, object body (object) C and background (background) D belonging to the marked data one by one, as shown in Figure 3. And the labeling data is stored in the image segmentation module. The animal entity recognition module marks out the areas belonging to the facial landmarks (facial landmarks) P1 to P7 on the object's face image in the labeling data one by one. As shown in Figure 4, the labeled data is stored in the animal entity recognition module. The animal behavior recognition module identifies the target joint points (joints points or decision points) belonging to the object in the labeled data on the image one by one. Mark it out, as shown in Figure 5, and store its marking data in the animal behavior recognition module.

Step 4S4: Input the data of the marked data group into the machine learning machine, and establish a recognition model through the machine learning algorithm.

Step 5S5: Deploy the recognition model to the host unit 40.

Step 6S6: Process the new image data acquired by the sensing unit 50 of the autonomous mobile device through steps 2 and 3 to form a new label data set, and input it into the machine learning machine, and combine it with the recognition The models are compared through calculations to obtain identification results that identify organisms and their feeding environment status. At the same time, the identification results are sent to a central management platform. The central management platform can be a local computer or a cloud virtual host. The source of information for monitoring and abnormal notification, thereby achieving the purpose of automatically monitoring and caring for and managing living creatures.

Among them, in step 6, the above-mentioned identification results of some abnormalities are reviewed by the administrator to evaluate whether they need to be re-labeled and classified, so as to form a new label data group so that the new label data group can be continuously input to the machine learning The machine is used to retrain the recognition model, thereby improving the accuracy of the recognition model.

Among them, the machine learning algorithm in step 4 can vary according to application requirements, ranging from rule-based calculations, such as clustering or support vector machine (SVM), etc. to learning-based algorithms, and the latter is mainly based on deep learning (Deep Learning) with neural networks as the core architecture.

Referring to FIGS. 6 to 10 , the present invention further discloses a first embodiment of a biological monitoring device 100 based on an automated system based on artificial intelligence, which is installed on a biological breeding cage 200 . The biological breeding cage 200 is equipped with There are a plurality of biological breeding cages 210. The artificial intelligence-based automatic system monitoring biological device 100 includes:

A track unit 10 includes at least one track group 11. The track group 11 has a first track 12. The first track 12 is provided on one side of the biological breeding cages 210. The first track 12 can be Landscape setting or portrait setting.

A driving unit 20 is electrically connected to the track unit 10. The driving unit 20 is provided with a driver 21 corresponding to the first track 12. In the embodiment of the present invention, the driver 21 can be a motor or a pneumatic cylinder. The driver 21 is provided at one end of the first rail 12, and the driver 21 can drive the first rail 12 to operate.

A support unit 30. The support unit 30 is provided with at least one support frame 31 corresponding to the first track 12. One end of the support frame 31 is fixed to the biological breeding cage 200, and the other end is fixed to the first Track 12.

A host unit 40. The host unit 40 is provided with at least one host 41 corresponding to the first track 12. The host 41 is located on the first track 12. The host 41 is an edge computing host. The edge computing host includes a central Processor (CPU), memory, graphics processing unit (GPU, Graphics processing unit), peripheral I/O interface, wireless transmission and data storage unit, and the bottom of the host 41 is provided with a first set of connecting boards 42. One set of connecting plates 42 is equipped with a second set of connecting plates 43. The second set of connecting plates 43 is located on the first track 12. In the embodiment of the present invention, the first set of connecting plates 42 is provided with a plurality of slides. Block 421, and the slide blocks 421 of the second set of connecting plates 43 corresponding to the first set of connecting plates 42 are provided with a plurality of slide grooves 431, and the slide blocks 421 are fastened to the slide grooves 431, so that the The main machine 41 is assembled on the first rail 12 and can move on the first rail 12. Another end of the main machine 41 is connected to an adjusting frame 44. The adjusting frame 44 includes a front frame body 45 and a rear frame body. 46. Both sides of the front frame 45 are connected to the rear frame 46 through a first fixing part 47, and the rear frame 46 is connected to the main unit 41 through a plurality of second fixing parts 48. Among them, by loosening the first fixing parts 47, the front frame body 45 can be rotated relative to the rear frame body 46, or by loosening the second fixing parts 48, the rear frame body 46 can be rotated relative to the main unit. 41Adjust the position up and down.

A sensing unit 50. The sensing unit 50 is provided with a sensor 51 corresponding to the host 41. The sensor 51 includes a camera module, infrared detection, a thermometer, a humidity meter, a directional microphone, and a vibration meter. and a pressure gauge or any combination of the above. The sensor 51 is electrically connected to the host 41 and is located on the front frame 45 of the host 41 .

A power supply unit 60 is provided with a power supply module 61 corresponding to the host 41 . The power supply module 61 can provide the power required by the host 41 and the sensor 51 .

Referring to Figures 11 and 12 in conjunction with Figure 7, the driver 21 drives the first rail 12 to rotate and drives the host 41, through which the host 41 performs lateral or axial reciprocating movement on the first rail 12, so that the device The sensor 51 in the host computer 41 can observe the activity status of the experimental animals in the biological breeding cages 210 for a long time and continuously, and further standardize the conditions in the biological breeding cages 210, only for those that need to be replaced. The replacement of the biological breeding cage 210 reduces the interference to the life of experimental animals and improves the quality of experimental animal care. In addition to solving the problem of manual rounds, it can also improve the abnormal detection rate and pollution control level.

Referring to FIGS. 13 to 16 , a second embodiment of the device of the present invention is disclosed. The difference between the second embodiment of the present invention and the aforementioned first embodiment is that the support unit 30 also includes a supporting frame 32 , the supporting frame 32 can be erected around the biological breeding cage 200 or on one side of the biological breeding cage 200. The supporting frame 32 is composed of a plurality of supporting rods 33, so that the supporting frame 32 The overall shape can be slightly inverted U-shaped or square. The track set 11 has a first track 12, two second tracks 13 and a synchronized round rod 14. The second tracks 13 are located on both sides of the bearing frame 32. side, and are respectively fixed to the corresponding support rods 33. The driver 21 is provided on one of the second rails 13. The first rail 12 is fixed to each of the second rails 13 through two fixing brackets 34, and the Both ends of the synchronous round rod 14 are respectively fixed to the second rails 13, whereby the driver 21 drives the corresponding second rail 13 and the synchronous round rod 14 to rotate at the same time, so that the synchronous round rod 14 drives the other synchronous round rod 14. When the second rail 13 is activated, the fixing brackets 34 move synchronously and in the same direction to link the first rail 12, so that the first rail 12 and the main machine 41 reciprocate along the long axis direction of the second rails 13. move, or another driver 21 may be further provided on the first rail 12, so that the other driver 21 drives the first rail 12 to rotate at the same time, so that the host 41 can move on the first rail 12 at the same time to lift the machine. Invent the convenience of use.

Referring to Figure 17, a third embodiment of the device of the present invention is disclosed. The difference between the third embodiment of the present invention and the aforementioned first embodiment is that the first assembly plate 42 is provided with a plurality of magnetic pieces. 422, and the magnetic pieces 422 of the second set of connecting plates 43 corresponding to the first set of connecting plates 42 are provided with a plurality of magnetic slots 432, and are attracted to the magnetic slots 432 through the magnetic pieces 422, The host 41 is assembled on the first rail 12 and can move on the first rail 12 .

Referring to Figure 18, a fourth embodiment of the device of the present invention is disclosed. The difference between the fourth embodiment of the present invention and the aforementioned first embodiment is that the first assembly plate 42 is provided with a push button on both sides. 423, and the second set of connecting plates 43 is provided with two pressing holes 433 corresponding to the two pressing parts 423 of the first set of connecting plates 42, and the pressing parts 423 are clamped in the pressing holes 433, so that the The host 41 is assembled on the first rail 12 and can move on the first rail 12 .

Referring to Figure 19, a fifth embodiment of the device of the present invention is disclosed. The fifth embodiment of the present invention is different from the first embodiment in that a card is provided on both sides of the first set of connecting plates 42. Fasteners 424, and the second set of connecting plates 43 corresponding to the two fastening parts 424 of the first set of connecting plates 42 are provided with two buckling holes 434, and the fastening parts 424 are fastened to the buckles. The hole 434 allows the main unit 41 to be assembled on the first rail 12 and move on the first rail 12 .

To sum up, the present invention has excellent practicality among similar products. At the same time, after reviewing the technical information on this type of structure at home and abroad, no similar structure has been found to exist in the literature. Therefore, This invention actually meets the requirements for an invention patent, and you need to file an application in accordance with the law.

However, the above is only one of the best possible embodiments of the present invention. Therefore, all equivalent structural changes made by applying the description and patent scope of the present invention should be included in the patent scope of the present invention.

200:Biological breeding cage 210:Biological breeding cage 100: Device for monitoring biological organisms through automated systems based on artificial intelligence 10:Track unit 11: Orbital group 12: First track 13:Second track 14:Synchronized round rod 20:Drive unit 21:Drive 30:Support unit 31: Support frame 32:Bearing frame 33:Support rod 34:fixed frame 40: Host unit 41:Host 42: The first set of connecting boards 421:Slider 422:Magnetic parts 423: Pressing parts 424: fasteners 43: The second set of connecting boards 431:Chute 432:Magnetic slot 433: Press hole 434:Buckle hole 44: Adjustment stand 45:Front frame body 46:Rear frame body 47:First fixing piece 48:Second fixing piece 50: Sensing unit 51: Sensor 60:Power supply unit 61:Power supply module A: bounding box B: Object outline C: Object body D:Background E: target joint point S1: Step 1 S2: Step 2 S3: Step 3 S4: Step 4 S5: Step 5 S6: Step 6 P1~P7: Facial feature points

[Fig. 1] is a schematic flow diagram of the present invention. [Fig. 2] is a schematic diagram of the object recognition module of the present invention. [Fig. 3] is a schematic diagram of the present invention applied to the image segmentation module. [Fig. 4] is a schematic diagram of the present invention applied to the animal entity recognition module. [Figure 5] is a schematic diagram of the animal behavior recognition module of the present invention. [Fig. 6] is a perspective view of the first embodiment of the present invention. [Fig. 7] is a partial enlarged view of the first embodiment of the present invention. [Fig. 8] is a schematic diagram of the main unit assembled on the first track according to the first embodiment of the present invention. [Fig. 9] is a partially exploded view of the first embodiment of the present invention. [Fig. 10] is a schematic diagram of the sensor operating on the host machine according to the first embodiment of the present invention. [Fig. 11] is a schematic diagram of the main machine reciprocating on the first track disposed laterally according to the first embodiment of the present invention. [Fig. 12] is a schematic diagram of the main machine reciprocating on the first track arranged longitudinally according to the first embodiment of the present invention. [Fig. 13] is a perspective view of the second embodiment of the present invention. [Fig. 14] is a schematic diagram of the first track reciprocating up and down according to the second embodiment of the present invention. [Fig. 15] is a schematic diagram of the first track reciprocating left and right according to the second embodiment of the present invention. [Fig. 16] is a schematic diagram of the host machine moving left and right on the first track and up and down on the second track according to the second embodiment of the present invention. [Fig. 17] is a schematic diagram of the main unit assembled on the first track according to the third embodiment of the present invention. [Fig. 18] is a schematic diagram of the main unit assembled on the first track according to the fourth embodiment of the present invention. [Fig. 19] is a schematic diagram of the main unit assembled on the first track according to the fifth embodiment of the present invention.

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Claims (17)

A method of monitoring organisms using an automated system based on artificial intelligence, which includes the following steps: Step 1: Acquire multiple image data through an autonomous mobile device, which includes a host unit and a sensing unit, to form an image data set, and the autonomous mobile device is placed next to a biological breeding cage; Step 2: For the image data of the image data group, use the motion detection algorithm and the key frame capture algorithm to capture the required image frames and convert them into static images; Step 3: Use a computer equipped with a graphical user interface software to mark the coordinates of the target object in the above static image through the graphical user interface software to form a mark data, and store the mark data in the computer , and can be assisted by graphics algorithms to quickly label the labeled data to form a labeled data group for training a machine learning machine; Step 4: Input the data of the marked data group into the machine learning machine, and establish a recognition model through the machine learning algorithm; Step 5: Deploy the identification model to the host unit; Step 6: Process the new image data acquired by the sensing unit of the autonomous mobile device through steps 2 and 3 to form a new label data set, and input it into the machine learning machine and the recognition model By performing computational comparisons, the identification results can be obtained to identify the organisms and their feeding environment status. At the same time, the identification results are sent to a central management platform as a source of information for monitoring and abnormal notifications, thereby achieving automated monitoring and care and management of organisms. the goal of. As claimed in claim 1, the method for monitoring living things by an automated system based on artificial intelligence, wherein the autonomous mobile device can be a linear track set, a self-propelled vehicle or a drone, and the host unit is an edge computing host, and the edge computing The host includes a central processing unit, memory, graphics computing unit, peripheral I/O interface, wireless transmission, data storage unit and power supply unit. The sensing unit includes a camera module, infrared detection, thermometer, humidity meter, pointing microphone, vibrator and pressure meter or any combination of the above. The method of biological monitoring by an automated system based on artificial intelligence as described in claim 1, wherein the image data set includes animal species, environmental conditions and animal behavior, and the image data set can be further normalized through computer vision algorithms. . The method of biological monitoring by an automated system based on artificial intelligence as described in claim 1, wherein the marking data set includes an object recognition module, an image segmentation module, an animal entity recognition module and an animal behavior recognition module . The method of biological monitoring by an automated system based on artificial intelligence as described in claim 4, wherein the object recognition module marks the location of the marked data in a bounding box manner one by one, and stores the marked data in the object In the recognition module, the image segmentation module marks out the pixels of the object outline, object body and background area belonging to the marked data one by one, and stores the marked data in the image segmentation module. The animal entity The recognition module marks out the facial feature points on the object's face image in the marked data one by one, and stores the marked data in the animal entity recognition module. The animal behavior recognition module marks out the facial feature points on the object's face image one by one. The target joint points belonging to the object in the mark data on the image are marked, and the mark data is stored in the animal behavior recognition module. The method of biological monitoring by an automated system based on artificial intelligence as described in claim 1, wherein in step 6, the above-mentioned identification results of some abnormalities are reviewed by the administrator to evaluate whether they need to be re-labeled and classified, so as to form a new The marking data set enables the new marking data set to be continuously input to the machine learning machine to retrain the recognition model, thereby improving the accuracy of the recognition model. An automated system-based biological monitoring device based on artificial intelligence is installed on a biological breeding cage, which is equipped with a plurality of biological breeding cages. The automated system-based biological monitoring device based on artificial intelligence includes: A track unit, which includes at least one track group, the track group has a first track, the first track is provided on one side of the biological breeding cage; A driving unit that is electrically connected to the track unit. The driving unit is provided with at least one driver corresponding to the track group, and the driver can drive the track group to operate; A host unit. The host unit is provided with at least one host corresponding to the first track. The bottom of the host is provided with a first set of connecting plates. The first set of connecting plates is equipped with a second set of connecting plates. The second set of connecting plates is provided with a first set of connecting plates. The connecting plate is located in the track group; A sensing unit is provided with a sensor corresponding to the host. The sensor is electrically connected to the host and is located at one end of the host. The biological monitoring device of an automated system based on artificial intelligence as described in claim 7, wherein the host is an edge computing host, including a central processing unit, a memory, a graphics computing unit, a peripheral I/O interface, a wireless transmission and Data storage unit, the sensor includes a camera module, infrared detection, temperature meter, humidity meter, directional microphone, vibration meter and pressure meter or any combination of the above. As claimed in claim 7, the biological monitoring device based on an automated system based on artificial intelligence, wherein one end of the host computer is connected to an adjusting frame, and the adjusting frame includes a front frame body and a rear frame body, and the two sides of the front frame body are The side systems are respectively connected to the rear frame body through a first fixing part, and the rear frame body is connected to the main engine through a plurality of second fixing parts. As claimed in claim 7, the device for monitoring living things by an automated system based on artificial intelligence, wherein the first track can be arranged horizontally or vertically. The device for monitoring living things by an automated system based on artificial intelligence as described in claim 7, further comprising a support unit, which is provided with at least one support frame corresponding to the first track, and one end of the support frame is fixed In the biological breeding cage, the other end is fixed on the first track. In addition, the support unit also includes a supporting frame. The supporting frame can be erected around the biological breeding cage or on the biological breeding cage. On one side of the cage, the support frame is composed of a plurality of support rods, so that the entire support frame can be slightly inverted U-shaped or square. As claimed in claim 11, the biological monitoring device of an automated system based on artificial intelligence, wherein the track set has one first track, two second tracks and a synchronized round rod, and the second tracks are located on the bearing. Both sides of the base frame are respectively fixed to the corresponding support rods. The driver is located on one of the second rails. The first rail is fixed to each of the second rails through two fixing brackets, and the synchronization circle The two ends of the rod are respectively fixed on the second rails. The biological monitoring device of an automated system based on artificial intelligence as described in claim 7, which further includes a power supply unit. The power supply unit is provided with a power supply module corresponding to the host, and the power supply module can provide the The power required by the host and the sensor. As claimed in claim 7, the biological monitoring device of an automated system based on artificial intelligence, wherein the first set of connecting boards is provided with a plurality of sliders, and the second set of connecting boards corresponds to the plurality of sliders of the first set of connecting boards. The slide block is provided with a plurality of slide grooves, and the slide blocks are fastened to the slide grooves, so that the main unit is placed on the first track and can move on the first track. As claimed in claim 7, the biological monitoring device of an automated system based on artificial intelligence, wherein the first set of connecting boards is provided with a plurality of magnetic attraction components, and the second set of connecting boards corresponds to the first set of connecting boards. The magnetic parts are provided with a plurality of magnetic grooves, and the magnetic parts are attracted to the magnetic grooves, so that the main unit is installed on the first track and can move on the first track. As claimed in claim 7, the biological monitoring device of an automated system based on artificial intelligence, wherein the first set of connecting plates is provided with a pressing member on both sides, and the second set of connecting plates corresponds to the first set of connecting plates. The two pressing parts are provided with two pressing holes, and the pressing parts are clamped in the pressing holes, so that the main unit is installed on the first track and can move on the first track. As claimed in claim 7, the biological monitoring device of an automated system based on artificial intelligence, wherein the first set of connecting plates is provided with a buckle on both sides, and the second set of connecting plates corresponds to the first set of connecting plates. The two buckle parts are provided with two buckle holes, and the buckle parts are fastened to the buckle holes, so that the main unit is placed on the first track and can move on the first track.