TWI907038B - Microfluidic detection system with self-standard benchmark and rapid deployment of artificial intelligence identification and method thereof - Google Patents

Abstract

A microfluidic detection system with self-standard benchmark and rapid deployment of artificial intelligence identification includes a sensing device, a microfluidic chip and a processor. The microfluidic chip corresponds to the sensing device and includes a plurality of injection holes, a standard part, an observation part, a plurality of microfluidic channels and an identification part. The standard part includes a standard product and has a standard space, and the standard product is disposed in the standard space. The observation part has an observation space. Two of the injection holes, the standard part and the observation part are connected to each other by one of the microfluidic channels. The identification part has an identification mark, and the identification mark is configured to be sensed by the sensing device to identify mark information. The processor is signally connected to the sensing device and the microfluidic chip, and includes an artificial intelligence identification module. The artificial intelligence identification module receives the mark information from the sensing device, and automatically imports a corresponding artificial intelligence identification process according to the mark information, and converts an observation result of the standard space into a calculation parameter according to the artificial intelligence identification process, and automatically identifies the observation space according to the calculation parameter to generate an experimental result. Therefore, the present disclosure utilizes artificial intelligence to shorten detection time and reduce manual detection errors.

Description

A microfluidic channel detection system and method with self-standard benchmark and rapid deployment of artificial intelligence recognition.

This invention relates to a microchannel detection system and method, and more particularly to a microchannel detection system and method with self-standard criterion and rapid deployment of artificial intelligence identification.

With advancements in testing science, in addition to professional laboratory procedures, many user-friendly test strips have been developed. Conventional test strips typically include a test chamber containing a specific reagent, the type of which depends on the target substance being tested. When using conventional test strips, simply add the sample to the test chamber, and after the reaction between the sample and reagent is complete, observe or measure the changes in the reagent's properties to obtain the test result.

However, conventional inspection requires manual inspection, which takes a long time and the inspection process is cumbersome. In addition, manual inspection can easily lead to errors in manual interpretation and inconsistent standards. It can be seen from this that there is currently a lack of a microfluidic detection system and method on the market that can effectively shorten the detection time and reduce manual interpretation errors, with self-standard benchmarks and rapid deployment of artificial intelligence identification. Therefore, relevant industries are looking for solutions.

The purpose of this invention is to provide a microfluidic channel detection system and method with self-standard benchmarks and rapid deployment of artificial intelligence recognition. It can be used in experiments involving physical and chemical biological cells, biochemistry, etc. By identifying the label information, the corresponding artificial intelligence recognition program can be automatically imported, and the detection results can be quickly obtained through automated identification in the observation area by the artificial intelligence recognition program. This can effectively shorten the detection time and reduce the error of human interpretation, so as to solve the problems of long learning detection time and human interpretation error.

According to one embodiment of the present invention, a microchannel detection system with self-standard datum and rapid deployment of artificial intelligence recognition is provided, comprising a sensing device, a microchannel chip, and a processor. The microchannel chip corresponds to the sensing device and includes a plurality of injection ports, a standard section, an observation section, a plurality of microchannels, and an identification section. The standard section includes a standard sample and has a standard sample space, in which the standard sample is disposed. The observation section has an observation space. The injection ports, the standard section, and the observation section are connected through one of the microchannels. The identification section has an identification mark for the sensing device to detect and identify a mark information. The processor is signal-connected to the sensing device and the microchannel chip and includes an artificial intelligence recognition module. The AI recognition module receives the tag information from the sensing device and automatically imports the corresponding AI recognition program based on the tag information. Based on the AI recognition program, it converts the observation result of the standard space into a calculation parameter and automatically identifies the observation space based on the calculation parameter to generate an experimental result.

Other embodiments of the aforementioned implementation method are as follows: The aforementioned standard includes a physical object or a chemical object, and either the standard space or the observation space is a three-dimensional space, which is a sphere or a cube. One of the standard part and the observation part is transparent and has low light scattering. One of these injection holes is connected to the standard part through these microchannels. The identification mark includes an RF tag, a two-dimensional barcode, a one-dimensional barcode, or text.

Other embodiments of the aforementioned implementation method are as follows: The aforementioned sensing device includes an observation device whose signal is connected to an artificial intelligence recognition module. The observation device observes the standard product located in the standard product space to obtain a digital image of the standard product, and transmits the digital image of the standard product to the artificial intelligence recognition module for recognition, with the digital image of the standard product corresponding to the observation result.

Other embodiments of the aforementioned implementation method are as follows: The artificial intelligence identification program of the aforementioned artificial intelligence identification module includes identifying and calculating based on the digital image of the standard product to obtain a state of the standard product; and calculating the state of the standard product to obtain an image adjustment parameter. The image adjustment parameter includes a light intensity or a focal length, and a corresponding calculation parameter.

Other embodiments of the aforementioned implementation method are as follows: The artificial intelligence identification program of the aforementioned artificial intelligence identification module further includes automatically adjusting the observation equipment according to the image adjustment parameters so that the observation equipment has the same observation state.

According to one embodiment of the present invention, a microchannel detection method with self-standard benchmark and rapid deployment of artificial intelligence identification is provided, comprising the following steps: identifying a mark information by sensing a mark in an identification part of a microchannel chip using a sensing device; receiving the mark information from the sensing device by an artificial intelligence identification module of a processor, and automatically importing a corresponding artificial intelligence identification program according to the mark information; and converting an observation result of a standard space in a standard part of a microchannel chip into a calculation parameter by the artificial intelligence identification program using the artificial intelligence identification module of the processor, and automatically identifying an observation space in an observation part of a microchannel chip according to the calculation parameter to generate an experimental result. The microchannel chip corresponds to a sensing device and includes a plurality of injection ports, a standard section, an observation section, a plurality of microchannels, and an identification section. The standard section includes a standard sample and has a standard sample space, in which the standard sample is disposed. The observation section has an observation space. The injection ports, the standard section, and the observation section are connected through one of the microchannels.

Other embodiments of the aforementioned implementation method are as follows: The aforementioned standard includes a physical object or a chemical object, and either the standard space or the observation space is a three-dimensional space, which is a sphere or a cube. One of the standard part and the observation part is transparent and has low light scattering. One of these injection holes is connected to the standard part through these microchannels. The identification mark includes an RF tag, a two-dimensional barcode, a one-dimensional barcode, or text.

Other embodiments of the aforementioned implementation method are as follows: The aforementioned microfluidic channel detection method with self-standard benchmark and rapid deployment of artificial intelligence identification further includes: observing a standard sample located in the standard sample space using an observation device of one of the sensing devices to obtain a digital image of the standard sample, and transmitting the digital image of the standard sample to an artificial intelligence identification module for identification. The digital image of the standard sample corresponds to the observation result.

Other embodiments of the aforementioned implementation method are as follows: The aforementioned artificial intelligence identification process includes: obtaining a state of the standard product by performing identification and calculation based on the digital image of the standard product using an artificial intelligence identification module; and obtaining an image adjustment parameter by calculating the state of the standard product using the artificial intelligence identification module. The image adjustment parameter includes a light intensity or a focal length, and corresponds to the calculation parameter.

Other embodiments of the aforementioned implementation method are as follows: The aforementioned artificial intelligence identification program further includes automatically adjusting the observation equipment according to the image adjustment parameters by the artificial intelligence identification module, so that the observation equipment has the same observation state.

Therefore, the microfluidic channel detection system and method of this invention, which features self-standardized benchmarks and rapid deployment of artificial intelligence recognition, can be used in experiments involving physical and chemical processes such as biological cells and biochemistry. By identifying marker information, the corresponding artificial intelligence recognition program can be automatically imported, effectively shortening the detection time. Furthermore, by automating the identification process within the observation area through artificial intelligence recognition, detection results can be quickly obtained, reducing errors from human interpretation.

Several embodiments of the present invention will be described below with reference to the drawings. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details should not be used to limit the present invention. That is, these practical details are not essential in some embodiments of the present invention. Furthermore, for the sake of simplicity, some conventional structures and components will be shown in the drawings in a simple schematic manner; and repeated components may be represented by the same designation.

Please refer to Figure 1, which is a block diagram illustrating a microchannel detection system 100 with self-standardized benchmark and rapid deployment of artificial intelligence recognition according to a first embodiment of the present invention. The microchannel detection system 100 with self-standardized benchmark and rapid deployment of artificial intelligence recognition includes a sensing device 200, a microchannel chip 300, and a processor 400. The microchannel chip 300 corresponds to the sensing device 200 and includes a plurality of injection holes 310, a standard section 320, an observation section 330, a plurality of microchannels 340, and an identification section 350. The injection holes 310, the standard section 320, and the observation section 330 are connected through one of the microchannels 340. The standard section 320 includes a standard sample and has a standard sample space, in which the standard sample is disposed. The observation unit 330 has an observation space. The identification unit 350 has an identification mark for the sensing device 200 to detect and identify a mark information. The processor 400 is signal-connected to the sensing device 200 and the microfluidic chip 300, and includes an artificial intelligence identification module 410. The artificial intelligence identification module 410 receives the mark information from the sensing device 200, automatically imports a corresponding artificial intelligence identification program according to the mark information, converts an observation result of the standard space into a calculation parameter according to the artificial intelligence identification program, and automatically identifies the observation space and generates an experimental result according to the calculation parameter.

Therefore, the microfluidic channel detection system 100 of this invention, which has a self-standard benchmark and rapid deployment of artificial intelligence recognition, can automatically import the corresponding artificial intelligence recognition program through the identification of mark information, and quickly obtain detection results through the automatic identification of the observation area by the artificial intelligence recognition program, which can effectively shorten the detection time and reduce the error of human interpretation.

The sensing device 200 includes an observation device 210, a mark recognition module 220, and an electrophysical module 230. The observation device 210 is signal-connected to the artificial intelligence recognition module 410. The observation device 210 observes a standard sample located in the standard sample space to obtain a digital image of the standard sample and transmits the digital image to the artificial intelligence recognition module 410 for identification. The digital image of the standard sample corresponds to the observation result. The mark recognition module 220 corresponds to and senses the identification mark of the identification unit 350 to identify the mark information. The mark recognition module 220 can complete the identification through radio frequency identification (RFID) electromagnetic wave sensing or video camera image recognition. The electrophysical module 230 may include an electrophysical sensing element disposed within the microfluidic chip 300 to detect the physical or chemical properties of the experimental subject and transmit the properties to the processor 400 for analysis. The experimental subject is located in the observation space.

The standard component of the standard section 320 includes a physical object (e.g., fluorescent beads, microbeads, magnetic beads, etc.) or a chemical object (e.g., a standard reagent). Either the standard component space or the observation space is a three-dimensional space, which is a sphere or cube, and is a closed three-dimensional space except for the connecting holes of the microchannels 340. One of the standard section 320 and the observation section 330 is translucent and has low light scattering. One of the injection holes 310 is connected to the standard section 320 through one of the microchannels 340. The identification mark includes an RFID tag (e.g., a two-dimensional barcode, such as a QR code), a one-dimensional barcode, or text. The identification mark can be laser-engraved (rulers, text, and symbols).

If the standard is a physical object, the injection port 310, the standard space of the standard section 320, and the observation space of the observation section 330 can be connected to each other through the microchannel 340. If the standard is a chemical object, the injection port 310 and the standard section 320 can be connected to each other through the microchannel 340, and the injection port 310 and the observation section 330 can be connected to each other through the microchannel 340, while the standard section 320 and the observation section 330 are not connected. Furthermore, the standard space can be connected to an electrode or metal for electrical or magnetic operation. The material of the standard is pre-placed in the standard space.

The processor 400 may be a central processing unit (CPU), a graphics processing unit (GPU), a computer, a mobile device processor, a cloud processor, or other high-performance computing processor, but the present invention is not limited thereto.

Please refer to Figures 1 and 2 together, where Figure 2 is a schematic diagram illustrating the standard sample SP of the microchannel chip 300a of the second embodiment of the present invention as a physical object. The microchannel chip 300a includes a first injection port 3101, a second injection port 3102, a third injection port 3103, a standard section 320a, an observation section 330a, and a plurality of microchannels 340a. The standard section 320a includes a standard sample SP and has a standard sample space SS, in which the standard sample SP is disposed. The observation section 330a has an observation space OS. The first injection port 3101, the second injection port 3102, the third injection port 3103, the standard sample space SS of the standard section 320a, and the observation space OS of the observation section 330a are interconnected through these microchannels 340a.

Please refer to Figures 1 and 3 together, where Figure 3 is a schematic diagram illustrating that the standard of the microchannel chip 300b of the third embodiment of the present invention is a chemical object. The microchannel chip 300b includes a first injection port 3101, a second injection port 3102, a third injection port 3103, a standard section 320b, an observation section 330b, and a plurality of microchannels 340b. The standard section 320b includes a standard (standard reagent) and has a standard space SS, into which the standard reagent can be injected through the microchannels 340b. The observation section 330b has an observation space OS. The first injection port 3101, the third injection port 3103 and the standard section 320b are connected to each other through the microchannel 340b, the second injection port 3102 and the observation section 330b are connected to each other through the microchannel 340b, while the standard section 320b and the observation section 330b are not connected (e.g. through a check valve).

Please refer to Figures 1 and 4 together, where Figure 4 is a schematic diagram illustrating the microchannel chip 300c of the fourth embodiment of the present invention. The microchannel chip 300c is of the plug type and can be disposable or reusable. The microchannel chip 300c includes a first injection port 3101, a second injection port 3102, a third injection port 3103, a standard section 320c, an observation section 330c, and a plurality of microchannels 340c. The first injection port 3101, the second injection port 3102, the third injection port 3103, and the standard section 320c are connected to each other through the microchannels 340c, and the standard section 320c and the observation section 330c are connected to each other through the microchannels 340c.

Please refer to Figures 1 and 5 together, where Figure 5 is a schematic diagram illustrating the microchannel chip 300d of the fifth embodiment of the present invention. The microchannel chip 300d is of the plug type and can be disposable or reusable. The microchannel chip 300d includes a first injection port 3101, a second injection port 3102, a third injection port 3103, a standard section 320d, an observation section 330d, and a plurality of microchannels 340d. The first injection port 3101, the second injection port 3102, the third injection port 3103, and the observation section 330d are interconnected through the microchannels 340d, and the standard section 320d and the observation section 330d are interconnected through the microchannels 340d.

Please refer to Figures 1 and 6 together, where Figure 6 is a schematic diagram illustrating the microchannel chip 300e of the sixth embodiment of the present invention. The microchannel chip 300e includes a standard section 320e, a plurality of observation sections 330e, and a plurality of microchannels 340e. Each observation section 330e and the standard section 320e are connected to each other through the microchannels 340e. A check valve 342 is provided near the observation section 330e in each microchannel 340e, and it has a microchannel check area 344, a buffer area 346, and a microchannel pressure reduction area 348. The check valve 342 is disposed between the microchannel check area 344 and the observation section 330e, and can be used to prevent the observation section 330e and the standard section 320e from interfering with each other. The buffer zone 346 is located between the microchannel check zone 344 and the microchannel depressurization zone 348.

Please refer to Figures 1 and 7 together. Figure 7 is a flowchart illustrating the microchannel detection method 500 with self-standard benchmark and rapid deployment of artificial intelligence recognition according to the seventh embodiment of the present invention. The microchannel detection method 500 with self-standard benchmark and rapid deployment of artificial intelligence recognition includes steps S02, S04, S06, S08, S10, S12, S14, S16, and S18.

Step S02 includes embedding the microfluidic chip 300. Step S04 includes scanning and identifying tag information using AI A, i.e., obtaining RFID tag information, QR code identification results, or laser-engraved tag identification results through contact or non-contact methods. Step S06 includes loading the corresponding AI B standby, i.e., obtaining the AI identification module 410 (AI B) information corresponding to the tag information, which can be obtained directly from the microfluidic chip 300 or remotely via a network located at an observation station or device (such as a computer or microscope). Steps S02, S04, and S06 can be considered as one step SA, where step SA executes AI A.

Step S08 includes loading real-time images using observation device 210. Step S10 includes retrieving the standard space of the microfluidic chip 300. Step S12 includes automated parameter adjustment using AI B. Step S14 includes automated adjustment of observation device 210 based on parameters. Step S16 includes loading real-time images into the adjusted observation device 210. Steps S08, S10, S12, S14, and S16 can be considered as one step SB, where step SB executes AI B.

As described above, step SB includes acquiring a digital image of the standard sample by observing the standard sample located in the standard sample space using the observation device 210 of the sensing device 200, and transmitting the digital image to the artificial intelligence recognition module 410 for recognition. The digital image of the standard sample corresponds to the observation result. Step SB further includes executing an artificial intelligence recognition program, which includes obtaining a state of the standard sample by recognizing and calculating based on the digital image of the standard sample using the artificial intelligence recognition module 410, and obtaining an image adjustment parameter by calculating the state of the standard sample using the artificial intelligence recognition module 410. The image adjustment parameter includes a brightness or a focal length, and corresponds to the calculation parameter. In addition, the artificial intelligence recognition program includes an artificial intelligence recognition module 410 that automatically adjusts the observation device 210 according to the image adjustment parameters, so that the observation device 210 has the same observation state during the observation process, and automatically calculates and adjusts the observed image (such as brightness, contrast or zoom in/out of the image preprocessing), and the adjustment parameter results can be used for the automatic adjustment of the observation device 210 and peripheral connected devices.

Step S18 involves automated image recognition using artificial intelligence (AI) C, that is, selecting the corresponding AI C based on the recognition results from AI A. Different AI Cs have specific recognition targets (such as specific cells, bacteria, viruses, or antibodies). Step S18 can be regarded as a step SC, which executes AI C.

The aforementioned artificial intelligences A, B, and C can be implemented through artificial intelligence software modules or artificial intelligence hardware modules. The artificial intelligence software module can be downloaded from a remote server (such as the cloud) via a network to a near-end edge computing device (connected to observation device 210) to perform image recognition operations on observation device 210. The artificial intelligence hardware module can be directly integrated into the near-end edge computing device (connected to observation device 210) for computation. Whether through software or hardware modules, automation can achieve the goal of rapid deployment of artificial intelligence recognition. The aforementioned artificial intelligence recognition program can be a neural network or deep learning-based system, but this invention is not limited to these.

As can be seen from the above implementation method, the present invention has the following advantages: First, it can be used in experiments involving physical and chemical biological cells, biochemistry, etc., and can automatically import corresponding artificial intelligence identification programs through the identification of label information, which can effectively shorten the detection time. Second, by using artificial intelligence identification programs to automatically identify in the observation area (i.e., the observation space) to quickly obtain detection results, the error of human interpretation can be reduced.

Although the invention has been disclosed above by way of implementation, it is not intended to limit the invention. Anyone skilled in the art may make various modifications and alterations without departing from the spirit and scope of the invention. Therefore, the scope of protection of the invention shall be determined by the scope of the appended patent application.

100: Microfluidic channel inspection system with self-standard benchmark and rapid deployment of AI recognition 200: Sensing device 210: Observation equipment 220: Mark recognition module 230: Electrophysical module 300, 300a, 300b, 300c, 300d, 300e: Microfluidic chip 310: Injection port 3101: First injection port 3102: Second injection port 3103: Third injection port 320, 320a, 320b, 320c, 320d, 320e: Standard section 330, 330a, 330b, 330c, 330d, 330e: Observation section 340, 340a, 340b, 340c, 340d, 340e: Microchannel 342: Check Valve 344: Microchannel Check Zone 346: Buffer Zone 348: Microchannel Pressure Reduction Zone 350: Identification Unit 400: Processor 410: Artificial Intelligence Identification Module 500: Microchannel Detection Method with Self-Standardized Baseline and Rapid Deployment of Artificial Intelligence Identification OS: Observation Space S02, S04, S06, S08, S10, S12, S14, S16, S18, SA, SB, SC: Steps SP: Standard Sample SS: Standard Sample Space

Figure 1 is a block diagram illustrating a microchannel inspection system with self-standardization and rapid deployment of AI recognition according to the first embodiment of the present invention; Figure 2 is a schematic diagram illustrating that the standard for the microchannel chip in the second embodiment of the present invention is a physical object; Figure 3 is a schematic diagram illustrating that the standard for the microchannel chip in the third embodiment of the present invention is a chemical object; Figure 4 is a schematic diagram illustrating a microchannel chip in the fourth embodiment of the present invention; Figure 5 is a schematic diagram illustrating a microchannel chip in the fifth embodiment of the present invention; Figure 6 is a schematic diagram illustrating a microchannel chip in the sixth embodiment of the present invention; and Figure 7 is a flowchart illustrating a microchannel inspection method with self-standardization and rapid deployment of AI recognition according to the seventh embodiment of the present invention.

100: A microfluidic channel inspection system with self-standardized benchmarks and rapid deployment of AI-based identification.

200: Sensing Device

210: Observational Equipment

220: Logo Recognition Module

230: Electromechanical Module

300: Microchannel chip

310: Injection Hole

320:Standards Department

330: Observation Department

340: Microchannel

350:Identification Department

400: Processor

410: Artificial Intelligence Recognition Module

Claims (10)

A microchannel detection system with self-standardized benchmarks and rapid deployment of AI recognition, comprising: a sensing device; a microchannel chip corresponding to the sensing device and including: a plurality of injection holes; a standard section including a standard and having a standard space, the standard being disposed in the standard space; an observation section having an observation space; a plurality of microchannels, wherein the injection holes, the standard section, and the observation section are connected through one of the microchannels; and an identification section having an identification mark, the identification mark being used by the sensing device to sense and identify a mark information; and A processor, signal-connected to the sensing device and the microfluidic chip, includes an artificial intelligence (AI) recognition module. This AI recognition module receives the marker information from the sensing device, automatically imports a corresponding AI recognition program based on the marker information, converts an observation result of the standard space into a calculation parameter based on the AI recognition program, and automatically identifies the observation space based on the calculation parameter to generate an experimental result. The microchannel detection system with self-standardized benchmark and rapid deployment of artificial intelligence identification as described in claim 1, wherein the standard comprises a physical object or a chemical object, and either the standard space or the observation space is a three-dimensional space, the three-dimensional space being a sphere or a cube, one of the standard part and the observation part having light transmittance and low light scattering, one of the injection holes being connected to the standard part through the microchannels, and the identification mark comprising an RF tag, a two-dimensional barcode, a one-dimensional barcode, or text. The microfluidic channel detection system with self-standardized benchmark and rapid deployment of artificial intelligence identification as described in claim 1, wherein the sensing device comprises: an observation device, signal-connected to the artificial intelligence identification module, the observation device observing the standard sample located in the standard sample space to acquire a digital image of the standard sample, and transmitting the digital image of the standard sample to the artificial intelligence identification module for identification, the digital image of the standard sample corresponding to the observation result. The microfluidic channel detection system with self-standard benchmark and rapid deployment of artificial intelligence recognition as described in claim 3, wherein the artificial intelligence recognition module's artificial intelligence recognition program includes: identifying and calculating a state of the standard product based on a digital image of the standard product; and calculating the state of the standard product to obtain an image adjustment parameter; wherein, the image adjustment parameter includes a light intensity or a focal length, and corresponds to the calculated parameter. The microfluidic channel detection system with self-standard benchmark and rapid deployment of artificial intelligence recognition as described in claim 4, wherein the artificial intelligence recognition module's artificial intelligence recognition program further includes: Automatically adjusting the observation equipment according to the image calibration parameters to ensure that the observation equipment has the same observation state. A microchannel detection method with self-standardized benchmarks and rapid deployment of artificial intelligence (AI) recognition includes the following steps: Sensing an identification mark on an identification section of a microchannel chip using a sensing device to identify a mark information; Receiving the mark information from the sensing device using an AI recognition module of a processor, and automatically importing a corresponding AI recognition program based on the mark information; The AI recognition module of the processor converts an observation result of a standard space in a standard section of the microchannel chip into a calculation parameter according to the AI recognition program, and automatically identifies an observation space in an observation section of the microchannel chip based on the calculation parameter to generate an experimental result; The microchannel chip corresponds to the sensing device and includes a plurality of injection ports, a standard section, an observation section, a plurality of microchannels, and an identification section. The standard section includes a standard sample and has a standard sample space disposed therein. The observation section has an observation space. The injection ports, the standard section, and the observation section are connected through one of the microchannels. The microchannel detection method with self-standard benchmark and rapid deployment of artificial intelligence identification as described in claim 6, wherein the standard comprises a physical object or a chemical object, either the standard space or the observation space is a three-dimensional space, the three-dimensional space being a sphere or a cube, one of the standard part and the observation part having light transmittance and low light scattering, one of the injection holes being connected to the standard part through the microchannels, and the identification mark comprising an RF tag, a two-dimensional barcode, a one-dimensional barcode, or text. The microfluidic channel detection method with self-standard benchmark and rapid deployment of artificial intelligence identification as described in claim 6 further comprises: observing the standard sample located in the standard sample space using an observation device of the sensing device to obtain a digital image of the standard sample, and transmitting the digital image of the standard sample to the artificial intelligence identification module for identification; wherein, the digital image of the standard sample corresponds to the observation result. The microfluidic channel detection method with self-standard benchmark and rapid deployment of artificial intelligence identification as described in claim 8, wherein the artificial intelligence identification process includes: obtaining a state of the standard product by performing identification and calculation based on a digital image of the standard product using the artificial intelligence identification module; and obtaining an image adjustment parameter by calculating the state of the standard product using the artificial intelligence identification module; wherein, the image adjustment parameter includes a light intensity or a focal length, and corresponds to the calculation parameter. The microchannel detection method with self-standard benchmark and rapid deployment of artificial intelligence identification as described in claim 9, wherein the artificial intelligence identification program further includes: automatically adjusting the observation equipment according to the image calibration parameters by the artificial intelligence identification module, so that the observation equipment has the same observation state.