Rapid detection equipment for planktonic animals and plants based on artificial intelligence technology and operation method

Abstract

The invention provides a planktonic animal and plant rapid detection device based on an artificial intelligence technology and an operation method, and relates to the field of artificial intelligence, the device comprises an image acquisition system, an artificial intelligence picture recognition system, an early warning system, a detection result display system, a detection result transmission system and a detection result storage system, wherein the image acquisition system is used for acquiring microscopic images of planktonic animals and plants and transmitting data; the artificial intelligence picture recognition system is used for processing, segmenting, feature extraction and classification recognition of the collected images; the early warning system is used for analyzing and judging data and informing related personnel when early warning is needed; the detection result display system is used for displaying the collected plankton image, the processed clear image and the recognition result; the detection result transmission system is used for transmitting plankton detection results to other equipment, platforms or storage systems; and the detection result storage system is used for managing storage resources and is responsible for receiving, transmitting and storing identification results.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology. Specifically, it particularly relates to a rapid detection device and operation method for plankton based on artificial intelligence technology. Background Art

[0002] In rapid plankton detection devices, through the built-in artificial intelligence system, the device can perform real-time analysis and recognition on the collected microscopic images. Using deep learning algorithms, it can quickly extract the characteristics of plankton, accurately identify their species and related information. The artificial intelligence technology can also automatically process images inside the device. By automatically processing the images, the images are more clearly displayed on the screen, facilitating subsequent recognition. When dangerous species or extremely high concentrations of plankton occur, the artificial intelligence system can quickly make a judgment and the warning light will turn on, providing a guarantee for taking timely countermeasures. In addition, the application of artificial intelligence technology improves the detection efficiency and accuracy, reduces the errors and workload of manual operations, and provides strong technical support for plankton research and ecological environment monitoring.

[0003] In plankton detection and recognition, traditional detection methods are unable to cope when faced with the complex and diverse forms of plankton and a large number of samples. To improve the detection and recognition efficiency and accuracy, in terms of image clarification, image enhancement algorithms are used. By adjusting the brightness and contrast, the outlines and details of plankton are highlighted, making them better distinguishable from the background. At the same time, noise reduction techniques such as median filtering are adopted to reduce the noise generated during image acquisition and avoid interference with the characteristics of plankton. In addition, super-resolution reconstruction technology based on deep learning can improve the image resolution and more clearly display the fine structure of plankton.

[0004] Embedded systems, with their characteristics of miniaturization, low power consumption, and high reliability, provide an ideal solution for plankton recognition devices. This embedded system can integrate key components such as data processing units and artificial intelligence chips to achieve efficient acquisition and processing of microscopic images. Through the built-in detection algorithms and warning modules, the embedded system can continuously analyze the collected images, promptly detect abnormal changes in dangerous species or plankton concentrations, and provide strong support for environmental warning and scientific research. At the same time, the stability and real-time performance of the embedded system ensure the accuracy and timeliness of plankton recognition, meet the requirements for data processing and analysis in different application scenarios, and play an important role in promoting plankton research and environmental protection.

[0005] Based on advanced artificial intelligence technologies and data analysis algorithms, the early warning module can monitor the dynamics of plankton in real time. It can quickly identify potential dangerous species, such as toxic plankton that can cause red tides, providing early warnings for environmental protection and ecological security. At the same time, by monitoring the concentration of plankton, the early warning module can judge the health status of the water ecosystem. When the concentration abnormally increases, it may indicate problems such as water eutrophication. In addition, through the collaborative effect with other technical modules, the early warning module can achieve comprehensive monitoring and analysis of plankton, providing strong data support for scientific research and environmental management decisions.

[0006] In the research of ecological environment, the rapid detection of plankton and nekton can timely reflect the ecological status of water areas. As the primary producers in water areas, the changes in the species and quantities of phytoplankton can reflect the water quality eutrophication level. Nekton is a key link in the food chain. The detection data of both can help understand the ecological balance state and provide a basis for evaluating the health degree of the water ecosystem. In the field of environmental protection, the abnormal changes of plankton and nekton caused by pollution can be detected as early as possible through rapid detection, so as to quickly take measures to control pollution sources and protect the water environment. For fisheries, plankton and nekton are important food sources for fish. Detection can help understand the bait situation of fishery resources, guide fishery breeding and fishing activities, and reasonably plan the utilization of fishery resources. At the same time, in response to sudden environmental events such as red tides, rapid detection can quickly determine the species and scale of plankton, providing key information for emergency treatment.

[0007] Traditional methods for detecting plankton and nekton mainly include microscope observation method, counting method, etc. However, these traditional methods have obvious defects: in terms of detection efficiency, microscope observation requires manual identification and counting one by one. For a large number of samples, this process is extremely time-consuming and laborious, and it is difficult to achieve rapid detection; in terms of accuracy, manual observation is easily affected by subjective factors. For example, the experience and fatigue degree of observers may lead to identification errors, and the detection results of the same sample by different personnel may deviate. Moreover, it is difficult to accurately classify plankton and nekton by traditional methods, especially some species with similar morphologies, which are prone to misjudgment. Furthermore, the data processing and storage of these methods are inconvenient, mostly relying on manual records, which is not conducive to the integration and analysis of long-term data, lacks timeliness for dynamically monitoring the changes of plankton and nekton, and is difficult to meet the requirements of modern ecological research, environmental monitoring and fishery development for rapid and accurate detection of plankton and nekton. Summary of the Invention

[0008] According to the above-mentioned technical problems, a rapid detection device for planktonic animals and plants based on artificial intelligence technology is provided, which has the advantages of high-efficiency and accurate recognition, intelligent and convenient operation, comprehensive system, safety and reliability, etc. The purpose of the present invention is to design a complete set of rapid detection solutions for planktonic animals and plants with the help of artificial intelligence technology. On the one hand, through the image acquisition system, the microscopic images of planktonic animals and plants are accurately obtained and quickly transmitted, and the artificial intelligence image recognition system is used for efficient processing to accurately identify their species and names, so as to meet the needs of accurate cognition of planktonic animals and plants in the fields of ecological research, environmental monitoring, etc.; on the other hand, with the help of the warning system, abnormal conditions are detected in a timely manner, and the warning light is lit to notify the personnel. At the same time, with the help of the detection result display, transmission and storage system, the data is intuitively presented, conveniently shared and properly stored, comprehensively improving the efficiency, accuracy and practicability of planktonic animals and plants detection, and providing strong support for the development of related industries.

[0009] The technical means adopted by the present invention are as follows:

[0010] A rapid detection device for planktonic animals and plants based on artificial intelligence technology, including: an image acquisition system, an artificial intelligence image recognition system, a warning system, a detection result display system, a detection result transmission system and a detection result storage system, wherein:

[0011] The image acquisition system is used to collect microscopic images of planktonic animals and plants and transmit data;

[0012] The artificial intelligence image recognition system is used to process, segment, extract features and classify and identify the collected images;

[0013] The warning system is used to analyze and judge data and notify relevant personnel when warning is required;

[0014] The detection result display system includes a display screen, which is used to display the collected plankton images, the processed clear images and the recognition results;

[0015] The detection result transmission system is used to transmit the plankton detection results to other devices, platforms or storage systems;

[0016] The detection result storage system is used to manage storage resources and is responsible for receiving, transmitting and storing the recognition results.

[0017] Furthermore, the image acquisition system includes an image acquisition module and a data transmission module, wherein:

[0018] The image acquisition module includes an external microscope, which is used to observe planktonic animals and plants and collect their microscopic images;

[0019] The data transmission module includes an information transmission interface and a data processing unit. Among them, the information transmission interface is used to realize the connection between devices, and the data processing unit is used for transmission control and management and cooperation with other modules. The two are jointly responsible for quickly transmitting the collected image data to other systems of the device.

[0020] Further, the artificial intelligence picture recognition system includes an artificial intelligence chip, an image preprocessing module, an image segmentation module, a feature extraction module, and a classification and recognition module integrated on the artificial intelligence chip. Among them:

[0021] The image preprocessing module is used to perform preliminary processing on the collected microscopic images of plankton to improve the image quality;

[0022] The image segmentation module is used to separate the plankton in the collected microscopic images of plankton from the background and analyze the plankton separately;

[0023] The feature extraction module is used to extract representative features from the segmented plankton images;

[0024] The classification and recognition module is used to classify and recognize plankton according to the extracted features to determine the types and names of plankton.

[0025] Further, the warning system includes a warning logic module and a warning notification module integrated on the artificial intelligence chip. Among them:

[0026] The warning logic module is used to analyze and judge the collected data to judge whether to trigger a warning;

[0027] The warning notification module is responsible for conveying the warning information to relevant personnel and turning on the warning light after the warning logic module triggers a warning.

[0028] Further, the detection result storage system includes an image storage chip connected to the artificial intelligence chip, a storage management module integrated on the image storage chip, and a data transmission module. Among them:

[0029] The storage management module is used to manage the allocation and use of storage resources;

[0030] The data transmission module is used to establish a connection with the storage platform, receive the detection result data, and transmit the recognition result to the storage platform.

[0031] The present invention also provides an operation method implemented based on the plankton and phytoplankton rapid detection device based on artificial intelligence technology, including:

[0032] S1. Press the power on / off button to turn on the device, connect to the microscope through the information transmission interface, and use the microscope for observation;

[0033] After the microscope captures a microscopic image, the device will automatically enhance the clarity of the captured microscopic image;

[0034] S3. Display the processed microscopic image on the display screen, and in real time identify planktonic animals and plants, and display the names and detailed information of the identified planktonic animals and plants;

[0035] S4. If the recognition result needs to be accurate, press the storage button to store the data. If re - shooting or recognition is required, press the return button to return or cancel the operation;

[0036] S5. If a dangerous species or a situation with extremely high concentration of plankton appears, the warning light will turn red, store the information and mark it, and wait for analysis after being transmitted to the computer terminal;

[0037] S6. After the work is completed, press the transmission button to directly connect the data to the computer terminal through the information transmission interface and transmit it to the computer cloud for further analysis;

[0038] S7. After use, press the power - on / off button to shut down.

[0039] Compared with the existing technology, the present invention has the following advantages:

[0040] 1. A rapid detection device for planktonic animals and plants based on artificial intelligence technology provided by the present invention uses artificial intelligence technology to be able to identify planktonic animals and plants in real time, greatly improving the detection speed and saving time.

[0041] 2. A rapid detection device for planktonic animals and plants based on artificial intelligence technology provided by the present invention, its embedded system provides a stable operating environment, precise control and efficient data management for the data processing unit and the artificial intelligence chip, improves the device processing speed, accuracy and overall performance, and ensures the smooth progress of plankton detection.

[0042] 3. A rapid detection device for planktonic animals and plants based on artificial intelligence technology provided by the present invention, its image processing system and artificial intelligence chip can automatically optimize the image, enhance clarity, contrast and color saturation, remove noise, and improve image quality and readability.

[0043] 4. A rapid detection device for planktonic animals and plants based on artificial intelligence technology provided by the present invention stores the recognition result in the internal chip, improves the system stability and operation efficiency, facilitates rapid data processing and calling, and reduces the risk of failure caused by complex connections.

[0044] 5. A rapid detection device for planktonic animals and plants based on artificial intelligence technology provided by the present invention can upload the stored data to a computer, which is conducive to centralized management and further in - depth research.

[0045] 6. The rapid detection device for planktonic animals and plants based on artificial intelligence technology provided by the present invention has a warning light at the top that turns on a red light when dangerous species or a too-high concentration of plankton is detected, which can promptly alert the staff and prevent potential risks.

[0046] 7. The rapid detection device for planktonic animals and plants based on artificial intelligence technology provided by the present invention has a transmission button responsible for transmitting data to the computer terminal, facilitating remote analysis and data sharing.

[0047] 8. The rapid detection device for planktonic animals and plants based on artificial intelligence technology provided by the present invention integrates multiple functions such as image processing, recognition, storage, warning, and transmission. The one-stop service reduces the working links and improves the overall working efficiency.

[0048] Based on the above reasons, the present invention can be widely promoted in the fields of plankton identification and biological warning, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 It is a three-dimensional view of the rapid detection device for planktonic animals and plants based on artificial intelligence technology of the present invention.

[0051] Figure 2 It is a plan view of the rapid detection device for planktonic animals and plants based on artificial intelligence technology of the present invention.

[0052] Figure 3 It is a bottom view of the rapid detection device for planktonic animals and plants based on artificial intelligence technology of the present invention.

[0053] Figure 4 It is an internal schematic diagram of the rapid detection device for planktonic animals and plants based on artificial intelligence technology of the present invention.

[0054] Figure 5 It is a schematic diagram of the artificial intelligence chip and the image storage chip of the rapid detection device for planktonic animals and plants based on artificial intelligence technology of the present invention.

[0055] Figure 6 It is a schematic diagram of the battery and the data processing unit of the rapid detection device for planktonic animals and plants based on artificial intelligence technology of the present invention.

[0056] Figure 7Schematic diagram of the connection between the phytoplankton and zooplankton rapid detection device based on artificial intelligence technology of the present invention and a microscope.

[0057] Figure 8 Partial technical route diagram of the phytoplankton and zooplankton rapid detection device based on artificial intelligence technology of the present invention.

[0058] Figure 9 Technical route diagram of the phytoplankton and zooplankton rapid detection device based on artificial intelligence technology of the present invention.

[0059] Figure 10 Schematic diagram of the screen display when the phytoplankton and zooplankton rapid detection device based on artificial intelligence technology of the present invention is operating.

[0060] In the figure: 1. Switch button; 2. Transmission button; 3. Circular button; 4. Warning light; 5. Display screen; 6. Storage button; 7. Return button; 8. Interface; 9. Artificial intelligence chip; 10. Image storage chip; 11. Battery; 12. Data processing unit. Detailed implementation manners

[0061] In order to enable those skilled in the art of the present technology to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0062] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0063] As Figure 1-6 shown, the present invention provides a phytoplankton and zooplankton rapid detection device based on artificial intelligence technology, including: an image acquisition system, an artificial intelligence image recognition system, a warning system, a detection result display system, a detection result transmission system, and a detection result storage system, wherein:

[0064] The image acquisition system is used to acquire microscopic images of plankton and transmit data;

[0065] The artificial intelligence image recognition system is used to process, segment, extract features and classify and recognize the acquired images;

[0066] The early warning system is used to analyze and judge data and notify relevant personnel when early warning is required;

[0067] The detection result display system includes a display screen 5, which is used to display the acquired plankton images, the processed clear images and the recognition results;

[0068] The detection result transmission system is used to transmit the plankton detection results to other devices, platforms or storage systems;

[0069] The detection result storage system is used to manage storage resources and is responsible for receiving, transmitting and storing the recognition results.

[0070] In specific implementation, as a preferred implementation manner of the present invention, the image acquisition system includes an image acquisition module and a data transmission module, wherein:

[0071] The image acquisition module includes an external microscope, which is used to observe plankton and acquire their microscopic images;

[0072] The data transmission module includes an information transmission interface 8 and a data processing unit 12. The information transmission interface 8 is used to realize the connection between devices, and the data processing unit 12 is used for transmission control and management and cooperation with other modules. The two jointly are responsible for quickly transmitting the acquired image data to other systems of the device.

[0073] In this embodiment, as Figure 7 shown, the present invention is connected to the microscope through a built-in data processing unit to obtain high-definition images of plankton samples. The built-in data processing unit 12 and the artificial intelligence chip 9 are connected to the microscope through an interface to ensure the smooth transmission of data. The microscope is equipped with a high-resolution camera, which can capture the details of tiny plankton and take clear microscopic images. The data processing unit 12 can quickly receive and process the original image data transmitted from the microscope, and adopts image preprocessing algorithms to preliminarily optimize the data, such as adjusting brightness, contrast, etc.

[0074] In specific implementation, as a preferred implementation manner of the present invention, the artificial intelligence image recognition system includes an artificial intelligence chip 9, an image preprocessing module, an image segmentation module, a feature extraction module and a classification recognition module integrated on the artificial intelligence chip 9, wherein:

[0075] The image preprocessing module is used to preliminarily process the collected microscopic images of plankton to improve the image quality;

[0076] The image segmentation module is used to separate the plankton in the collected microscopic images of plankton from the background and analyze the plankton separately;

[0077] The feature extraction module is used to extract representative features from the segmented plankton images;

[0078] The classification and recognition module is used to classify and recognize the plankton according to the extracted features to determine the types and names of the plankton.

[0079] In this embodiment, as Figure 8 shown, the artificial intelligence chip 9, based on the deep learning model, can further analyze the image. The algorithms stored in the artificial intelligence chip 9 are trained with a large amount of data and can accurately identify and extract the feature information of plankton. Through the artificial intelligence recognition technology, using the deep learning algorithm, a multi-layer neural network model is constructed. This model is trained with a large amount of microscopic image data of plankton and can automatically extract the features in the image, such as the outline and internal structural characteristics of plankton. During the analysis and processing, the convolutional layer in the network can effectively capture local features, the pooling layer reduces the dimension and filters the features, and the fully connected layer synthesizes these features for classification and recognition. By continuously adjusting the network parameters and optimizing the model performance, it can accurately identify information such as the types and quantities of plankton, and finally present these recognition results on the display screen 5 to provide users with intuitive and accurate detection results.

[0080] In this embodiment, as Figure 8 shown, the artificial intelligence technology starts from the data acquisition module and obtains the original microscopic images from devices such as microscopes. Subsequently, the data enters the preprocessing module, where operations such as noise removal and image parameter adjustment are performed to improve the data quality. The processed data is stored in the data storage module for subsequent use on the one hand; on the other hand, it enters the model inference module. Combining the continuously optimized model performance of the model training module, it accurately identifies information such as the types of plankton and displays the results in the recognition and display module. At the same time, the early warning logic module continuously monitors the data. When it judges that there are dangerous species or the concentration of plankton is extremely high, it triggers the early warning notification module to issue an alarm, providing a guarantee for relevant personnel to take timely measures.

[0081] In this embodiment, the data processing unit inside the device and the artificial intelligence chip 9 cooperate to deeply process the pictures. The data processor first preliminarily processes the collected microscopic images, such as adjusting brightness, contrast, and color balance, etc., to make the images clearer and easier to identify. The artificial intelligence chip 9 exerts its powerful computing ability and uses deep learning algorithms to further analyze the images. By learning a large amount of plankton image data, the artificial intelligence chip 9 can automatically extract the features in the images, identify information such as the types, morphologies, and quantities of plankton, and improve the accuracy and efficiency of identification. At the same time, it can also remove the noise in the images, enhance the details of the images, and make the features of the plankton more prominent.

[0082] In the embedded system design of the device in the above embodiment, the internal data processing unit and the artificial intelligence chip can work efficiently. The embedded system provides a stable operating environment for the data processing unit and the artificial intelligence chip, including a suitable hardware architecture and a resource allocation mechanism, ensuring that they can smoothly process data. The embedded system contains driver programs specifically for the data processing unit and the artificial intelligence chip, realizing the effective docking of hardware and software, enabling the operating system to accurately control the operation of the hardware, ensuring the accurate transmission of data and the correct execution of processing instructions. The embedded system is responsible for organizing and storing the collected microscopic image data. It can efficiently process the read and write operations of the data, classify and index the image data, and at the same time ensure the integrity and security of the data, preventing data loss or damage caused by unexpected situations (such as sudden power failure), so as to ensure the smooth and stable operation of the entire device system.

[0083] In specific implementation, as a preferred implementation manner of the present invention, the warning system includes a warning logic module and a warning notification module integrated on the artificial intelligence chip, where:

[0084] The warning logic module is used to analyze and judge the collected data to determine whether to trigger a warning;

[0085] The warning notification module is used to convey the warning information to relevant personnel and turn on the warning light 4 after the warning logic module triggers a warning.

[0086] In this embodiment, a warning light 4 is provided at the top of the device, which is connected to the detection and identification system. When a dangerous species is identified, such as a type of plankton that is harmful to the ecological environment or other organisms, the warning light 4 immediately turns on a red light. At the same time, when the plankton concentration reaches an extremely high level, which may indicate a change in the water body ecological environment, etc., the red light will also turn on. This function allows the staff to promptly know the abnormal situation and take corresponding measures in a timely manner, such as the prevention and control of dangerous species, the further detection of the water body environment, etc., effectively ensuring the safety of the water body ecology and the smooth progress of related research work.

[0087] In specific implementation, as a preferred implementation manner of the present invention, the detection result transmission system also performs data transmission through the information transmission interface 8 and the data processing unit 12. In this embodiment, the device is provided with a transmission button 2, and the transmission button 2 is responsible for data transmission and can transmit the locally stored data to the computer cloud. This function is based on a stable data transmission protocol to ensure the integrity and accuracy of the data during transmission. It can achieve seamless connection between the device and the cloud, enabling the collected data to be further analyzed in depth in the cloud without being restricted by the computing resources of the local device. At the same time, its switch button 1 is adjacent to it for convenient operation. This design makes the entire data processing process more efficient, and both on-site rapid detection and subsequent comprehensive analysis can proceed smoothly, greatly improving the efficiency of plankton research.

[0088] In specific implementation, as a preferred implementation manner of the present invention, the detection result storage system includes an image storage chip 10 connected to the artificial intelligence chip 9, a storage management module and a data transmission module integrated on the image storage chip 10, where:

[0089] The storage management module is used to manage the allocation and use of storage resources;

[0090] The data transmission module is used to establish a connection with the storage platform, receive the detection result data and transmit the recognition result to the storage platform.

[0091] In this embodiment, the device can locally store the plankton-related data after artificial intelligence recognition, and uses a dedicated storage mechanism to ensure the integrity and security of the data. Its storage function is controlled by a specific button for convenient operation. The upload function is designed for subsequent in-depth analysis and can upload the stored data to the main computer. This design allows the staff to perform data transmission at an appropriate time, avoiding the risk of data loss caused by network problems or operation errors, and ensuring the smooth data flow from the device end to the computer end.

[0092] As Figure 9 shown, the present invention also provides an operation method implemented by a plankton and phytoplankton rapid detection device based on artificial intelligence technology, including:

[0093] S1. Press the switch button 1 to turn on the machine, connect to the microscope through the information transmission interface 8, and use the microscope for observation;

[0094] S2. After the microscope captures a microscopic image, the device will automatically process the original microscopic picture to make the microscopic image clearer;

[0095] S3. Display the processed microscopic images on the display screen 5, and identify phytoplankton and zooplankton in real time, displaying the identified names and detailed information. In the present invention, the screen display of the device during the operation process is as follows Figure 10 shown. First, perform real-time shooting. After the shooting is completed, the user can select "Save", "Confirm", or "Return". Then, the image is automatically processed, and the processed picture is clearer. The user can choose to perform "Save", "Confirm", or "Return" operations. After confirming the recognition, the user can view the recognition result. The result is marked with different color frames and accompanied by category labels, and corresponding operation options are provided at the same time. During the operation process, if it is necessary to save the picture, after pressing the storage button, a prompt of successful transmission will appear, and the saved images can be viewed. The entire process constitutes a complete operation chain for detecting phytoplankton and zooplankton.

[0096] S4. If accurate recognition results are required, press the storage button 6 to store the data. If re-shooting or recognition is needed, press the return button 7 to return or cancel the operation.

[0097] S5. If dangerous species or extremely high concentrations of plankton occur, the warning light 4 will turn on a red light, and the information will be stored and analyzed in detail when transmitted to the computer terminal. Specifically, when the warning module continuously analyzes the collected microscopic images through the built-in detection algorithm, once an abnormal situation of dangerous species or extremely high concentrations of plankton is found, the warning module will immediately send a signal to the warning light 4. The warning light 4 usually adopts a high-brightness and eye-catching red light design so that it can be clearly seen in various environments. When the warning light 4 lights up, relevant personnel can quickly learn that an emergency has occurred and take further measures in a timely manner, such as dealing with dangerous species or conducting in-depth analysis and adjustment of the abnormal situation of plankton concentration. At the same time, the connection between the warning light 4 and the warning module also reflects the intelligent design of the device, improving the reliability and practicality of the device in plankton monitoring.

[0098] S6. After the work is completed, press the transmission button 2 to directly connect the data to the computer terminal through the information transmission interface 8 and transmit it to the computer cloud for further analysis.

[0099] S7. After use, press the switch button 1 to turn off the machine.

[0100] In several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in electrical or other forms.

[0101] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0103] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks or optical disks and other various media that can store program codes.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of each embodiment of the present invention.

Claims

1. A rapid detection device for plankton based on artificial intelligence technology, characterized in that, Including: An image acquisition system, an artificial intelligence picture recognition system, a warning system, a detection result display system, a detection result transmission system, and a detection result storage system. Among them: The image acquisition system is used to acquire microscopic images of plankton and transmit data; The artificial intelligence picture recognition system is used to process, segment, extract features, and classify and recognize the acquired images; The warning system is used to analyze and judge data and notify relevant personnel when a warning is required; The detection result display system includes a display screen and is used to display the acquired plankton images, the processed clear images, and the recognition results; The detection result transmission system is used to transmit the plankton detection results to other devices, platforms, or storage systems; The detection result storage system is used to manage storage resources and is responsible for receiving, transmitting, and storing the recognition results.

2. The rapid detection device for plankton based on artificial intelligence technology according to claim 1, wherein The image acquisition system includes an image acquisition module and a data transmission module. Among them: The image acquisition module includes an external microscope, which is used to observe plankton and acquire its microscopic images; The data transmission module includes an information transmission interface and a data processing unit. Among them, the information transmission interface is used to realize the connection between devices, and the data processing unit is used for transmission control and management and cooperation with other modules. The two jointly are responsible for quickly transmitting the acquired image data to other systems of the device.

3. The rapid detection device for planktonic animals and plants based on artificial intelligence technology according to claim 1, characterized in that, The artificial intelligence picture recognition system includes an artificial intelligence chip, an image preprocessing module, an image segmentation module, a feature extraction module, and a classification and recognition module integrated on the artificial intelligence chip. Among them: The image preprocessing module is used to perform preliminary processing on the acquired microscopic images of plankton to improve the image quality; The image segmentation module is used to separate the plankton in the acquired microscopic images of plankton from the background and analyze the plankton separately; The feature extraction module is used to extract representative features from the segmented plankton images; The classification and recognition module is used to classify and recognize plankton according to the extracted features to determine the types and names of plankton.

4. The rapid detection device for planktonic animals and plants based on artificial intelligence technology according to claim 1, characterized in that, The warning system includes a warning logic module and a warning notification module integrated on the artificial intelligence chip. Among them: The warning logic module is used to analyze and judge the acquired data to determine whether to trigger a warning; The warning notification module is used to convey the warning information to relevant personnel and turn on the warning light after the warning logic module triggers a warning.

5. A rapid detection device for planktonic animals and plants based on artificial intelligence technology according to claim 1, characterized in that, The detection result storage system includes an image storage chip connected to the artificial intelligence chip, a storage management module and a data transmission module integrated on the image storage chip. Among them: The storage management module is used to manage the allocation and use of storage resources; The data transmission module is used to receive the recognition result data and transmit it to the storage platform.

6. An operation method implemented by a rapid detection device for planktonic animals and plants based on artificial intelligence technology according to any one of claims 1-5, characterized in that, Including: S1. Press the switch button to turn on the machine, connect to the microscope through the information transmission interface, and use the microscope for observation; S2. After the microscope captures microscopic images, the device will automatically perform image clarification processing on the acquired microscopic images; S3. Display the processed microscopic images on the display screen, and identify phytoplankton and zooplankton in real time, displaying the names and detailed information of the identified phytoplankton and zooplankton; S4. If accurate identification results are required, press the storage button to store the data. If re - shooting or re - identification is needed, press the return button to return or cancel the operation; S5. If dangerous species or a situation with extremely high concentrations of plankton appear, the warning light turns red, store the information and mark it, and wait for analysis after transmission to the computer terminal; S6. After the work is completed, press the transmission button to directly connect the data to the computer terminal through the information transmission interface and transmit it to the computer cloud for further analysis; S7. After use, press the switch button to turn off the machine.

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