Intelligent plankton collection net system

Abstract

The invention discloses an intelligent plankton collection net system which comprises a control unit, and the control unit is connected with an execution module used for collecting plankton; the parameter setting module is used for setting and changing the sampling depth, the sampling time, the sampling frequency and the sampling amount in the previous and later periods; the real-time environment detection module is used for monitoring water area environment parameters in real time after the system is started and transmitting data to the control unit; the optimization module is integrated in the control unit, processes and analyzes the real-time environment data through a self algorithm, and optimizes an acquisition strategy according to a preset condition or the real-time data; compared with the prior art, the system has remarkable advantages in automation, intelligence and sampling efficiency, can automatically monitor the environment and automatically collect and store plankton samples, realizes the integration of real-time monitoring of environmental parameters, intelligent optimization of a sampling strategy and automatic and accurate sampling, and remarkably improves the efficiency, accuracy and scientific research value of plankton sample collection.

Description

Technical Field

[0001] The invention belongs to the field of plankton and automation technology, and in particular relates to an intelligent plankton collection net system. Background Art

[0002] Traditional plankton collection methods rely on manual operations, and have problems such as low sampling efficiency, high cost, and susceptibility to human factors. With the development of science and technology, automation and intelligent technologies have been widely used in various fields, but in the field of plankton collection, there is no perfect intelligent collection system. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent plankton collection net system that can automatically monitor environmental parameters, intelligently select sampling locations, and automatically collect and store plankton samples to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: an intelligent plankton collection net system, comprising a control unit, connected to the control unit:

[0005] An execution module is used for collecting plankton;

[0006] The parameter setting module is used to set and modify the sampling depth, sampling time, sampling frequency and sampling volume before and after the sampling.

[0007] The real-time environment detection module monitors the water environment parameters in real time after starting the system and transmits the data to the control unit;

[0008] The optimization module is integrated in the control unit and processes and analyzes real-time environmental data through its own algorithm, and optimizes the collection strategy according to preset conditions or real-time data, including selecting the best sampling location and adjusting the sampling mesh size;

[0009] The sampling unit receives the data from the optimization module and selects the sampling mode. The sampling unit and the execution module are controlled by the control unit, and the control unit controls the execution mechanism to perform automatic sampling operation;

[0010] A sample processing module is used to automatically close the sampling net after the sampling is completed, and transfer the collected plankton samples to a storage unit;

[0011] The storage unit is used to store plankton and ensure the biological characteristics of the plankton.

[0012] As a preferred technical solution of the present invention, the sampling unit comprises

[0013] The automatic sampling module sends instructions to the actuator according to the optimized sampling strategy, drives the sampling net to sink to the target depth, and automatically opens the sampling net to collect plankton samples;

[0014] The data recording module is used to record the sampling data in real time during the sampling process and store the data in the internal memory or the cloud.

[0015] As a preferred technical solution in the present invention, the storage unit is provided with a temperature control module with temperature and humidity control functions, and the temperature control module is used to ensure that the plankton samples maintain their biological characteristics and research value during long-term storage.

[0016] As a preferred technical solution in the present invention, a communication module is also connected to the control unit, and a remote monitoring module is connected to the communication module, and the sampling data and status information are transmitted in real time. The remote monitoring module provides a user login window, allowing the user to remotely access, view real-time data, sampling results and system status, and perform required operations and controls.

[0017] As a preferred technical solution in the present invention, the storage unit is further provided with an adding module, which is used to add a fixative to the collected plankton samples to maintain the freshness and stability of the samples.

[0018] As a preferred technical solution in the present invention, the algorithm used by the optimization module includes:

[0019] Sliding Window Method:

[0020] Divide the data into windows of fixed size, such as processing the most recent 100 data points each time, calculate the statistics of the data in the window, and dynamically adjust the sampling frequency or strategy based on these statistics;

[0021] Adaptive sampling method:

[0022] Adjust the sampling frequency according to the changes in real-time data, and use indicators such as change rate and data fluctuation to decide whether to sample and the sampling frequency;

[0023] Event-driven approach:

[0024] Monitor the occurrence of specific events, such as emergencies, abnormal events, etc., and determine the sampling strategy based on the occurrence of the events.

[0025] As a preferred technical solution in the present invention, the data recording module includes recording of sampling time, sampling depth, sampling volume, and environmental parameters.

[0026] As a preferred technical solution in the present invention, the execution module includes a sampling network, a motor, and a propeller connected to the motor; the real-time environment detection module is composed of a variety of sensors for detecting water depth, water flow velocity, light intensity, and temperature.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] In the present invention, significant advantages are shown in automation, intelligence and sampling efficiency. It can automatically monitor the environment, automatically collect and store plankton samples, realize the integration of real-time monitoring of environmental parameters, intelligent optimization of sampling strategies and automatic and precise sampling, and significantly improve the efficiency, accuracy and scientific research value of plankton sample collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a system diagram of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] See also Figure 1 The present invention provides a technical solution: an intelligent plankton collection net system, including a control unit, connected to the control unit:

[0032] An execution module is used for collecting plankton;

[0033] The parameter setting module is used to set and modify the sampling depth, sampling time, sampling frequency and sampling volume before and after the sampling.

[0034] The real-time environment detection module monitors the water environment parameters in real time after starting the system and transmits the data to the control unit;

[0035] The optimization module is integrated in the control unit and processes and analyzes real-time environmental data through its own algorithm, and optimizes the collection strategy according to preset conditions or real-time data, including selecting the best sampling location and adjusting the sampling mesh size;

[0036] The sampling unit receives the data from the optimization module and selects the sampling mode. The sampling unit and the execution module are controlled by the control unit, and the control unit controls the execution mechanism to perform automatic sampling operation;

[0037] A sample processing module is used to automatically close the sampling net after the sampling is completed, and transfer the collected plankton samples to a storage unit;

[0038] The storage unit is used to store plankton and ensure the biological characteristics of the plankton.

[0039] In this embodiment, the sampling unit includes

[0040] The automatic sampling module sends instructions to the actuator according to the optimized sampling strategy, drives the sampling net to sink to the target depth, and automatically opens the sampling net to collect plankton samples;

[0041] The data recording module is used to record the sampling data in real time during the sampling process and store the data in the internal memory or the cloud.

[0042] In this embodiment, a temperature control module with temperature and humidity control functions is provided on the storage unit, and the temperature control module is used to ensure that the plankton samples maintain their biological characteristics and research value during long-term storage.

[0043] In this embodiment, a communication module is also connected to the control unit, and a remote monitoring module is connected to the communication module, and the sampling data and status information are transmitted in real time. The remote monitoring module provides a user login window, allowing the user to remotely access, view real-time data, sampling results and system status, and perform necessary operations and controls.

[0044] In this embodiment, an adding module is also provided on the storage unit, and the adding module is used to add a fixative to the collected plankton samples to maintain the freshness and stability of the samples.

[0045] In this embodiment, the algorithms used by the optimization module include:

[0046] Sliding Window Method:

[0047] Divide the data into windows of fixed size, such as processing the most recent 100 data points each time, calculate the statistics of the data in the window, and dynamically adjust the sampling frequency or strategy based on these statistics;

[0048] Adaptive sampling method:

[0049] Adjust the sampling frequency according to the changes in real-time data, and use indicators such as change rate and data fluctuation to decide whether to sample and the sampling frequency;

[0050] Event-driven approach:

[0051] Monitor the occurrence of specific events, such as emergencies, abnormal events, etc., and determine the sampling strategy based on the occurrence of the events.

[0052] In this embodiment, the data recording module includes recording the sampling time, sampling depth, sampling volume, and environmental parameters.

[0053] In this embodiment, the execution module includes a sampling network, a motor, and a propeller connected to the motor; the real-time environment detection module is composed of a variety of sensors for detecting water depth, water flow speed, light intensity, and temperature.

[0054] Although embodiments of the present invention have been shown and described (see the above detailed description for details), it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent plankton collection net system, comprising a control unit, characterized in that: The following connections are made to the control unit: An execution module is used for collecting plankton; The parameter setting module is used to set and modify the sampling depth, sampling time, sampling frequency and sampling volume before and after the sampling. The real-time environment detection module monitors the water environment parameters in real time after starting the system and transmits the data to the control unit; The optimization module is integrated in the control unit, processes and analyzes real-time environmental data through its own algorithm, and optimizes the collection strategy based on preset conditions or real-time data; The sampling unit receives the data from the optimization module and selects the sampling mode. The sampling unit and the execution module are controlled by the control unit, and the control unit controls the execution mechanism to perform automatic sampling operation; A sample processing module is used to automatically close the sampling net after the sampling is completed, and transfer the collected plankton samples to a storage unit; The storage unit is used to store plankton and ensure the biological characteristics of the plankton.

2. The intelligent plankton collection net system according to claim 1, characterized in that: The sampling unit comprises The automatic sampling module sends instructions to the actuator according to the optimized sampling strategy, drives the sampling net to sink to the target depth, and automatically opens the sampling net to collect plankton samples; The data recording module is used to record the sampling data in real time during the sampling process and store the data in the internal memory or the cloud.

3. The intelligent plankton collection net system according to claim 1, characterized in that: The storage unit is provided with a temperature control module with temperature and humidity control functions, and the temperature control module is used to ensure that the plankton samples maintain their biological characteristics and research value during long-term storage.

4. The intelligent plankton collection net system according to claim 1, characterized in that: The control unit is also connected to a communication module, and the communication module is connected to a remote monitoring module, which transmits sampling data and status information in real time. The remote monitoring module provides a user login window, allowing the user to remotely access, view real-time data, sampling results and system status, and perform necessary operations and controls.

5. The intelligent plankton collection net system according to claim 3, characterized in that: The storage unit is also provided with an adding module, which is used to add a fixative to the collected plankton samples to maintain the freshness and stability of the samples.

6. The intelligent plankton collection net system according to claim 1, characterized in that: The algorithms used by the optimization module include: Sliding Window Method: Divide the data into windows of fixed size, calculate the statistics of the data in the window, and dynamically adjust the sampling frequency or strategy based on these statistics; Adaptive sampling method: Adjust the sampling frequency according to the changes in real-time data, and use indicators such as change rate and data fluctuation to decide whether to sample and the sampling frequency; Event-driven approach: Monitor the occurrence of specific events and decide on sampling strategies based on the occurrence of the events.

7. The intelligent plankton collection net system according to claim 1, characterized in that: The data recording module includes recording the sampling time, sampling depth, sampling volume, and environmental parameters.

8. The intelligent plankton collection net system according to claim 1, characterized in that: The execution module includes a sampling network, a motor, and a propeller connected to the motor; the real-time environment detection module is composed of a variety of sensors for detecting water depth, water flow speed, light intensity, and temperature.

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