CN117296762A - Prawn production detection and intelligent management and control system based on Internet of things - Google Patents

Abstract

The application provides a shrimp production detects and intelligent management and control system based on thing networking relates to the technical field that shrimp was bred, the method includes: presetting characteristic data of a plurality of growth stages of the prawns, establishing a characteristic data identification library of the prawns, setting a data threshold of the characteristic data identification library of the prawns, configuring the characteristic data identification library in an intelligent control system, then acquiring real-time data of the prawn culture, preprocessing the acquired data, comparing the data with the data threshold of the preset characteristic data identification library of the prawns, and when the real-time acquired characteristic data of the prawns exceeds the data threshold of the preset characteristic data identification library of the prawns, matching an optimal data adjustment scheme by the intelligent control system, and controlling an automation device to execute specific operation of the adjustment scheme so that the characteristic data of the prawns are within the data threshold of the preset characteristic data identification library of the prawns. The intelligent management system can automatically execute the cultivation management task, improves the cultivation efficiency, and promotes the modernization and technical progress of the cultivation prawn industry.

Description

Prawn production detection and intelligent management and control system based on Internet of things

Technical Field

The invention relates to the technical field of shrimp culture, in particular to a shrimp production detection and intelligent management and control system based on the Internet of things.

Background

The shrimp farming industry increasingly adopts intelligent monitoring systems, and intelligent self-adaptive shrimp farming is mainly realized by means of Internet of things technology and intelligent equipment. For example, the water quality is monitored by using a water quality sensor, a dissolved oxygen sensor and other devices, and the intelligent decision and control are performed on the cultivation process by using a data analysis and prediction model.

The prior art is through thing networking monitoring system: the monitoring equipment comprises a temperature sensor, an illumination intensity sensor, water body dissolved oxygen, a pH value, ammonia nitrogen content, nitrite content and the like, and is used for monitoring various information parameters of the water area, which influence the growth of the shrimps, in real time and eliminating adverse factors in time.

In the prior art, various information of shrimp growth is monitored in real time, so that a user can obtain data in time, when the data of the shrimp growth in real time is obtained, deviation occurs, or a sensor has a problem, frequent error prompt is caused, the user does not have a great deal of time to deal with the problem in time, the problem is rapidly analyzed, an adjustment scheme is determined, and the shrimp culture cannot reach the expected effect.

Disclosure of Invention

The method comprises the steps of presetting feature data of a plurality of growth stages of the prawns, establishing a feature data identification library of the prawns, setting a data threshold of the feature data identification library of the prawns, configuring the feature data identification library in an intelligent control system, then acquiring real-time data of prawn culture, preprocessing the acquired data, comparing the data with the preset data threshold of the feature data identification library of the prawns, and when the real-time acquired feature data of the prawns exceeds the data threshold of the feature data identification library of the preset prawns, matching an optimal data adjustment scheme by the intelligent control system, and controlling an automation device to execute specific operation of the adjustment scheme so that the feature data of the prawns are within the data threshold of the feature data identification library of the preset prawns.

In view of the above problems, an embodiment of the present application provides a method for detecting and controlling production of prawns based on internet of things, and in a first aspect, the embodiment of the present application provides a method for detecting and controlling production of prawns based on internet of things, where the system includes: the method comprises the steps of establishing a prawn growth characteristic data identification library and a prawn growth environment characteristic data identification library, setting a prawn growth stage characteristic data identification library data threshold and a prawn growth environment characteristic data identification library data threshold, acquiring prawn growth data through an image acquisition system and a video photographing device, transmitting acquired data to an intelligent management and control system, acquiring prawn growth characteristic data through a central monitoring system, transmitting the acquired characteristic data to the intelligent management and control system, judging whether the acquired prawn characteristic data are in the set characteristic identification library data threshold, repeatedly acquiring prawn culture environment parameters through the central monitoring system if the acquired prawn characteristic data are in the set characteristic identification library data threshold, acquiring prawn growth data through the image acquisition system, transmitting the acquired characteristic data to the intelligent management and control system, calculating a deviation value based on the set characteristic identification library data threshold and the acquired data if the acquired prawn characteristic data are not in the set characteristic identification library data threshold, transmitting an alarm to a user terminal when the deviation value is larger than the alarm value, matching an optimal adjustment data scheme, and controlling the automatic device to execute specific adjustment scheme operation to a remote user terminal.

In a second aspect, an embodiment of the present application provides a prawn production detection and intelligent management and control system based on the internet of things, the system includes: the characteristic recognition library configuration module is used for configuring an initialization data standard aiming at a defined multi-stage growth stage of the prawns, configuring the initialization data standard in the intelligent management and control system, and the central monitoring system module is used for monitoring and acquiring real-time prawn growth characteristic data based on a monitoring sensing device, processing the acquired data and sending the processed data to the intelligent management and control system, the image acquisition system module is used for analyzing and processing the characteristic data of a shot image and video prawn growth environment and sending the processed data to the intelligent management and control system, the intelligent management and control system module is used for monitoring the prawn growth characteristic and the prawn growth environment characteristic data by using a sensor, analyzing and processing the collected characteristic data and automatically executing an adjustment scheme,

the automatic adjustment program module is used for preparing a preset problem data exceeding a set data threshold, aiming at the preset problem data, preparing a wanted corresponding adjustment scheme, executing the preset problem data to prepare the wanted corresponding adjustment scheme when the acquired characteristic data exceeds the set data threshold, and sending an alarm to the user terminal when the deviation value of the acquired characteristic data is larger than the alarm value based on the intelligent management and control system, and sending the alarm to the user terminal when the acquired characteristic data is not in the preset characteristic recognition library.

One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:

the application provides a prawn production detection and intelligent control method based on the Internet of things, which relates to the technical field of prawn culture, and comprises the following steps: presetting characteristic data of a plurality of growth stages of the prawns, establishing a characteristic data identification library of the prawns, setting a data threshold of the characteristic data identification library of the prawns, configuring the characteristic data identification library in an intelligent control system, then acquiring real-time data of prawn culture, preprocessing the acquired data, comparing the data with the data threshold of the characteristic data identification library of the preset prawns, and starting the intelligent control system to execute an adjustment scheme when the characteristic data of the prawns obtained in real time exceeds the data threshold of the characteristic data identification library of the preset prawns.

The intelligent control system uses sensors to monitor key parameters in the culture pond or facility, such as water temperature, salinity, dissolved oxygen, pH, ammonia nitrogen, etc. According to the real-time data, the system can automatically adjust the environmental conditions to maintain the optimal growth conditions and improve the growth efficiency of the prawns. Meanwhile, a large amount of culture data can be collected, stored and analyzed, rapid data analysis is also helpful for optimizing feeding, culture period and culture strategy, the intelligent management and control system can set alarm conditions, once environmental abnormality or deviation threshold is detected, the system can give an alarm, the intelligent management system can automatically execute culture management tasks, and according to deviation of collected prawn growth data, adjustment schemes such as automatic feeding, water quality adjustment, dissolved oxygen increase and the like are automatically matched, so that culture efficiency is improved, labor force requirements are reduced, and management cost is reduced.

The foregoing description is merely an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.

Drawings

For a clearer description of the present disclosure or of the prior art, the drawings used in the description of the embodiments or of the prior art will be briefly described, it being obvious that the drawings in the description below are only exemplary and that other drawings may be obtained, without inventive effort, by a person skilled in the art, from the provided drawings.

Fig. 1 is a schematic flow chart of a prawn production detection and intelligent control method based on the internet of things in the embodiment of the application;

fig. 2 is a schematic structural diagram of a central monitoring system in a method for detecting and controlling the production of prawns based on the internet of things according to an embodiment of the present application;

fig. 3 is a schematic flow chart of a method for judging whether the collected prawn feature data is within a set feature recognition library data threshold in the prawn production detection and intelligent management and control method based on the internet of things according to the embodiment of the application;

fig. 4 is a schematic flow chart of a method for determining a set data threshold and calculating a deviation value of collected feature data in a prawn production detection and intelligent control method based on internet of things according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a prawn production detection and intelligent management and control system based on the internet of things.

Reference numerals illustrate: the system comprises a feature recognition library configuration module 20, a central monitoring system module 21, an image acquisition system module 22, an intelligent management and control system module 23, an automatic adjustment program module 24 and a warning feedback module 25.

Detailed Description

The method and the device mainly solve the problems that the complexity of the system is high, and a user is difficult to master and maintain. And monitoring the real-time state, carrying out fuzzy anomaly identification and emergency self-adjusting execution, determining an abnormal breeding point, carrying out feedback adjustment, and carrying out execution management and control.

Providing a powerful support for sustainable development of the prawn farming industry in order to better understand the above technical solutions, the following detailed description of the above solutions will be given with reference to the accompanying drawings and specific embodiments:

example 1

The method for detecting and intelligently managing and controlling the production of the prawns based on the Internet of things as shown in fig. 1 comprises the following steps:

establishing a prawn growth characteristic data identification library and a prawn growth environment characteristic data identification library, and setting a prawn growth stage characteristic data identification library data threshold value and a prawn growth environment characteristic data identification library data threshold value;

specifically, the prawn growth characteristic data identification library acquires prawn growth characteristic data by using a sensor, a measuring tool or a manual measuring method. Including weight, length, molting period, sex, age, mortality, etc. of the prawn. After the characteristic data of the real-time growth of the prawns are obtained each time, the characteristic data of the growth of the prawns are recorded, the characteristic data of the growth environment of the prawns are preset for each growth period of the prawns, the water temperature data of the growth environment of the prawns, the water quality data of the growth environment, the oxygen content data of the growth environment and the salinity data of the growth environment, and after the characteristic data of the growth environment of the prawns are obtained each time, the characteristic data of the growth environment of the prawns are recorded. Water quality data: including water temperature, pH, oxygen content, salinity data, dissolved oxygen, etc. These indices should be kept within a suitable range to ensure proper growth of the prawns. Dissolved oxygen: the concentration of dissolved oxygen in the water body, the prawn needs sufficient dissolved oxygen to maintain normal life activities. In the cultivation process, the monitoring of dissolved oxygen is an important component of water quality monitoring and is used for evaluating the health condition and pollution degree of water. Salinity data: proper salinity can help the prawns maintain good health condition and reduce the risk of infection diseases. In the cultivation process, the salinity should be monitored and adjusted according to the species, growth stage and local water quality condition of the shrimps in the cultivation process of the shrimps. Temperature: prawns are warm-blooded animals that require a suitable temperature to maintain their normal growth. And analyzing the obtained data by utilizing a data visualization tool, and drawing a growth curve, a box diagram, a trend diagram and the like so as to better understand each growth period of the prawns, the weight of the prawns, the body length of the prawns and the molting period of the prawns, and the data of the growth environment of the prawns, and establishing a prawn growth characteristic data identification library and a prawn growth environment characteristic data identification library so as to predict whether the growth and development of the prawns are in a normal range. In the cultivation process, the shrimp growth characteristic data identification library and the shrimp growth environment characteristic data identification library are configured as initialized cultivation standards, and are applied to an intelligent medium control system. The system comprises a sensor, a camera device, a controller and the like so as to meet the requirements of monitoring and automatic adjustment control of the growth environment of the prawns. Appropriate hardware and software configurations, including sensors, controllers, actuators, communication protocols, etc., are selected to ensure the stability and reliability of the intelligent control system. The method can not only improve the growth speed and survival rate of the prawns, but also reduce the culture cost and risk. Thereby bringing greater economic and ecological benefits to farmers.

The method comprises the steps of acquiring prawn culture environment parameters through an image acquisition system, acquiring prawn growth data through video photographing equipment, transmitting the acquired data to an intelligent management and control system, acquiring prawn growth characteristic data through a central monitoring system, and transmitting the acquired characteristic data to the intelligent management and control system;

specifically, the image acquisition system is used for prawn culture environment parameters. For example, a high resolution camera device, preferably a camera with night vision, is provided to ensure that data is collected under a variety of lighting conditions, the camera device being configured to take pictures or to record video continuously, while a sensor is placed in the vicinity of the camera device to monitor environmental parameters. The acquired images or videos are analyzed by using image processing and computer vision technology, the number, the size, the behaviors and other characteristics of the shrimps are tracked by using a computer vision tool, the analysis results are visualized so as to better understand the growth trend and the behaviors of the shrimps, meanwhile, charts, images and animations are created to present data, and the acquired data are transmitted to an intelligent management and control system. The central monitoring system is used for collecting growth characteristic data of the prawns, so that key growth characteristics of the prawns can be monitored and recorded in real time, and monitoring equipment for prawn culture, such as a water quality sensor, a temperature sensor, a dissolved oxygen sensor, a pH sensor and the like. These sensors may be data collected and recorded by a central monitoring system. The water quality sensor can monitor water quality parameters in real time, evaluate the health condition, the water quality characteristics and the pollution degree of the water body, and is connected with the central monitoring system to ensure that data can be transmitted to the monitoring system in real time. The temperature sensor outputs a temperature value in a digital or analog form so as to monitor and control a temperature-related process, and the sensor is connected with a central monitoring system to ensure that data can be transmitted to the monitoring system in real time. Dissolved oxygen sensors measure the concentration of dissolved oxygen in water, typically in milligrams per liter (mg/L), which is critical to the survival of aquatic organisms. And the pH sensor is used for measuring the pH value of the water body. And the intelligent control system is connected with the central monitoring system and ensures that data can be transmitted to the intelligent control system in real time. Setting a data set characteristic identification library data threshold of the prawn stage growth environment, wherein the data set characteristic identification library data threshold of the prawn stage growth environment sets a maximum value and a minimum value which accord with the characteristic data of the prawn stage growth environment for the data of each stage growth environment of the prawn. For example, the growth and metabolism of the prawn is affected by temperature, and the setting of the feature recognition library and threshold values may include a suitable temperature range to ensure that the prawn is in an optimal temperature condition, as exemplified by the feature recognition library: the normal growth temperature ranges from 25 ℃ to 30 ℃. Data threshold example: the temperature is 20 ℃ to 35 ℃. Different prawn species have different adaptability to salinity, so that appropriate salinity data thresholds need to be set according to the species of culture, for example, a feature recognition library example: suitable salinity ranges for white shrimps are 5ppt to 20ppt, data threshold examples: salinity was 3ppt to 25ppt. Monitoring water quality parameters such as dissolved oxygen, ammonia nitrogen, nitrate, pH, etc., is important, for example, dissolved oxygen signature libraries: the dissolved oxygen should be maintained above 5mg/L and the pH range is 7.5 to 8.5. Data threshold: dissolved oxygen 3mg/L, pH in the range of 7.0 to 9.0. Meanwhile, the central monitoring system can also perform preliminary processing and analysis on the acquired data, and more visual and accurate data support is provided for breeding personnel.

Judging whether the collected prawn feature data is within a set feature recognition library data threshold;

specifically, as shown in fig. 3, after the collected real-state data is analyzed and sorted, the collected prawn feature data is compared with a set feature recognition library data threshold value, and the measured value of each feature is compared with a corresponding threshold value. Defining the obtained characteristic data to be set as Z, wherein the minimum data of the data threshold value is X, and the maximum data is Y, and the method comprises the following steps: judging the data characteristic value of the current Z and the minimum data in a preset data threshold value as X, and the maximum data as Y, wherein X is not less than Z and not more than Y; and judging that the obtained characteristic data does not exceed the range of the set threshold value, and judging that the obtained characteristic data exceeds the range of the set threshold value if Z < X or X > Y.

The collected prawn characteristic data is within a set characteristic recognition library data threshold, the central monitoring system is repeatedly used for collecting prawn culture environment parameters, the image collecting system is used for collecting prawn growth data, and the obtained characteristic data is transmitted to the intelligent management and control system;

specifically, the central monitoring system is repeatedly used for collecting the shrimp culture environment parameters, the image collecting system is used for collecting the shrimp growth data, and the obtained characteristic data are transmitted to the intelligent management and control system; the system realizes the survival data of the circulating real-time monitoring object.

If the acquired prawn feature data is not in the set feature recognition library data threshold, calculating a deviation value based on the set feature recognition library data threshold and the acquired data, and sending an alarm to the user terminal when the deviation value is larger than the alarm value;

specifically, as shown in fig. 4, the acquired prawn feature data is not within the set feature recognition library data threshold, a deviation value is calculated based on the set data threshold and the acquired feature data, the deviation value is defined as M, the data of M is initialized, the alarm value is N, when the deviation value is larger than the alarm value, the deviation value is calculated based on the set data threshold and the acquired feature data, and when the data feature value of the current Z is judged to be smaller than the minimum data in the data threshold as X, namely: whether Z is less than X, if so, the deviation value is: m= |Z-X|, the pair calculates a deviation value based on a set data threshold and the collected characteristic data, and when judging that the data characteristic value of the current Z is larger than the data threshold, the maximum data is Y, namely: if Y > X, if so, the offset value is: m= |y-x|, when the deviation value is greater than the alarm value, namely: if M > N, then sending alarm to user terminal. For each feature, comparing the collected data with a set feature recognition library data threshold, calculating a deviation value of each feature, namely the difference between the measured value and the threshold, triggering an alarm if the deviation value exceeds a set threshold range, and taking adjustment environmental conditions or management measures as required.

Matching the optimal scheme for adjusting the data, and controlling the automation equipment to execute the specific operation of the adjustment scheme;

specifically, Q adjustment schemes are formulated for P preset problem data according to the P preset problem data exceeding the set data threshold, when the collected feature data exceeds the set data threshold, the P problem feature data is judged to be met, specific feature data problem points are determined, one adjustment scheme of the corresponding Q adjustment schemes is executed, and when the collected feature data exceeds the set data threshold and the P preset problem data exceeding the set data threshold is not met, an alarm is sent to the user terminal. For example, the automatic adjustment scheme is configured into the intelligent management and control system Chinese, and when corresponding abnormal data is triggered, the intelligent management and control system can automatically execute the adjustment. The intelligent control system is used for executing an automatic adjustment scheme, adjusting environmental conditions according to the monitored data, including adjusting water temperature, salinity, water level, illumination, oxygen supply, a feed feeder and the like, and the execution mechanism is used for automatically adjusting the automatic adjustment scheme, such as automatically adjusting the water temperature by a heater, adding dissolved oxygen by an oxygen supply device, adjusting the salinity by a desalination device and the like. The intelligent control system is used for recording and storing sensor data, automatically adjusting operation logs and historical data so as to analyze and optimize system performance later, and triggering an alarm when the automatic adjustment of the system fails or manual intervention is needed.

Further, as shown in fig. 2, the method of the present application, the central monitoring system, the method further includes:

the temperature sensor system is a group of temperature sensors arranged in water so as to monitor the water temperature and send the detected water temperature data to the intelligent management and control system;

the water quality sensor system is a water quality sensor group arranged in water and is used for monitoring water quality parameters including pH value, ammonia nitrogen content, nitrate nitrogen content, hydrogen sulfide content and dissolved oxygen content in water for prawn growth;

a salinity sensor system for mounting in water a group of salinity sensors for monitoring salinity in a body of water;

a light sensitive sensor system, which is a light sensitive sensor for being installed in water, for monitoring the light level;

a pressure sensor system built for a pressure sensor installed in water, hard to monitor water depth;

an oxygen sensor system, which is an oxygen sensor assembly for being installed in water, for monitoring a stirring effect and an oxygen concentration;

a flow sensor system, which is a flow sensor assembly for installation in water, for monitoring the water flow rate;

a turbidity sensor system, which is a turbidity sensor assembly for installation in water, the turbidity sensor monitoring the clarity of the water;

a weather sensor system, which is a weather sensor assembly for installation in water, for monitoring data of temperature, humidity, wind speed and precipitation;

and the central monitoring system transmits the acquired data to the intelligent management and control system.

Specifically, the central monitoring system acquires data in real time by being connected to various sensors and monitoring devices, the data acquired in real time is transmitted from scattered places to the central monitoring center, and the central monitoring center transmits data summarizing analysis to the intelligent management and control system.

And transmitting the processed information to a remote user terminal.

Specifically, the intelligent control system is connected to the remote user terminal through the internet, and when the intelligent control system detects that the environment data needs to be adjusted or specific measures are taken, processing information is generated and sent to the remote user terminal. Including the current state of the farming environment, the adjustment measures taken, the predicted outcome, etc. The processed information may be displayed on a remote user terminal for viewing by a user. In addition, the system can also notify the user of information by a notification mode, such as a mobile phone application notification, an email, a short message, etc., so that the user can know the condition of the cultivation environment at any time, and meanwhile, the processed information is usually stored as historical data, so that the user can check the past processing records at any time and perform trend analysis or review the changes in the cultivation process.

Example two

Based on the same inventive concept as the prawn production detection and intelligent control method based on the internet of things in the foregoing embodiments, as shown in fig. 5, the present application provides a prawn production detection and intelligent control system based on the internet of things, the system includes:

the characteristic recognition library configuration module is used for configuring an initialization data standard aiming at a defined multi-stage growth stage of the prawns and configuring the initialization data standard in an intelligent management and control system;

the central monitoring system module is used for acquiring real-time prawn growth characteristic data based on monitoring of the monitoring sensing device, processing the acquired data and transmitting the processed data to the intelligent management and control system;

the image acquisition system module is used for analyzing and processing the characteristic data of the growth environment of the prawns through the shot images and videos, processing the obtained data and sending the processed data to the intelligent management and control system;

the intelligent control system module is used for monitoring the growth characteristics of the prawns and the growth environment characteristic data of the prawns by using a sensor, analyzing and processing the collected characteristic data and automatically executing an adjustment scheme;

the automatic adjustment program module is used for presetting problem data exceeding a set data threshold, making a wanted corresponding adjustment scheme aiming at the preset problem data, and executing the preset problem data to make the wanted corresponding adjustment scheme when the acquired characteristic data exceeds the set data threshold;

and the warning feedback module is based on the intelligent management and control system, and transmits warning to the user terminal when the deviation value of the obtained characteristic data is larger than the warning value, and transmits warning to the user terminal when the obtained characteristic data is not in the preset characteristic recognition library.

Through the foregoing detailed description of the method and the system for detecting and controlling the production of the prawns based on the internet of things, those skilled in the art can clearly know the system for detecting and controlling the production of the prawns based on the internet of things in the embodiment, and for the system disclosed in the embodiment, the description is simpler because the system corresponds to the device disclosed in the embodiment, and relevant places refer to the description of the method.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. Prawn production detection and intelligent control method and system based on Internet of things, and is characterized in that the system comprises:

establishing a prawn growth characteristic data identification library and a prawn growth environment characteristic data identification library, and setting a prawn growth stage characteristic data identification library data threshold value and a prawn growth environment characteristic data identification library data threshold value;

the method comprises the steps of acquiring prawn culture environment parameters through an image acquisition system, acquiring prawn growth data through video photographing equipment, transmitting the acquired data to an intelligent management and control system, acquiring prawn growth characteristic data through a central monitoring system, and transmitting the acquired characteristic data to the intelligent management and control system;

judging whether the collected prawn feature data is within a set feature recognition library data threshold;

if the acquired prawn feature data is within the set feature recognition library data threshold, repeatedly acquiring prawn culture environment parameters through the central monitoring system, acquiring prawn growth data through the image acquisition system, and transmitting the acquired feature data to the intelligent management and control system;

if the acquired prawn feature data is not in the set feature recognition library data threshold, calculating a deviation value based on the set feature recognition library data threshold and the acquired data, and sending an alarm to the user terminal when the deviation value is larger than the alarm value;

matching the optimal scheme for adjusting the data, and controlling the automation equipment to execute the specific operation of the adjustment scheme;

and transmitting the processed information to a remote user terminal.

2. The method of claim 1, wherein the prawn growth characteristic data identification library and the prawn growth environment characteristic data identification library comprise:

the prawn growth characteristic data identification library is used for presetting data of each growth period of the prawns, the weight of the prawns, the body length of the prawns and the molting period of the prawns;

after the characteristic data of the real-time growth of the prawns are obtained each time, the characteristic data of the growth of the prawns are recorded;

the characteristic data of the growth environment of the prawns are preset, and each growth period of the prawns is preset, wherein the water temperature data of the growth environment of the prawns, the water quality data of the growth environment, the oxygen content data of the growth environment and the salinity data of the growth environment are obtained;

after the growth environment characteristic data of the prawns are obtained each time, the growth environment characteristic data of the prawns are recorded.

3. The method of claim 1, wherein the setting of the data threshold for the prawn growth stage feature identification library and the prawn growth environment feature identification library comprises:

setting a data set characteristic identification library data threshold of the prawn stage growth environment, wherein the data set characteristic identification library data threshold of the prawn stage growth environment sets a maximum value and a minimum value which accord with the characteristic data of the prawn stage growth environment for the data of each stage growth environment of the prawn.

4. The method of claim 1, wherein the central monitoring system comprises: a temperature sensor system, a water quality sensor system, a salinity sensor system, a light sensitive sensor system, a pressure sensor system, an oxygen sensor system, a flow rate sensor system, a turbidity sensor system, a weather sensor system, the method comprising:

the temperature sensor system is a group of temperature sensors arranged in water so as to monitor the water temperature and send the detected water temperature data to the intelligent management and control system;

the water quality sensor system is a water quality sensor group arranged in water and is used for monitoring water quality parameters including pH value, ammonia nitrogen content, nitrate nitrogen content, hydrogen sulfide content and dissolved oxygen content in water for prawn growth;

a salinity sensor system for mounting in water a group of salinity sensors for monitoring salinity in a body of water;

a light sensitive sensor system, which is a light sensitive sensor for being installed in water, for monitoring the light level;

a pressure sensor system built for a pressure sensor installed in water, hard to monitor water depth;

an oxygen sensor system, which is an oxygen sensor assembly for being installed in water, for monitoring a stirring effect and an oxygen concentration;

a flow sensor system, which is a flow sensor assembly for installation in water, for monitoring the water flow rate;

a turbidity sensor system, which is a turbidity sensor assembly for installation in water, the turbidity sensor monitoring the clarity of the water;

a weather sensor system, which is a weather sensor assembly for installation in water, for monitoring data of temperature, humidity, wind speed and precipitation;

and the central monitoring system transmits the acquired data to the intelligent management and control system.

5. The method of claim 1, wherein the image acquisition system comprises:

acquiring image data shot by a camera, preprocessing, denoising, enhancing and color correcting the data by using a computer vision technology, and acquiring growth characteristic data of the prawns;

acquiring video data shot by video shooting equipment, and extracting characteristics of the prawns from the images by using a computer vision technology, wherein the characteristics comprise characteristic data of the length of the prawns, the weight of the prawns and the body color of the prawns;

and combining the photographed image and video data to obtain the characteristic data of the current growth of the prawns.

6. A method according to claim 3, wherein said determining whether said collected prawn feature data is within a set feature recognition library data threshold defines an obtained feature data set as Z, and said data threshold has a minimum data of X and a maximum data of Y, the method comprising:

the acquired data is stored in real time;

judging the data characteristic value of the current Z and the minimum data in a preset data threshold value as X, and the maximum data as Y, wherein X is not less than Z and not more than Y; judging that the obtained characteristic data does not exceed the range of the set threshold value;

if Z < X or X > Y, judging that the obtained characteristic data exceeds the range of the set threshold value.

7. The method of claim 6, wherein the pair calculates a deviation value based on the set data threshold and the collected feature data, defines the deviation value as M, initializes the data of M, and the alarm value as N, and when the deviation value is greater than the alarm value, the method comprises:

the pair calculates a deviation value based on a set data threshold and the collected characteristic data, and when judging that the data characteristic value of the current Z is smaller than the minimum data in the data threshold, the minimum data is X, namely: whether Z is less than X;

if yes, the deviation value is: m= |z-x|;

the pair calculates a deviation value based on the set data threshold and the collected characteristic data, and when judging that the data characteristic value of the current Z is larger than the data threshold, the maximum data is Y, namely: whether Y > X;

if yes, the deviation value is: m= |y-x|;

when the deviation value is greater than the alarm value, namely: whether M > N;

if yes, an alarm is sent to the user terminal.

8. The method of claim 4, wherein the automatic adjustment scheme comprises:

according to P preset problem data exceeding a set data threshold, setting Q adjustment schemes for the P preset problem data;

when the collected characteristic data exceeds a set data threshold, judging the characteristic data conforming to P problems, determining specific characteristic data problem points, and executing one of corresponding Q adjustment schemes;

and when the acquired characteristic data exceeds the set data threshold value and the preset P kinds of problem data exceeding the set data threshold value are not met, sending an alarm to the user terminal.

9. Prawn production detection and intelligent control method and system based on Internet of things, and is characterized in that the system comprises:

the characteristic recognition library configuration module is used for configuring an initialization data standard aiming at a defined multi-stage growth stage of the prawns and configuring the initialization data standard in an intelligent management and control system;

the central monitoring system module is used for acquiring real-time prawn growth characteristic data based on monitoring of the monitoring sensing device, processing the acquired data and transmitting the processed data to the intelligent management and control system;

the image acquisition system module is used for analyzing and processing the characteristic data of the growth environment of the prawns through the shot images and videos, processing the obtained data and sending the processed data to the intelligent management and control system;

the intelligent control system module is used for monitoring the growth characteristics of the prawns and the growth environment characteristic data of the prawns by using a sensor, analyzing and processing the collected characteristic data and automatically executing an adjustment scheme;

the automatic adjustment program module is used for presetting problem data exceeding a set data threshold, making a wanted corresponding adjustment scheme aiming at the preset problem data, and executing the preset problem data to make the wanted corresponding adjustment scheme when the acquired characteristic data exceeds the set data threshold;

and the warning feedback module is based on the intelligent management and control system, and transmits warning to the user terminal when the deviation value of the obtained characteristic data is larger than the warning value, and transmits warning to the user terminal when the obtained characteristic data is not in the preset characteristic recognition library.

10. A computer readable storage medium, characterized in that the storage medium comprises a stored program, wherein the program, when run, performs the method of any one of claims 1 to 8.