A smart farming method for mandarin fish

Abstract

This invention provides an intelligent aquaculture method for mandarin fish, comprising: obtaining first distribution location information of an image acquisition device; acquiring images of a first aquaculture pond using the image acquisition device to obtain a first image acquisition set; acquiring mandarin fish distribution information based on the first distribution location information and the first image acquisition set to obtain a first mandarin fish distribution time variation pattern; distributing dissolved oxygen sensors according to the first mandarin fish distribution time variation pattern to obtain a first sensor distribution result; acquiring oxygen content information of the first aquaculture pond to obtain a first acquisition result; analyzing the growth stage of mandarin fish based on the first image acquisition set to obtain a first growth analysis result; inputting the first acquisition result and the first growth analysis result into a distributed oxygen content analysis model to obtain a first distribution area oxygenation supplementation result and a first identified oxygen content acquisition time area, and performing mandarin fish aquaculture management in the first aquaculture pond.

Description

Technical Field

[0001] This invention relates to the field of intelligent aquaculture technology, specifically to an intelligent aquaculture method for mandarin fish. Background Technology

[0002] Mandarin fish, with its delicious and tender flesh, high protein content, and rich nutrition, is a relatively precious freshwater fish and a delicacy served at banquets throughout history. To ensure the healthy growth and balanced nutrition of mandarin fish, fry are typically raised and then artificially cultured to produce marketable fish. The length of the rearing period depends on the availability and palatability of feed fish, water quality, and proper management. The main farming methods include monoculture in ponds, polyculture in small reservoirs and ponds for adult fish, and cage culture. Monitoring and adjusting the aquaculture environment in a rational manner is of great importance.

[0003] Currently, the main method of adjustment involves regularly monitoring the breeding environment and relying on human experience. However, this manual monitoring and adjustment process is inefficient and depends heavily on human judgment, requiring a high level of expertise from the breeders and increasing labor costs.

[0004] However, in the process of implementing the inventive technical solution in the embodiments of this application, the inventors of this application discovered that the above-mentioned technology has at least the following technical problems:

[0005] Existing technologies rely heavily on manual monitoring and adjustment of the aquaculture environment, resulting in low efficiency and weak stability. Summary of the Invention

[0006] This application provides an intelligent aquaculture method for mandarin fish, solving the technical problems of low efficiency and weak stability in existing technologies that rely mainly on manual monitoring and adjustment of the aquaculture environment. The first step involves using an image acquisition device to collect real-time images of the fishpond, obtaining the distribution pattern of mandarin fish over time. Based on this distribution pattern, a dissolved oxygen sensor collects the oxygen content at the main distribution locations at different times. The second step analyzes the growth stages of the mandarin fish from the images. Based on the growth stages, the oxygen content collected at different times is analyzed to determine if it meets the requirements, identifying the distribution areas requiring oxygen supplementation and corresponding monitoring time markers. This allows for oxygenation of the corresponding distribution areas and monitoring of oxygen content at the marked times to determine if the standards are met, ensuring the healthy growth of the mandarin fish and achieving the technical effect of intelligent monitoring and management.

[0007] In view of the above problems, this application provides an intelligent farming method for mandarin fish.

[0008] In a first aspect, embodiments of this application provide an intelligent aquaculture method for mandarin fish, wherein the method is applied to a mandarin fish aquaculture monitoring system, the system being communicatively connected to an image acquisition device and a dissolved oxygen sensor, the method comprising: obtaining first distribution location information of the image acquisition device; acquiring images of a first aquaculture pond through the image acquisition device to obtain a first image acquisition set; acquiring mandarin fish distribution information based on the first distribution location information and the first image acquisition set to obtain a first mandarin fish distribution time variation pattern; and distributing the dissolved oxygen sensor according to the first mandarin fish distribution time variation pattern. The distribution results of the first sensing device are obtained; based on the first sensor distribution results, the oxygen content information of the first fishpond is collected using the dissolved oxygen sensing device to obtain the first collection result; the growth stage of mandarin fish is analyzed based on the first image collection set to obtain the first growth analysis result; the first collection result and the first growth analysis result are input into the distributed oxygen analysis model to obtain the oxygenation supplementation result of the first distribution area and the first identified oxygen collection time area; the mandarin fish farming management of the first fishpond is carried out using the oxygenation supplementation result of the first distribution area and the first identified oxygen collection time area.

[0009] On the other hand, embodiments of this application provide an intelligent aquaculture system for mandarin fish, wherein the system includes: a first obtaining unit, the first obtaining unit being used to obtain first distribution location information of an image acquisition device, and to acquire images of a first aquaculture pond through the image acquisition device to obtain a first image acquisition set; a second obtaining unit, the second obtaining unit being used to acquire mandarin fish distribution information based on the first distribution location information and the first image acquisition set to obtain a first mandarin fish distribution time variation pattern; a third obtaining unit, the third obtaining unit being used to distribute the dissolved oxygen sensing device based on the first mandarin fish distribution time variation pattern to obtain a first sensing device distribution result; and a fourth obtaining unit, the fourth obtaining unit... The system is configured to: 1) collect oxygen content information of the first fishpond based on the dissolved oxygen sensing device using the first sensor distribution results, and obtain a first collection result; 2) obtain a fifth obtaining unit, which is configured to analyze the growth stage of mandarin fish based on the first image collection set, and obtain a first growth analysis result; 3) obtain a sixth obtaining unit, which is configured to input the first collection result and the first growth analysis result into a distributed oxygenation analysis model to obtain a first distribution area oxygenation supplementation result and a first identified oxygenation collection time area; and 4) execute a first executing unit, which is configured to manage the mandarin fish farming in the first fishpond using the first distribution area oxygenation supplementation result and the first identified oxygenation collection time area.

[0010] Thirdly, embodiments of this application provide an intelligent aquaculture system for mandarin fish, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described in any of the first aspects.

[0011] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0012] By employing the acquisition of first distribution location information of image acquisition devices, images are acquired from the first fishpond using these devices to obtain a first image acquisition set. Based on the first distribution location information and the first image acquisition set, mandarin fish distribution information is acquired to obtain the temporal variation pattern of the first mandarin fish distribution. Based on the temporal variation pattern of the first mandarin fish distribution, the dissolved oxygen sensing devices are distributed to obtain the first sensing device distribution result. Using the first sensor distribution result, oxygen content information of the first fishpond is acquired based on the dissolved oxygen sensing devices to obtain a first acquisition result. Based on the first image acquisition set, the growth stage of the mandarin fish is analyzed to obtain a first growth analysis result. The first acquisition result and the first growth analysis result are input into a distributed oxygenation analysis model to obtain the oxygenation supplementation result for the first distribution area. The technical solution for managing mandarin fish farming in the first fishpond is based on the oxygenation results and the oxygenation time area of ​​the first distribution area. The solution involves: 1) acquiring real-time images of the fishpond using an image acquisition device to obtain the distribution pattern of mandarin fish over time; 2) using a dissolved oxygen sensor to collect oxygen content at different times in the main distribution locations based on the distribution pattern; 3) analyzing the growth stages of the mandarin fish from the images; 4) analyzing whether the oxygen content collected at different times meets the requirements based on the growth stages of the mandarin fish, obtaining the distribution areas requiring oxygen supplementation and the corresponding monitoring time information; 5) adding oxygen to the corresponding distribution areas and collecting oxygen content at the designated time to determine if the standards are met, ensuring the healthy growth of the mandarin fish and achieving intelligent monitoring and management.

[0013] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0014] Figure 1 This application provides a schematic flowchart of an intelligent aquaculture method for mandarin fish.

[0015] Figure 2This application provides a schematic flowchart of a method for monitoring feed fish in intelligent aquaculture of mandarin fish.

[0016] Figure 3 This application provides a schematic flowchart of a method for intelligent aquaculture water quality monitoring of mandarin fish.

[0017] Figure 4 This application provides a schematic diagram of an intelligent aquaculture system for mandarin fish.

[0018] Figure 5 This is a schematic diagram of the structure of an exemplary electronic device according to an embodiment of this application.

[0019] Explanation of reference numerals in the attached drawings: First obtaining unit 11, Second obtaining unit 12, Third obtaining unit 13, Fourth obtaining unit 14, Fifth obtaining unit 15, Sixth obtaining unit 16, First execution unit 17, Electronic device 300, Memory 301, Processor 302, Communication interface 303, Bus architecture 304. Detailed Implementation

[0020] This application provides an intelligent aquaculture method and system for mandarin fish, solving the technical problems of low efficiency and weak stability in existing technologies that rely mainly on manual monitoring and adjustment of the aquaculture environment. The first step involves using an image acquisition device to collect real-time images of the fishpond, obtaining the distribution pattern of mandarin fish over time. Based on this distribution pattern, a dissolved oxygen sensor collects the oxygen content at the main distribution locations at different times. The second step analyzes the growth stages of the mandarin fish from the images. Based on the growth stages, the analysis checks whether the oxygen content collected at different times meets the requirements, identifying the distribution areas requiring oxygen supplementation and corresponding monitoring time markers. This allows for oxygenation of the corresponding distribution areas and monitoring of oxygen content at the marked times to determine if the standards are met, ensuring the healthy growth of the mandarin fish and achieving the technical effect of intelligent monitoring and management.

[0021] Application Overview

[0022] Mandarin fish, with its delicious and tender flesh, high protein content, and rich nutrition, is a relatively precious freshwater fish and a delicacy served at banquets throughout history. To ensure the healthy growth and balanced nutrition of mandarin fish, fry are typically raised and then artificially cultured to produce marketable fish. The length of the rearing process depends on the availability and palatability of feed, water quality, and proper management. The main cultivation methods include monoculture in ponds, polyculture in small reservoirs, and cage culture. Monitoring and adjusting the cultivation environment is crucial. Currently, adjustments are mainly made through regular monitoring of the environment, relying on human experience. However, this manual monitoring and adjustment process is inefficient and dependent on human judgment, requiring a high level of expertise from the personnel and increasing labor costs. Furthermore, the current technology, which relies heavily on manual monitoring and adjustment of the cultivation environment, suffers from low efficiency and weak stability.

[0023] To address the aforementioned technical problems, the overall approach of the technical solution provided in this application is as follows:

[0024] This application provides an intelligent aquaculture method for mandarin fish, wherein the method is applied to a mandarin fish aquaculture monitoring system, the system being communicatively connected to an image acquisition device and a dissolved oxygen sensor. The method includes: obtaining first distribution location information of the image acquisition device; acquiring images of a first aquaculture pond using the image acquisition device to obtain a first image acquisition set; acquiring mandarin fish distribution information based on the first distribution location information and the first image acquisition set to obtain a first mandarin fish distribution time variation pattern; distributing the dissolved oxygen sensor according to the first mandarin fish distribution time variation pattern to obtain a first sensor distribution result; acquiring oxygen content information of the first aquaculture pond based on the dissolved oxygen sensor using the first sensor distribution result to obtain a first acquisition result; analyzing the growth stage of mandarin fish based on the first image acquisition set to obtain a first growth analysis result; inputting the first acquisition result and the first growth analysis result into a distributed oxygen content analysis model to obtain a first distribution area oxygenation supplementation result and a first identified oxygen content acquisition time area; and managing the mandarin fish aquaculture in the first aquaculture pond using the first distribution area oxygenation supplementation result and the first identified oxygen content acquisition time area.

[0025] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0026] Example 1

[0027] like Figure 1As shown in the figure, this application provides an intelligent aquaculture method for mandarin fish, wherein the method is applied to a mandarin fish aquaculture monitoring system, the system being communicatively connected to an image acquisition device and a dissolved oxygen sensor, and the method includes:

[0028] S100: Obtain the first distribution location information of the image acquisition device, and use the image acquisition device to acquire images of the first fishpond to obtain a first image acquisition set;

[0029] Specifically, the image acquisition device is a device deployed at multiple locations within the pond to acquire images of the mandarin fish farming pond, preferably a high-definition camera; the first distribution location information is the deployment location of the image acquisition device in the first farming pond, including but not limited to: the specific coordinates of multiple locations such as the bottom of the pond, the inner wall of the pond, and the top of the pond; the first image acquisition set is the image information of the pond collected 24 hours a day by the image acquisition device. Further, optionally, the first image acquisition set and the first distribution location information can be stored correspondingly. One distribution location may correspond to multiple images, and the images corresponding to multiple distribution locations may be of the same area but acquired from different angles. This allows for the acquisition of multi-angle image sets from different areas within the pond, which can clearly characterize the activity and growth status of the mandarin fish.

[0030] By collecting image information from fishponds, the mandarin fish farming monitoring system can monitor and analyze the activity and growth status of mandarin fish in real time, providing a basis for further monitoring and farming regulation, and improving the intelligence of mandarin fish farming monitoring.

[0031] S200: Collect mandarin fish distribution information based on the first distribution location information and the first image acquisition set to obtain the first mandarin fish distribution time change pattern;

[0032] Specifically, based on the distribution locations and image storage methods described above, images collected at different time points are further stored according to their acquisition sequence. For example, a 24-hour acquisition cycle is preset for storage, obtaining image data of different areas within the pond within 24 hours. This allows analysis of the distribution location changes of mandarin fish within 24 hours. Furthermore, the mandarin fish distribution images within the 24-hour cycle are labeled with weather and season information. Seasonal labels include spring, summer, autumn, and winter, and weather labels include, but are not limited to, rainy, sunny, cloudy, and snowy days. This allows for the analysis of the distribution location changes of mandarin fish within 24 hours under multiple different weather conditions and seasons.

[0033] By collecting data on the distribution patterns of mandarin fish in the pond over time, the first fishpond can be divided into zones. The main distribution locations of mandarin fish at different time periods can be marked as the main monitoring areas. At the corresponding time points, various indicators in these areas can be monitored in detail, and adjustments can be made accordingly to ensure the healthy growth of mandarin fish.

[0034] S300: Dissolve oxygen sensing devices are distributed according to the distribution time variation pattern of the first mandarin fish to obtain the distribution result of the first sensing device;

[0035] S400: Based on the distribution results of the first sensor, the oxygen content information of the first fishpond is collected using the dissolved oxygen sensing device to obtain the first collection result;

[0036] Specifically, the dissolved oxygen sensing device is a device for collecting oxygen content information of the first fishpond, preferably multiple devices. The distribution of the first sensing devices is determined based on the temporal distribution pattern of the mandarin fish. The first sensing devices are deployed in the areas where the mandarin fish are mainly distributed, while a small number of first sensing devices are evenly deployed in other locations. The deployment orientation is then transmitted to obtain the distribution results of the first sensing devices. The distribution of the first sensing devices needs to be adaptively adjusted according to different seasons or different weather conditions to ensure a high degree of correlation between the oxygen content monitoring results and the distribution location of the mandarin fish.

[0037] The first acquisition result is real-time oxygen content data in the first fishpond collected by multiple first sensing devices. Optionally, the oxygen content data can be stored according to the previously divided regions based on the acquisition results at the same time point, and the acquisition results at different time points in the same region can be stored according to the acquisition sequence. For example, the oxygen content changes over 24 hours can be collected and stored in correspondence with the distribution location of mandarin fish to obtain the oxygen content of the time period corresponding to the main distribution location of mandarin fish as the main monitoring indicator.

[0038] By monitoring the real-time oxygen content data in the first fishpond, a data foundation is provided for subsequent analysis of oxygen content in different locations of mandarin fish at different time periods, thereby enabling dynamic adjustment and improving intelligence.

[0039] S500: Based on the first image acquisition set, analyze the growth stage of mandarin fish to obtain the first growth analysis result;

[0040] Specifically, the first growth analysis result is obtained by extracting image features from the mandarin fish images in the first image acquisition set, and further analyzing the extracted features to obtain information representing the growth stage of the mandarin fish. An exemplary determination method is as follows: the first image acquisition set is input into a mandarin fish feature extraction model trained on a convolutional neural network. The model is trained using multiple sets of image information and mandarin fish feature identification information, and then input into the first image acquisition set to obtain the growth characteristic information of the mandarin fish, preferably size information. For example, without limitation, the average body length of 1-year-old fish is 17.5 cm; 2-year-old fish is 23.6 cm; 3-year-old fish is 32.8 cm; and 4-year-old fish is 42.5 cm. The feature extraction model trained on a convolutional neural network has strong image feature extraction capabilities, resulting in more accurate results.

[0041] S600: Input the first acquisition result and the first growth analysis result into the distributed oxygen content analysis model to obtain the oxygen replenishment result of the first distribution area and the first identified oxygen content acquisition time area;

[0042] S700: The mandarin fish farming management of the first aquaculture pond is carried out based on the oxygenation supplementation results of the first distribution area and the oxygen collection time area of ​​the first identifier.

[0043] Specifically, the distributed oxygen content analysis model is a model trained based on a neural network model to analyze the degree of adaptation between the growth stages of mandarin fish and the monitored oxygen content in different areas of a pond under different climatic conditions. The artificial neural network uses fully connected layers to simulate the thinking process of the human brain. Multiple sets of training data are used, each set including: the first collection result and the first growth analysis result as input training information, and labeling information indicating the adaptation between oxygen content and mandarin fish growth, as well as supervisory information. Once the model converges, the distributed oxygen content analysis model is obtained.

[0044] Furthermore, the first collection results and the first growth analysis results are input into the distributed oxygen content analysis model. Based on the output results, the corresponding regions at different time points where oxygen content and mandarin fish growth stages are not compatible can be obtained. These regions are set as the oxygen supplementation results of the first distribution area, and oxygen content can be supplemented to the corresponding low oxygen content areas. The first identified oxygen content collection time area is the monitoring time point set for real-time monitoring of oxygen content in the area after oxygen supplementation. Oxygen content is collected in the corresponding area according to the first identified oxygen content collection time area, and then the compatibility between the supplemented oxygen content and the mandarin fish growth stage is judged. If the requirements are met, no further adjustments are made. The compatibility can be set by the breeders themselves based on theoretical knowledge and actual breeding conditions.

[0045] By using intelligent models to analyze the compatibility between oxygen content in different regions and at different times and the growth stages of mandarin fish, oxygenation operations are carried out in areas with low oxygen content to ensure the healthy growth of mandarin fish. This achieves the technical effect of improving feeding quality based on intelligent monitoring and regulation.

[0046] Furthermore, such as Figure 2 As shown, the method further includes step S800:

[0047] S810: Obtain a first set of baitfish images, classify the baitfish according to their size, and construct a first baitfish level database based on the classification results and the first set of baitfish images.

[0048] S820: Obtain a first image feature matching instruction; according to the first image feature matching instruction, perform image feature matching on the first image acquisition set through the first bait fish grade database to obtain a first matching result;

[0049] S830: Calculate the distribution density of different grades of bait fish in the first fishpond based on the first matching result, and obtain the bait fish grade density distribution result;

[0050] S840: Supplement baitfish based on the baitfish grade density distribution results.

[0051] Specifically, the first set of bait fish images is a set of images of bait fish used to raise mandarin fish. Preferably, the bait fish should be fed at fixed times and locations. During the period when mandarin fish fry are 3-6 cm long, 4-8 bait fish should be fed daily per fish, with the bait fish's body length not exceeding 55-60% of the mandarin fish's body length. After 6 cm, 4-5 bait fish should be fed daily, with their body length not exceeding 50-55% of the mandarin fish's body length. Therefore, in order for the system to automatically feed bait fish of appropriate size, it is necessary to classify the bait fish by size according to the first set of bait fish images and store the classification results to obtain the first bait fish grade number. According to the database; further, the first image feature matching instruction is to match a custom preset quantity and size of bait fish based on the size feature information and quantity feature information of mandarin fish in the first image acquisition set, and record it as the first matching result; further, the quantity feature and size feature of bait fish are extracted from the first image acquisition set, the extracted features are compared with the first matching result, and the quantity information of bait fish corresponding to different size levels is calculated, and recorded as the bait fish level density distribution result; bait fish that do not meet the custom requirements are supplemented to ensure that the mandarin fish are fed with rich nutrition and thus grow healthily.

[0052] Furthermore, method step S840 also includes:

[0053] S841: Obtain a second image feature matching instruction, perform image feature matching on the first image acquisition set according to the second image feature matching instruction, and obtain a second matching result;

[0054] S842: Obtain the grade distribution density of mandarin fish based on the second matching result;

[0055] S843: Obtain the predation ratio of mandarin fish and baitfish;

[0056] S844: The grade matching degree is evaluated by the predation ratio, the grade distribution density of mandarin fish, and the grade density distribution results of bait fish to obtain the first evaluation result;

[0057] S845: Distribute supplementation of different bait fish grades based on the first evaluation results.

[0058] Specifically, the second image feature matching instruction analyzes the quantity information of mandarin fish of different sizes in the fishpond based on the first image acquisition set. An exemplary matching process involves: extracting features from the first image acquisition set using a convolutional neural network model to obtain the quantity information of mandarin fish at different size levels, which is recorded as the second matching result. Further, the quantity and size information of mandarin fish at different levels are jointly stored to obtain the mandarin fish level distribution density information. This facilitates the subsequent quick retrieval of corresponding mandarin fish density data based on different sizes of the fish.

[0059] The predation ratio refers to the size requirement of the bait fish for different sized mandarin fish, as well as the feeding quantity requirement within a preset time period. For example, it is customized as follows: during the period when the mandarin fish fry are 3-6 cm long, 4-8 bait fish are fed every 24 hours, with the bait fish body length not exceeding 55-60% of the mandarin fish body length; after 6 cm, 4-5 bait fish are fed every 24 hours, with their body length not exceeding 50-55% of the mandarin fish body length. The first evaluation result is based on the predation ratio, comparing whether the bait fish size and quantity represented by the bait fish level density distribution result corresponding to the mandarin fish level density under different levels match. For bait fish under the corresponding level that do not match, bait is supplemented until a match is reached.

[0060] The quantity and size characteristics of mandarin fish are extracted from the first image acquisition set to obtain the quantity information of mandarin fish at different size levels. Further analysis is conducted based on the customized predation ratio to determine whether the density distribution of the bait fish level meets the requirements. Bait fish that do not meet the customized requirements will be supplemented to ensure that the mandarin fish are fed with rich nutrition and thus grow healthily.

[0061] Furthermore, such as Figure 3 As shown, the system is also communicatively connected to a pH measuring device, and the method further includes step S900:

[0062] S910: The image acquisition device is used to acquire a water image of the first fishpond to obtain a second image;

[0063] S920: Water transparency is monitored using the second image to obtain the first water transparency monitoring result;

[0064] S930: The pH value of the water body is measured by the pH measuring device to obtain the first pH value measurement result;

[0065] S940: Adjust the water quality based on the first water body transparency monitoring results and the first pH value measurement results.

[0066] Specifically, the second image is the result of acquiring an image of the water surface of the first fishpond using an image acquisition device; the first water transparency monitoring result is information representing the water's characteristics obtained by extracting image features from the second image. For example, transparency is characterized by the clarity of the mandarin fish in the water; the clearer the mandarin fish image, the higher the corresponding transparency; the first pH value measurement result is the pH value of the water in the first fishpond monitored by the pH value measuring device. The preferred monitoring period is set to 24 hours, because excessive pH changes within 24 hours indicate an unstable water environment. The water quality is judged based on water transparency and pH value. When the preset requirements are not met, relevant personnel need to be reminded to make adjustments. For example, the preset water pH is 7-8.5. If the water is opaque, there are many algae and phytoplankton, the pH may be too high, and timely pH monitoring and adjustment are required; if the pH is low, the water is acidic, the concentration of metal ions in the water increases, which may lead to toxicity, and the water needs to be replaced and the pH adjusted.

[0067] By monitoring water transparency in real time and pH periodically, the water quality of the fishpond is controlled in an environment suitable for the growth of mandarin fish, ensuring the healthy growth of mandarin fish. This achieves the technical effect of intelligent monitoring and regulation of the mandarin fish farming environment, greatly reducing the instability and untimeliness of manual monitoring.

[0068] Furthermore, the method further includes step S1000:

[0069] S1010: Obtain the first weather change prediction information for the first fishpond;

[0070] S1020: Incremental learning of the distributed oxygen content analysis model is performed using weather change prediction training data and labeling information indicating the impact of weather on water oxygen content to obtain an incremental distributed oxygen content analysis model.

[0071] S1030: Input the first weather change prediction information into the incremental distributed oxygen content analysis model to obtain the first oxygen content impact result;

[0072] S1040: Based on the first oxygen content influence result, adjust the oxygen supplementation result of the first distribution area and the first marked oxygen collection time area to obtain the first adjustment result;

[0073] S1050: Conduct mandarin fish farming management in the first aquaculture pond according to the first adjustment result.

[0074] Specifically, the first weather change prediction information is the weather change information of the first fishpond within a preset time period, preferably the weather change information within the next 24 hours; the result of the weather's influence on the oxygen content of the water is the relationship between the influence of different atmospheric environments on the oxygen content of the water, preferably determined by: collecting multiple sets of water oxygen content under different weather conditions at different time points, and then obtaining the influence law of weather on the oxygen content of the water, which is recorded as the result of the weather's influence on the oxygen content of the water; the incremental distributed oxygen content analysis model is a new model obtained after convergence by training the distributed oxygen content analysis model based on weather change prediction training data and the identification information of the result of weather's influence on the oxygen content of the water through incremental learning. Incremental learning refers to using data with high similarity to the data sample features of the original model to train a new model on the basis of the original model. By adding the influence of weather elements on the oxygen content in the fishpond through incremental learning, the intelligence of the model is further improved. The first oxygen content impact result is the predicted change information of oxygen content in the fishpond in the next 24 hours obtained by processing weather forecast information through the incremental distributed oxygen content analysis model. Furthermore, the first adjustment result is the oxygen supplementation result of the first distribution area and the adjustment plan of the first marked oxygen collection time area based on the predicted change information of oxygen content in the fishpond, in order to cope with the changes of oxygen content in the fishpond in the next 24 hours and ensure that the water environment is always a suitable living environment for mandarin fish.

[0075] By training a new model through incremental learning, the model can predict changes in oxygen levels in fishponds based on future weather conditions, and then make targeted adjustments to ensure the suitability of the aquaculture environment and the growth of mandarin fish, thereby improving the intelligence of the system.

[0076] Furthermore, the system is also communicatively connected to a temperature sensor, and the method further includes step S1100:

[0077] S1110: Continuously monitor the temperature of the first fishpond using the temperature sensor to obtain the temperature change curve of the first fishpond.

[0078] S1120: Obtain the external air temperature change curve of the first fishpond;

[0079] S1130: Analyze the temperature response time based on the temperature change curve and the external air temperature change curve to obtain the first-time response result;

[0080] S1140: Control the temperature of the first fishpond based on the first time response result.

[0081] Specifically, the temperature change curve of the first fishpond is obtained by collecting temperature data within the fishpond over a preset time period and storing the data corresponding to the collection time nodes. The result is obtained by plotting the temperature-time change curve based on multiple sets of collected data. The preset time period is preferably 24 hours. The external air temperature change curve of the first fishpond is the temperature-time change curve outside the fishpond, and the plotting method and process are the same as those of the temperature change curve of the first fishpond. The first time response result is obtained by analyzing the temperature-time change curves inside and outside the fishpond to understand the influence of external temperature on the fishpond temperature over time. Furthermore, based on the influence of external temperature on the fishpond temperature over time, the temperature inside the first fishpond is dynamically adjusted in real time by monitoring the temperature of the first fishpond and the external air temperature of the first fishpond, ensuring that the temperature is maintained within the temperature range preferred by the mandarin fish and guaranteeing their healthy growth.

[0082] Furthermore, the method step S1100 also includes step S1150:

[0083] S1151: Obtain first weather temperature change information through the first weather change prediction information;

[0084] S1152: Obtain a first temperature adjustment threshold, wherein the first temperature adjustment threshold is a threshold for adjusting the temperature obtained through big data on mandarin fish farming;

[0085] S1153: Based on the first time response result and the first weather temperature change information, the first temperature adjustment threshold is corrected to obtain the second temperature adjustment threshold;

[0086] S1154: Temperature control of the first fishpond is performed using the second temperature adjustment threshold.

[0087] Specifically, the first weather temperature change information is the temperature change data predicted for the next 24 hours based on the first weather forecast information; the first temperature adjustment threshold is a preset temperature adjustment threshold in the system after summarizing the suitable temperature range for mandarin fish growth through big data analysis of mandarin fish farming, to ensure that the water temperature is always maintained within the preset temperature range. For example, if the optimal water temperature range is preset to 23℃-26℃, then the preset adjustment threshold is to maintain it at around 25℃; the second temperature adjustment threshold is based on the influence law of external temperature on water temperature obtained from the first time response result, and then the influence law is used to obtain the influence of real-time external temperature on water temperature. The result obtained by correcting the first temperature adjustment threshold based on the response value is exemplified as follows: If the first time response result indicates that the outside temperature and the water temperature exceed 5℃-8℃, it will have an impact of 3℃ on the water temperature. When the water temperature is 25℃ and the outside temperature is 31℃, if the temperature adjustment threshold is 25℃, the actual water temperature is 28℃, which exceeds 26℃. At this time, the temperature adjustment threshold should be corrected to 22℃ to ensure that the water temperature is maintained at around 25℃. By adjusting the water temperature through the corrected second temperature adjustment threshold, the water temperature is maintained in a suitable temperature environment for mandarin fish, ensuring the healthy growth of mandarin fish and improving the intelligence of intelligent aquaculture.

[0088] In summary, the intelligent aquaculture method and system for mandarin fish provided in this application have the following technical effects:

[0089] 1. This application provides an intelligent aquaculture method and system for mandarin fish, solving the technical problems of low efficiency and weak stability in existing technologies that rely mainly on manual monitoring and adjustment of the aquaculture environment. The first step involves using an image acquisition device to collect real-time images of the fishpond, obtaining the distribution pattern of mandarin fish over time. Based on this distribution pattern, a dissolved oxygen sensor collects the oxygen content at the main distribution locations at different times. The second step analyzes the growth stages of the mandarin fish from the images. Based on the growth stages, the analysis checks whether the oxygen content collected at different times meets the requirements, identifying the distribution areas requiring oxygen supplementation and corresponding monitoring time markers. This allows for oxygenation of the corresponding distribution areas and assessment of oxygen content collection at the marked times to determine if standards are met, ensuring the healthy growth of the mandarin fish and achieving the technical effect of intelligent monitoring and management.

[0090] 2. Extract the quantity and size characteristics of mandarin fish from the first image acquisition set to obtain the quantity information of mandarin fish at different size levels. Further analyze whether the density distribution results of the bait fish level meet the requirements based on the customized predation ratio. Supplement the bait fish that do not meet the customized requirements to ensure that the mandarin fish are fed with rich nutrition and grow healthily.

[0091] 3. By training a new model through incremental learning, the model can predict changes in oxygen content in fishponds based on future weather conditions, and then make targeted adjustments to ensure the suitability of the breeding environment and the growth of mandarin fish, thereby improving the intelligence of the system.

[0092] Example 2

[0093] Based on the same inventive concept as the intelligent aquaculture method for mandarin fish in the foregoing embodiments, such as Figure 4 As shown in the figure, this application embodiment provides an intelligent aquaculture system for mandarin fish, wherein the system includes:

[0094] The first obtaining unit 11 is used to obtain the first distribution location information of the image acquisition device, and to acquire images of the first fish pond through the image acquisition device to obtain a first image acquisition set.

[0095] The second obtaining unit 12 is used to collect mandarin fish distribution information based on the first distribution location information and the first image acquisition set, and obtain the first distribution time change pattern of mandarin fish.

[0096] The third obtaining unit 13 is used to distribute the dissolved oxygen sensing device according to the distribution time change law of the first mandarin fish, and obtain the distribution result of the first sensing device;

[0097] The fourth obtaining unit 14 is used to collect oxygen content information of the first fishpond based on the dissolved oxygen sensing device through the distribution result of the first sensor, and obtain the first collection result.

[0098] The fifth obtaining unit 15 is used to analyze the growth stage of mandarin fish based on the first image acquisition set and obtain the first growth analysis result;

[0099] The sixth obtaining unit 16 is used to input the first acquisition result and the first growth analysis result into the distributed oxygen content analysis model to obtain the oxygen supplementation result of the first distribution area and the first identified oxygen content acquisition time area;

[0100] The first execution unit 17 is used to manage the mandarin fish farming in the first aquaculture pond by using the oxygenation supplementation results of the first distribution area and the oxygen collection time area of ​​the first identifier.

[0101] Furthermore, the system also includes:

[0102] The seventh obtaining unit is used to obtain a first set of bait fish images, classify the bait fish according to their size, and construct a first bait fish level database based on the level classification results and the first set of bait fish images.

[0103] The eighth obtaining unit is used to obtain a first image feature matching instruction, and according to the first image feature matching instruction, to perform image feature matching on the first image acquisition set through the first bait fish level database to obtain a first matching result;

[0104] The ninth obtaining unit is used to calculate the distribution density of different grades of bait fish in the first fishpond based on the first matching result, and obtain the bait fish grade density distribution result.

[0105] The second execution unit is used to supplement the bait fish based on the bait fish grade density distribution results.

[0106] Furthermore, the system also includes:

[0107] The tenth obtaining unit is used to obtain a second image feature matching instruction, and to perform image feature matching on the first image acquisition set according to the second image feature matching instruction to obtain a second matching result;

[0108] The eleventh obtaining unit is used to obtain the grade distribution density of mandarin fish based on the second matching result;

[0109] The twelfth obtaining unit is used to obtain the predation ratio between mandarin fish and bait fish;

[0110] The thirteenth obtaining unit is used to evaluate the grade matching degree through the predation ratio, the mandarin fish grade distribution density and the bait fish grade density distribution results, and obtain the first evaluation result;

[0111] The third execution unit is used to distribute and supplement different bait fish levels based on the first evaluation result.

[0112] Furthermore, the system also includes:

[0113] The fourteenth obtaining unit is used to acquire water images of the first fishpond through the image acquisition device to obtain a second image;

[0114] The fifteenth obtaining unit is used to perform water transparency monitoring through the second image to obtain a first water transparency monitoring result;

[0115] The sixteenth obtaining unit is used to measure the pH value of water using a pH measuring device to obtain a first pH value measurement result.

[0116] The fourth execution unit is used to adjust the water quality based on the first water transparency monitoring result and the first pH value measurement result.

[0117] Furthermore, the system also includes:

[0118] The seventeenth obtaining unit is used to obtain the first weather change prediction information of the first fish pond;

[0119] The eighteenth obtaining unit is used to perform incremental learning of the distributed oxygen content analysis model by using weather change prediction training data and identification information that identifies the impact of weather on water oxygen content, thereby obtaining an incremental distributed oxygen content analysis model.

[0120] The nineteenth obtaining unit is used to input the first weather change prediction information into the incremental distributed oxygen content analysis model to obtain the first oxygen content influence result.

[0121] The twentieth obtaining unit is used to adjust the oxygen supplementation result of the first distribution area and the first marked oxygen collection time area based on the first oxygen content influence result, and obtain the first adjustment result.

[0122] The fifth execution unit is used to manage the mandarin fish farming in the first fishpond according to the first adjustment result.

[0123] Furthermore, the system also includes:

[0124] The 21st obtaining unit is used to continuously monitor the temperature of the first fish pond through a temperature sensor and obtain the temperature change curve of the first fish pond.

[0125] The 22nd obtaining unit is used to obtain the external air temperature change curve of the first fish pond;

[0126] The twentieth obtaining unit is used to analyze the temperature response time based on the temperature change curve and the external air temperature change curve to obtain the first time response result.

[0127] The sixth execution unit is used to control the temperature of the first fishpond based on the first time response result.

[0128] Furthermore, the system also includes:

[0129] The 21st obtaining unit is used to obtain the first weather temperature change information through the first weather change prediction information;

[0130] The 22nd obtaining unit is used to obtain a first temperature adjustment threshold, wherein the first temperature adjustment threshold is a threshold for adjusting temperature obtained through big data on mandarin fish farming.

[0131] The 23rd obtaining unit is used to correct the first temperature adjustment threshold based on the first time response result and the first weather temperature change information to obtain the second temperature adjustment threshold.

[0132] The seventh execution unit is used to control the temperature of the first fishpond by means of the second temperature adjustment threshold.

[0133] Exemplary electronic devices

[0134] The following is for reference. Figure 5 To describe the electronic device in the embodiments of this application,

[0135] Based on the same inventive concept as the intelligent aquaculture method for mandarin fish in the foregoing embodiments, this application also provides an intelligent aquaculture system for mandarin fish, including: a processor coupled to a memory, the memory being used to store a program, and when the program is executed by the processor, causing the system to perform the method described in any of the first aspects.

[0136] The electronic device 300 includes a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. The communication interface 303, processor 302, and memory 301 can be interconnected via the bus architecture 304; the bus architecture 304 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus architecture 304 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0137] Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits used to control the execution of programs according to the present application.

[0138] Communication interface 303 is used in any transceiver system for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), wired access network, etc.

[0139] Memory 301 can be ROM or other types of static storage devices capable of storing static information and instructions, RAM or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory can exist independently and be connected to the processor via bus architecture 304. Memory can also be integrated with the processor.

[0140] The memory 301 stores computer execution instructions for implementing the scheme of this application, and the processor 302 controls the execution. The processor 302 executes the computer execution instructions stored in the memory 301, thereby realizing the intelligent aquaculture method for mandarin fish provided in the above embodiments of this application.

[0141] Optionally, the computer execution instructions in the embodiments of this application may also be referred to as application code, and the embodiments of this application do not specifically limit this.

[0142] This application provides an intelligent aquaculture method and system for mandarin fish, solving the technical problems of low efficiency and weak stability in existing technologies that rely mainly on manual monitoring and adjustment of the aquaculture environment. The first step involves using an image acquisition device to collect real-time images of the fishpond, obtaining the distribution pattern of mandarin fish over time. Based on this distribution pattern, a dissolved oxygen sensor collects the oxygen content at the main distribution locations at different times. The second step analyzes the growth stages of the mandarin fish from the images. Based on the growth stages, the analysis checks whether the oxygen content collected at different times meets the requirements, identifying the distribution areas requiring oxygen supplementation and corresponding monitoring time markers. This allows for oxygenation of the corresponding distribution areas and monitoring of oxygen content at the marked times to determine if the standards are met, ensuring the healthy growth of the mandarin fish and achieving the technical effect of intelligent monitoring and management.

[0143] Those skilled in the art will understand that the various numerical designations, such as "first" and "second," used in this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application, nor do they indicate a sequential order. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one" refers to one or more. "At least two" refers to two or more. "At least one," "any one," or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0144] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0145] The various illustrative logic units and circuits described in the embodiments of this application can be implemented or operate the described functions using a general-purpose processor, digital signal processor, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA) or other programmable logic system, discrete gate or transistor logic, discrete hardware components, or any combination thereof. The general-purpose processor can be a microprocessor; alternatively, it can also be any conventional processor, controller, microcontroller, or state machine. The processor can also be implemented using a combination of computing systems, such as a digital signal processor and a microprocessor, multiple microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

[0146] The steps of the methods or algorithms described in the embodiments of this application can be directly embedded in hardware, software units executed by a processor, or a combination of both. The software units can be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and storage medium can be disposed in an ASIC, which can be disposed in a terminal. Optionally, the processor and storage medium can also be disposed in different components of the terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0147] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of the claims and their equivalents, this application intends to include such modifications and modifications.

Claims

1. A smart farming method for mandarin fish, wherein, The method is applied to a mandarin fish farming monitoring system, which is communicatively connected to an image acquisition device and a dissolved oxygen sensor. The method includes: Obtain the first distribution location information of the image acquisition device, and use the image acquisition device to acquire images of the first fishpond to obtain the first image acquisition set; Based on the first distribution location information and the first image acquisition set, mandarin fish distribution information is collected to obtain the first distribution time variation pattern of mandarin fish; The distribution of the dissolved oxygen sensing device is determined based on the distribution time variation pattern of the first mandarin fish, and the distribution result of the first sensing device is obtained. Based on the distribution results of the first sensor, the oxygen content information of the first fishpond is collected using the dissolved oxygen sensing device to obtain the first collection result; Based on the first image acquisition set, the growth stage of mandarin fish is analyzed to obtain the first growth analysis result; The first collection result and the first growth analysis result are input into the distributed oxygen content analysis model to obtain the oxygen replenishment result of the first distribution area and the first identified oxygen content collection time area. The mandarin fish farming management of the first aquaculture pond is carried out based on the oxygenation supplementation results of the first distribution area and the oxygen collection time area of ​​the first identifier. The method further includes: Obtain the first weather change forecast information for the first fishpond. The distributed oxygen content analysis model is incrementally learned by using weather change prediction training data and labeling information that identifies the impact of weather on water oxygen content. The first weather change prediction information is input into the incremental distributed oxygen content analysis model to obtain the first oxygen content impact result; Based on the first oxygen content influence result, the oxygen supplementation result of the first distribution area and the first marked oxygen content collection time area are adjusted to obtain the first adjustment result; Based on the first adjustment result, the mandarin fish farming management of the first aquaculture pond shall be carried out; The system is also communicatively connected to a temperature sensor, and the method further includes: The temperature of the first fishpond is continuously monitored using the temperature sensor to obtain the temperature change curve of the first fishpond. Obtain the external air temperature change curve of the first fish farming pond; Based on the temperature change curve and the external air temperature change curve, the temperature response time is analyzed to obtain the first-time response result; Temperature control of the first fishpond is performed based on the first time response result. The method further includes: First weather temperature change information is obtained through the first weather change prediction information; A first temperature adjustment threshold is obtained, wherein the first temperature adjustment threshold is a threshold for adjusting the temperature obtained through big data on mandarin fish farming; The first temperature adjustment threshold is corrected based on the first time response result and the first weather temperature change information to obtain the second temperature adjustment threshold; The temperature of the first fishpond is controlled by the second temperature adjustment threshold.

2. The method as described in claim 1, wherein, The method further includes: Obtain a first set of baitfish images, classify the baitfish according to their size, and construct a first baitfish level database based on the level classification results and the first set of baitfish images. A first image feature matching instruction is obtained, and based on the first image feature matching instruction, the first image acquisition set is matched with the first bait fish grade database to obtain a first matching result. The distribution density of different grades of bait fish in the first fishpond is calculated based on the first matching result to obtain the density distribution result of bait fish grade. The baitfish are supplemented based on the density distribution results of the baitfish grades.

3. The method as described in claim 2, wherein, The method further includes: Obtain a second image feature matching instruction, and perform image feature matching on the first image acquisition set according to the second image feature matching instruction to obtain a second matching result; The grade distribution density of mandarin fish is obtained based on the second matching result; Obtain the predation ratio between mandarin fish and baitfish; The first evaluation result is obtained by evaluating the matching degree of the predation ratio, the distribution density of the mandarin fish by grade, and the distribution density of the bait fish by grade. Based on the results of the first evaluation, distributed supplementation of different bait fish grades is carried out.

4. The method of claim 1, wherein, The system is also communicatively connected to a pH measuring device, and the method further includes: The image acquisition device is used to acquire water images of the first fishpond to obtain a second image. The water transparency is monitored using the second image to obtain the first water transparency monitoring result. The pH value of the water body is measured using the pH measuring device to obtain the first pH value measurement result; Water quality is adjusted based on the first water body transparency monitoring results and the first pH value measurement results.

5. An intelligent aquaculture system for mandarin fish, wherein, The system is used to execute an intelligent aquaculture method for mandarin fish as described in any one of claims 1 to 4, comprising: The first obtaining unit is used to obtain the first distribution location information of the image acquisition device, and to acquire images of the first fishpond through the image acquisition device to obtain a first image acquisition set. The second obtaining unit is used to collect mandarin fish distribution information based on the first distribution location information and the first image acquisition set, and obtain the first distribution time change pattern of mandarin fish. The third obtaining unit is used to distribute the dissolved oxygen sensing device according to the distribution time variation law of the first mandarin fish, and obtain the distribution result of the first sensing device; The fourth obtaining unit is used to collect oxygen content information of the first fishpond based on the dissolved oxygen sensing device using the distribution results of the first sensor, and obtain the first collection result. The fifth obtaining unit is used to analyze the growth stage of mandarin fish based on the first image acquisition set and obtain the first growth analysis result; The sixth obtaining unit is used to input the first acquisition result and the first growth analysis result into the distributed oxygen content analysis model to obtain the oxygen replenishment result of the first distribution area and the first identified oxygen content acquisition time area; The first execution unit is used to manage the mandarin fish farming in the first aquaculture pond based on the oxygenation supplementation results of the first distribution area and the oxygen collection time area of ​​the first identifier.

6. An intelligent aquaculture system for mandarin fish, comprising: A processor coupled to a memory for storing a program that, when executed by the processor, causes the system to perform the method as described in any one of claims 1 to 4.

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