Growth information acquisition system for aquaculture

Abstract

The invention relates to the technical field of aquaculture, and discloses a growth information acquisition system for aquaculture, which comprises distributed acquisition node modules deployed in a plurality of key areas of a farm and used for acquiring data through a water quality information acquisition module, a biological information acquisition module and an external environment information acquisition module; the data processing module is used for carrying out denoising, time synchronization and regionalization integration on the acquired data; the data transmission module is used for uploading data of the acquisition nodes to the central data processing unit; the central data processing unit is used for integrating and storing the uploaded data and monitoring the running state of the uploaded data; the water quality information acquisition module comprises a multi-parameter water quality sensor and is used for acquiring temperature, salinity, pH value, dissolved oxygen and ammonia nitrogen. According to the invention, through the reasonable deployment of the distributed acquisition nodes, the key areas, including the center, corners and different depth positions of the water body, of the farm are covered, so that the spatial comprehensiveness and regional representativeness of data acquisition are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aquaculture, and in particular to a growth information collection system for aquaculture. BACKGROUND

[0002] Aquaculture is a production activity of breeding, cultivating and harvesting aquatic plants and animals under artificial control. With the rapid development of aquaculture, more and more modern farming methods have been widely used, such as net cage culture, flow water culture, etc. At the same time, the ecological fishery technology of three-dimensional utilization of water area and water-land composite production is continuously matured, which promotes the sustainable and efficient development of aquaculture. The technology of maintaining sustainable utilization of fishery resources is increasingly concerned by the industry and society, which provides technical support for meeting the growing demand for aquatic products.

[0003] The collection and monitoring equipment of the existing technology of the farming environment information has been applied in some farms, such as monitoring the dissolved oxygen, temperature and pH value through fixed water quality sensors, recording the growth state of the cultured organisms by using the camera, or obtaining the external meteorological parameters through the environmental collection equipment. The application of these technologies provides a certain basis for the monitoring of the aquaculture environment, and also promotes the progress of the aquaculture production towards automation and digitization. However, the application of these technologies is mostly focused on the collection of local areas or single parameters, and lacks systematicness and comprehensiveness.

[0004] The traditional fixed equipment can usually only collect data at a single position, and cannot reflect the environmental differences of different areas of the farm, such as the water quality at the center, corners and different depths of the water body. In addition, many devices only focus on the collection of a single dimension of water quality or external environment, and cannot realize the multi-dimensional synchronous collection of water quality, biological and external environmental information. This technical limitation directly leads to insufficient spatial representativeness of the data, which cannot provide reliable data support for the global monitoring and decision-making of the farming environment. SUMMARY

[0005] In order to make up for the above shortcomings, the present application provides a growth information collection system for aquaculture, which aims to improve the problem of the existing technology that the collection is focused on local areas or single parameters, and the systematicness and comprehensiveness are poor.

[0006] In the first aspect, the present application provides the following technical scheme, a growth information collection system for aquaculture, comprising: The distributed collection node module is deployed in multiple key areas of the farm, and collects data through the water quality information collection module, the biological information collection module and the external environment information collection module; The data processing module is used for denoising, time synchronization and regional integration of the collected data; The data transmission module is used to upload the data from the acquisition nodes to the central data processing unit; The central data processing unit integrates, stores, and monitors the operational status of the uploaded data.

[0007] Preferably, the water quality information acquisition module includes a multi-parameter water quality sensor for collecting key water quality indicators such as temperature, salinity, pH value, dissolved oxygen, and ammonia nitrogen. The sensor sampling frequency is dynamically adjustable when the parameter change rate... When the sampling frequency exceeds the set threshold, the sampling frequency switches to high-frequency mode.

[0008] Preferably, the bio-information acquisition module includes a high-definition underwater camera and a computer vision algorithm unit, used to acquire multi-angle images of the cultured organisms and extract individual body length and weight information, wherein: Body length is calculated using the following formula:

[0009] Where L is the body length, Length in pixels is the pixel density, and scale is the ratio factor between the pixels and the actual length; Weight is estimated using the following formula:

[0010] in, For weight, For body length, This is the calibration coefficient for a specific variety.

[0011] Preferably, the external environment information acquisition module includes an integrated weather station for collecting real-time temperature, humidity, air pressure, and wind speed, wherein: The air pressure is related to dissolved oxygen and is corrected using the following formula:

[0012] in, The current air pressure. This is the standard atmospheric pressure.

[0013] Preferably, the data processing module includes a data denoising unit, a time synchronization unit, and a regional integration unit, wherein: The data denoising unit filters high-frequency noise from water quality data using wavelet transform. The time synchronization unit aligns the data of each node according to the system's base time to form a unified timestamp. The regional integration unit groups the data collected from the nodes according to their geographical location to form a regional data view.

[0014] Preferably, the data transmission module adopts a wireless communication mode and supports LoRa and Wi-Fi protocols.

[0015] Preferably, the central data processing unit comprises a system fault tolerance module, and the system fault tolerance module comprises a node redundancy design and a data cross verification unit, wherein: the node redundancy design is used for arranging nodes in each collection area, and when a main node fails, a standby node automatically takes over the collection task. The data cross verification unit corrects abnormal values through average calculation of multi-node data, and the correction formula is:

[0016] wherein, is the corrected data, is the collection data of the i th node.

[0017] In a second aspect, the present application provides the following technical solution: a growth information collection method for aquaculture, and the method steps are as follows: S1: distributed collection nodes are arranged at the center, corners and key depth positions of the water body of the breeding farm, and each node comprises a water quality information collection unit, a biological information collection unit and an external environment collection unit; S2: key water quality indexes such as temperature, salinity, pH value, dissolved oxygen and ammonia nitrogen are collected in real time through a multi-parameter water quality sensor, and the sampling data are recorded according to time stamps; S3: multi-angle images of organisms are collected by using an underwater high-definition camera, individual body length is extracted, and body weight is calculated by combining an empirical formula; S4: external parameters such as air temperature, humidity, air pressure and wind speed are collected by the external environment collection unit and are synchronized to the collection system; S5: water quality and biological data are subjected to signal noise reduction, time synchronization and regional integration to form a structured data set; S6: the collected data are uploaded to a central data processing unit in real time by using wireless communication or wired network, and the data are temporarily stored when the network is interrupted; S7: a standby node takes over the collection task of a failed node and corrects abnormal or missing data through cross verification; S8: after all the collected data are classified and integrated, the data are stored as a multi-dimensional database, and time, area and parameter information are recorded.

[0018] In a third aspect, the present application provides the following technical solution: a computer device comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, and the processor implements the growth information collection method for aquaculture as described above when executing the computer program.

[0019] ​In a fourth aspect, the present application provides a readable storage medium, wherein the readable storage medium stores a computer program, and the computer program is executed by a processor to implement the growth information collection method for aquaculture.

[0020] The present application has the following advantages: 1. In the present application, by deploying distributed collection nodes, the key areas of the aquaculture farm are covered, including the center, corners and different depth positions of the water body, ensuring the spatial comprehensiveness and regional representativeness of data collection; at the same time, each node collects water quality, biological and external environmental information in multiple dimensions, solving the problem of insufficient coverage of traditional single-point collection equipment.

[0021] 2. In the present application, high-frequency dynamic collection strategy and wireless communication technology are used to realize real-time uploading and processing of water quality, biological and environmental data; in the case of network interruption, the node supports temporary data storage and breakpoint resume function, ensuring the integrity and losslessness of data.

[0022] 3. In the present application, by classifying and integrating water quality, biological and external environmental data, a multidimensional database is formed, which is stored according to time, region and parameter type, supporting efficient data query and analysis; the multidimensional database provides structured and traceable data support for subsequent growth trend analysis, abnormal environment monitoring and aquaculture management optimization. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 A system framework diagram of a growth information collection system for aquaculture is provided for the present application; Figure 2 A step flowchart of a growth information collection method for aquaculture is provided for the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. Embodiment one:

[0025] Reference Figure 1 In the first embodiment of the present application, the present application provides a growth information collection system for aquaculture, comprising: The distributed collection node module is deployed in multiple key areas of the aquaculture farm, and collects data through the water quality information collection module, the biological information collection module and the external environmental information collection module; Specifically, the distributed data acquisition node module is deployed in multiple key areas of the farm. By integrating water quality information acquisition module, biological information acquisition module and external environment information acquisition module, it covers different functional areas of the farm (such as feeding area, drainage area and water center) to achieve multi-dimensional data acquisition for the entire farm.

[0026] Each node has independent operating capabilities, and redundant deployment is implemented among nodes. When a primary node fails, the backup node automatically takes over the data collection task.

[0027] The water quality information acquisition module includes a multi-parameter water quality sensor for collecting data on temperature, salinity, pH, dissolved oxygen, and ammonia nitrogen. The sensor sampling frequency can be dynamically adjusted based on the parameter change rate. When the sampling frequency exceeds the set threshold, the sampling frequency switches to high-frequency mode.

[0028] Specifically, module description: The water quality information acquisition module monitors temperature in real time through multi-parameter sensors. Key water quality indicators include salinity (S), pH value, dissolved oxygen (DO), and ammonia nitrogen (NH3).

[0029] Detailed implementation: For high-altitude or areas with abnormal air pressure, the system dynamically corrects dissolved oxygen levels using the following formula:

[0030] in, The current air pressure. This is the standard atmospheric pressure.

[0031] Based on the rate of change of water quality parameters ( Dynamically adjust the sampling frequency when When a threshold is set, the sensor switches to high-frequency sampling mode to capture rapidly fluctuating dynamic changes. The system has a built-in sensor calibration unit that corrects sensor drift through regular standard solution testing or built-in calibration algorithms, ensuring long-term data accuracy.

[0032] Reference Figure 1 The bioinformatics acquisition module includes a high-definition underwater camera and a computer vision algorithm unit, used to acquire multi-angle images of farmed organisms and extract individual body length and weight information, wherein: Body length is calculated using the following formula:

[0033] in, For body length, Length in pixels is the pixel density, and scale is the ratio factor between the pixels and the actual length; The body weight is estimated by the following formula:

[0034] wherein, is the body weight, is the body length, is the standard coefficient of the specific breed.

[0035] Specifically, the biological information acquisition module obtains multi-angle images of the cultured organisms through a high-definition underwater camera, and realizes high-precision extraction of the body length and the body weight in combination with a computer vision algorithm, while supporting behavior feature recording.

[0036] Referring to Figure 1 , the external environment information acquisition module includes an integrated weather station for collecting real air temperature, humidity, air pressure and wind speed, wherein: The air pressure is associated with the dissolved oxygen, and is corrected by the following formula:

[0037] wherein, is the current air pressure, is the standard air pressure.

[0038] Specifically, the external environment information acquisition module collects air temperature (T), humidity (H), air pressure (P) and wind speed (V) through the integrated chemical monitoring, and synchronously integrates with the internal environment data.

[0039] Referring to Figure 1 , the data processing module is used for denoising, time synchronization and regional integration of the collected data; The data processing module includes a data denoising unit, a time synchronization unit and a regional integration unit, wherein: The data denoising unit filters high-frequency noise of the water quality data through wavelet transform; The time synchronization unit aligns the data of each node according to the system reference time to form a unified time stamp; The regional integration unit groups the data of the collection nodes according to the geographical position to form a regional data view.

[0040] Specifically, the data denoising unit filters the high-frequency noise existing in the water quality data through wavelet transform, to ensure the accuracy and stability of the data.

[0041] ​​​​In the implementation process, the original water quality signal is first decomposed into low-frequency components and high-frequency components by discrete wavelet transform, wherein the low-frequency components retain the main trend of the signal, and the high-frequency components mainly contain noise. The system sets a noise threshold, and filters out the part of the high-frequency components that exceeds the threshold. Then the low-frequency and remaining high-frequency components are reconstructed into a denoised signal. The denoised signal can retain the characteristics of the original data to the greatest extent while significantly reducing the interference in the sensor acquisition process.

[0042] The formula is described as follows: for the original signal The wavelet decomposition formula is:

[0043] wherein is the decomposition coefficient, is the wavelet base function.

[0044] The water quality data after denoising processing will be transmitted to the next stage of time synchronization unit and regional integration unit.

[0045] The time synchronization unit is used to align the data uploaded by the distributed acquisition nodes according to the system reference time, and to ensure the time consistency of the data of different nodes.

[0046] This unit relies on the unified clock of the central data processing unit, and periodically calibrates the local clock of each acquisition node through the network time protocol, so as to eliminate the time deviation between nodes.

[0047] Each piece of data will be attached with a local timestamp when uploaded to the central unit. The central unit corrects the timestamp according to the time deviation of the node and the reference clock so that the final data record has a unified time reference.

[0048] The calculation formula of time alignment is:

[0049] wherein is the corrected time, is the local time of the node, is the difference between the node and the system reference time.

[0050] After time synchronization processing, all data have a unified time identifier.

[0051] The regional integration unit groups the data uploaded by the acquisition nodes according to the geographical position, generates a regional data view, and provides support for analyzing the water quality and biological growth state of different regions.

[0052] The uploaded data of each collection node contains a geographic identifier, such as GPS coordinates or node number, by which the system classifies data in the same geographic area as a regional data set.

[0053] The integrated data is stored in a structured form, including water quality parameters, biological information and external environmental data collected by each node, and is sorted by time sequence for subsequent trend analysis.

[0054] The regional integrated data structure is as follows:

[0055] Among them represents the regional data set, is the data sampling value of the region, the moment, is the corresponding timestamp.

[0056] Through regional integration, the system can form a data heat map of each region.

[0057] The data transmission module is used to upload the data of the collection node to the central data processing unit; the data transmission module adopts a wireless communication mode, supporting LoRa and Wi-Fi protocols.

[0058] Specifically, the data transmission module is responsible for uploading the data collected by the distributed collection node to the central data processing unit in real time, realizing efficient transmission and centralized management of data. This module adopts a wireless communication mode, supporting low-power wide-area network (LoRa) and local wireless network (Wi-Fi) protocols to adapt to different transmission scenarios and needs.

[0059] In long-distance transmission or complex environment, the module can preferentially use LoRa protocol, and the low-power feature and long transmission distance of LoRa can meet the distributed collection needs of large-scale breeding farms; while in the local area with strong signal coverage, the module can switch to Wi-Fi protocol to improve the transmission rate and data throughput.

[0060] In addition, the data transmission module also has the function of breakpoint resume, when the network is interrupted, the collection node will temporarily store the data in the local storage unit, and automatically upload the untransmitted data after the network is restored, to ensure the integrity and continuity of the data. Through flexible communication protocol and fault-tolerant design, this module realizes reliable connection between the collection node and the central data processing unit, and builds a robust data transmission link.

[0061] Referring to Figure 1 , the central data processing unit integrates, stores and monitors the running state of the uploaded data.

[0062] The central data processing unit includes a system fault-tolerant module, which comprises node redundancy design and data cross-validation units, wherein: The node redundancy design deploys backup nodes in each acquisition area. When the primary node fails, the backup node automatically takes over the acquisition task. The data cross-validation unit corrects outliers by averaging data from multiple nodes. The correction formula is as follows:

[0063] in, For the corrected data, For the first Data collected from each node.

[0064] Specifically, the system's fault-tolerant module incorporates redundant node design, deploying backup nodes in each acquisition area. When a primary node failure or communication anomaly is detected, the system automatically activates the corresponding backup node to take over the acquisition task. The backup node synchronizes the primary node's operating parameters and historical data status in real time to ensure no information loss or interruption after takeover.

[0065] This mechanism uses the status monitoring function of the central data processing unit to judge the health status of the master node in real time, and ensures the continuity of data collection tasks through a fast switching algorithm, adapting to the large-scale, long-term operation environment of aquaculture farms.

[0066] The data cross-validation unit is responsible for correcting any abnormal or missing data that may occur during the data collection process. By cross-comparing and averaging data collected from multiple nodes, it ensures the accuracy and consistency of the final output data.

[0067] In the specific implementation process, the system first filters out the relevant datasets uploaded by each node in the same area, and then corrects them using the following formula:

[0068] in: These are the corrected data values; For the first Data collected from each node; This represents the number of nodes participating in the computation.

[0069] This formula enables the system to effectively smooth out outliers (such as instantaneous fluctuations or sensor errors) during the acquisition process and generate more stable and reliable data records.

[0070] The central data processing unit integrates and processes data uploaded from multiple nodes, including timestamp alignment, data format standardization, and regional classification. The integrated data is stored in a structured format in a central database, categorized and indexed according to collection time, regional location, and parameter type, facilitating subsequent retrieval and analysis.

[0071] And by analyzing the node operation log and real-time state indicators, it is determined whether the node has a fault or performance degradation, and the fault-tolerant mechanism is automatically triggered when an exception is found.

[0072] Embodiment two: Referring to Figure 2 In the second embodiment of the present application, the present application provides a growth information collection method for aquaculture, the method steps are: S1: Distribute distributed collection nodes at the center, corners and key depth positions of the water body of the breeding farm, each node containing a water quality information collection unit, a biological information collection unit and an external environment collection unit; Specifically, distributed collection nodes are arranged at the center, corners and different depths of the water body of the breeding farm to ensure comprehensive coverage of the breeding environment and biological information. Each collection node integrates a water quality information collection unit, a biological information collection unit and an external environment information collection unit, and the position of the node is optimally deployed according to the topography and water flow characteristics of the breeding farm. The node density is increased in key areas such as the feeding area and the drainage outlet to capture possible water quality changes and the concentration trend of biological activities. The collection node is designed to be corrosion-resistant and waterproof to meet the environmental requirements of long-term operation.

[0073] S2: Real-time collection of temperature, salinity, pH value, dissolved oxygen and ammonia nitrogen and other key water quality indicators by multi-parameter water quality sensors, and recording of sampling data by time stamp; Specifically, the temperature, salinity, pH value, dissolved oxygen and ammonia nitrogen and other key parameters of the breeding water body are collected in real time by multi-parameter water quality sensors, and a time stamp and node position identifier are added to each collection data to ensure that the data source is clear and traceable. The water quality sensor supports dynamic sampling frequency adjustment, automatically switching to high-frequency sampling mode when the water quality fluctuates sharply to capture rapid changes in a short period of time. The collected water quality data is transmitted to the central data processing unit after preliminary processing to form a basic environmental data set.

[0074] S3: Use underwater high-definition camera to collect multi-angle images of the organism, extract individual body length and calculate body weight combined with empirical formula; Specifically, underwater high-definition cameras are used to collect multi-angle images of the breeding organism, and computer vision algorithms are used to extract the body length and contour features of the organism, and the body weight of the organism is estimated combined with relevant empirical data. The camera's collection range covers the main biological activity area in the breeding pond, capturing biological behavior characteristics such as swimming trajectory and feeding frequency. All biological information is stored in time series and associated with water quality data to support subsequent health assessment and production management.

[0075] S4: Collect external parameters such as air temperature, humidity, air pressure and wind speed through the external environment collection unit and synchronize them to the collection system; Specifically, the external environment data, including air temperature, humidity, air pressure, wind speed and other external conditions, are collected in real time by the integrated weather station to record the dynamic changes of the external environment. The external environment data are integrated with water quality data and biological information to analyze the potential impact of environmental fluctuations on water conditions and biological growth. All collected external environment data are stored by timestamp and node location for tracing and analysis.

[0076] S5: Signal denoising, time synchronization and regional integration of water quality and biological data to form a structured data set; Specifically, the collected water quality, biological and external environment data are signal denoised, time synchronized and regionally integrated to form a structured data set. Water quality data is filtered by signal processing method to reduce data bias that may be caused by sensors, and image data is preprocessed to improve the accuracy of feature recognition. Time synchronization is achieved through a central clock to align all node data to a unified time reference, ensuring the time consistency of multi-node data. Regional integration groups data by the geographical location and time identification of the collection nodes to provide data support for subsequent regional dynamic analysis.

[0077] S6: Real-time upload of collected data to the central data processing unit using wireless communication or wired network, and temporary storage of data in case of network interruption; Specifically, the collected data is uploaded in real time to the central data processing unit through the wireless communication module, supporting multiple communication protocols to adapt to different network conditions. In case of network interruption, data will be temporarily stored in the local storage unit of the node, and automatically uploaded after network recovery. The system performs integrity check during transmission to ensure the safety and reliability of data transmission.

[0078] S7: Enable backup nodes to take over the collection task of failed nodes and correct abnormal or missing data through cross-validation; Specifically, backup nodes are configured in each collection area to automatically take over the collection task when the main node fails. The backup node synchronizes the collection plan and operating status of the main node in real time, and can quickly recover data collection without manual intervention after taking over. In addition, the system cross- validates the multi-node data in the same area to correct abnormal data or fill in missing data during collection, ensuring the accuracy and consistency of the collection results.

[0079] S8: Store all collected data as a multi-dimensional database after classification and integration, recording its time, region and parameter information.

[0080] Specifically, the collected data is classified and integrated and stored as a multi-dimensional database, stored according to time, region and parameter type, forming a structured multi-dimensional data record. The integrated data is backed up regularly and stored in the cloud and local storage servers to ensure data security.

[0081] Embodiment three The third embodiment of the present application is based on the same inventive concept, and the present application provides a computer readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the growth information acquisition method for aquaculture of the above-mentioned embodiments.

[0082] Embodiment four The fourth embodiment of the present application is based on the same inventive concept, and the present application provides a computer device, which comprises a processor, a memory, the processor and the memory communicate with each other, the memory is used to store instructions, and the processor is used to execute the instructions in the memory to implement the growth information acquisition method for aquaculture of the above-mentioned embodiments.

[0083] It should be understood that parts of the present application can be realized by hardware, software, firmware or their combination. In the above-mentioned embodiments, a plurality of steps or methods can be realized by software or firmware stored in the memory and executed by a suitable instruction execution system. For example, if realized by hardware, and as in another embodiment, it can be realized by any one or their combination of the following technologies known in the art: discrete logic circuit with logic gate circuit for implementing logic function on data signal, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA) and the like.

[0084] Finally, it should be noted that: the above only describes the preferred embodiments of the present application and is not used to limit the present application, although the present application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application, should be included in the protection scope of the present application.

Claims

1. An aquaculture growth information collection system characterized by comprising: The application relates to an aquaculture growth information acquisition system. The distributed acquisition node module is arranged in multiple key areas of a breeding farm, and data is collected through a water quality information acquisition module, a biological information acquisition module and an external environment information acquisition module; The data processing module is used for denoising, time synchronization and regional integration of the collected data; The data transmission module is used for uploading the data of the acquisition node to a central data processing unit; The central data processing unit integrates, stores and monitors the running state of the uploaded data.

2. The growth information collection system for aquaculture according to claim 1, wherein The water quality information acquisition module comprises a multi-parameter water quality sensor for collecting temperature, salinity, pH value, dissolved oxygen and ammonia nitrogen, the sensor sampling frequency setting dynamic adjustment option, when the parameter change rate is greater than the set threshold, the sampling frequency is switched to high frequency mode.

3. The growth information collection system for aquaculture according to claim 1, wherein The biological information acquisition module comprises a high-definition underwater camera and a computer vision algorithm unit, which are used for acquiring multi-angle images of breeding organisms and extracting individual body length and weight information, wherein: The body length is calculated through the following formula: wherein, is the body length, is the pixel length, is the pixel density, and scale is the scale factor of the pixel to the actual length; Body weight is estimated by the following formula: where, is the body weight, is the body length, is the breed-specific calibration factor.

4. The growth information collection system for aquaculture according to claim 1, wherein The external environment information acquisition module comprises an integrated weather station, which is used for collecting real air temperature, humidity, air pressure and wind speed, wherein: The air pressure is associated with dissolved oxygen and is corrected through the following formula: wherein, Pcurrent is the current air pressure, Pstandard is the standard air pressure.

5. The growth information collection system for aquaculture according to claim 1, wherein The data processing module comprises a data denoising unit, a time synchronization unit and a regional integration unit, wherein: The data denoising unit filters high-frequency noise of water quality data through wavelet transform; The time synchronization unit aligns the node data according to the system reference time to form a unified time stamp; The regional integration unit groups the data of the acquisition node according to the geographical position to form a regional data view.

6. The growth information collection system for aquaculture according to claim 1, wherein The data transmission module adopts a wireless communication mode and supports LoRa and Wi-Fi protocols.

7. The growth information collection system for aquaculture according to claim 1, wherein The central data processing unit comprises a system fault tolerance module, and the system fault tolerance module comprises a node redundancy design and a data cross-validation unit, wherein: The node redundancy design is used for arranging a node in each acquisition area, and when the main node fails, the standby node automatically takes over the acquisition task; The data cross-validation unit corrects abnormal values through average calculation of the multi-node data, and the correction formula is: wherein, is the corrected data, is the data collected for the node.

8. A method for collecting growth information for aquaculture, characterized by, The application relates to an aquaculture growth information acquisition system. S1: distributed acquisition nodes are arranged at the center, corners and key depth positions of the water body of a breeding farm, and each node comprises a water quality information acquisition unit, a biological information acquisition unit and an external environment acquisition unit; S2: a multi-parameter water quality sensor is used for collecting temperature, salinity, pH value, dissolved oxygen and ammonia nitrogen in real time, and the sampling data are recorded according to time stamps; S3: a high-definition underwater camera is used for collecting multi-angle images of organisms, extracting individual body length and calculating weight according to an empirical formula; S4: an external environment acquisition unit is used for collecting air temperature, humidity, air pressure and wind speed external parameters and synchronizing the parameters to the acquisition system; S5: water quality and biological data are subjected to signal denoising, time synchronization and regional integration to form a structured data set; S6: wireless communication or wired network is used for uploading the collected data to a central data processing unit in real time, and the data are temporarily stored when the network is interrupted; S7: a standby node is enabled to take over the acquisition task of a fault node and correct abnormal or missing data through cross-validation; S8: after all the collected data are classified and integrated, the data are stored as a multidimensional database, and time, area and parameter information are recorded.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the growth information collection method for aquaculture in claim 8.

10. A readable storage medium, characterized by, The readable storage medium stores the computer program, and the computer program is executed by the processor to realize the growth information collection method for aquaculture in claim 8.

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