Breeding water quality remote monitoring and early warning system based on Bluetooth wireless data transmission

Abstract

The invention discloses an aquaculture water quality remote monitoring and early warning system based on Bluetooth wireless data transmission, and belongs to the technical field of SoC. Comprising a water quality continuous detection module, a data processing module connected with the output end of the water quality continuous detection module, a water quality module controller connected with the output end of the data processing module, a host connected with the output end of the water quality module controller, and data transfer equipment connected with the host, the cloud server is connected with the data transfer equipment; and the mobile monitoring terminal is connected with the cloud server. According to the breeding water quality remote monitoring and early warning system based on Bluetooth wireless data transmission, data can be transmitted to the mobile terminal through Bluetooth, and workers can conveniently monitor the water quality in real time.

Description

Technical Field

[0001] The present invention relates to the field of SoC technology, and in particular to a remote monitoring and early warning system for aquaculture water quality based on Bluetooth wireless data transmission. Background Art

[0002] As an important part of agriculture, aquaculture is of great significance to ensuring food safety and promoting farmers' income growth.

[0003] However, in the field of aquaculture, traditional farming models face problems such as lagging environmental monitoring, untimely disease warning, and low farming efficiency. The intelligent detection and early warning system based on SoC (system-on-chip) technology provides a new direction for the precise management of aquaculture by integrating sensors, data processing, communication modules and algorithms. Summary of the Invention

[0004] The purpose of the present invention is to provide a remote monitoring and early warning system for aquaculture water quality based on Bluetooth wireless data transmission to solve the problems existing in the background technology.

[0005] To achieve the above-mentioned objectives, the present invention provides a remote monitoring and early warning system for aquaculture water quality based on Bluetooth wireless data transmission, comprising a continuous water quality detection module, a data processing module connected to the output end of the continuous water quality detection module, a water quality module controller connected to the output end of the data processing module, a host connected to the output end of the water quality module controller, a data transfer device connected to the host, a cloud server connected to the data transfer device, and a mobile monitoring terminal connected to the cloud server.

[0006] Preferably, the water quality continuous detection module is used to obtain various parameter data of the aquaculture water environment in real time, including pH value, dissolved oxygen content value, water temperature value, turbidity, ammonia nitrogen concentration value, nitrite concentration value, and nitrate concentration value.

[0007] Preferably, the host includes a central controller, a communication module and a first RF module. The central controller is connected to the water quality module controller, the communication module and the first RF module respectively. The central controller is used to receive data transmitted by the water quality module controller.

[0008] Preferably, the data transfer device includes a second RF module, a microprocessor and a Bluetooth module, the first RF module is connected to the second RF module, the microprocessor is connected to the second RF module and the Bluetooth module respectively, and the Bluetooth module is matched with the mobile monitoring terminal via Bluetooth.

[0009] Preferably, a cloud server is further included, and the cloud server is connected to the communication module and the mobile monitoring terminal respectively.

[0010] Preferably, the cloud server is provided with a remote monitoring and early warning module, which is used to realize water pollution degree evaluation and abnormal early warning.

[0011] Preferably, the data processing module is used to perform time series analysis on the multi-source data transmitted by the water quality acquisition module to obtain a time series feature vector of basic water quality parameters, including:

[0012] The basic water quality parameter data sorting unit is responsible for classifying multi-source data and arranging them by time dimension to generate a time series input matrix of basic water quality parameters;

[0013] The water quality basic parameter time series feature generation unit converts the water quality basic parameter time series input matrix into the water quality basic parameter time series feature matrix through the water quality basic parameter time series change attention device;

[0014] The water quality basic parameter time series feature dimensionality reduction unit reduces the dimension of the water quality basic parameter time series feature matrix to obtain the corresponding time series feature parameter time series feature vector.

[0015] Preferably, the water quality basic parameter temporal change focus is a convolutional neural network model in which adjacent layers use mutually transposed convolution kernels.

[0016] Therefore, the present invention adopts the above-mentioned aquaculture water quality remote monitoring and early warning system based on Bluetooth wireless data transmission, which has the following beneficial effects:

[0017] (1) By setting up a continuous water quality detection module, various water quality parameters are collected in real time to collect data for subsequent real-time monitoring;

[0018] (2) By setting up a cloud server, water quality can be evaluated and abnormal conditions can be detected;

[0019] (3) By setting up a Bluetooth module, data can be transmitted to a mobile terminal, making it convenient for staff to monitor water quality in real time.

[0020] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a structural schematic diagram of a remote monitoring and early warning system for aquaculture water quality based on Bluetooth wireless data transmission. DETAILED DESCRIPTION

[0022] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort shall fall within the scope of protection of the present invention.

[0023] See also Figure 1 A remote aquaculture water quality monitoring and early warning system based on Bluetooth wireless data transmission, based on SoC technology, includes a continuous water quality detection module, a data processing module connected to the output of the continuous water quality detection module, a water quality module controller connected to the output of the data processing module, a host connected to the output of the water quality module controller, a data transfer device connected to the host, a cloud server connected to the data transfer device, and a mobile monitoring terminal connected to the cloud server. SoC technology is highly integrated, integrating these modules on a single chip reduces the number of peripheral components, lowering hardware complexity, cost, and size.

[0024] The continuous water quality monitoring module is used to obtain real-time data on various parameters of the aquaculture water environment, including pH, dissolved oxygen content, water temperature, turbidity, ammonia nitrogen concentration, nitrite concentration, and nitrate concentration. The corresponding sensors include pH water quality sensors, dissolved oxygen sensors, water temperature sensors, ammonia nitrogen sensors, nitrite sensors, and nitrate sensors. The output of the sensors is connected to the central controller in the host computer through filters, amplifiers, and A / D converters. The pH value reflects the acidity and alkalinity of the water body, and different aquatic organisms have specific adaptability ranges to water pH. Dissolved oxygen is a substance necessary for aquatic animals to breathe, and the availability of dissolved oxygen in the water is directly related to whether aquatic organisms can respire normally. Water temperature has a significant impact on aquatic organisms, affecting their metabolic rate, feeding intensity, and reproductive cycle. When the water temperature is too high or too low, aquatic organisms will be in a state of stress, affecting their normal growth and development, and may also change the activity of microorganisms in the water and the rate of chemical reactions. Ammonia nitrogen mainly comes from nitrogen-containing organic matter such as excrement and leftover bait of aquatic organisms. When ammonia nitrogen concentrations are too high in water, they are highly toxic to aquatic animals, damaging their gill tissue, affecting their respiration and osmotic pressure regulation, inhibiting their growth, and even causing death. Nitrite, an intermediate product in the conversion of ammonia nitrogen, is also highly toxic to aquatic animals. It can oxidize ferrous hemoglobin in the blood into methemoglobin, causing it to lose its oxygen-carrying capacity, resulting in hypoxia and poisoning in aquatic animals, seriously affecting their health. Excessive nitrate concentrations can lead to eutrophication of water bodies, triggering problems such as algae blooms, disrupting the ecological balance of the water body, affecting water quality stability, and the living environment of aquatic organisms. Taken together, these indicators reflect the chemical properties, biological suitability, and pollution level of the water body from different perspectives. Only when the values of each indicator are within the range suitable for the survival of the corresponding aquatic organisms and meet the water quality standards can the water quality of the aquaculture water environment be judged to be up to standard. In other words, these data are interrelated and influence each other, and together they form the basis for judging water quality compliance. Considering that the aquaculture water environment is a dynamic system, it is affected by many factors, such as weather changes (sunny, cloudy, rainy, etc. will affect water temperature, dissolved oxygen, etc.), the growth stage of the cultured organisms (excrement and food intake at different stages will affect water quality indicators), and feeding conditions (feeding amount, feeding time, etc. will affect the nutrient content of the water body). Only obtaining data at a single time point can reflect the water condition at that time, and it is impossible to know the trend of water quality changes over time. Through real-time data, the fluctuations of various water quality indicators at different times can be observed, and the real dynamic changes of the water body can be grasped more comprehensively and accurately, providing a more sufficient basis for accurately judging whether the water quality meets the standards.

[0025] The host includes a central controller, a communication module, and a first RF module. The central controller is connected to the water quality module controller, the communication module, and the first RF module, respectively, and is used to receive data transmitted by the water quality module controller. The communication module can be a 2G, 3G, 4G, or 5G communication module, which is used to transmit the real-time collected data to the cloud server.

[0026] The data transfer device includes a second RF module, a microprocessor and a Bluetooth module. The first RF module is connected to the second RF module. The microprocessor is connected to the second RF module and the Bluetooth module respectively. The Bluetooth module is matched with the mobile monitoring terminal via Bluetooth.

[0027] The cloud server is connected to the communication module and the mobile monitoring terminal. The mobile monitoring terminal is connected to a mobile phone or tablet. The Bluetooth device built into the mobile phone or tablet matches the Bluetooth module in the data transfer device to receive data, which can then be viewed on the mobile phone or iPad.

[0028] The cloud server is equipped with a remote monitoring and early warning module, which is used to evaluate water quality and provide warnings of abnormalities. After the cloud server evaluates water quality, it sends the data to the mobile terminal for users to monitor water quality in real time.

[0029] The data processing module is used to perform time series analysis on the multi-source data transmitted by the water quality acquisition module to obtain the time series feature vectors of basic water quality parameters, including:

[0030] The basic water quality parameter data organization unit is responsible for classifying multi-source data and arranging them by time to generate a time-series input matrix for basic water quality parameters. This unit is responsible for arranging pH values, dissolved oxygen content, and water temperature values at multiple time points in an orderly temporal order to generate a time-series input matrix for basic water quality parameters. Because water quality indicators such as pH, dissolved oxygen, and water temperature are constantly changing, arranging data by time can visually demonstrate the temporal evolution of each indicator, helping to uncover potential patterns of water quality change.

[0031] Furthermore, data arrangement integrates scattered data into a unified framework, making data organization more standardized and orderly, facilitating subsequent data management and comprehensive analysis. In the generated time series input matrix for basic water quality parameters, each row corresponds to monitoring data at a different time point, while each column represents a different basic water quality parameter, such as pH, dissolved oxygen, and water temperature.

[0032] The water quality parameter temporal feature generation unit converts the water quality parameter temporal input matrix into a water quality parameter temporal feature matrix through a water quality parameter temporal change attention unit. This unit is a convolutional neural network model in which adjacent layers use transposed convolution kernels. The original water quality parameter temporal input matrix only records the water quality data at each time point, but does not yet reveal the underlying characteristics and patterns within the data. From this raw data, it is difficult to intuitively understand the long-term variation patterns of water quality parameters, nor can it clarify the complex interconnected relationships between parameters. To extract more valuable information from the data and provide a reliable basis for water pollution early warning, feature extraction and abstraction processing of the raw data are necessary. To this end, an encoding method is adopted, which feeds the water quality parameter temporal input matrix into the water quality parameter temporal change attention unit. This method not only captures the temporal variation trends of each water quality parameter, such as linear change, periodic fluctuation, or random fluctuation, but also explores the correlation characteristics between different parameters.

[0033] The water quality basic parameter time series feature dimensionality reduction unit reduces the dimension of the water quality basic parameter time series feature matrix to obtain the corresponding time series feature parameter time series feature vector.

[0034] Therefore, the present invention adopts the above-mentioned aquaculture water quality remote monitoring and early warning system based on Bluetooth wireless data transmission, which can transmit data to a mobile terminal via Bluetooth, making it convenient for staff to monitor water quality in real time.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A remote monitoring and early warning system for aquaculture water quality based on Bluetooth wireless data transmission, characterized by: The invention comprises a continuous water quality detection module, a data processing module connected to the output end of the continuous water quality detection module, a water quality module controller connected to the output end of the data processing module, a host connected to the output end of the water quality module controller, a data transfer device connected to the host, a cloud server connected to the data transfer device, and a mobile monitoring terminal connected to the cloud server.

2. The aquaculture water quality remote monitoring and early warning system based on Bluetooth wireless data transmission according to claim 1 is characterized in that: The water quality continuous detection module is used to obtain various parameter data of the aquaculture water environment in real time, including pH value, dissolved oxygen content value, water temperature value, turbidity, ammonia nitrogen concentration value, nitrite concentration value, and nitrate concentration value.

3. The aquaculture water quality remote monitoring and early warning system based on Bluetooth wireless data transmission according to claim 2 is characterized in that: The host includes a central controller, a communication module and a first RF module. The central controller is connected to the water quality module controller, the communication module and the first RF module respectively. The central controller is used to receive data transmitted by the water quality module controller.

4. The aquaculture water quality remote monitoring and early warning system based on Bluetooth wireless data transmission according to claim 3 is characterized in that: The data transfer device includes a second RF module, a microprocessor and a Bluetooth module. The first RF module is connected to the second RF module. The microprocessor is connected to the second RF module and the Bluetooth module respectively. The Bluetooth module is matched with the mobile monitoring terminal via Bluetooth.

5. The aquaculture water quality remote monitoring and early warning system based on Bluetooth wireless data transmission according to claim 4 is characterized in that: It also includes a cloud server, which is connected to the communication module and the mobile monitoring terminal respectively.

6. The aquaculture water quality remote monitoring and early warning system based on Bluetooth wireless data transmission according to claim 5 is characterized in that: The cloud server is provided with a remote monitoring and early warning module, which is used to realize water quality evaluation and abnormal early warning.

7. The aquaculture water quality remote monitoring and early warning system based on Bluetooth wireless data transmission according to claim 1 is characterized in that: The data processing module is used to perform time series analysis on the multi-source data transmitted by the water quality acquisition module to obtain a time series feature vector of basic water quality parameters, including: The basic water quality parameter data sorting unit is responsible for classifying multi-source data and arranging them by time dimension to generate a time series input matrix of basic water quality parameters; The water quality basic parameter time series feature generation unit converts the water quality basic parameter time series input matrix into the water quality basic parameter time series feature matrix through the water quality basic parameter time series change attention device; The water quality basic parameter time series feature dimensionality reduction unit reduces the dimension of the water quality basic parameter time series feature matrix to obtain the corresponding time series feature parameter time series feature vector.

8. The aquaculture water quality remote monitoring and early warning system based on Bluetooth wireless data transmission according to claim 7 is characterized in that: The attention detector for temporal changes in basic water quality parameters is a convolutional neural network model in which adjacent layers use mutually transposed convolution kernels.

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