Intelligent fish and rice symbiosis ecological control system and method based on Internet of Things

Abstract

The invention discloses an intelligent fish and rice symbiosis ecological control system and method based on the Internet of Things. The system comprises a fish breeding area, a rice irrigation area and a circulating water channel arranged between the fish breeding area and the rice irrigation area. The central control platform is used for collecting a dissolved oxygen (DO) value and a pH value in a water body in real time through a plurality of water quality sensors configured in the system; the dissolved oxygen DO value and the pH value are water quality data; and analyzing the dissolved oxygen DO value, generating a water quality regulation and control instruction, and regulating the oxygenation equipment according to the water quality regulation and control instruction. The automatic buffering agent adding device is used for automatically adding a buffering agent according to an instruction sent by the central control platform; the oxygenation equipment comprises an aeration device and is used for adjusting the content of dissolved oxygen in the water body. According to the intelligent fish and rice symbiosis ecological control system based on the Internet of Things, the fish culture area, the rice irrigation area and the circulating water channel are integrated, effective management and control of the fish and rice symbiosis ecological system are achieved, the water body environment is optimized, and the symbiosis benefits of fishes and rice are improved.

Description

Technical Field

[0001] The present invention relates to the field of fish-rice symbiosis system control, and in particular to an intelligent fish-rice symbiosis ecological control system and method based on the Internet of Things. Background Art

[0002] The fish-rice symbiotic system combines fish farming with rice cultivation, using fish excrement as fertilizer for rice. At the same time, fish can control pests and weeds in the water, achieving the effect of double increase in production and ecological balance.

[0003] However, the study found that the efficient operation of the fish-rice symbiosis system depends on the stable control of water quality, especially the precise regulation of dissolved oxygen (DO) and pH. The study also found that in the traditional fish-rice symbiosis system, water quality control mainly depends on manual monitoring and manual adjustment. This method has the following major problems: Manual monitoring of water quality parameters usually relies on timed sampling and laboratory testing, which cannot achieve real-time monitoring, resulting in the inability to respond to water quality changes in a timely manner, affecting the growth of fish and rice. Because the monitoring and adjustment process relies on manual operation, there is a time delay, and it is impossible to make rapid and precise adjustments according to the dynamic changes in water quality, which can easily cause water quality fluctuations, affect the stability of the ecosystem, and thus affect the healthy ecology of the fish-rice symbiosis system. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent fish-rice symbiosis ecological control system and method based on the Internet of Things, which solves the above-mentioned technical problems pointed out in the prior art.

[0005] The present invention provides an intelligent fish-rice symbiotic ecological control system based on the Internet of Things, comprising: a fish breeding area and a rice irrigation area and a circulating water channel arranged between the fish breeding area and the rice irrigation area;

[0006] A variety of water quality sensors are used to collect the dissolved oxygen DO value and pH value in the water of the circulating water channel in real time;

[0007] A wireless communication module, used to transmit the dissolved oxygen DO value collected by the sensor to a central control platform via the wireless communication module;

[0008] The central control platform is used to collect the dissolved oxygen DO value and pH value in the water body in real time through various water quality sensors configured in the system; the dissolved oxygen DO value and pH value are water quality data; the dissolved oxygen DO value is analyzed to generate water quality control instructions, and the oxygenation equipment is adjusted according to the water quality control instructions.

[0009] An automated buffer adding device for automatically adding buffer according to instructions from a central control platform;

[0010] Aeration equipment, including aeration devices, is used to regulate the dissolved oxygen content in water bodies.

[0011] Preferably, as an implementable scheme, it also includes an environmental monitoring module; the environmental monitoring module is used to monitor the attribute information of the fish breeding area and the attribute information of the rice irrigation area; the attribute information of the fish breeding area includes water quality temperature, dissolved oxygen DO value and pH value in the water body; the attribute information of the rice irrigation area includes soil pH value and nitrogen, phosphorus and potassium content.

[0012] Preferably, as an implementable scheme, it also includes an irrigation system, which is used to start the irrigation system to supply water to the rice irrigation area when the irrigation water volume is still insufficient after the circulating water channel is supplemented with water from the fish breeding area to the rice irrigation area;

[0013] The wireless communication module includes one or more of LoRa, NB-IoT, WiFi or Bluetooth.

[0014] The present invention provides an intelligent fish-rice symbiosis ecological control method based on the Internet of Things, comprising the following steps:

[0015] The dissolved oxygen DO value and pH value in the water body are collected in real time through various water quality sensors configured in the system; the dissolved oxygen DO value and pH value are water quality data;

[0016] The dissolved oxygen DO value collected by the sensor is transmitted to the central control platform through the wireless communication module;

[0017] The dissolved oxygen DO value is analyzed based on the central control platform to generate a water quality control instruction, and the oxygenation equipment is adjusted according to the water quality control instruction.

[0018] Preferably, as an implementable solution, the central control platform is used to analyze the dissolved oxygen DO value, generate a water quality control instruction, and adjust the oxygenation equipment according to the water quality control instruction, including the following steps:

[0019] Establish a historical water quality data database in the central control platform;

[0020] Use machine learning algorithms to train historical water quality data to generate prediction models;

[0021] Based on the prediction model, the future trend of water quality data changes is predicted, and the operating parameters of the oxygenation equipment are adjusted; the operating parameters of the oxygenation equipment include the opening time and frequency.

[0022] Preferably, as an implementable solution, the prediction of future water quality change trends based on the prediction model and adjustment of the operating parameters of the aeration equipment specifically include the following steps:

[0023] Dynamically adjust the opening time and frequency of the aeration equipment according to the predicted water quality change trend;

[0024] Within a preset observation period, real-time monitoring of adjusted water quality parameters; the water quality parameters include dissolved oxygen DO value;

[0025] The adjusted water quality parameters are fed back to the prediction model to repeatedly optimize the operating parameters of the oxygenation equipment within a future preset time period.

[0026] Preferably, as an implementable solution, the machine learning algorithm is used to train the historical water quality data to generate a prediction model; based on the prediction model, the trend of future water quality data changes is predicted, and the operating parameters of the aeration equipment are adjusted, which specifically includes the following steps:

[0027] Establish a database in the central control platform to store historical water quality data, including dissolved oxygen;

[0028] Select the linear regression model as the prediction model; use the historical water quality data stored in the database to train the selected prediction model to obtain the target prediction model;

[0029] A portion of historical water quality data is used as the prediction accuracy of the target prediction model of the validation set. When the accuracy of the target prediction model is ensured to be higher than the basic accuracy value, the final prediction model after convergence is obtained;

[0030] Adjust the operating parameters of the aeration equipment based on the final prediction model;

[0031] Input current data: Input current water quality data into the final prediction model that has been generated.

[0032] The final prediction model predicts the dissolved oxygen DO value in the future period based on the input data;

[0033] Compare the predicted water quality parameters with the target range of dissolved oxygen DO values ​​to determine whether the aeration equipment needs to be adjusted; if the predicted dissolved oxygen DO value is lower than the lower limit of the target range of dissolved oxygen DO values, control to increase the operating time or frequency of the aeration equipment; if the predicted dissolved oxygen DO value is higher than the upper limit of the target range of dissolved oxygen DO values, control to reduce the operating time or frequency of the aeration equipment;

[0034] The target range of dissolved oxygen DO value is 5-8 mg / L.

[0035] Preferably, as an implementable method, the following steps are also included:

[0036] Equipped with an automated buffer adding device;

[0037] automatically adding a buffer in the automated buffer adding device according to real-time pH data;

[0038] Adjust the amount of buffer added in the automated buffer adding device to maintain the pH value of the water within a preset range.

[0039] Preferably, as an implementable method, the following steps are also included:

[0040] Set the calculation formula for the amount of buffer added:

[0041] Where Q is the amount of buffer added, pH 设定 is the target pH value, pH 实时 is the real-time measured pH value, V is the water volume; K is the buffer coefficient, and C is the buffer concentration;

[0042] Dynamically calculate the required amount of buffer added according to the calculation formula for the amount of buffer added;

[0043] The calculated buffer addition amount instruction is sent to the buffer addition device.

[0044] Preferably, as an implementable method, the following steps are also included:

[0045] The buffer coefficient K in the calculation formula of the amount of buffer added is dynamically adjusted, and the adjustment formula is as follows: K = α × e -βt +γ;

[0046] Among them, α, β, γ are parameters trained based on historical data, and t is the current time;

[0047] Recalculate the amount of buffer added using the adjusted buffer coefficient K;

[0048] Based on recalculated buffer addition amounts.

[0049] Compared with the prior art, the embodiments of the present invention have at least the following technical advantages:

[0050] It can be seen from the analysis of the above-mentioned intelligent fish-rice symbiosis ecological control system and method based on the Internet of Things provided by the present invention that the intelligent fish-rice symbiosis ecological control system based on the Internet of Things includes: a fish breeding area and a rice irrigation area and a circulating water channel arranged between the fish breeding area and the rice irrigation area;

[0051] A variety of water quality sensors for real-time collection of dissolved oxygen DO values ​​and pH values ​​in the water of the circulating water channel; a wireless communication module for transmitting the dissolved oxygen DO values ​​collected by the sensors to the central control platform through the wireless communication module; the central control platform for real-time collection of dissolved oxygen DO values ​​and pH values ​​in the water through a variety of water quality sensors configured in the system; the dissolved oxygen DO values ​​and pH values ​​are water quality data; the dissolved oxygen DO values ​​are analyzed to generate water quality control instructions, and the oxygenation equipment is adjusted according to the water quality control instructions. An automated buffer addition device is used to automatically add buffers according to instructions issued by the central control platform; the oxygenation equipment includes an aeration device for adjusting the dissolved oxygen content in the water.

[0052] The above technical solution proposes an intelligent fish-rice symbiotic ecological control system based on the Internet of Things. By integrating fish breeding areas, rice irrigation areas and circulating water channels, it can achieve effective management and control of the fish-rice symbiotic ecosystem. The application of a variety of water quality sensors can monitor the dissolved oxygen and pH value in the water in real time to ensure the stability of water quality. Among them, the central control platform generates control instructions by analyzing real-time water quality data, automatically adjusts the oxygenation equipment, optimizes the water environment, and improves the symbiotic benefits of fish and rice. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A schematic diagram of an intelligent fish-rice symbiosis ecological control system based on the Internet of Things provided in Embodiment 1 of the present invention;

[0055] Figure 2 A schematic diagram of a processing control object of an intelligent fish-rice symbiosis ecological control system based on the Internet of Things provided in Embodiment 1 of the present invention;

[0056] Figure 3 A flow chart of an intelligent fish-rice symbiosis ecological control method based on the Internet of Things provided in Example 1 of the present invention;

[0057] Figure 4 A specific flow chart of an intelligent fish-rice symbiosis ecological control method based on the Internet of Things provided in Embodiment 1 of the present invention;

[0058] Figure 5 Another specific flow chart of an intelligent fish-rice symbiosis ecological control method based on the Internet of Things provided in Embodiment 1 of the present invention;

[0059] Figure 6 The present invention provides another specific flow chart of an intelligent fish-rice symbiosis ecological control method based on the Internet of Things in the first embodiment of the present invention.

[0060] Label:

[0061] Fish breeding area 10; rice irrigation area 20; circulating water channel 30; water quality sensor 40; wireless communication module 50; central control platform 60; automatic buffer adding device 70; oxygenation equipment 80. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] The present invention is further described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0064] Embodiment 1

[0065] See also Figure 1 and Figure 2 , the present invention provides an intelligent fish-rice symbiotic ecological control system based on the Internet of Things, comprising: a fish breeding area 10 and a rice irrigation area 20 and a circulating water channel 30 arranged between the fish breeding area and the rice irrigation area;

[0066] A variety of water quality sensors 40 for real-time acquisition of dissolved oxygen DO value and pH value in the water of the circulating water channel;

[0067] A wireless communication module 50 is used to transmit the dissolved oxygen DO value collected by the sensor to a central control platform via the wireless communication module;

[0068] The central control platform 60 is used to collect the dissolved oxygen DO value and pH value in the water body in real time through various water quality sensors configured in the system; the dissolved oxygen DO value and pH value are water quality data; the dissolved oxygen DO value is analyzed to generate water quality control instructions, and the oxygenation equipment is adjusted according to the water quality control instructions.

[0069] An automated buffer adding device 70, for automatically adding a buffer according to instructions issued by a central control platform;

[0070] The oxygenation equipment 80 includes an aeration device for adjusting the dissolved oxygen content in the water body.

[0071] Preferably, as an implementable scheme, it also includes an environmental monitoring module; the environmental monitoring module is used to monitor the attribute information of the fish breeding area and the attribute information of the rice irrigation area; the attribute information of the fish breeding area includes water quality temperature, dissolved oxygen DO value and pH value in the water body; the attribute information of the rice irrigation area includes soil pH value and nitrogen, phosphorus and potassium content.

[0072] Analysis of the above technical solutions shows that after adding the environmental monitoring module, the system can not only monitor water quality parameters, but also obtain more attribute information (such as water temperature, soil pH and nutrients) of fish breeding areas and rice irrigation areas in real time, and realize comprehensive ecological environment monitoring. This helps to more accurately regulate water quality and soil conditions, and further promote the healthy development of the fish-rice symbiotic ecosystem.

[0073] Preferably, as an implementable scheme, it also includes an irrigation system, which is used to start the irrigation system to supply water to the rice irrigation area when the irrigation water volume is still insufficient after the circulating water channel is supplemented with water from the fish breeding area to the rice irrigation area;

[0074] Analysis of the above technical solutions shows that after the irrigation system is introduced into this system, the system can automatically start irrigation when the circulating water channel is insufficient to provide the necessary water guarantee for rice. This enhances the system's water resource management capabilities, ensures that the irrigation needs of rice are met, and improves the stability of crop yield and quality.

[0075] Preferably, as an implementable embodiment, the wireless communication module includes one or more of LoRa, NB-I oT, Wi-Fi or Bluetooth.

[0076] By analyzing the above technical solutions, it can be seen that this IoT system supports multiple wireless communication technologies such as LoRa, NB-IoT, Wi-Fi or Bluetooth, which improves the flexibility and adaptability of the system. The compatibility of different communication technologies enables the system to operate stably in different environments and application scenarios, and enhances the reliability and coverage of data transmission.

[0077] The present invention provides an intelligent fish-rice symbiosis ecological control system based on the Internet of Things. By deploying a variety of sensors, the Internet of Things system can collect environmental and water quality data in real time, and transmit the data to a central control platform through wireless communication technology, thereby realizing intelligent monitoring and control of the symbiotic system.

[0078] At the same time, most existing systems lack advanced prediction models and optimization algorithms, and cannot achieve active management and intelligent regulation of water quality changes. However, the intelligent fish-rice symbiotic ecological control method based on the Internet of Things (using machine learning algorithms to train historical water quality data and generate prediction models; predicting future water quality data change trends based on prediction models, and adjusting the operating parameters of the aeration equipment) adopted in the embodiments of the present invention can achieve intelligent algorithm management and control under different environmental conditions and climate changes, maintaining the stability of water quality and the balance of the ecosystem.

[0079] Embodiment 2

[0080] like Figure 3 As shown, the present invention proposes an intelligent fish-rice symbiosis ecological control method based on the Internet of Things, comprising the following operating steps:

[0081] S10, collecting dissolved oxygen DO value and pH value in the water body in real time through various water quality sensors configured in the system; the dissolved oxygen DO value and pH value are water quality data;

[0082] S20, transmitting the dissolved oxygen DO value collected by the sensor to the central control platform through a wireless communication module;

[0083] S30, analyzing the dissolved oxygen DO value based on the central control platform, generating a water quality control instruction, and adjusting the oxygenation equipment according to the water quality control instruction.

[0084] Through real-time data collection and wireless transmission of multiple water quality sensors, the central control platform can timely obtain key water quality parameters (dissolved oxygen and pH value) in the water body, and generate control instructions based on these data to automatically adjust the oxygenation equipment. This improves the real-time and accuracy of water quality management and ensures the stable operation of the ecosystem.

[0085] See also Figure 4 The central control platform is used to analyze the dissolved oxygen DO value, generate a water quality control instruction, and adjust the oxygenation equipment according to the water quality control instruction, including the following steps:

[0086] S31. Establish a historical water quality data database in the central control platform;

[0087] S32. Use machine learning algorithms to train historical water quality data to generate a prediction model;

[0088] S33. Predict the future trend of water quality data changes based on the prediction model and adjust the operating parameters of the oxygenation equipment.

[0089] Use historical water quality data to train machine learning models, predict future water quality trends, and dynamically adjust the operating parameters of the aeration equipment (such as opening duration and frequency). This predictive control method improves the system's ability to predict water quality changes, optimizes the efficiency of the aeration equipment, saves energy, and improves the accuracy of water quality control. At the same time, in subsequent operations, the system can continuously optimize the accuracy of the prediction model by dynamically adjusting the operating parameters of the aeration equipment and monitoring the water quality parameters in real time within the preset observation period. The adjusted water quality parameters are fed back to the prediction model to achieve closed-loop control, ensuring that the water quality is always within the target range, and enhancing the system's adaptability and stability.

[0090] Preferably, as a feasible implementation scheme, the operating parameters of the oxygen enrichment equipment include the opening time and frequency.

[0091] See also Figure 5 The method of predicting the future water quality change trend based on the prediction model and adjusting the operating parameters of the oxygenation equipment specifically includes the following steps:

[0092] S331. Dynamically adjust the opening time and frequency of the aeration equipment according to the predicted water quality change trend;

[0093] S332. During a preset observation period, real-time monitoring of the adjusted water quality parameters; the water quality parameters include dissolved oxygen DO value;

[0094] S333, the adjusted water quality parameters are fed back to the prediction model, and the operating parameters of the oxygenation equipment are repeatedly optimized within a future preset time period.

[0095] Analysis of the above technical solutions shows that this system can continuously optimize the accuracy of the prediction model by dynamically adjusting the operating parameters of the aeration equipment and monitoring the water quality parameters in real time within the preset observation period. The adjusted water quality parameters are fed back to the prediction model to achieve closed-loop control, ensuring that the water quality is always maintained within the target range, and enhancing the system's adaptability and stability.

[0096] See also Figure 6 The method uses a machine learning algorithm to train historical water quality data to generate a prediction model; predicts future water quality data change trends based on the prediction model, and adjusts the operating parameters of the aeration equipment, specifically including the following steps:

[0097] S321. Establish a database in the central control platform to store historical water quality data, including dissolved oxygen;

[0098] S322, selecting a linear regression model as a prediction model; using the historical water quality data stored in the database, training the selected prediction model to obtain a target prediction model;

[0099] S323, using a portion of historical water quality data as the prediction accuracy of the target prediction model of the validation set, and obtaining the final prediction model after convergence when ensuring that the accuracy of the target prediction model is higher than the basic accuracy value;

[0100] S324, adjusting the operating parameters of the oxygen enrichment equipment based on the final prediction model;

[0101] S325, input current data: input current water quality data (such as current dissolved oxygen DO value) into the generated final prediction model.

[0102] S326, the final prediction model predicts the dissolved oxygen DO value in the future period of time (such as the next hour or the next day) based on the input data;

[0103] S327, comparing the predicted water quality parameters with the target range of dissolved oxygen DO value to determine whether the aeration equipment needs to be adjusted; if the predicted dissolved oxygen DO value is lower than the lower limit of the target range of dissolved oxygen DO value, controlling to increase the operating time or frequency of the aeration equipment; if the predicted dissolved oxygen DO value is higher than the upper limit of the target range of dissolved oxygen DO value, controlling to reduce the operating time or frequency of the aeration equipment;

[0104] The target range of dissolved oxygen DO value is 5-8 mg / L.

[0105] It should be noted that if the predicted dissolved oxygen DO value is lower than the lower limit, the operating time or frequency of the oxygenation equipment needs to be increased. If the predicted dissolved oxygen DO value is higher than the upper limit, the operating time or frequency of the oxygenation equipment can be reduced to save energy. The technical solution adopted in the embodiment of the present invention uses a linear regression model to predict water quality data, and ensures the accuracy of the prediction model through training and verification. Based on accurate prediction results, the system can accurately adjust the oxygenation equipment to keep the dissolved oxygen within the target range of 5-8 mg / L. This ensures the optimal state of the fish farming and rice growth environment, and improves the benefits of the overall ecosystem.

[0106] Preferably, as an implementable method, after the final prediction model predicts the dissolved oxygen DO value in the future period of time based on the input data, it also includes the following steps:

[0107] S3271, if the predicted dissolved oxygen DO value is lower than the lower limit of the dissolved oxygen DO value target range, and the difference between the predicted dissolved oxygen DO value and the lower limit of the dissolved oxygen DO value target range is greater than the first low amount warning value, then control the wireless communication module 50 to send a low amount warning alarm message to the central control platform 60;

[0108] S3272: If the predicted dissolved oxygen DO value is greater than the upper limit of the target range of the dissolved oxygen DO value, and the difference between the upper limit of the target range of the dissolved oxygen DO value and the predicted dissolved oxygen DO value is greater than the first high-level warning value, the wireless communication module 50 is controlled to send a high-level warning alarm message to the central control platform 60;

[0109] The central control platform 60 sends early warning alarm information to management personnel via mobile terminals or servers.

[0110] The system can monitor dissolved oxygen (DO) values ​​in real time and accurately predict future DO values ​​to ensure dynamic understanding of water quality conditions. By comparing the predicted DO value with the target range, when the DO value is lower than the lower limit or higher than the upper limit and the difference exceeds the set first low or high warning value, the system can promptly identify potential risks. Efficient communication and response: The central control platform sends early warning information to management personnel through mobile terminals or servers to ensure that relevant personnel can be informed of abnormal conditions and take corresponding measures at the first time.

[0111] In the above-mentioned dissolved oxygen (DO) monitoring system, the content of the early warning alarm information should be detailed and comprehensive to ensure that management personnel can quickly and accurately understand the current water quality conditions and take appropriate measures.

[0112] The following are the contents of early warning alarm information and specific supplementary cases:

[0113] Among them, the components of early warning alarm information are:

[0114] Timestamp: records the specific time when the warning information is generated. Function: helps managers understand the time when the event occurred and conduct follow-up tracking and analysis.

[0115] Current DO value: The current dissolved oxygen concentration value at the monitoring point. Function: Provide real-time water quality data.

[0116] Predicted DO value: The system predicts the future DO value based on historical data and trends. Function: Identify potential water quality changes in advance and conduct preventive management.

[0117] Dissolved oxygen DO value target range, including upper and lower limits. Function: Serves as a benchmark for judging whether the DO value is abnormal.

[0118] Difference value: the difference between the predicted DO value and the upper and lower limits of the target range. Function: determine the warning level and judge whether it exceeds the warning value.

[0119] Alert level: The alert level determined based on the difference value, such as low-volume warning and high-volume warning. Function: Helps managers understand the severity of the warning and decide on response measures.

[0120] Monitoring point information: specific monitoring location or area name. Function: locate the specific location where the abnormality occurs to facilitate on-site inspection and processing.

[0121] Sensor ID: Description: Unique identifier of the sensor or device that generates the warning information. Function: Facilitates the traceability and maintenance of sensors to ensure data accuracy. Recommended measures: Description: Response suggestions or operational guidelines provided based on the type of warning. Function: Guide managers to take specific response measures to reduce the impact of abnormal water quality.

[0122] Case 1: An abnormally low dissolved oxygen value was detected at a monitoring point in a certain aquaculture area. Warning alarm information content: Timestamp: 2024-04-27 14:30 Current DO value: 3.2 mg / L Forecast DO value: 2.8 mg / L Target range: 4.0-8.0 mg / L Difference value: 4.0-2.8 = 1.2 mg / L Warning level: First low level warning; Monitoring point information: East bank of the aquaculture area; Sensor ID: S12345; Recommended measures: Check whether there are any pollution sources in the surrounding area, such as industrial wastewater or agricultural runoff. Start or adjust the aeration equipment to increase the DO level.

[0123] Through the detailed composition and specific cases of the above early warning alarm information, it can be seen that this system can not only monitor and predict DO values ​​in real time, but also generate detailed early warning information according to actual conditions. Among them, the comprehensiveness and specificity of the above early warning alarm information ensure that managers can quickly understand the nature and severity of water quality abnormalities.

[0124] Preferably, as an implementable method, the following steps are also included:

[0125] Equipped with an automated buffer adding device;

[0126] automatically adding a buffer in the automated buffer adding device according to real-time pH data;

[0127] Adjust the amount of buffer added in the automated buffer adding device to maintain the pH value of the water within a preset range.

[0128] From the analysis of the above technical solutions, it can be seen that the system is equipped with an automated buffer adding device, which automatically adds buffer according to the real-time pH value, dynamically adjusts the amount of buffer added, and keeps the pH value of the water body within the preset range. This helps to stabilize the pH value of the water body, prevent the adverse effects of drastic fluctuations in pH value on fish and rice, and optimize the ecological environment.

[0129] Preferably, as an implementable method, the method further comprises the following steps: setting a calculation formula for the amount of buffer added:

[0130] Where Q is the amount of buffer added, pH 设定 is the target pH value, pH 实时 is the real-time measured pH value, V is the water volume; K is the buffer coefficient, and C is the buffer concentration;

[0131] Dynamically calculate the required amount of buffer added according to the calculation formula for the amount of buffer added;

[0132] The calculated buffer addition amount instruction is sent to the buffer addition device.

[0133] Analysis of the above technical solutions shows that the system used in the embodiment of the present invention realizes automatic adjustment and stabilization of the pH value of the water body by configuring an automated buffer addition device, combining real-time pH value monitoring and intelligent calculation formulas. This automated operation not only improves management efficiency and system reliability, but also effectively protects the ecological environment, ensures the healthy growth of aquatic organisms and crops, and saves resources and reduces operating costs.

[0134] Preferably, as an implementable embodiment, the method further includes the following steps of optimizing the amount of buffer added:

[0135] The buffer coefficient K in the calculation formula of the amount of buffer added is dynamically adjusted, and the adjustment formula is as follows: K = α × e -βt +γ;

[0136] Among them, α, β, γ are parameters trained based on historical data, and t is the current time;

[0137] Recalculate the amount of buffer added using the adjusted buffer coefficient K;

[0138] Based on the recalculated buffer addition amounts, the automatic buffer addition strategy is optimized.

[0139] Analysis of the above technical solutions shows that the system used in the embodiment of the present invention can continuously monitor the pH value of the water body, automatically control the addition of buffer according to the real-time pH value data, realize automatic adjustment of the pH value of the water body, reduce human intervention, and improve the accuracy and efficiency of the operation. Through the set buffer addition amount calculation formula (Q = K × (target pH value - real-time pH value) × V / C), the required buffer addition amount is dynamically calculated to ensure that the amount of buffer used is highly matched with the actual demand.

[0140] At the same time, by dynamically adjusting the amount of buffer: according to the changes in water volume (V), buffer concentration (C) and buffer coefficient (K), the amount of buffer added is accurately adjusted to avoid excessive or insufficient amounts and optimize the buffer effect. The buffer coefficient (K) is dynamically adjusted using the parameters obtained through historical data training to make the calculation formula more in line with the actual situation of the current water body and improve the system's adaptive ability. By recalculating the amount of buffer added and optimizing the automatic addition strategy of the buffer, this system can ensure that the pH value can be effectively maintained stable under different environmental conditions.

[0141] In summary, the above-mentioned intelligent fish-rice symbiosis ecological control system and method based on the Internet of Things provided by the present invention realizes real-time monitoring and dynamic regulation of water quality and environmental parameters through the integration of multiple sensors, wireless communications, a central control platform and intelligent algorithms, and significantly improves the management efficiency and ecological benefits of the fish-rice symbiosis ecosystem.

[0142] 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 it. A person skilled in the art may modify the technical solutions described in the above embodiments, or replace part or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent fish-rice symbiotic ecological control system based on the Internet of Things, characterized in that: include: Fish culture areas and rice irrigation areas and circulation channels between fish culture areas and rice irrigation areas; A variety of water quality sensors are used to collect the dissolved oxygen DO value and pH value in the water of the circulating water channel in real time; A wireless communication module, used to transmit the dissolved oxygen DO value collected by the sensor to a central control platform via the wireless communication module; The central control platform is used to collect the dissolved oxygen DO value and pH value in the water body in real time through various water quality sensors configured in the system; the dissolved oxygen DO value and pH value are water quality data; the dissolved oxygen DO value is analyzed to generate water quality control instructions, and the oxygenation equipment is adjusted according to the water quality control instructions. An automated buffer adding device for automatically adding buffer according to instructions from a central control platform; Aeration equipment, including aeration devices, is used to regulate the dissolved oxygen content in water bodies.

2. The intelligent fish-rice symbiosis ecological control system based on the Internet of Things according to claim 1 is characterized in that: It also includes an environmental monitoring module; the environmental monitoring module is used to monitor the attribute information of the fish breeding area and the attribute information of the rice irrigation area; the attribute information of the fish breeding area includes water quality temperature, dissolved oxygen DO value and pH value in the water body; the attribute information of the rice irrigation area includes soil pH value and nitrogen, phosphorus and potassium content.

3. The intelligent fish-rice symbiosis ecological control system based on the Internet of Things according to claim 1 is characterized in that: It also includes an irrigation system, which is used to start the irrigation system to supply water to the rice irrigation area when the irrigation water volume is still insufficient after the circulating water channel is supplemented with water from the fish breeding area to the rice irrigation area; The wireless communication module includes one or more of LoRa, NB-IoT, Wi-Fi or Bluetooth.

4. An intelligent fish-rice symbiotic ecological control method based on the Internet of Things, characterized in that: The following steps are involved: The dissolved oxygen DO value and pH value in the water body are collected in real time through various water quality sensors configured in the system; the dissolved oxygen DO value and pH value are water quality data; The dissolved oxygen DO value collected by the sensor is transmitted to the central control platform through the wireless communication module; The dissolved oxygen DO value is analyzed based on the central control platform to generate a water quality control instruction, and the oxygenation equipment is adjusted according to the water quality control instruction.

5. The intelligent fish-rice symbiosis ecological control method based on the Internet of Things according to claim 4 is characterized in that: The method of analyzing the dissolved oxygen DO value based on the central control platform, generating a water quality control instruction, and adjusting the oxygenation equipment according to the water quality control instruction includes the following steps: Establish a historical water quality data database in the central control platform; Use machine learning algorithms to train historical water quality data to generate prediction models; Based on the prediction model, the future trend of water quality data changes is predicted, and the operating parameters of the aeration equipment are adjusted; the operating parameters of the aeration equipment include the opening time and frequency.

6. The intelligent fish-rice symbiosis ecological control method based on the Internet of Things according to claim 5 is characterized in that: The method of predicting the future water quality change trend based on the prediction model and adjusting the operating parameters of the oxygenation equipment specifically includes the following steps: Dynamically adjust the opening time and frequency of the aeration equipment according to the predicted water quality change trend; Within a preset observation period, real-time monitoring of adjusted water quality parameters; the water quality parameters include dissolved oxygen DO value; The adjusted water quality parameters are fed back to the prediction model to repeatedly optimize the operating parameters of the oxygenation equipment within a future preset time period.

7. The intelligent fish-rice symbiosis ecological control method based on the Internet of Things according to claim 6 is characterized in that: The method uses a machine learning algorithm to train historical water quality data to generate a prediction model; predicts the trend of future water quality data changes based on the prediction model, and adjusts the operating parameters of the aeration equipment, specifically including the following steps: Establish a database in the central control platform to store historical water quality data, including dissolved oxygen; Select the linear regression model as the prediction model; use the historical water quality data stored in the database to train the selected prediction model to obtain the target prediction model; A portion of historical water quality data is used as the prediction accuracy of the target prediction model of the validation set. When the accuracy of the target prediction model is ensured to be higher than the basic accuracy value, the final prediction model after convergence is obtained; Adjust the operating parameters of the aeration equipment based on the final prediction model; Input current data: Input current water quality data into the final prediction model that has been generated. The final prediction model predicts the dissolved oxygen DO value in the future period based on the input data; Compare the predicted water quality parameters with the target range of dissolved oxygen DO values ​​to determine whether the aeration equipment needs to be adjusted; if the predicted dissolved oxygen DO value is lower than the lower limit of the target range of dissolved oxygen DO values, control to increase the operating time or frequency of the aeration equipment; if the predicted dissolved oxygen DO value is higher than the upper limit of the target range of dissolved oxygen DO values, control to reduce the operating time or frequency of the aeration equipment; The target range of dissolved oxygen DO value is 5-8 mg / L.

8. The intelligent fish-rice symbiosis ecological control method based on the Internet of Things according to claim 7 is characterized in that: The following steps are also included: Equipped with an automated buffer adding device; automatically adding a buffer in the automated buffer adding device according to real-time pH data; Adjust the amount of buffer added in the automated buffer adding device to maintain the pH value of the water within a preset range.

9. The intelligent fish-rice symbiosis ecological control method based on the Internet of Things according to claim 8 is characterized in that: The following steps are also included: Set the calculation formula for the amount of buffer added: Where Q is the amount of buffer added, pH 设定 is the target pH value, pH 实时 is the real-time measured pH value, V is the water volume; K is the buffer coefficient, and C is the buffer concentration; Dynamically calculate the required amount of buffer added according to the calculation formula for the amount of buffer added; The calculated buffer addition amount instruction is sent to the buffer addition device.

10. The intelligent fish-rice symbiosis ecological control method based on the Internet of Things according to claim 9 is characterized in that: The following steps are also included: The buffer coefficient K in the calculation formula of the amount of buffer added is dynamically adjusted, and the adjustment formula is as follows: K = α × e -βt +γ; Among them, α, β, γ are parameters trained based on historical data, and t is the current time; Recalculate the amount of buffer added using the adjusted buffer coefficient K; Based on recalculated buffer addition amounts.

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