Intelligent water temperature control system and method for land-based aquaculture

Abstract

The invention provides an intelligent water temperature control system and method for land-based aquaculture, and the system comprises a multi-stage temperature sensing network, a multi-mode decision module and an execution network, and the multi-stage temperature sensing network is used for collecting water temperature data in real time and monitoring the spatial distribution of the water temperature data; the multi-mode decision module is used for receiving the water temperature data and calculating the deviation state and trend of the current water temperature relative to a preset suitable interval; the multi-mode decision-making module is used for generating a cooperative regulation and control strategy for maintaining the water temperature in a proper interval or enabling the water temperature to rapidly return to the interval according to the auxiliary data and the deviation state and trend of the current water temperature; and the execution network is used for executing heating, cooling or thermal diffusion operation on a specific area to enable the overall water temperature of the culture pond to converge to a preset appropriate interval. By integrating multi-level sensing, intelligent decision making and accurate execution, internal and external interference is dynamically overcome, the temperature of the aquaculture water body is continuously and stably maintained within the preset suitable interval, the optimal growth environment is created for aquaculture creatures, and the aquaculture benefits are improved.

Description

Technical Field

[0001] This invention relates to the field of land-based aquaculture technology, and in particular to an intelligent water temperature control system and method for land-based aquaculture. Background Technology

[0002] Land-based recirculating aquaculture systems, as an intensive and controlled aquaculture method, can significantly increase stocking density and water recycling rate.

[0003] Water temperature is one of the most critical environmental factors affecting the growth, metabolism, immunity, and feed conversion rate of aquatic organisms. Different aquatic species and their different growth stages have specific suitable water temperature ranges (i.e., the interval between the first threshold and the second threshold). Current land-based aquaculture systems rely heavily on manual experience or simple temperature control equipment for water temperature control, making it difficult to achieve high-precision and high-stability constant or circulating temperature control. Especially when faced with external climate fluctuations, uneven heat distribution in the aquaculture pond (heat island effect), or changes in aquaculture load, water temperatures often exceed the suitable range, which can range from affecting growth to causing stress or disease outbreaks.

[0004] Therefore, there is an urgent need to invent an intelligent control method and system that can accurately and adaptively control the temperature of aquaculture water within a preset suitable range, so as to meet the core needs of efficient, stable and intelligent production in land-based aquaculture. Summary of the Invention

[0005] This invention provides an intelligent water temperature control system and method for land-based aquaculture to solve one or more technical problems encountered in the prior art.

[0006] In a first aspect, embodiments of the present invention provide an intelligent water temperature control system for land-based aquaculture, comprising: A multi-level temperature sensing network is distributed across multiple aquaculture ponds and water bodies at different depths in the land-based aquaculture workshop to collect water temperature data in real time and monitor its spatial distribution. A multimodal decision-making module with range control as its core is connected to a multi-level temperature sensing network. The multimodal decision-making module is used to receive the water temperature data and calculate the deviation and trend of the current water temperature relative to the preset suitable range. The multimodal decision-making module is used to fuse auxiliary data from water quality sensors and biological behavior monitoring devices. The multimodal decision-making module is used to generate a coordinated control strategy based on the auxiliary data and the deviation and trend of the current water temperature to maintain the water temperature within the suitable range or to make it quickly return to the range. The execution network is zonal and controllable. It includes distributed temperature control devices and water circulation disturbance devices that can act independently or collaboratively according to a strategy. It is used to perform heating, cooling or heat diffusion operations on specific areas. The execution network is communicatively connected to the multimodal decision module. The execution network is used to drive the temperature control devices and water circulation disturbance devices to converge the overall water temperature of the aquaculture pond to the preset suitable range according to the collaborative control strategy generated by the multimodal decision module.

[0007] In a preferred embodiment, the multi-level temperature sensing network includes a vertical sensor array deployed in different water layers within a single aquaculture pond and a horizontal sensor network deployed in aquaculture ponds at different geographical locations, for constructing a three-dimensional temperature field of the aquaculture water body; based on this three-dimensional temperature field, the multimodal decision module is used to identify low-temperature regions where the water temperature is below a first threshold, high-temperature regions where the water temperature is above a second threshold, and potential thermal disturbance regions with large gradient differences within the suitable range.

[0008] In a preferred embodiment, the multimodal decision-making module with interval control as its core incorporates a rule-model hybrid engine, whose decision logic includes: When the water temperature is detected to be lower than the first threshold, the heating strategy is triggered, and the water circulation disturbance device is activated based on the dissolved oxygen concentration. When the water temperature is detected to be higher than the second threshold, a cooling or natural heat dissipation strategy is triggered, and the water circulation intensity is adjusted according to the trend of ammonia nitrogen concentration change. When a large gradient difference is detected in the water temperature within the suitable range, an equalization strategy is triggered to control the water circulation disturbance device to eliminate the heat island effect and maintain the temperature uniformity within the range. All strategies reference real-time biological behavior data. When reduced feeding activity or stress behavior of abnormal clusters is detected, the multimodal decision-making model smooths the rate or magnitude of temperature control.

[0009] In a preferred embodiment, the multimodal decision module further includes an adaptive interval optimization submodule, which optimizes the values ​​of the first threshold and the second threshold and learns the optimal control parameters for maintaining water temperature within the dynamic interval under different environmental conditions based on the aquaculture species, growth stage and historical growth performance data.

[0010] In a preferred embodiment, the distributed temperature control device in the zone-adjustable execution network includes a main heat pump unit and multiple auxiliary heating / cooling units deployed in key aquaculture ponds or specific areas within the ponds; the multimodal decision module is used to control the auxiliary units in the execution network to precisely supplement heating in low-temperature areas or locally cool high-temperature areas according to water temperature deviations, and to control the execution network to coordinate the water circulation disturbance device to promote uniform heat diffusion, so that the overall water temperature quickly and stably returns to the preset suitable range.

[0011] In a preferred embodiment, the multimodal decision module includes an edge-cloud collaborative architecture, which includes: Edge control nodes, deployed in the aquaculture workshop, are used for real-time data processing and rapid decision-making, and provide millisecond-level response to emergencies where the water temperature exceeds the preset suitable range; The cloud-based analytics platform is used to aggregate long-term data, run predictive models, predict the impact of changes in the external environment on the water temperature range in the next few hours to days, and send optimization strategies or model parameters to the edge control nodes in advance to enhance the predictability and robustness of the range control.

[0012] In a preferred embodiment, the predictive model integrated by the cloud analysis platform is used to predict the risk probability and timing of the breach of the preset suitable range by combining external weather forecasts, historical water temperature data and workshop operation logs; based on this prediction, the cloud analysis platform is used to generate forward-looking maintenance strategies including advance heat storage, pre-cooling or adjustment of circulation mode.

[0013] In a preferred embodiment, the system further includes a unified data interface for accessing various environmental sensors; the unified data interface is communicatively connected to the multimodal decision module, and is used to set the first threshold and the second threshold in the multimodal decision module through a human-computer interaction interface, or for the aquaculture management system to automatically send the first threshold and the second threshold set in the aquaculture model to the multimodal decision module through the unified data interface.

[0014] In a preferred embodiment, the system further includes an energy efficiency optimization subsystem, which is communicatively connected to the execution network. The energy efficiency optimization subsystem is used to optimize the operation combination and timing of each device in the execution network, prioritizing the scheduling of devices with high energy efficiency ratios, and optimizing energy consumption costs by utilizing time-of-use pricing policies, under the primary premise of ensuring that the water temperature is stably maintained within the preset suitable range.

[0015] Secondly, embodiments of the present invention provide a land-based aquaculture water temperature intelligent control method based on the above-mentioned system, with the core objective of maintaining the aquaculture water temperature within a preset suitable range defined by a first threshold and a second threshold, comprising the following steps: Temperature data of aquaculture water is collected in real time through a multi-level temperature sensing network, and multimodal data on water quality and biological behavior are obtained. Based on the temperature data, determine the deviation relationship between the current overall and local water temperature and the preset suitable range; By integrating the aforementioned water temperature deviation relationship, real-time water quality data, and biological behavior data, a collaborative regulation strategy is generated with the core objective of correcting deviations and maintaining range stability. The strategy is executed through a partitioned and controllable execution network, driving the distributed temperature control device and water circulation disturbance device to perform precise actions; The system continuously monitors whether the water temperature after regulation returns to and stabilizes within the preset suitable range, and collects multimodal feedback data to evaluate the control effect and iteratively optimize the decision logic.

[0016] One of the above technical solutions has the following advantages or beneficial effects: by integrating multi-level sensing, intelligent decision-making and precise execution, it dynamically overcomes internal and external interference, and continuously and stably maintains the temperature of the aquaculture water within a preset suitable range, thereby creating the best growth environment for aquaculture organisms and improving aquaculture efficiency.

[0017] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0018] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in the invention and should not be construed as limiting the scope of the invention.

[0019] Figure 1 This is a simplified diagram of the overall structure and connection of the land-based aquaculture water temperature intelligent control system in this embodiment. Detailed Implementation

[0020] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0021] Firstly, this embodiment provides an intelligent water temperature control system for land-based aquaculture, see [link to relevant documentation]. Figure 1 As shown, the intelligent water temperature control system includes a multi-level temperature sensing network 100, a multi-modal decision-making module 200 with interval control as its core, and an execution network 300 that can be zoned and controlled.

[0022] The multi-level temperature sensing network 100 is distributed across multiple aquaculture ponds and water bodies at different depths in the land-based aquaculture workshop. The multi-level temperature sensing network 100 is used to collect water temperature data in real time and monitor its spatial distribution.

[0023] The multimodal decision module 200 is connected to the multi-level temperature sensing network 100. The multimodal decision module 200 is used to receive the water temperature data and calculate the deviation state and trend of the current water temperature relative to the preset suitable range. The multimodal decision module 200 is used to fuse auxiliary data from water quality sensors and biological behavior monitoring devices. The multimodal decision module 200 is used to generate a coordinated control strategy based on the auxiliary data and the deviation state and trend of the current water temperature to maintain the water temperature within the suitable range or to make it quickly return to the range.

[0024] The execution network 300 includes a distributed temperature control device 310 and a water circulation disturbance device 320 that can operate independently or collaboratively according to a strategy. The execution network 300 is used to perform heating, cooling, or heat diffusion operations on a specific area. The execution network 300 is communicatively connected to the multimodal decision module 200. The execution network 300 is used to drive the temperature control device 310 and the water circulation disturbance device 320 to converge the overall water temperature of the aquaculture pond to the preset suitable range according to the collaborative control strategy generated by the multimodal decision module 200.

[0025] This embodiment integrates multi-level sensing, intelligent decision-making, and precise execution to dynamically overcome internal and external interference, continuously and stably maintaining the temperature of the aquaculture water within a preset suitable range, thereby creating the best growth environment for aquaculture organisms and improving aquaculture efficiency.

[0026] In one specific embodiment, see Figure 1 As shown, the multi-level temperature sensing network 100 includes a vertical sensor array 110 deployed in different water layers within a single aquaculture pond and a horizontal sensor network 120 deployed in aquaculture ponds at different geographical locations. The multi-level temperature sensing network 100 is used to construct a three-dimensional temperature field of the aquaculture water body. Based on this three-dimensional temperature field, the multimodal decision module 200 is used to identify low-temperature regions where the water temperature is below a first threshold, high-temperature regions where the water temperature is above a second threshold, and potential thermal disturbance regions with large gradient differences within the suitable range.

[0027] In one specific embodiment, the multimodal decision module 200, with interval control as its core, incorporates a rule-model hybrid engine, whose decision logic includes: When the water temperature is detected to be lower than the first threshold, the heating strategy is triggered, and the water circulation disturbance device is activated based on the dissolved oxygen concentration. When the water temperature is detected to be higher than the second threshold, a cooling or natural heat dissipation strategy is triggered, and the water circulation intensity is adjusted according to the trend of ammonia nitrogen concentration change. When a large gradient difference is detected in the water temperature within the suitable range, an equalization strategy is triggered to control the water circulation disturbance device to eliminate the heat island effect and maintain the temperature uniformity within the range. All strategies reference real-time biological behavior data. When reduced feeding activity or stress behavior of abnormal clusters is detected, the multimodal decision-making model smooths the rate or magnitude of temperature control.

[0028] In one specific embodiment, the multimodal decision module 200 further includes an adaptive interval optimization submodule 210. This submodule optimizes the values ​​of the first threshold and the second threshold and learns the optimal control parameters for maintaining the water temperature within the dynamic interval under different environmental conditions based on the aquaculture species, growth stage and historical growth performance data.

[0029] In one specific embodiment, the distributed temperature control device in the zone-adjustable execution network 300 includes a main heat pump unit and multiple auxiliary heating / cooling units deployed in key aquaculture ponds or specific areas within the ponds; the multimodal decision module 200 is used to control the auxiliary units in the execution network 300 to precisely supplement heating in low-temperature areas or locally cool high-temperature areas according to water temperature deviations, and to control the execution network 300 to coordinate the water circulation disturbance device 320 to promote uniform heat diffusion, so that the overall water temperature quickly and stably returns to the preset suitable range.

[0030] In one specific embodiment, the multimodal decision module 200 includes an edge-cloud collaborative architecture, which includes: Edge control nodes, deployed in the aquaculture workshop, are used for real-time data processing and rapid decision-making, and provide millisecond-level response to emergencies where the water temperature exceeds the preset suitable range; The cloud-based analytics platform is used to aggregate long-term data, run predictive models, predict the impact of changes in the external environment on the water temperature range in the next few hours to days, and send optimization strategies or model parameters to the edge control nodes in advance to enhance the predictability and robustness of the range control.

[0031] In one specific embodiment, the predictive model integrated by the cloud analysis platform is used to predict the risk probability and timing of the breach of the preset suitable range by combining external weather forecasts, historical water temperature data and workshop operation logs; based on this prediction, the cloud analysis platform is used to generate forward-looking maintenance strategies including advance heat storage, pre-cooling or adjustment of circulation mode.

[0032] In one specific embodiment, the system further includes a unified data interface 400, which is used to access various environmental sensors. The unified data interface 400 is communicatively connected to the multimodal decision module 200. The unified data interface 400 is used to set the first threshold and the second threshold in the multimodal decision module 200 through a human-computer interaction interface, or the aquaculture management system can automatically send the first threshold and the second threshold set in the aquaculture model to the multimodal decision module 200 through the unified data interface 400.

[0033] In one specific embodiment, the system further includes an energy efficiency optimization subsystem 500, which is communicatively connected to the execution network 300. The energy efficiency optimization subsystem 500 is used to optimize the operation combination and timing of each device in the execution network 300 under the primary premise of ensuring that the water temperature is stably maintained within the preset suitable range, prioritize scheduling devices with high energy efficiency ratios, and optimize energy consumption costs using time-of-use electricity pricing policies.

[0034] Secondly, this embodiment provides a method for intelligent control of water temperature in land-based aquaculture, with the core objective of maintaining the temperature of the aquaculture water within a preset suitable range defined by a first threshold and a second threshold, including the following steps: Temperature data of aquaculture water is collected in real time through a multi-level temperature sensing network, and multimodal data on water quality and biological behavior are obtained. Based on the temperature data, determine the deviation relationship between the current overall and local water temperature and the preset suitable range; By integrating the aforementioned water temperature deviation relationship, real-time water quality data, and biological behavior data, a collaborative regulation strategy is generated with the core objective of correcting deviations and maintaining range stability. The strategy is executed through a partitioned and controllable execution network, driving the distributed temperature control device and water circulation disturbance device to perform precise actions; The system continuously monitors whether the water temperature after regulation returns to and stabilizes within the preset suitable range, and collects multimodal feedback data to evaluate the control effect and iteratively optimize the decision logic.

[0035] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0036] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0037] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A land-based intelligent water temperature control system for aquaculture, characterized in that, include: A multi-level temperature sensing network is distributed across multiple aquaculture ponds and water bodies at different depths in the land-based aquaculture workshop to collect water temperature data in real time and monitor its spatial distribution. A multimodal decision module with interval control as its core is connected to a multi-level temperature sensing network. The multimodal decision module is used to receive the water temperature data and calculate the deviation state and trend of the current water temperature relative to the preset suitable interval. The multimodal decision module is used to integrate auxiliary data from water quality sensors and biological behavior monitoring devices. The multimodal decision module is used to generate a coordinated control strategy based on the auxiliary data and the deviation status and trend of the current water temperature to maintain the water temperature within the appropriate range or to make it quickly return to the range. The execution network is zonal and controllable. It includes distributed temperature control devices and water circulation disturbance devices that can act independently or collaboratively according to a strategy. It is used to perform heating, cooling or heat diffusion operations on specific areas. The execution network is communicatively connected to the multimodal decision module. The execution network is used to drive the temperature control devices and water circulation disturbance devices to converge the overall water temperature of the aquaculture pond to the preset suitable range according to the collaborative control strategy generated by the multimodal decision module.

2. The system according to claim 1, characterized in that, The multi-level temperature sensing network includes a vertical sensor array deployed in different water layers within a single aquaculture pond and a horizontal sensor network deployed in aquaculture ponds at different geographical locations, used to construct a three-dimensional temperature field of the aquaculture water body; based on this three-dimensional temperature field, the multimodal decision module is used to identify low-temperature regions where the water temperature is below a first threshold, high-temperature regions where the water temperature is above a second threshold, and potential thermal disturbance regions with large gradient differences within the suitable range.

3. The system according to claim 2, characterized in that, The multimodal decision-making module, with interval control at its core, incorporates a rule-model hybrid engine. Its decision logic includes: When the water temperature is detected to be lower than the first threshold, the heating strategy is triggered, and the water circulation disturbance device is activated based on the dissolved oxygen concentration. When the water temperature is detected to be higher than the second threshold, a cooling or natural heat dissipation strategy is triggered, and the water circulation intensity is adjusted according to the trend of ammonia nitrogen concentration change. When a large gradient difference is detected in the water temperature within the suitable range, an equalization strategy is triggered to control the water circulation disturbance device to eliminate the heat island effect and maintain the temperature uniformity within the range. All strategies reference real-time biological behavior data. When reduced feeding activity or stress behavior of abnormal clusters is detected, the multimodal decision-making model smooths the rate or magnitude of temperature control.

4. The system according to claim 3, characterized in that, The multimodal decision-making module also includes an adaptive interval optimization submodule. This submodule optimizes the values ​​of the first threshold and the second threshold based on the aquaculture species, growth stage, and historical growth performance data, and learns the optimal control parameters for maintaining water temperature within the dynamic interval under different environmental conditions.

5. The system according to claim 1, characterized in that, The distributed temperature control device in the partitioned controllable execution network includes a main heat pump unit and multiple auxiliary heating / cooling units deployed in key aquaculture ponds or specific areas within the ponds. The multimodal decision module is used to control the auxiliary units in the execution network to precisely supplement heating in low-temperature areas or locally cool high-temperature areas based on water temperature deviations, and to control the execution network to coordinate the water circulation disturbance device to promote uniform heat diffusion so that the overall water temperature can quickly and stably return to the preset suitable range.

6. The system according to claim 1, characterized in that, The multimodal decision-making module includes an edge-cloud collaborative architecture, which includes: Edge control nodes, deployed in the aquaculture workshop, are used for real-time data processing and rapid decision-making, and provide millisecond-level response to emergencies where the water temperature exceeds the preset suitable range; The cloud-based analytics platform is used to aggregate long-term data, run predictive models, predict the impact of changes in the external environment on the water temperature range in the next few hours to days, and send optimization strategies or model parameters to the edge control nodes in advance to enhance the predictability and robustness of the range control.

7. The system according to claim 6, characterized in that, The predictive model integrated into the cloud-based analysis platform is used to predict the probability and timing of the breach of the preset suitable range by combining external weather forecasts, historical water temperature data, and workshop operation logs. Based on this prediction, the cloud-based analysis platform is used to generate forward-looking maintenance strategies, including advance heat storage, pre-cooling, or adjustment of circulation modes.

8. The system according to claim 1, characterized in that, It also includes a unified data interface, which is used to access various environmental sensors; the unified data interface is communicatively connected to the multimodal decision module, and is used to set the first threshold and the second threshold in the multimodal decision module through a human-computer interaction interface, or the aquaculture management system can automatically send the first threshold and the second threshold set in the aquaculture model to the multimodal decision module through the unified data interface.

9. The system according to claim 1, characterized in that, It also includes an energy efficiency optimization subsystem, which is connected to the execution network. Under the primary premise of ensuring that the water temperature is kept stable within the preset suitable range, the energy efficiency optimization subsystem optimizes the operation combination and timing of each device in the execution network, prioritizes the scheduling of devices with high energy efficiency ratios, and optimizes energy consumption costs using time-of-use pricing policies.

10. A method for intelligent control of water temperature in land-based aquaculture based on the system described in any one of claims 1-9, characterized in that, With the core objective of maintaining the aquaculture water temperature within a preset suitable range defined by a first threshold and a second threshold, the process includes the following steps: Temperature data of aquaculture water is collected in real time through a multi-level temperature sensing network, and multimodal data on water quality and biological behavior are obtained. Based on the temperature data, determine the deviation relationship between the current overall and local water temperature and the preset suitable range; By integrating the aforementioned water temperature deviation relationship, real-time water quality data, and biological behavior data, a collaborative regulation strategy is generated with the core objective of correcting deviations and maintaining range stability. The strategy is executed through a partitioned and controllable execution network, driving the distributed temperature control device and water circulation disturbance device to perform precise actions; The system continuously monitors whether the water temperature after regulation returns to and stabilizes within the preset suitable range, and collects multimodal feedback data to evaluate the control effect and iteratively optimize the decision logic.

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