Intelligent light prawn control system
Through the intelligent light control system, the spectrum and lighting parameters are dynamically adjusted, the problem of instability of the light environment in traditional shrimp farming is solved, the growth rate, immunity and feed conversion rate are improved, and efficient lighting management is achieved.
Patent Information
- Application Number
- CN202510684039.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-26
- Publication Date
- 2025-08-19
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The light environment in traditional shrimp farming is unstable and cannot be accurately regulated, resulting in inefficient growth efficiency and health problems, affecting the growth rate, immunity and feed conversion rate of shrimp.
The intelligent light control system is adopted, including a light generation module, perception and feedback module, algorithm optimization module and control execution module. Through adjustable spectral LED light source, sensor and intelligent algorithm, the spectral wavelength, light intensity and light time are dynamically adjusted to achieve precise light control.
Significantly improve the growth rate, immunity and feed conversion rate of shrimps, reduce energy consumption, achieve accurate and efficient lighting regulation, and improve breeding efficiency.
Smart Images
Figure CN120509674A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of shrimp farming, and in particular to an intelligent light-controlled shrimp system. Background Art
[0002] Shrimp are an important aquaculture species worldwide, and their growth rate and health are directly related to the economic benefits of aquaculture. However, traditional shrimp farming relies primarily on natural light or fixed light sources for lighting, failing to fully consider the impact of light environment parameters such as light intensity, spectrum, and photoperiod on shrimp growth, immunity, and behavior. This simple and extensive light control method can easily lead to the following problems:
[0003] Instability of the light environment: Natural light is greatly affected by weather and seasonal changes and cannot provide stable lighting conditions, which may affect the biological rhythm and growth performance of shrimp.
[0004] Low breeding efficiency: Traditional lighting cannot be precisely regulated for different growth stages and goals (such as growth rate, feed conversion rate, and disease resistance), resulting in waste of resources and insufficient growth potential.
[0005] Shrimp health issues: Unreasonable light exposure may cause physiological stress in shrimp, reduce their immunity, make them more susceptible to diseases, and increase breeding risks.
[0006] In recent years, with the development of intelligent control technologies and environmental monitoring methods, intelligent light-controlled aquaculture has gradually become a research hotspot. By building a scientific light environment control system, combined with the physiological needs of shrimp and environmental conditions, it is possible to achieve precise and personalized light control, thereby significantly improving aquaculture efficiency.
[0007] To this end, we propose an intelligent light control shrimp system to solve the existing problems. Summary of the Invention
[0008] The purpose of the present invention is to address the problems existing in the background technology and to provide an intelligent light control system for shrimps.
[0009] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent light control system for shrimp, comprising a light generation module, a perception and feedback module, an algorithm optimization module and a control execution module, wherein the light generation module is used to provide adjustable light covering the ultraviolet to infrared spectrum range, the light generation module realizes dynamic adjustment of wavelength and light intensity through an adjustable spectrum LED light source, and adopts pulse width modulation technology for stepless light intensity regulation to meet the light intensity requirements of different depths of the culture pond, the perception and feedback module includes a light intensity sensor, a water quality sensor, a temperature and humidity sensor and an underwater camera, for realizing The system monitors the light distribution, environmental parameters and activity status of the culture pond in real time, and transmits the collected data to the central processing unit for analysis. The algorithm optimization module dynamically optimizes the light control parameters by combining the real-time feedback data with an intelligent algorithm based on deep reinforcement learning, and automatically adjusts the spectrum wavelength, light intensity and illumination time to improve the growth rate, immune ability and feed conversion rate of the shrimp. The control execution module adjusts the spectrum and light intensity output by the light generation module in real time according to the environmental data transmitted by the perception and feedback module and the parameters generated by the algorithm optimization module, ensuring that the lighting environment is always in the optimal growth state for the shrimp.
[0010] Preferably, the light generating module further includes a directional reflector and a diffuser, which are used to improve the directionality and uniformity of the light to avoid the phenomenon of excessive or weak local illumination. The directional reflector adopts multi-layer dielectric coating technology, and can achieve uniform light distribution while improving light utilization efficiency through optimized design of reflection and scattering. The diffuser is made of a composite material with high light transmittance, which can maintain a stable light diffusion effect in different spectral ranges, thereby providing a more consistent lighting environment for the shrimp and avoiding stress reactions caused by local light intensity differences.
[0011] Preferably, the light intensity sensor of the perception and feedback module can cover different depth levels in the breeding pond, and provide a comprehensive environmental monitoring function in combination with a water quality sensor and a camera, wherein the data collection time interval is once every 10 seconds.
[0012] Preferably, the reward function in the control execution module performs multi-objective optimization on the dynamic adjustment of lighting parameters by combining data correlation analysis of the lighting environment and the growth behavior of shrimp. The selection of spectral wavelength is based on the physiological needs of shrimp in different growth stages, and the light intensity and illumination time are optimized in real time according to feeding behavior and daily activity intensity, thereby achieving a comprehensive improvement in growth rate, immune capacity and feed conversion rate.
[0013] Preferably, the weight adjustment of the reward function is combined with real-time environmental conditions and historical data, and dynamic adjustment is achieved through a simulated annealing algorithm. Specifically, for different seasons, temperatures and water quality conditions, the system gives priority to parameter combinations that can significantly improve the growth rate of shrimp, while taking into account economic costs and energy consumption, making aquaculture management more flexible and adaptable.
[0014] Preferably, the control execution module adopts an improved proportional-integral-differential control algorithm, which avoids the integral saturation problem caused by long-term error accumulation by setting an integral upper limit, and adopts a differential advance strategy to improve the response speed to error changes; the control system further sets an error tolerance interval and a parameter adaptive adjustment function to adapt to environmental changes in aquaculture ponds of different sizes, ensuring the accuracy and stability of light control regulation.
[0015] Preferably, the perception and feedback module transmits real-time data of the breeding environment to the central processing unit through low-power long-distance wireless communication technology, wherein the communication protocol adopts an anti-interference design to ensure the stability of the data in a complex farm environment; in addition, the central processing unit further improves the accuracy and reliability of data transmission through a built-in data redundancy check mechanism.
[0016] Preferably, the algorithm optimization module dynamically adjusts the wavelength, intensity and duration of light according to the spectral demand model of different growth stages, wherein the early growth stage focuses on short-wavelength light to promote metabolism, the middle growth stage adopts medium-wavelength light to improve immunity, and the late growth stage introduces long-wavelength light to optimize feed conversion rate, thereby achieving efficient growth of shrimp at each stage.
[0017] Preferably, the system's autonomous learning function automatically updates the light control parameter optimization model by comparing and analyzing historical experimental data and real-time monitoring results. The learning process combines reinforcement learning with a multi-objective decision-making algorithm to continuously improve the adaptability to environmental changes and the accuracy of the light control scheme. In addition, the system can also generate forecast reports based on big data analysis to provide long-term strategic planning support for aquaculture management.
[0018] Compared with the prior art, the present invention has the following beneficial effects:
[0019] Improve shrimp growth rate: The system uses spectrum optimization technology and light control optimization based on reinforcement learning. Different from traditional empirical rules, it has dynamic adaptability and dynamically adjusts the light wavelength and intensity according to the spectrum requirements of shrimp at different growth stages, significantly promoting metabolism and tissue growth.
[0020] Optimize feed conversion rate: Through intelligent light intensity control and photoperiod optimization, the system can improve shrimp digestion and absorption efficiency of feed, reduce feed waste, and thus reduce breeding costs;
[0021] Energy saving and high efficiency: Through improved PID real-time adjustment, the system can optimize energy consumption while achieving light control goals. Through scientific light regulation, energy waste is significantly reduced, and precise control of light is achieved, which is in line with the sustainable development concept of green farming.
[0022] In summary, the intelligent light-controlled shrimp system of the present invention, based on a scientific light-control model and combined with modern intelligent control technology, achieves precise, personalized, and efficient regulation of the lighting environment. Compared with traditional aquaculture methods, this system offers significant advantages in increasing shrimp growth rate, enhancing immunity, optimizing feed utilization, and reducing energy consumption. It effectively addresses the shortcomings of existing technologies and provides innovative technical support for the development of the modern aquaculture industry. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION
[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.
[0025] Example 1
[0026] like Figure 1 As shown in FIG, the present invention proposes an intelligent light control system for shrimp, including a light generation module, a perception and feedback module, an algorithm optimization module and a control execution module:
[0027] Light Generation Module
[0028] It is used to provide adjustable light covering the ultraviolet to infrared spectrum range. The light generation module uses a spectrum-adjustable LED light source to achieve dynamic adjustment of wavelength and light intensity, and adopts pulse width modulation technology for stepless light intensity regulation to meet the light intensity requirements of different depths of the aquaculture pond;
[0029] Further,
[0030] The light generation module also includes a directional reflector and a diffuser to improve the directionality and uniformity of light to avoid localized over-intensity or under-intensity of light. The directional reflector uses multi-layer dielectric coating technology, which can achieve uniform light distribution while improving light utilization efficiency through optimized reflection and scattering design. The diffuser is made of a composite material with high light transmittance and can maintain a stable light diffusion effect in different spectral ranges, thereby providing a more consistent lighting environment for shrimp and avoiding stress reactions caused by local light intensity differences.
[0031] At the same time, each breeding pond is equipped with a separate light source to avoid light interference, and the light source installation angle is controlled at 45° to simulate the natural light environment.
[0032] Perception and feedback module
[0033] It includes light intensity sensors, water quality sensors, temperature and humidity sensors, and underwater cameras, which are used to monitor the light distribution, environmental parameters, and shrimp activity status of the aquaculture pond in real time, and transmit the collected data to the central processing unit for analysis;
[0034] Further,
[0035] The light intensity sensor in the perception and feedback module can cover different depth levels within the aquaculture pond. The water quality sensor detects dissolved oxygen, pH, and ammonia nitrogen concentrations. Underwater cameras capture the activity of shrimp, analyzing their feeding and swimming patterns, providing comprehensive environmental monitoring. Data is collected every 10 seconds.
[0036] The perception and feedback module uses the LoRa wireless communication module to transmit real-time data of the farming environment to the central processing unit. The communication protocol adopts an anti-interference design to ensure the stability of data in complex farm environments. In addition, the central processing unit further improves the accuracy and reliability of data transmission through a built-in data redundancy check mechanism.
[0037] Algorithm optimization module
[0038] Through the TensorFlow intelligent algorithm based on deep reinforcement learning, combined with real-time feedback data, we dynamically optimize light control parameters and automatically adjust the spectral wavelength, light intensity, and lighting time to improve the growth rate, immune capacity, and feed conversion rate of shrimp. The specific examples are as follows:
[0039] Effect of spectrum on growth rate
[0040] The difference in the relationship between spectral wavelength and shrimp growth rate in the growth rate model follows a Gaussian distribution:
[0041]
[0042] in:
[0043] GR(λ,L): represents the growth rate of shrimp under wavelength λ and light intensity L, with the unit of g / day, which is used to measure the growth increment of shrimp per unit time.
[0044] GR max : Indicates the optimal spectral wavelength λ of shrimp opt and the maximum growth rate that can be achieved under optimal light intensity conditions.
[0045] λ: represents the wavelength of the spectrum, measured in nanometers (nm). Shrimp are usually sensitive to a specific range of the spectrum, mainly concentrated between 400-700nm (visible light region). For example, blue light (about 450nm) and red light (about 630nm) have different effects on different growth stages.
[0046] λ opt : Indicates the optimal spectral wavelength when shrimp growth rate reaches its peak. Its specific value depends on the growth stage of the shrimp. For example, blue light may be more suitable for promoting early metabolism, while red light may be more suitable for feed utilization in the later stage.
[0047] σ: represents the distribution range of shrimp's sensitivity to spectral response, measured in nanometers (nm). A smaller standard deviation σ indicates a more selective shrimp for spectral wavelengths; conversely, a smaller standard deviation indicates the shrimp can adapt to a wider range of spectral wavelengths.
[0048] L: represents light intensity, the unit is lux. Light intensity determines the energy density of light in the environment, thus affecting the behavior and growth of shrimp.
[0049] L min : Indicates the lowest light intensity threshold that shrimp can perceive. <L min , the light-promoting effect on shrimp growth is insufficient and the growth rate cannot be increased.
[0050] k L k: A parameter indicating the sensitivity of light intensity to growth rate. L The larger it is, the faster the growth rate responds to changes in light intensity, and its specific value is obtained by experimental fitting.
[0051] represents an indicator function. When L≥L min When L <L min When , the function value is 0.
[0052] The effect of the above spectrum on the growth rate of shrimp is expressed by the Gaussian distribution function, which reflects the sensitivity of shrimp to specific wavelengths. By adjusting the spectral wavelength, the light configuration can be optimized for different growth stages, and customized light control strategies can be provided for different growth stages and environmental conditions.
[0053] Optimization of photoperiod
[0054] Expand the immune capacity model to a multifactorial model to consider the impact of circadian rhythm on the immune system:
[0055]
[0056] in:
[0057] I0 is the basic immunity level;
[0058] a is the amplitude of regulation of immune ability by photoperiod fluctuation;
[0059] P is the mean value of the light intensity fluctuation amplitude;
[0060] b is the adjustment coefficient of the fluctuation factor.
[0061] Through the above formula calculation, the system can dynamically adjust the lighting time, intensity and duration to enhance immunity. For example, the fluctuation of the light cycle synchronized with the circadian rhythm helps maximize the activity of the immune system. When the circadian rhythm is disturbed (such as too long light or complete darkness) or the environmental parameters are unsuitable, the model can reflect the downward trend of immune ability and provide early warning signals for the regulatory system.
[0062] Relationship between light intensity and feed conversion rate
[0063] The feed conversion rate F(L) shows a bimodal characteristic as the light intensity L changes. The model expression is:
[0064]
[0065] F(L) is the feed conversion rate under light intensity LLL, which is defined as the weight gain of shrimp promoted by unit feed weight. The unit is usually kg shrimp / kg feed.
[0066] F max : Indicates the theoretical maximum value of feed conversion rate, corresponding to the optimal light intensity L opt The maximum feed utilization efficiency that the system can achieve.
[0067] L: Indicates the current light intensity in lux.
[0068] L opt : Indicates the optimal light intensity for maximum feed conversion, corresponding to the optimal light requirement for shrimp. This value is closely related to the shrimp's growth stage, species, and environmental conditions.
[0069] L min : Indicates the minimum light intensity required for shrimp to perceive light and trigger physiological responses to light. If the light intensity L is less than L min, the light physiological effect of shrimp cannot be activated and the model will output a zero value.
[0070] k: This parameter represents the sensitivity of light intensity to the increase in feed conversion rate. A larger value indicates a faster response of feed conversion rate to changes in light intensity. Its specific value is obtained by fitting experimental data.
[0071] e: represents the base of the natural logarithm (approximately 2.718), which is used to calculate the exponential relationship between light intensity and feed conversion rate.
[0072] Represents an indicator function, when L is greater than or equal to L min When L is less than L min When , the function value is 0, indicating that the light intensity is not enough to affect the feed conversion rate.
[0073] The model describes the nonlinear relationship of feed conversion rate gradually tending to saturation with the increase of light intensity in the form of exponential form. When the light intensity is too low, the feed conversion rate is limited; when the light intensity gradually increases, the feed conversion rate increases rapidly; when the light intensity approaches the optimal value, the feed conversion rate tends to saturation. According to the model, the optimal light intensity L can be found. opt , in order to maximize feed conversion rate. At the same time, avoid excessive increase of light intensity which may lead to energy waste or physiological stress of shrimp.
[0074] Control execution module
[0075] Based on the environmental data transmitted by the perception and feedback module and the parameters generated by the algorithm optimization module, the spectrum and light intensity output by the light generation module are adjusted in real time to ensure that the lighting environment is always in the optimal state for shrimp growth;
[0076] The system uses a deep Q-learning (DQL) algorithm to optimize light control parameters in real time based on environmental feedback:
[0077] State space: includes the current wavelength λ, light intensity L, illumination duration T, and environmental parameters (temperature, dissolved oxygen, etc.).
[0078] Action space: a three-dimensional vector [Δλ, ΔL, ΔT], representing the increments of spectral wavelength, light intensity, and illumination duration, respectively.
[0079] The reward function is:
[0080] R=w1·G(λ,t)+w2·I(T,P)+w3·F(L)-w4·E
[0081] in:
[0082] w1, w2, w3, w4: weight coefficients, dynamically adjusted based on economic benefits and breeding goals;
[0083] E: Lighting energy consumption, which depends on the power of the LED lamp and the duration of use.
[0084] Use the improved PID control algorithm to adjust the light intensity L:
[0085]
[0086] L adjusted : Indicates the adjustment value of light intensity. It is the light intensity adjustment value output by the control execution module based on real-time error calculation. It is used to dynamically adjust the spectral intensity of the light generation module.
[0087] K p : Represents the proportional gain parameter. Its function is to directly calculate the adjustment amount according to the size of the error value e. Increasing K p It can improve the response speed, but too large a value may cause system oscillation.
[0088] e: represents the current illumination error, that is, the difference between the target illumination intensity and the actual measured illumination intensity:
[0089] e=L target -L measured
[0090] Among them, L target is the target light intensity required for shrimp growth, L measured It is the actual light intensity value detected in real time by the perception and feedback module.
[0091] K i : Indicates the integral gain parameter. The integral part gradually eliminates the long-term error by integrating the accumulated error value over time, making the actual light intensity value closer to the target value. i The excessive value may lead to integral saturation and cause system instability.
[0092] It represents the cumulative integral of the error value over time, from the initial moment to the current moment. By accumulating the historical error value, the deviation in the system is compensated, which is particularly suitable for dealing with situations where the deviation persists.
[0093] K d : Indicates the differential gain parameter. The differential part adjusts the output value according to the error change rate, which can predict the error change trend and make corrections in advance, thereby suppressing error fluctuations and response overshoot. d A high value may cause the control output to be too sensitive and increase the effect of noise.
[0094] This represents the rate of change of the error over time, reflecting the trend of the error. A positive value indicates an increase in the error, while a negative value indicates a decrease. The derivative term provides predictive control by calculating this rate of change.
[0095] The improvement mechanism is:
[0096] Anti-integral saturation mechanism: By setting the integral upper limit or integral error elimination condition, it prevents long-term error accumulation from causing the integral value to be too large, resulting in excessive adjustment of the light intensity.
[0097] Differential-first strategy: Before the proportional and integral terms take effect, the rate of change of the error is prioritized to improve the system's ability to respond quickly to sudden changes in ambient light.
[0098] Through this algorithm, the light intensity can be quickly adjusted according to real-time environmental changes to meet the lighting needs of shrimp at different growth stages, while avoiding the oscillation, delay and over-adjustment problems that may occur in traditional PID control algorithms.
[0099] The control execution module adopts an improved proportional-integral-differential control algorithm, which avoids the integral saturation problem caused by long-term error accumulation by setting an integral upper limit, and adopts a differential priority strategy to improve the response speed to error changes; the control system further sets an error tolerance range and parameter adaptive adjustment function to adapt to environmental changes in aquaculture ponds of different sizes, ensuring the accuracy and stability of light control regulation.
[0100] The reward function in the control execution module performs multi-objective optimization on the dynamic adjustment of lighting parameters by combining data correlation analysis between the lighting environment and the growth behavior of shrimp. The selection of spectral wavelength is based on the physiological needs of shrimp in different growth stages, and the light intensity and illumination time are optimized in real time according to feeding behavior and daily activity intensity, thereby achieving a comprehensive improvement in growth rate, immune capacity and feed conversion rate.
[0101] The weight adjustment of the reward function is combined with real-time environmental conditions and historical data, and dynamic adjustment is achieved through the simulated annealing algorithm. Specifically, for different seasons, temperatures and water quality conditions, the system gives priority to parameter combinations that can significantly improve the growth rate of shrimp, while taking into account economic costs and energy consumption, making aquaculture management more flexible and adaptable.
[0102] The algorithm optimization module dynamically adjusts the wavelength, intensity and duration of light according to the spectral demand model of different growth stages. In the early growth stage, short-wavelength light is emphasized to promote metabolism, medium-wavelength light is used in the middle growth stage to improve immunity, and long-wavelength light is introduced in the late growth stage to optimize feed conversion rate, thereby achieving efficient growth of shrimp at each stage.
[0103] The system's autonomous learning function automatically updates the light control parameter optimization model by comparing and analyzing historical experimental data and real-time monitoring results. The learning process combines reinforcement learning with multi-objective decision-making algorithms to continuously improve adaptability to environmental changes and the accuracy of light control solutions. In addition, the system can also generate predictive reports based on big data analysis to provide long-term strategic planning support for aquaculture management.
[0104] The above specific embodiments are only several preferred embodiments of the present invention. Based on the technical solutions of the present invention and the relevant inspirations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.
[0105] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations that come within the meaning and range of equivalents of the claims be embraced therein.
Claims
1. An intelligent light control system for shrimp, comprising a light generation module, a perception and feedback module, an algorithm optimization module, and a control execution module, characterized in that: The light generation module is used to provide adjustable light covering the ultraviolet to infrared spectrum range. The light generation module dynamically adjusts the wavelength and light intensity through an adjustable spectrum LED light source, and adopts pulse width modulation technology for stepless light intensity regulation to meet the light intensity requirements of different depths in the aquaculture pond. The perception and feedback module includes a light intensity sensor, a water quality sensor, a temperature and humidity sensor, and an underwater camera, which is used to monitor the light distribution, environmental parameters, and shrimp activity status of the aquaculture pond in real time, and transmit the collected data to the central processing unit for analysis. The algorithm optimization module dynamically optimizes the light control parameters through an intelligent algorithm based on deep reinforcement learning and combined with real-time feedback data, automatically adjusting the spectral wavelength, light intensity, and lighting time to improve the growth rate, immune capacity, and feed conversion rate of the shrimp. The control execution module adjusts the spectrum and light intensity output by the light generation module in real time based on the environmental data transmitted by the perception and feedback module and the parameters generated by the algorithm optimization module to ensure that the lighting environment is always in the optimal growth state for the shrimp.
2. The intelligent light control system for shrimp according to claim 1, characterized in that: The light generation module also includes a directional reflector and a diffuser, which are used to improve the directionality and uniformity of light to avoid localized over-intensity or under-intensity of light. The directional reflector uses multi-layer dielectric coating technology and can achieve uniform light distribution while improving light utilization efficiency through optimized reflection and scattering design. The diffuser is made of a composite material with high light transmittance and can maintain a stable light diffusion effect within different spectral ranges, thereby providing a more consistent lighting environment for the shrimp and avoiding stress reactions caused by local light intensity differences.
3. The intelligent light control system for shrimp according to claim 1, characterized in that: The light intensity sensor of the perception and feedback module can cover different depth levels in the aquaculture pond, and combined with the water quality sensor and camera to provide comprehensive environmental monitoring functions, where the data collection time interval is once every 10 seconds.
4. The intelligent light control system for shrimp according to claim 1, characterized in that: The reward function in the control execution module performs multi-objective optimization on the dynamic adjustment of lighting parameters by combining data correlation analysis between the lighting environment and the growth behavior of shrimp. The selection of spectral wavelength is based on the physiological needs of shrimp in different growth stages, and the light intensity and illumination time are optimized in real time according to feeding behavior and daily activity intensity, thereby achieving a comprehensive improvement in growth rate, immune capacity and feed conversion rate.
5. The intelligent light control system for shrimps according to claim 4, characterized in that: The weight adjustment of the reward function is combined with real-time environmental conditions and historical data, and dynamic adjustment is achieved through a simulated annealing algorithm. Specifically, for different seasons, temperatures and water quality conditions, the system prioritizes parameter combinations that can significantly improve the growth rate of shrimp, while taking into account economic costs and energy consumption, making aquaculture management more flexible and adaptable.
6. The intelligent light control system for shrimp according to claim 1, characterized in that: The control execution module adopts an improved proportional-integral-differential control algorithm, which avoids the integral saturation problem caused by long-term error accumulation by setting an integral upper limit, and adopts a differential-first strategy to improve the response speed to error changes. The control system further sets an error tolerance range and parameter adaptive adjustment function to adapt to environmental changes in aquaculture ponds of different sizes, ensuring the accuracy and stability of light control regulation.
7. The intelligent light control system for shrimp according to claim 1, characterized in that: The perception and feedback module transmits real-time data about the farming environment to the central processing unit via low-power, long-distance wireless communication technology. The communication protocol adopts an anti-interference design to ensure the stability of the data in complex farm environments. In addition, the central processing unit further improves the accuracy and reliability of data transmission through a built-in data redundancy check mechanism.
8. The intelligent light control system for shrimps according to claim 1, characterized in that: The algorithm optimization module dynamically adjusts the wavelength, intensity, and duration of light according to the spectral demand model of different growth stages. In the early growth stage, short-wavelength light is emphasized to promote metabolism, medium-wavelength light is used in the middle growth stage to improve immunity, and long-wavelength light is introduced in the late growth stage to optimize feed conversion rate, thereby achieving efficient growth of shrimp at each stage.
9. The intelligent light control system for shrimps according to claim 1, characterized in that: The system's autonomous learning function automatically updates the light-control parameter optimization model by comparing and analyzing historical experimental data and real-time monitoring results. The learning process combines reinforcement learning with a multi-objective decision-making algorithm to continuously improve adaptability to environmental changes and the accuracy of light-control solutions. In addition, the system can also generate predictive reports based on big data analysis, providing long-term strategic planning support for aquaculture management.