Environmental cooperative control digital twinning method and system

Abstract

The invention discloses an environment coordinated regulation digital twinning method and system, and belongs to the field of environment regulation, and the environment coordinated regulation digital twinning method comprises the following steps: constructing a dynamic digital model of a fish and vegetable symbiotic system in a virtual space based on a digital twinning technology, the dynamic digital model copies all components of the fish and vegetable symbiotic system; compared with the prior art, the method has the advantages that compared with traditional management depending on artificial experience, the dynamic digital model is constructed, real-time mapping and interaction between a physical system and a virtual model are achieved, fuzzy experience judgment is converted into accurate analysis based on multi-dimensional data, and the method is easy to implement and high in practicability. Through a simulation algorithm and an ecological knowledge base, optimal environmental parameters can be dynamically deduced, biological abnormality can be intelligently diagnosed, and finally, cooperative regulation and control are performed through automatic control equipment, so that the limitation of traditional management is thoroughly changed, and all-weather accurate regulation and control and risk early warning are really achieved.

Description

Technical Field

[0001] This invention belongs to the field of environmental control, and particularly relates to a digital twin method and system for coordinated environmental control. Background Technology

[0002] Aquaponics is a complex ecological system that combines aquaculture and hydroponics. Microorganisms decompose fish waste to provide nutrients for plants, while plant roots purify the water, achieving a circular model of "raising fish without changing the water and growing vegetables without applying fertilizer." The system consists of three parts: aquaculture ponds, planting areas, and a water circulation and treatment device. Its core principle is the ecological balance between animals, plants, and microorganisms.

[0003] As a complex ecosystem that relies on the dynamic balance of animals, plants, and microorganisms, the stable operation of aquaponics is extremely sensitive to fluctuations in environmental parameters. Traditional management relies heavily on human experience, making it difficult to achieve precise control and risk warning around the clock, and therefore needs improvement. Summary of the Invention

[0004] Therefore, it is necessary to provide a digital twin method and system for coordinated environmental control to address the above-mentioned problems.

[0005] The present invention is implemented as follows: the digital twin method for coordinated environmental regulation includes the following steps:

[0006] Based on digital twin technology, a dynamic digital model of an aquaponics system is constructed in virtual space. This dynamic digital model replicates all components of the aquaponics system (including aquaculture ponds, planting areas, water recycling and treatment devices, and the interactions between animals, plants, and microorganisms). The dynamic digital model can continuously evolve, integrating newly emerging biological information (such as invasive species or competitors detected through image recognition or environmental DNA analysis) with an ecological knowledge base and simulation logic, thereby simulating the overall operating mechanism, ecological processes, and potential biological competition dynamics of the aquaponics system.

[0007] Real-time collection of multi-dimensional data from the aquaponics system (such as environmental parameters like water temperature, pH, dissolved oxygen, and ammonia nitrogen concentration, as well as biological information parameters like fish behavior images, plant leaf status, and changes in aquatic microbial communities) and continuous transmission of this multi-dimensional data to the dynamic digital model. This allows the dynamic digital model to be updated in real time according to the latest status of the aquaponics system, achieving synchronous mapping and two-way interaction between the dynamic digital model and the aquaponics system.

[0008] By using dynamic digital models to run simulation algorithms to analyze multi-dimensional data, and combining ecological balance principles with real-time biological information, the optimal environmental data range required to maintain the ecological balance of animals, plants, and microorganisms is dynamically inferred to obtain the first analysis content. The monitored biological abnormal signals (such as the appearance of invasive species, competitors, abnormal fish behavior, etc.) are analyzed and judged to obtain the second analysis content.

[0009] Based on the analysis, the aquaponics system is coordinated and regulated by automatic control equipment (such as water pumps, heaters, aerators, or targeted ultraviolet sterilization devices) to cope with environmental fluctuations and biological anomalies.

[0010] In one embodiment, the present invention provides a digital twin method for coordinated environmental regulation. The steps of using a dynamic digital model to run simulation algorithms to analyze multi-dimensional data, integrating ecological balance principles and real-time biological information, dynamically inferring the optimal environmental data range required to maintain the ecological balance of animals, plants, and microorganisms to obtain a first analysis content, and analyzing and judging monitored biological anomalies to obtain a second analysis content, specifically include:

[0011] The simulation algorithm runs through a dynamic digital model to calculate and simulate trends of multi-dimensional data (especially environmental parameters such as water temperature, pH value, dissolved oxygen, and ammonia nitrogen concentration) collected in real time and stored in history. Combined with the constraints defined by the principle of ecological balance (such as the suitable temperature range for nitrifying bacteria communities and the optimal pH range for plant nutrient absorption), and referring to the state of the aquaponics system reflected by real-time biological information (such as microbial community structure and plant growth rate), the optimal target range of various environmental parameters that can most effectively maintain the ecological balance among animals, plants, and microorganisms under the current state is dynamically deduced to obtain the first analysis content.

[0012] When the monitored biological anomaly signal (monitored through image recognition or environmental DNA analysis) is identified as an invasive organism (such as alien algae, competitive aquatic organisms, or pathogenic microorganisms), the control dynamic digital model initiates a dynamic simulation of biological competition. Based on the principles of species interaction relationships (competition, predation, parasitism, etc.) in the ecological knowledge base, it simulates the impact path and extent of the invasive organism on the ecological balance of existing animals, plants, and microorganisms, determines the threat level of the invasive organism and the possible chain ecological reactions, generates an assessment conclusion containing response strategy recommendations, and obtains the second analysis content.

[0013] When monitored biological anomalies (e.g., through image recognition) are identified as abnormal fish behavior (fish may exhibit agitation, refusal to eat, or mutual aggression due to environmental stress, such as excessive light or human disturbance), all environmental parameter records for the period in which the abnormal fish behavior occurred are cross-referenced. If abnormal environmental parameters are found (automatic environmental control malfunctions, such as a sudden drop in dissolved oxygen or excessive ammonia nitrogen concentration), it is determined to be due to environmental factors. If all environmental parameters are within the optimal range and there are no other abnormalities, it is initially inferred to be caused by non-systematic environmental factors (such as human fright or operational interference). For non-systematic environmental factors, automatic control is not activated. If the abnormal fish behavior does not slow down or disappear on its own after a set time, the initial inference is considered a misjudgment, and a secondary inference is made to identify unidentified potential environmental factors or persistent stressors. This specific set of environmental parameters and the continuous abnormal behavior sequence are recorded through a dynamic digital model to obtain the second analysis content. The knowledge base of the dynamic digital model regarding the relationship between fish behavior and the environment is updated through the second analysis content.

[0014] In one embodiment, the present invention provides a digital twin method for coordinated environmental regulation, further comprising:

[0015] Integrate external energy data (such as time-of-use electricity prices and future weather forecasts) to calculate and predict the energy consumption demand of the aquaponics system in the future (such as the operating load of water pumps, heaters, and aerators), and formulate the optimal energy consumption scheduling strategy.

[0016] In one embodiment, the present invention provides a digital twin method for coordinated environmental regulation, further comprising:

[0017] By integrating external energy data (such as time-of-use electricity prices and future weather forecasts) and combining them with environmental parameters from multi-dimensional data of aquaponics systems, resource planning is carried out across time scales. The thermodynamic and hydrodynamic changes of aquaponics systems and fluctuations in external energy data over a future period are simulated, and a path for regulating environmental parameters with the lowest cost is calculated. All decisions are based on a preset range of environmental parameter fluctuations that is permissible to ensure ecological security.

[0018] In one embodiment, the present invention provides a digital twin method for coordinated environmental regulation, further comprising:

[0019] When a power outage is anticipated, the natural trajectory of environmental parameters (with water temperature as the core) during the entire power outage is simulated based on the expected duration of the outage and the thermodynamic and hydrodynamic characteristics of the aquaponics system. The estimated survival rate of the highest priority biological population (such as fish) upon power recovery is the highest optimization index. An environmental parameter that must be achieved before the power outage is calculated and environmental parameter adjustments are performed.

[0020] In one embodiment, the present invention provides an environmental collaborative control digital twin system, comprising:

[0021] A dynamic digital model construction module is used to construct a dynamic digital model of an aquaponics system in virtual space based on digital twin technology. The dynamic digital model replicates all components of the aquaponics system (including aquaculture ponds, planting areas, and water circulation and treatment devices, as well as the interactions between animals, plants, and microorganisms). The dynamic digital model can continuously evolve, integrating newly emerging biological information (such as invasive species or competitors detected through image recognition or environmental DNA analysis) through an ecological knowledge base and simulation logic, thereby simulating the overall operating mechanism, ecological processes, and potential biological competition dynamics of the aquaponics system.

[0022] The dynamic digital model real-time update module is used to collect multi-dimensional data of the aquaponics system in real time (such as environmental parameters such as water temperature, pH value, dissolved oxygen, and ammonia nitrogen concentration, as well as biological information parameters such as fish behavior images, plant leaf status, and changes in aquatic microbial communities). The multi-dimensional data is continuously transmitted to the dynamic digital model, so that the dynamic digital model can be updated in real time according to the latest status of the aquaponics system, realizing synchronous mapping and two-way interaction between the dynamic digital model and the aquaponics system.

[0023] The analysis content acquisition module is used to run simulation algorithms using dynamic digital models to analyze multi-dimensional data, integrate ecological balance principles and real-time biological information, dynamically infer the optimal environmental data range required to maintain the ecological balance of animals, plants and microorganisms, and obtain the first analysis content. It also analyzes and judges the monitored biological abnormal signals (such as the appearance of invasive species, competitors, abnormal fish behavior, etc.) to obtain the second analysis content.

[0024] The collaborative control module is used to coordinate and regulate the aquaponics system based on the analysis content through automatic control equipment (such as water pumps, heaters, aerators, or targeted ultraviolet sterilization devices), while responding to environmental fluctuations and biological anomalies.

[0025] In one embodiment, the present invention provides an environmental collaborative control digital twin system, wherein the analysis content acquisition module includes:

[0026] The environmental analysis unit is used to run simulation algorithms through dynamic digital models to calculate and simulate trends of multi-dimensional data (especially environmental parameters such as water temperature, pH value, dissolved oxygen, and ammonia nitrogen concentration) collected in real time and stored in history. It combines the constraints defined by the principle of ecological balance (such as the suitable temperature range of nitrifying bacteria community and the optimal pH range for plant nutrient absorption) and refers to the state of the aquaponics system reflected by real-time biological information (such as microbial community structure and plant growth rate) to dynamically deduce the optimal target range of various environmental parameters that can most effectively maintain the ecological balance among animals, plants, and microorganisms under the current state, and obtain the first analysis content.

[0027] The Invasive Organism Analysis Unit is used to control the dynamic digital model to initiate a dynamic simulation of biological competition when the monitored abnormal biological signals (monitored through image recognition or environmental DNA analysis) are identified as invasive organisms (such as alien algae, competitive aquatic organisms, or pathogenic microorganisms). Based on the principles of species interaction relationships (competition, predation, parasitism, etc.) in the ecological knowledge base, it simulates the impact path and degree of the invasive organism on the ecological balance of existing animals, plants, and microorganisms, determines the threat level of the invasive organism, the possible chain ecological reactions it may trigger, generates an assessment conclusion containing response strategy recommendations, and obtains the second analysis content.

[0028] The fish abnormal behavior analysis unit is used to cross-reference all environmental parameter records during the period when monitored biological abnormal signals (e.g., through image recognition) are identified as abnormal fish behavior (fish may exhibit agitation, refusal to eat, or mutual aggression due to environmental stress, such as excessive light or human disturbance). If abnormal environmental parameters are found (automatic environmental control malfunctions, such as a sudden drop in dissolved oxygen or excessive ammonia nitrogen concentration), it is determined to be due to environmental factors. If all environmental parameters are within the optimal range and there are no other abnormalities, it is initially inferred to be caused by non-systematic environmental factors (such as human fright or operational interference). For non-systematic environmental factors, automatic control is not activated. If the abnormal fish behavior does not subside or disappear on its own after a set time, the initial inference is considered a misjudgment, and a secondary inference is made to identify unidentified potential environmental factors or persistent stressors. This specific set of environmental parameters and the continuous abnormal behavior sequence are recorded through a dynamic digital model to obtain the second analysis content. The knowledge base of the dynamic digital model regarding the relationship between fish behavior and the environment is updated through the second analysis content.

[0029] In one embodiment, the present invention provides an environmental collaborative control digital twin system, which further includes:

[0030] The energy consumption optimization module is used to integrate external energy data (such as time-of-use electricity prices and future weather forecasts), calculate and predict the energy consumption demand of the aquaponics system in the future (such as the operating load of water pumps, heaters, and aerators), and formulate the optimal energy consumption scheduling strategy.

[0031] In one embodiment, the present invention provides an environmental collaborative control digital twin system, which further includes:

[0032] The cost-optimal adjustment module integrates external energy data (such as time-of-use electricity prices and future weather forecasts) and combines it with environmental parameters from multi-dimensional data of the aquaponics system to perform cross-timescale resource planning. It simulates the thermodynamic and hydrodynamic changes of the aquaponics system and fluctuations in external energy data over a future period, and calculates the lowest-cost environmental parameter control path. All decisions are based on a preset range of environmental parameter fluctuations that is permissible to ensure ecological security.

[0033] In one embodiment, the present invention provides an environmental collaborative control digital twin system, which further includes:

[0034] The optimal survival rate adjustment module is used to simulate the natural change trajectory of environmental parameters (with water temperature as the core) during the entire power outage period when a power outage is expected to occur, based on the predicted duration of the power outage and the thermodynamic and hydrodynamic characteristics of the aquaponics system. The module uses the estimated survival rate of the highest priority biological group (such as fish) at the time of power recovery as the highest optimization index to calculate an environmental parameter that must be achieved before the power outage and then performs environmental parameter adjustments.

[0035] Compared with existing technologies, the beneficial effects of this invention are as follows: Compared with traditional management that relies on human experience, this invention constructs a dynamic digital model, realizes real-time mapping and interaction between the physical system and the virtual model, transforms fuzzy experience judgments into precise analysis based on multi-dimensional data, and can dynamically infer optimal environmental parameters and intelligently diagnose biological anomalies through simulation algorithms and ecological knowledge bases. Finally, it can achieve coordinated regulation through automatic control equipment, which completely changes the limitations of traditional management and truly achieves all-weather precise regulation and risk warning. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the first part of the digital twin method for coordinated environmental regulation provided in an embodiment of the present invention.

[0037] Figure 2 This is a flowchart illustrating the multi-dimensional data analysis process provided in an embodiment of the present invention.

[0038] Figure 3 This is a schematic diagram of the second part of the digital twin method for coordinated environmental regulation provided in an embodiment of the present invention.

[0039] Figure 4 This is a schematic diagram of the third part of the digital twin method for coordinated environmental regulation provided in an embodiment of the present invention.

[0040] Figure 5 This is a schematic diagram of the fourth part of the digital twin method for coordinated environmental regulation provided in an embodiment of the present invention.

[0041] Figure 6 This is a schematic diagram of the first part of the digital twin system for coordinated environmental control provided in an embodiment of the present invention.

[0042] Figure 7 This is a schematic diagram of the analysis content acquisition module provided in an embodiment of the present invention.

[0043] Figure 8 This is a schematic diagram of the second part of the digital twin system for coordinated environmental control provided in an embodiment of the present invention.

[0044] Figure 9 This is a schematic diagram of the third part of the digital twin system for coordinated environmental control provided in an embodiment of the present invention.

[0045] Figure 10 This is a schematic diagram of the fourth part of the digital twin system for coordinated environmental control provided in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0047] It is understood that the terms "first," "second," etc., used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of this application, a first script may be referred to as a second script, and similarly, a second script may be referred to as a first script.

[0048] In one embodiment, such as Figure 1 As shown, the digital twin method for coordinated environmental regulation includes the following steps:

[0049] Step S1: Based on digital twin technology, a dynamic digital model of the aquaponics system is constructed in virtual space. The dynamic digital model replicates all components of the aquaponics system (including aquaculture ponds, planting areas, and water circulation and treatment devices, as well as the interactions between animals, plants, and microorganisms). The dynamic digital model can continuously evolve, integrating newly emerging biological information (such as invasive species or competitors detected through image recognition or environmental DNA analysis) through an ecological knowledge base and simulation logic, thereby simulating the overall operating mechanism, ecological processes, and potential biological competition dynamics of the aquaponics system.

[0050] Step S2: Collect multi-dimensional data of the aquaponics system in real time (such as environmental parameters such as water temperature, pH value, dissolved oxygen, and ammonia nitrogen concentration, as well as biological information parameters such as fish behavior images, plant leaf status, and changes in aquatic microbial communities). Continuously transmit the multi-dimensional data to the dynamic digital model, so that the dynamic digital model can be updated in real time according to the latest status of the aquaponics system, realizing synchronous mapping and two-way interaction between the dynamic digital model and the aquaponics system.

[0051] Step S3: Utilize the dynamic digital model to run simulation algorithms to analyze multi-dimensional data, integrate ecological balance principles and real-time biological information, dynamically infer the optimal environmental data range required to maintain the ecological balance of animals, plants, and microorganisms, and obtain the first analysis content. Analyze and judge the monitored biological abnormal signals (such as the appearance of invasive species, competitors, abnormal fish behavior, etc.) to obtain the second analysis content.

[0052] Step S4: Based on the analysis, the aquaponics system is coordinated and regulated by automatic control equipment (such as water pumps, heaters, aerators, or targeted ultraviolet sterilization devices) to cope with environmental fluctuations and biological anomalies.

[0053] In one embodiment, such as Figure 2 As shown, in the digital twin method for coordinated environmental regulation, step S3, which involves using a dynamic digital model to run simulation algorithms to analyze multi-dimensional data, integrating ecological balance principles and real-time biological information, dynamically inferring the optimal environmental data range required to maintain the ecological balance of animals, plants, and microorganisms, and obtaining the first analysis content, and then analyzing and judging the monitored abnormal biological signals to obtain the second analysis content, specifically includes:

[0054] Step S31: The simulation algorithm is run through a dynamic digital model to calculate and simulate the trends of multi-dimensional data (especially environmental parameters such as water temperature, pH value, dissolved oxygen, and ammonia nitrogen concentration) collected in real time and stored in history. Combined with the constraints defined by the principle of ecological balance (such as the suitable temperature range of nitrifying bacteria community and the optimal pH range for plant nutrient absorption), and referring to the state of the aquaponics system reflected by real-time biological information (such as microbial community structure and plant growth rate), the optimal target range of various environmental parameters that can most effectively maintain the ecological balance of animals, plants and microorganisms under the current state is dynamically deduced to obtain the first analysis content.

[0055] Step S32: When the monitored biological anomaly signal (monitored by image recognition or environmental DNA analysis) is determined to be an invasive organism (such as alien algae, competitive aquatic organisms or pathogenic microorganisms), the dynamic digital model is controlled to start the biological competition dynamic simulation. Based on the principles of species interaction relationships (competition, predation, parasitism, etc.) in the ecological knowledge base, the impact path and degree of the invasive organism on the ecological balance of existing animals, plants and microorganisms are simulated. The threat level of the invasive organism and the possible chain ecological reactions are determined. An assessment conclusion containing response strategy suggestions is generated to obtain the second analysis content.

[0056] Step S33: When the monitored biological abnormal signal (e.g., through image recognition) is determined to be abnormal fish behavior (fish may exhibit agitation, refusal to eat, or mutual aggression due to environmental stress, such as excessive light or human disturbance), cross-compare all environmental parameter records during the period in which the abnormal fish behavior occurs. If abnormal environmental parameters are found (automatic environmental control malfunction, such as a sudden drop in dissolved oxygen or excessive ammonia nitrogen concentration), it is determined to be due to environmental factors. If all environmental parameters are within the optimal range and there are no other abnormalities, it is initially inferred to be caused by non-systematic environmental factors (such as human fright or operational interference). For non-systematic environmental factors, automatic control is not activated. If the abnormal fish behavior does not slow down or disappear on its own after a set time, the initial inference is considered a misjudgment, and a secondary inference is made to identify unidentified potential environmental factors or persistent stressors. This set of specific environmental parameters and the continuous abnormal behavior sequence are recorded through a dynamic digital model to obtain the second analysis content. The knowledge base of the dynamic digital model regarding the relationship between fish behavior and the environment is updated through the second analysis content.

[0057] In step S4, the first analysis content generated in step S31 (i.e., the optimal environmental data range required to maintain ecological balance) and the second analysis content generated in steps S32 and S33 (i.e., the judgment and strategies for various biological anomalies) are comprehensively calculated to generate a coordinated regulation strategy. This strategy aims to simultaneously correct environmental parameter deviations and suppress biological anomalies, while ensuring that various control commands are coordinated and avoid conflicts. Subsequently, the system drives the corresponding automatic control equipment to perform precise operations through the instruction set:

[0058] To address environmental fluctuations: Real-time environmental parameters are compared with the optimal environmental data range deduced in step S31. If a deviation occurs, control commands are immediately issued. For example, when dissolved oxygen is below the optimal range, aerators are activated; when water temperature is abnormal, heaters or cooling devices are adjusted; when an increase in ammonia nitrogen concentration is detected, water pumps are coordinated to increase the water circulation rate to accelerate microbial decomposition and plant absorption. These control actions aim to quickly stabilize environmental parameters within the optimal range, laying the foundation for the ecological balance of animals, plants, and microorganisms.

[0059] Addressing biological anomalies: For invasive species or competitors identified in step S32, a pre-set biological inhibition program will be activated. For example, if a large-scale proliferation of harmful algae is detected, the model will adjust the operating time of the water circulation filter in the instructions and may activate a specific wavelength of ultraviolet sterilization device to precisely inhibit algae growth without affecting beneficial microorganisms.

[0060] For the persistent abnormal fish behavior ultimately determined in step S33 to be caused by potential environmental factors, the dynamic digital model will invoke the new knowledge learned from this case. It will incorporate the recorded "abnormal behavior-environmental parameter" correlation data into the real-time control logic, not only striving to adjust the current environment to the general optimal range, but also making fine adjustments to avoid the identified specific parameter combinations that will cause stress to the fish, thereby achieving a fundamental resolution of biological abnormalities.

[0061] In one embodiment, such as Figure 3 As shown, the digital twin method for coordinated environmental regulation also includes:

[0062] Step S5: Integrate external energy data (such as time-of-use electricity prices and future weather forecasts), calculate and predict the energy consumption demand of the aquaponics system in the future (such as the operating load of water pumps, heaters, and aerators), and formulate the optimal energy consumption scheduling strategy.

[0063] For example, water can be heated or circulation increased during off-peak electricity periods, while during peak periods, the system's thermal inertia and the water's buffering capacity are relied upon to minimize operating costs while ensuring that environmental parameters do not exceed permissible fluctuation ranges. This allows the aquaponics system to maintain ecological balance while achieving refined and low-cost management of energy use.

[0064] In one embodiment, such as Figure 4 As shown, the digital twin method for coordinated environmental regulation also includes:

[0065] Step S6: Integrate external energy data (such as time-of-use electricity prices and future weather forecasts) and combine them with environmental parameters from multi-dimensional data of the aquaponics system to perform cross-timescale resource planning. Simulate the thermodynamic and hydrodynamic changes of the aquaponics system and fluctuations in external energy data over a future period to calculate the lowest-cost environmental parameter regulation path. All decisions are based on a preset range of environmental parameter fluctuations that is permissible to ensure ecological security.

[0066] By storing energy in advance during low-price periods or reducing energy consumption during high-price periods, the operating status of equipment can be dynamically adjusted to ensure that environmental parameters do not exceed safe thresholds at any time, while achieving overall optimization of energy efficiency.

[0067] For example, if a drop in nighttime temperature is predicted and electricity prices are low, even if the current water temperature is at the midpoint of its optimal range, it may instruct the heaters to start earlier, raising the water temperature to near the upper limit of the safety threshold to utilize the low-cost electricity to store heat energy. Thus, when electricity prices rise, even with reduced heating, thermal inertia can maintain the water temperature within the safety threshold. This scheduling is a predictive control based on cost and physical laws, always constraining all environmental parameters of the aquaponics system within the range of fluctuations allowed for ecological safety, thereby achieving optimal economic efficiency.

[0068] In one embodiment, such as Figure 5 As shown, the digital twin method for coordinated environmental regulation also includes:

[0069] Step S7: When it is known that a power outage will occur in the future, based on the expected duration of the power outage and combined with the thermodynamic and hydrodynamic characteristics of the aquaponics system, simulate the natural change trajectory of environmental parameters (with water temperature as the core) during the entire power outage period. The estimated survival rate of the highest priority biological group (such as fish) when the power outage is restored is the highest optimization index. Calculate an environmental parameter that must be achieved before the power outage and perform environmental parameter adjustments.

[0070] For example, when it is known that a power outage will last for 12 hours, and the ambient temperature will drop continuously from the suitable point of 25°C, conventional strategies are ineffective. Emergency calculations are initiated, retrieving survival rate data for fish and plants under different low-temperature intensities and durations from the knowledge base, and weighing the pros and cons. The calculations may reveal that if the water temperature is only preheated to the normal safe upper limit (e.g., 28°C) before the power outage, the water temperature will drop to 10°C after the power outage, leading to mass fish mortality. However, if an overshoot is performed before the power outage, actively raising the water temperature and temporarily exceeding the safe constraint threshold to 32°C (this temperature, while causing short-term stress responses such as slowed growth, will not immediately lead to death), the water temperature will only drop to 15°C after the power outage, resulting in a much higher fish survival rate than the former approach. For nitrifying microorganisms that may be partially damaged at this high temperature, this loss is recorded, and a plan is made to automatically add compound microbial agents to quickly replenish and repair the microbial community after power is restored. Ultimately, this plan will be implemented, heating the water to 32°C before the power outage. This will prioritize the survival of core populations of higher organisms such as fish, at the cost of short-term, repairable microbial activity, thereby maximizing the overall survival probability of the system.

[0071] In one embodiment, such as Figure 6 As shown, the environmental collaborative control digital twin system includes:

[0072] The dynamic digital model construction module 1 is used to construct a dynamic digital model of an aquaponics system in virtual space based on digital twin technology. The dynamic digital model replicates all components of the aquaponics system (including aquaculture ponds, planting areas, and water circulation and treatment devices, as well as the interactions between animals, plants, and microorganisms). The dynamic digital model can continuously evolve and integrate newly emerging biological information (such as invasive species or competitors detected through image recognition or environmental DNA analysis) with an ecological knowledge base and simulation logic, thereby simulating the overall operating mechanism, ecological processes, and potential biological competition dynamics of the aquaponics system.

[0073] The dynamic digital model real-time update module 2 is used to collect multi-dimensional data of the aquaponics system in real time (such as environmental parameters such as water temperature, pH value, dissolved oxygen, and ammonia nitrogen concentration, as well as biological information parameters such as fish behavior images, plant leaf status, and changes in aquatic microbial communities). The multi-dimensional data is continuously transmitted to the dynamic digital model, so that the dynamic digital model can be updated in real time according to the latest status of the aquaponics system, realizing synchronous mapping and two-way interaction between the dynamic digital model and the aquaponics system.

[0074] The analysis content acquisition module 3 is used to run simulation algorithms using dynamic digital models to analyze multi-dimensional data, integrate ecological balance principles and real-time biological information, dynamically infer the optimal environmental data range required to maintain the ecological balance of animals, plants and microorganisms, and obtain the first analysis content. It also analyzes and judges the monitored biological abnormal signals (such as the appearance of invasive species, competitors, abnormal fish behavior, etc.) to obtain the second analysis content.

[0075] The collaborative regulation module 4 is used to coordinate and regulate the aquaponics system based on the analysis content through automatic control equipment (such as water pumps, heaters, aerators, or targeted ultraviolet sterilization devices), while responding to environmental fluctuations and biological anomalies.

[0076] First, an evolving dynamic digital model is created in the dynamic digital model construction module 1 as a virtual brain. Then, the real-time update module 2 enables real-time data synchronization and interaction between the physical system and the virtual model, providing a foundation for analysis. Next, the analysis content acquisition module 3 comprehensively analyzes multi-dimensional data to generate control criteria. Finally, the collaborative control module 4 uses automated control equipment for regulation. This upgrades the traditional decentralized management that relies on manual experience into a comprehensive intelligent system that can operate automatically around the clock, deeply perceive and simultaneously coordinate and handle environmental fluctuations and biological anomalies, thereby significantly improving the accuracy, timeliness, and overall stability of aquaponics system management.

[0077] In one embodiment, such as Figure 7 As shown, the environmental collaborative control digital twin system, analysis content acquisition module 3 includes:

[0078] The environmental analysis unit 31 is used to run simulation algorithms through a dynamic digital model to calculate and simulate trends of multi-dimensional data (especially environmental parameters such as water temperature, pH value, dissolved oxygen, and ammonia nitrogen concentration) collected in real time and stored in history. It combines the constraints defined by the principle of ecological balance (such as the suitable temperature range of nitrifying bacteria community and the optimal pH range for plant nutrient absorption) and refers to the state of the aquaponics system reflected by real-time biological information (such as microbial community structure and plant growth rate) to dynamically deduce the optimal target range of various environmental parameters that can most effectively maintain the ecological balance among animals, plants, and microorganisms under the current state, and obtain the first analysis content.

[0079] Invasive organism analysis unit 32 is used to control the dynamic digital model to start a dynamic simulation of biological competition when the monitored abnormal biological signals (monitored by image recognition or environmental DNA analysis) are identified as invasive organisms (such as alien algae, competitive aquatic organisms or pathogenic microorganisms). Based on the principles of species interaction relationships (competition, predation, parasitism, etc.) in the ecological knowledge base, it simulates the impact path and degree of invasive organisms on the ecological balance of existing animals, plants and microorganisms, judges the threat level of invasive organisms and the possible chain ecological reactions, generates an assessment conclusion containing response strategy suggestions, and obtains the second analysis content.

[0080] The fish abnormal behavior analysis unit 33 is used to cross-compare all environmental parameter records during the period when the monitored biological abnormal signals (e.g., through image recognition) are identified as fish abnormal behavior (fish may exhibit agitation, refusal to eat, and mutual aggression due to environmental stress, such as excessive light or human disturbance). If abnormal environmental parameters are found (automatic environmental control malfunction, such as a sudden drop in dissolved oxygen or excessive ammonia nitrogen concentration), it is determined to be due to environmental factors. If all environmental parameters are within the optimal range and there are no other abnormalities, it is initially inferred to be caused by non-systematic environmental factors (such as human fright or operational interference). For non-systematic environmental factors, automatic control is not activated. If the fish abnormal behavior does not slow down or disappear on its own after a set time, the initial inference is considered a misjudgment, and a secondary inference is made to identify unidentified potential environmental factors or persistent stressors. This set of specific environmental parameters and the continuous abnormal behavior sequence are recorded through a dynamic digital model to obtain the second analysis content. The knowledge base of the dynamic digital model regarding the relationship between fish behavior and the environment is updated through the second analysis content.

[0081] The analysis content acquisition module 3 is further refined with specialized division of labor, establishing dedicated analysis and judgment paths for different types of disturbance sources within the aquaponics system. The environmental analysis unit 31 focuses on maintaining the homeostasis of the aquaponics system, determining the optimal environmental target through quantitative deduction; the invasive organism analysis unit 32 addresses explicit biological invasions, predicting their ecological impact through simulation; and the abnormal fish behavior analysis unit 33 is responsible for diagnosing latent stresses and possesses the ability to learn from misjudgments and self-correct.

[0082] In one embodiment, such as Figure 8 As shown, the environmental collaborative control digital twin system also includes:

[0083] The energy consumption optimization adjustment module 5 is used to integrate external energy data (such as time-of-use electricity prices and future weather forecasts), calculate and predict the energy consumption demand of the aquaponics system in the future (such as the operating load of water pumps, heaters, and aerators), and formulate the optimal energy consumption scheduling strategy.

[0084] The energy consumption optimization adjustment module 5 incorporates external data such as time-of-use electricity pricing, using energy cost as the core optimization objective to achieve peak shaving and valley filling of energy consumption. Its advantage lies in reducing electricity costs and achieving low-cost automated management while absolutely ensuring the normal operation of the aquaponics system.

[0085] In one embodiment, such as Figure 9 As shown, the environmental collaborative control digital twin system also includes:

[0086] The cost-optimal adjustment module 6 is used to integrate external energy data (such as time-of-use electricity prices and future weather forecasts) and combine them with environmental parameters of multi-dimensional data of the aquaponics system to perform cross-time scale resource planning. It simulates the thermodynamic and hydrodynamic changes of the aquaponics system and fluctuations of external energy data in the future period, and calculates the lowest-cost environmental parameter regulation path. All decisions are based on a preset range of environmental parameter fluctuations that is allowed to ensure ecological security.

[0087] The cost-optimal adjustment module 6 deeply integrates ecological constraints with economic goals. Its core benefit is that it can plan a cost-optimal long-term operating path, maximizing energy efficiency and economic benefits while ensuring ecological security.

[0088] In one embodiment, such as Figure 10 As shown, the environmental collaborative control digital twin system also includes:

[0089] The optimal survival rate adjustment module 7 is used to simulate the natural change trajectory of environmental parameters (with water temperature as the core) during the entire power outage period when it is known that a power outage will occur in the future, based on the expected duration of the power outage and the thermodynamic and hydrodynamic characteristics of the aquaponics system. The module calculates an environmental parameter that must be achieved before the power outage and performs environmental parameter adjustment, with the estimated survival rate of the highest priority biological group (such as fish) at the time of power recovery as the highest optimization index.

[0090] After the survival rate optimal adjustment module 7 is activated and power is restored, whether to activate the energy consumption optimal adjustment module 5, the cost optimal adjustment module 6, or directly return to the most suitable environment depends on the actual state and external conditions of the aquaponics system at this time.

[0091] After power is restored, immediately conduct a comprehensive assessment of your own condition and the external environment:

[0092] If the assessment finds that the survival rate optimization adjustment module 7 has been executed effectively, the highest priority biological population is stable, all environmental parameters, although deviating from the optimal environment, are within normal safety constraints, and there are no drastic external weather changes or significant energy price fluctuations in the near future, then the energy consumption optimization adjustment module 5 will be activated. The primary task at this point is to adjust the system's environmental parameters back to the optimal range from the current state at the lowest cost and smoothly, while ensuring safety.

[0093] If the assessment finds that environmental parameters are within safe limits, but significant external disturbances are present or predicted (e.g., a cold wave / heat wave has not yet ended), activate the cost-optimal adjustment module 6. This is because resource planning across time scales is required at this point to calculate a cost-optimal regression path, rather than simply performing cost optimization at the current moment.

[0094] If the assessment finds that the environmental parameters are very close to or within the optimal environmental range after power restoration, and the external conditions are stable, the control mode will be directly switched to and maintained in the optimal environment. This is an ideal state, meaning that the contingency plan of the optimal survival rate adjustment module 7 is very successful, the power outage has almost no substantial disturbance to the system, and therefore there is no need to activate any economic scheduling strategy, directly providing the best conditions for biological growth.

[0095] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0096] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0097] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0099] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A digital twin method for coordinated environmental regulation, characterized in that, This environmental collaborative regulation digital twin method includes the following steps: Based on digital twin technology, a dynamic digital model of an aquaponics system is constructed in virtual space. The dynamic digital model replicates all components of the aquaponics system. The dynamic digital model can continuously evolve and integrate newly emerging biological information through an ecological knowledge base and simulation logic, thereby simulating the overall operation mechanism, ecological processes, and potential biological competition dynamics of the aquaponics system. Real-time collection of multi-dimensional data from the aquaponics system and continuous transmission of this data to the dynamic digital model enable the dynamic digital model to be updated in real time according to the latest status of the aquaponics system, achieving synchronous mapping and two-way interaction between the dynamic digital model and the aquaponics system. By using a dynamic digital model to run simulation algorithms to analyze multi-dimensional data, and combining the principles of ecological balance with real-time biological information, the optimal range of environmental data required to maintain the ecological balance of animals, plants and microorganisms is dynamically inferred to obtain the first analysis content. The monitored abnormal biological signals are then analyzed and judged to obtain the second analysis content. Based on the analysis, the aquaponics system is coordinated and regulated by automatic control equipment to cope with environmental fluctuations and biological anomalies.

2. The digital twin method for coordinated environmental regulation according to claim 1, characterized in that, The steps of using a dynamic digital model to run simulation algorithms to analyze multi-dimensional data, integrating ecological balance principles and real-time biological information, dynamically inferring the optimal environmental data range required to maintain the ecological balance of animals, plants, and microorganisms to obtain the first analysis content, and analyzing and judging the monitored abnormal biological signals to obtain the second analysis content, specifically include: By running simulation algorithms through dynamic digital models, calculations and trend simulations are performed on multi-dimensional data collected in real time and stored in history. Combined with the constraints defined by the principle of ecological balance and referring to the state of the aquaponics system reflected by real-time biological information, the optimal target range of various environmental parameters that can most effectively maintain the ecological balance of animals, plants and microorganisms under the current state is dynamically deduced to obtain the first analysis content. When the monitored abnormal biological signals are identified as invasive organisms, the dynamic digital model is activated to simulate biological competition. Based on the principles of species interaction in the ecological knowledge base, the model simulates the impact path and extent of the invasive organism on the ecological balance of existing animals, plants, and microorganisms, determines the threat level of the invasive organism and the possible chain ecological reactions, generates an assessment conclusion containing suggestions for response strategies, and obtains the second analysis content. When the monitored biological abnormal signals are identified as abnormal fish behavior, all environmental parameter records during the period in which the abnormal fish behavior occurred are cross-referenced. If abnormal environmental parameters are found, it is determined that the behavior is influenced by environmental factors. If all environmental parameters are within the optimal range and there are no other abnormalities, it is initially inferred that the behavior is caused by non-systematic environmental factors. For non-systematic environmental factors, automatic regulation is not initiated. If the abnormal fish behavior does not slow down or disappear on its own after a set time, the initial inference is considered a misjudgment, and a secondary inference is made that it is an unidentified potential environmental factor or a persistent stressor. This set of specific environmental parameters and the continuous abnormal behavior sequence are recorded through a dynamic digital model to obtain the second analysis content. The knowledge base of the dynamic digital model regarding the relationship between fish behavior and the environment is updated through the second analysis content.

3. The digital twin method for coordinated environmental regulation according to claim 1, characterized in that, Also includes: By integrating external energy data, we can calculate and predict the energy consumption demand of the aquaponics system in the future and formulate the optimal energy consumption scheduling strategy.

4. The digital twin method for coordinated environmental regulation according to claim 1, characterized in that, Also includes: By integrating external energy data and combining it with environmental parameters from multi-dimensional data of the aquaponics system, resource planning is carried out across time scales. The thermodynamic and hydrodynamic changes of the aquaponics system and fluctuations in external energy data over a period of time are simulated, and a path for regulating environmental parameters with the lowest cost is calculated. All decisions are based on a preset range of environmental parameter fluctuations that is allowed to ensure ecological security.

5. The digital twin method for coordinated environmental regulation according to claim 3 or 4, characterized in that, Also includes: When a power outage is anticipated, the natural trajectory of environmental parameters during the entire power outage is simulated based on the expected duration of the outage and the thermodynamic and hydrodynamic characteristics of the aquaponics system. The estimated survival rate of the highest priority biological population at the time of power recovery is used as the highest optimization index. An environmental parameter that must be achieved before the power outage is then calculated and environmental parameter adjustments are performed.

6. A digital twin system for coordinated environmental control, characterized in that, include: The dynamic digital model construction module is used to construct a dynamic digital model of an aquaponics system in virtual space based on digital twin technology. The dynamic digital model replicates all components of the aquaponics system. The dynamic digital model can continuously evolve and integrate newly emerging biological information through an ecological knowledge base and simulation logic to simulate the overall operation mechanism, ecological processes, and potential biological competition dynamics of the aquaponics system. The dynamic digital model real-time update module is used to collect multi-dimensional data of the aquaponics system in real time and continuously transmit the multi-dimensional data to the dynamic digital model, so that the dynamic digital model can be updated in real time according to the latest status of the aquaponics system, realizing synchronous mapping and two-way interaction between the dynamic digital model and the aquaponics system. The analysis content acquisition module is used to run simulation algorithms using dynamic digital models to analyze multi-dimensional data, integrate ecological balance principles and real-time biological information, dynamically infer the optimal environmental data range required to maintain the ecological balance of animals, plants and microorganisms, and obtain the first analysis content. It also analyzes and judges the monitored biological abnormal signals to obtain the second analysis content. The collaborative regulation module is used to coordinate and regulate the aquaponics system through automatic control equipment based on the analysis content, while responding to environmental fluctuations and biological anomalies.

7. The environmental collaborative control digital twin system according to claim 6, characterized in that, The content acquisition module includes: The environmental analysis unit is used to run simulation algorithms through dynamic digital models to calculate and simulate trends of multi-dimensional data collected in real time and stored in history. Combining the constraints defined by the principle of ecological balance and referring to the state of the aquaponics system reflected by real-time biological information, it dynamically deduces the optimal target range of various environmental parameters that can most effectively maintain the ecological balance of animals, plants and microorganisms under the current state, and obtains the first analysis content. The invasive organism analysis unit is used to control the dynamic digital model to start a dynamic simulation of biological competition when the monitored abnormal biological signal is identified as an invasive organism. Based on the principles of species interaction in the ecological knowledge base, it simulates the impact path and degree of the invasive organism on the ecological balance of existing animals, plants and microorganisms, judges the threat level of the invasive organism and the possible chain ecological reactions, generates an assessment conclusion containing response strategy suggestions, and obtains the second analysis content. The fish abnormal behavior analysis unit is used to cross-compare all environmental parameter records during the period when the monitored biological abnormal signals are identified as fish abnormal behavior. If abnormal environmental parameters are found, it is determined that the behavior is due to environmental factors. If all environmental parameters are within the optimal range and there are no other abnormalities, it is initially inferred that the behavior is caused by non-systematic environmental factors. For non-systematic environmental factors, automatic regulation is not initiated. If the fish abnormal behavior does not slow down or disappear on its own after a set time, the initial inference is considered a misjudgment, and a secondary inference is made that it is an unidentified potential environmental factor or a persistent stressor. The specific set of environmental parameters and the continuous abnormal behavior sequence are recorded through a dynamic digital model to obtain the second analysis content. The knowledge base of fish behavior and environmental correlation within the dynamic digital model is updated through the second analysis content.

8. The environmental collaborative control digital twin system according to claim 6, characterized in that, Also includes: The energy consumption optimization adjustment module is used to integrate external energy data, calculate and predict the energy consumption demand of the aquaponics system in the future, and formulate the optimal energy consumption scheduling strategy.

9. The environmental collaborative control digital twin system according to claim 6, characterized in that, Also includes: The cost-optimal adjustment module integrates external energy data and combines it with environmental parameters from multi-dimensional data of the aquaponics system to perform resource planning across time scales. It simulates the thermodynamic and hydrodynamic changes of the aquaponics system and fluctuations in external energy data over a future period, and calculates the lowest-cost environmental parameter regulation path. All decisions are based on a preset range of environmental parameter fluctuations that is permissible to ensure ecological security.

10. The environmental collaborative control digital twin system according to claim 8 or 9, characterized in that, Also includes: The survival rate optimization module is used to simulate the natural change trajectory of environmental parameters during the entire power outage period when a power outage is expected, based on the predicted duration of the power outage and the thermodynamic and hydrodynamic characteristics of the aquaponics system. The module calculates an environmental parameter that must be achieved before the power outage and performs environmental parameter adjustments, with the estimated survival rate of the highest priority biological population at the time of power recovery as the highest optimization index.

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