Remote management method, device and equipment for fish and vegetable symbiosis based on digital twinning

Abstract

The invention discloses a remote management method, device and equipment for fish and vegetable symbiosis based on digital twinning, and the method comprises the steps: obtaining the physical parameter data of a physical fish pond and a physical plant shed in a fish and vegetable symbiosis system, creating twin models corresponding to fish schools, fish ponds, hydroponic plants, plant sheds and physical equipment of the fish and vegetable symbiotic system based on the physical parameter data; acquiring fish school culture parameters and plant growth parameters acquired by a sensor and a camera deployed in the fish and vegetable symbiotic system in real time, and acquiring equipment operation parameters; analyzing according to the fish stock culture parameters, the plant growth parameters and the equipment operation parameters through a fish and vegetable symbiotic digital twinborn platform to obtain an analysis result; and triggering an early warning mechanism according to an analysis result, generating a regulation and control instruction, and dynamically adjusting the operation state of corresponding physical equipment in the fish and vegetable symbiotic system according to the regulation and control instruction, thereby realizing remote collaborative management. Remote management of the fish and vegetable symbiotic system can be realized, and the resource utilization rate and convenience are improved.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a remote management method, device and equipment for fish and vegetable symbiosis based on digital twin. Background Art

[0002] Fish-vegetable symbiosis is an ecological agricultural model that combines aquaculture with soilless cultivation. It provides nutrition to plants through fish excrement, which is then absorbed by plants and purified before returning to the fish pond, forming a closed-loop ecosystem of "fish-fertilized water → vegetable-purified water → water-raised fish", which has significant advantages in resource recycling. However, traditional fish-vegetable symbiosis systems are highly dependent on manual management, which is not only labor-intensive and inefficient, but also difficult to achieve precise linkage control and remote monitoring, and can easily lead to unstable system operation due to human negligence or lack of experience. Summary of the invention

[0003] In view of this, the purpose of the present invention is to propose a remote management method, device and equipment for fish and vegetable symbiosis based on digital twins, aiming to solve the problems of low efficiency and easy errors in manual management of traditional systems, and to achieve precise linkage control, remote monitoring and intelligent optimization.

[0004] To achieve the above object, the present invention provides a remote management method of aquaponics based on digital twins, the method comprising: Obtain physical parameter data of a physical fish pond and a physical plant shed in the aquaponics system, and create twin models corresponding to the fish school, fish pond, hydroponic plants, plant shed, and physical equipment of the aquaponics system based on the physical parameter data to obtain a digital twin platform for aquaponics; Real-time acquisition of fish farming parameters, plant growth parameters, and equipment operation parameters collected by sensors and cameras deployed in the aquaponics system, and upload to a cloud server; The fish-vegetable symbiosis digital twin platform performs analysis based on the fish culture parameters, plant growth parameters, and equipment operation parameters to obtain analysis results; According to the analysis results, an early warning mechanism is triggered and a control instruction is generated. According to the control instruction, the operating status of the corresponding physical equipment in the fish-vegetable symbiosis system is dynamically adjusted to realize remote collaborative management of fish farming, plant growth and equipment operation.

[0005] Preferably, the physical parameter data includes: The structural layout parameters of the physical fish pond, including the type of fish pond, layered design, size and shape of the fish pond, and the location of the fish pond; The layout parameters of the physical plant shed include the type of planting rack, the size and shape of the planting rack, and the location of the planting rack; Device information of physical devices, including device type, model, number, installation location, and device operating parameters.

[0006] Preferably, the step of creating twin models corresponding to fish schools, fish ponds, hydroponic plants, plant sheds, and physical equipment in the aquaponics system based on the physical parameter data includes: A three-dimensional model is constructed based on the structural layout parameters of the physical fish pond, and the environmental parameters of the fish pond are mapped to obtain a twin model of the fish pond including the fish school and the fish pond; A three-dimensional model is constructed based on the layout parameters of the physical plant shed, and the plant environment parameters are mapped to obtain a twin model of the plant shed including hydroponic plants and planting racks; Create virtual entities for physical equipment including sensors, cameras, water pumps, aerators, ultraviolet sterilizers, microfiltration machines, fill lights, and nutrient solution mixing devices, and synchronize the device information of the physical equipment to obtain a device twin model.

[0007] Preferably, the fish breeding parameters include fish pond water temperature, dissolved oxygen concentration, water quality parameters, pH value, and fish image data; the plant growth parameters include plant shed temperature, humidity, light intensity, carbon dioxide concentration, nutrient solution pH value, and plant image data; the equipment operation parameters include water pump flow, aerator power, ultraviolet sterilizer status, microfiltration machine working cycle, fill light spectrum parameters, ventilation window opening and closing angles, and nutrient solution mixing ratio.

[0008] Preferably, the analysis performed by the aquaponics digital twin platform according to the fish farming parameters, plant growth parameters and equipment operation parameters includes: When the fish pond environmental parameters meet the preset growth environment range, the fish health status of fish in different fish ponds is analyzed and the growth trend of fish is predicted based on the fish image data combined with the feeding amount, wherein the fish pond environmental parameters include fish pond water temperature, dissolved oxygen concentration, water quality parameters, and pH value; When the plant environmental parameters meet the preset growth environment range, the plant growth status of hydroponic plants in different planting racks is analyzed and the plant growth trend and yield are predicted based on the plant image data combined with the biological characteristics of the plants, wherein the plant environmental parameters include plant shed temperature, humidity, light intensity, carbon dioxide concentration, and nutrient solution pH value; When there is abnormal growth in the health status of fish or plant growth, the historical disease database is called for matching to obtain a treatment plan; The operating parameters of the equipment are analyzed to detect whether the physical equipment has equipment abnormalities, obtain an abnormality treatment plan, and use the treatment plan and / or the abnormality treatment plan as the analysis result.

[0009] Preferably, triggering the early warning mechanism according to the analysis results includes: The target abnormal area is marked with a red floating window in the fish-vegetable symbiosis digital twin platform, and the twin models of the corresponding fish schools, hydroponic plants and / or physical equipment are displayed as red holographic images.

[0010] Preferably, dynamically adjusting the operating state of corresponding physical equipment in the aquaponics system according to the control instructions includes: When the analysis result indicates that the dissolved oxygen concentration should be adjusted, the aerator is started and the power is adjusted; When the analysis result indicates that the water quality parameters need to be adjusted, the microfiltration machine is started for circulating filtration, the ultraviolet sterilizer is turned on, and the water pump is started and the flow rate is adjusted; When the analysis result indicates to adjust the lighting, adjust the intensity of the supplementary light and the spectrum to the wavelength band required by the plants; When the analysis result indicates that the temperature should be adjusted, adjusting the opening and closing angle of the ventilation window; When the analysis result indicates that the carbon dioxide concentration should be adjusted, a ventilation device or a supplementary air source is activated; When the analysis result indicates that the pH value of the nutrient solution should be adjusted, the nutrient solution mixing device is started and the mixing ratio is adjusted.

[0011] To achieve the above object, the present invention also provides a remote management device for fish and vegetable symbiosis based on digital twins, the device comprising: A model building unit, used to obtain physical parameter data of a physical fish pond and a physical plant shed in the aquaponics system, and create twin models corresponding to fish schools, fish ponds, hydroponic plants, plant sheds, and physical equipment in the aquaponics system based on the physical parameter data to obtain a digital twin platform for aquaponics; A data acquisition unit, used to acquire fish farming parameters, plant growth parameters, and equipment operation parameters collected by sensors and cameras deployed in the aquaponics system in real time, and upload them to a cloud server; A data analysis unit, configured to analyze the fish farming parameters, plant growth parameters, and equipment operation parameters through the aquaponics digital twin platform to obtain analysis results; The remote management unit is used to trigger the early warning mechanism and generate control instructions according to the analysis results, dynamically adjust the operating status of the corresponding physical equipment in the fish-vegetable symbiosis system according to the control instructions, and realize remote collaborative management of fish farming, plant growth and equipment operation.

[0012] In order to achieve the above-mentioned objectives, the present invention also proposes a remote management device for fish and vegetable symbiosis based on digital twins, comprising a processor, a memory, and a computer program stored in the memory, wherein the computer program is executed by the processor to implement the steps of a remote management method for fish and vegetable symbiosis based on digital twins as described in the above-mentioned embodiment.

[0013] In order to achieve the above-mentioned purpose, the present invention also proposes a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the steps of a remote management method of fish and vegetable symbiosis based on digital twins as described in the above embodiment.

[0014] In order to achieve the above-mentioned objectives, the present invention also proposes a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of a remote management method of fish and vegetable symbiosis based on digital twins as described in the above-mentioned embodiment.

[0015] Beneficial effects: The above scheme, by building a fish-vegetable symbiotic digital twin platform to integrate fish farming, plant growth and equipment operation data, can realize remote collaborative management of the fish-vegetable symbiotic system, dynamically adjust the equipment operation status through analysis results, ensure stable operation of the system, and reduce losses caused by abnormal parameters. In addition, based on the analysis and regulation of real-time data, it can more accurately meet the growth needs of fish and plants and avoid waste of resources, thereby improving resource utilization efficiency, management efficiency and convenience, and reducing manual inspection costs. It is especially suitable for large-scale farms or unmanned scenarios.

[0016] Clarifying the detailed parameter information of the physical fish pond, physical plant shed, and physical equipment provides a basis for building an accurate twin model, so that the digital twin platform can more realistically reflect the state and characteristics of the physical system, thereby improving the accuracy of management. Then, based on the corresponding parameter information, a three-dimensional model is constructed and the environmental parameters are mapped, and virtual entities are established for physical equipment, which can accurately simulate the various components of the fish-vegetable symbiotic system, provide detailed virtual model support for subsequent analysis and regulation, improve the credibility of virtual simulation, and ensure that the virtual platform is dynamically consistent with the physical system.

[0017] It covers a full range of parameters for fish farming, plant growth and equipment operation, and realizes comprehensive monitoring of the operating status of the fish-vegetable symbiotic system. It provides a rich data basis for subsequent precise analysis and regulation, and can timely capture various changes and problems in the system. Moreover, different types of parameter data can reflect the operating status of the system from multiple dimensions, such as the health status of fish, the growth trend of plants, the operating efficiency of equipment, etc., which is helpful for comprehensive analysis and evaluation and the formulation of more reasonable management strategies.

[0018] By analyzing the environmental parameters of fish ponds and plant sheds, combined with image data and biological characteristics, it is possible to accurately analyze the health status and growth trends of fish populations, as well as the growth status and yield forecasts of plants, providing a strong basis for scientific management and making production plans and resource allocations in advance. When there are growth abnormalities or equipment abnormalities, it is possible to call up the historical disease database or perform equipment abnormality detection to obtain corresponding treatment plans and abnormality handling plans, and take timely measures to solve problems, reduce losses, and ensure the normal operation of the system.

[0019] The target abnormal area is marked with a red floating window, and the corresponding twin model is displayed as a red holographic image, which can intuitively show the user the abnormal situation in the system, allowing the user to quickly locate the problem, improve the timeliness and efficiency of problem handling, and facilitate overall management and decision-making.

[0020] Automatically generate control instructions based on the analysis results, and dynamically adjust the operating status of physical equipment, realizing automatic control of equipment, reducing manual intervention, improving the accuracy and efficiency of management, and being able to quickly respond to various changes and needs in the system; then, specifically targeting different abnormal environmental parameters, take corresponding specific control measures, such as adjusting dissolved oxygen concentration, water quality parameters, light, temperature, carbon dioxide concentration and nutrient solution pH value, etc., which can finely control the growth environment of the fish-vegetable symbiotic system, provide more suitable growth conditions for fish and plants, and promote their healthy growth and high yield. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A flowchart of a remote management method for aquaponics based on digital twins is provided in accordance with an embodiment of the present invention.

[0023] Figure 2 This is a display effect diagram of a fish growth laboratory provided in one embodiment of the present invention.

[0024] Figure 3 This is a display effect diagram of a plant growth laboratory provided by an embodiment of the present invention.

[0025] Figure 4 A schematic structural diagram of a remote management device for fish and vegetable symbiosis based on digital twins provided in one embodiment of the present invention.

[0026] The realization of the purpose, functional features and advantages of the invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than 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 work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] The content of the present invention is described in detail below with reference to embodiments.

[0029] Reference Figure 1 The figure is a flow chart of a remote management method of aquaponics based on digital twins provided by an embodiment of the present invention.

[0030] In this embodiment, the method includes: S11, obtaining the physical parameter data of the physical fish pond and the physical plant shed in the aquaponic system, and creating twin models corresponding to the fish school, fish pond, hydroponic plants, plant shed and physical equipment of the aquaponic system based on the physical parameter data to obtain a digital twin platform for aquaponic system.

[0031] Furthermore, the physical parameter data includes: The structural layout parameters of the physical fish pond, including the type of fish pond, layered design, size and shape of the fish pond, and the location of the fish pond; The layout parameters of the physical plant shed include the type of planting rack, the size and shape of the planting rack, and the location of the planting rack; Device information of physical devices, including device type, model, number, installation location, and device operating parameters.

[0032] Further, in step S11, the twin models corresponding to the fish school, fish pond, hydroponic plants, plant shed and physical equipment of the aquaponics system are created based on the physical parameter data, including: S11-1, constructing a three-dimensional model based on the structural layout parameters of the physical fish pond, and mapping the environmental parameters of the fish pond to obtain a twin model of the fish pond including the fish school and the fish pond; S11-2, constructing a three-dimensional model based on the layout parameters of the physical plant shed, and mapping the plant environment parameters to obtain a twin model of the plant shed including hydroponic plants and planting racks; S11-3, establish virtual entities for physical equipment including sensors, cameras, water pumps, aerators, ultraviolet sterilizers, microfiltration machines, fill lights and nutrient solution mixing devices, and synchronize the device information of the physical equipment to obtain a device twin model.

[0033] In this embodiment, based on the fish, breeding equipment and environmental parameters of the fish-vegetable symbiotic system, as well as the hydroponic plants, planting equipment and environmental parameters, the corresponding twin model is constructed according to the proportion of the fish-vegetable symbiotic system and based on the Python framework, and the physical equipment and equipment twin model are matched by setting sequence numbers to build a fish-vegetable symbiotic digital twin platform. Figure 2 and Figure 3 As shown, the aquaponic digital twin platform includes aquaponic laboratory, which is divided into two floors. The first floor is a fish growth laboratory, and the second floor is a hydroponic plant growth laboratory. The fish growth laboratory on the first floor contains different types of fish ponds (including fish ponds, sedimentation tanks, square tanks, reservoirs, biochemical tanks), water pumps, control cabinets, microfiltration machines, aerators, ultraviolet sterilizers, sensors (including water temperature sensors, dissolved oxygen sensors, water quality detectors, humidity sensors, pressure sensors), and smart cameras; the hydroponic plant growth laboratory on the second floor contains different types of planting racks (including T-shaped cultivation racks, square cultivation racks), environmental monitors, plant fill lights, sensors (including pH sensors, light sensors, humidity sensors), and smart cameras.

[0034] By determining the location and number of different twin models in the aquaponics digital twin platform according to the relevant location information in the aquaponics system, the aquaponics digital twin platform can accurately simulate the details of the physical equipment in the real aquaponics system, further improving the accuracy and real-time performance of monitoring. A device file is further established for each physical device to record the type, model, specification, number and other information of the physical device for quick management and query, and to determine the fish pond and current location and movement trajectory of the virtual fish and the physical fish according to the GIS (geographic information system) of the fish pond, corresponding to different physical fish according to the fish mark. Different sensors are placed on each planting rack of the plant shed in the aquaponics system, and each sensor is used as the identification of the planting rack to facilitate recording the growth of plants on the planting rack, such as each planting rack is equipped with a three-color light and a smart camera.

[0035] S12, obtaining fish farming parameters, plant growth parameters, and equipment operation parameters collected by sensors and cameras deployed in the fish-vegetable symbiosis system in real time, and uploading them to a cloud server.

[0036] Furthermore, the fish breeding parameters include fish pond water temperature, dissolved oxygen concentration, water quality parameters, pH value, and fish image data; the plant growth parameters include plant shed temperature, humidity, light intensity, carbon dioxide concentration, nutrient solution pH value, and plant image data; the equipment operation parameters include water pump flow, aerator power, ultraviolet sterilizer status, microfiltration machine working cycle, fill light spectrum parameters, ventilation window opening and closing angle, and nutrient solution mixing ratio.

[0037] S13, analyzing the fish farming parameters, plant growth parameters and equipment operation parameters through the aquaponics digital twin platform to obtain analysis results.

[0038] Further, in step S13, the fish-vegetable symbiosis digital twin platform performs analysis based on the fish farming parameters, plant growth parameters, and equipment operation parameters, including: S13-1, when the fish pond environmental parameters meet the preset growth environment range, the fish health status of the fish in different fish ponds is analyzed and the growth trend of the fish is predicted based on the fish image data combined with the feeding amount, wherein the fish pond environmental parameters include the fish pond water temperature, dissolved oxygen concentration, water quality parameters, and pH value; S13-2, when the plant environmental parameters meet the preset growth environment range, the plant growth status of the hydroponic plants in different planting racks is analyzed according to the plant image data combined with the biological characteristics of the plants, and the plant growth trend and yield are predicted, wherein the plant environmental parameters include plant shed temperature, humidity, light intensity, carbon dioxide concentration, and nutrient solution pH value; S13-3, when there is abnormal growth in the health status of fish or the growth status of plants, the historical disease database is called for matching to obtain a treatment plan; S13-4, analyzing the equipment operation parameters, detecting whether the physical equipment has equipment abnormality, obtaining an abnormality treatment plan, and using the treatment plan and / or the abnormality treatment plan as the analysis result.

[0039] Among them, the fish image data combined with the feeding amount are used to analyze the health status of fish in different fish ponds and predict the growth trend of fish, including: Preprocess the fish image data and extract image features to obtain fish appearance characteristics; The fish appearance characteristics are respectively input into the constructed health status analysis model and fish growth prediction model to obtain the health status analysis results and fish growth prediction results respectively.

[0040] Specifically, the intelligent cameras deployed in the fish ponds collect fish images of fish schools in real time, including different fish species, postures and growth stages; the fish images are annotated, including the fish species, body length, body surface lesions (such as white spots, ulcers), gill status and other health indicators, and the corresponding feeding data are recorded; the fish images are preprocessed including data enhancement and fish standardization; the pre-trained ResNet50 model (removing the top classification layer) is used to extract image features, and the output of the residual block is retained as the fish appearance features; further, the feature pyramid network (FPN) can be used to integrate shallow details (such as lesion edges) and deep semantic information (such as overall morphology) for multi-scale feature fusion. The fish appearance features are input into the health status analysis model based on the dual-branch multi-task learning model (DB-MTL), and the health status analysis results including health level (such as healthy, sub-healthy, sick) and disease type (such as parasites, bacterial infection) are output.

[0041] The fish's apparent characteristics, fish environmental parameters, and historical time series (such as growth rate and length / weight changes over the past 7 days) are input into the fish growth prediction model built based on the LSTM model, and the fish growth prediction results of weight growth rate and length change in the next 7 days are output. The model architecture includes an input layer (multimodal features after the fusion of fish's apparent characteristics, fish environmental parameters, and historical time series), an LSTM layer (including 128 hidden units to capture time dependency), and an output layer for prediction. The mean square error (MSE) loss function is used to optimize the growth trend prediction accuracy.

[0042] Among them, the plant growth status of hydroponic plants in different planting racks is analyzed based on plant image data combined with the biological characteristics of the plants, and the plant growth trend and yield are predicted, including: Perform image segmentation and image feature extraction on plant image data to obtain multispectral features; The multispectral features are respectively input into the constructed growth state analysis model and the plant growth prediction model to obtain the growth state analysis results and the plant growth prediction results respectively.

[0043] Specifically, plant images (including leaf images) are collected through a hyperspectral camera, covering the visible light (400-700nm) and near-infrared (700-1000nm) bands; key growth indicators are annotated on plant images, including leaf area index (LAI), chlorophyll content, and diseased area (yellowing, brown spots); the U-Net model is used to segment the leaf area of ​​the plant image, remove background noise, and extract multispectral features to obtain multispectral features including texture features (such as gray-level co-occurrence matrix (GLCM) calculation of contrast, energy, homogeneity), morphological features (such as diseased area ratio, edge irregularity (fractal dimension)) and spectral features (such as near-infrared band reflectance (negatively correlated with chlorophyll content)). Multispectral features, plant environmental parameters (such as CO 2 The growth state analysis model (including random forest classifier and isolation forest algorithm) is input with data such as nutrient concentration, light intensity, and nutrient solution pH value, and the output includes growth state analysis results including growth state level (such as normal, mild stress, and severe stress) and plant disease type (identifying abnormal samples that deviate from the normal distribution (such as nutrient deficiency)).

[0044] The SARIMA model is used to decompose the seasonal cycle of light and temperature data (such as day and night, seasonal changes) to predict the trend of environmental parameters in the next 30 days; the multispectral features are combined with the environmental parameter trends to input the plant growth prediction model built based on the LSTM-Transformer hybrid model, and the plant growth prediction results of LAI and yield in the next 30 days are output. Among them, the architecture of the model includes the input layer (multispectral features, historical LAI sequence, SARIMA predicted environmental parameter trends), LSTM layer (capturing long-term dependencies), Transformer encoder (weighted attention to key growth stages (such as flowering period)) and output layer for prediction. The model parameters are updated according to the real-time collected image data and environmental parameters, and the sliding window mechanism (such as window size = 30 days) is used to continuously optimize the prediction accuracy. If fish diseases cause water quality deterioration (such as increased ammonia nitrogen), the associated growth abnormalities are predicted by analyzing the absorption efficiency of plant roots, and the closed-loop optimization of the fish-vegetable symbiotic system is achieved.

[0045] Furthermore, in another embodiment, a mathematical model including a water quality model, a plant growth model, and a fish growth model is established as historical data, and the sensor data and image data collected by sensors and cameras at different locations in real time are analyzed based on the historical data through the fish-vegetable symbiosis digital twin platform, and the analysis results are saved and the historical data is updated. Analyze and predict the fish schools based on fish images and feeding amount, such as judging whether the current feeding amount of the fish schools is on a downward trend, judging whether the growth environment parameters of the fish schools are within the growth environment range, and the environmental parameters include water quality (including ammonia nitrogen, nitrite, hydrogen sulfide, total alkalinity, total hardness), temperature, humidity, and various gases in the air (including ammonia, carbon dioxide, sulfur dioxide, nitrogen, etc.), that is, judging whether the currently collected values ​​are within the optimal water temperature threshold, the optimal humidity threshold, the optimal water quality threshold, and the optimal threshold of various gases in the air. If they are not within this range, a warning prompt is issued. If they are within this range, the fish schools are analyzed and predicted based on the fish images and feeding amount. When the current state of the fish schools has abnormal growth, the historical disease database is called for matching to obtain similar treatment plans. The growth of the fish schools is predicted by extracting features from fish images (such as color abnormalities, texture changes, morphological features), capturing fish surface lesions (ulcers, white spots, congestion, etc.), and identifying tissue changes that are invisible to the naked eye (such as changes in reflectivity caused by gill hypoxia).

[0046] Similarly, the growth status of hydroponic plants is analyzed based on plant image data combined with the biological characteristics of plants, and the growth trend and yield of plants are predicted. By collecting plant pictures and performing feature extraction, the surface characteristics of plants such as vegetables, such as leaf color, texture, spots (such as yellowing, brown spots) and local textures of spots, mold layers, and holes (analyzed by gray-level co-occurrence matrix GLCM), the edge contours of lesions (circular, irregular shapes) and distribution density, and the reflectivity anomalies of specific bands in multispectral data (such as near-infrared bands are sensitive to chlorophyll) are captured to analyze the growth of plants. Based on fish images and biological characteristics based on plant growth, such as varieties, growth stages, physiological indicators, etc., the growth trend and yield of hydroponic plants are predicted, such as using the SARIMA (Seasonal Autoregressive Integrated Moving Average) algorithm to deal with the influence of periodic environmental factors such as light and temperature, and using the LSTM (Long Short-Term Memory Network) algorithm to capture nonlinear growth dynamics. By inputting environmental parameters (CO 2 , concentration, temperature and humidity), leaf area index (LAI) time series and other data; and finally output the predicted value of stem height / leaf area in the next 7 days. It also links whether abnormal fish health has a corresponding abnormal impact on plant growth, and vice versa, whether plant diseases will affect fish growth, and records these data in the digital twin platform for data improvement.

[0047] The water quality model collects water-related data information through various sensors (such as pressure sensors, flow sensors, water quality sensors, etc.) deployed at different key locations such as fish ponds, square ponds, reservoirs, biochemical ponds, water pumps, etc. These sensors can sense and measure various physical and chemical parameters in the environment or water in real time, such as pH value, dissolved oxygen, turbidity, temperature, etc. The collected sensor data can be further converted into electrical signals and preliminarily processed and converted, usually including operations such as signal amplification, filtering, analog-to-digital conversion, etc., to convert them into digital signals that can be used for subsequent analysis. The data transmission technology transmits the processed sensor data to the cloud server via wired or wireless means (such as based on LoRa wireless communication technology).

[0048] The establishment of the plant growth model includes simulating the growth of hydroponic plants under different environmental conditions based on the biological principles of plant growth and by collecting monitoring data of the growth process of hydroponic plants. Specifically, the establishment of the plant growth model includes collecting environmental parameters such as temperature, humidity, light, and carbon dioxide concentration in the plant growth environment through the deployed sensors (such as soil moisture sensors accurately obtain soil moisture content by measuring the dielectric constant of the soil; light sensors can accurately sense light intensity, duration, and spectral distribution to provide data support for plant photosynthesis; carbon dioxide sensors monitor the concentration of carbon dioxide in the air in real time, because carbon dioxide is an important raw material for plant photosynthesis, and the appropriate concentration helps to improve the photosynthetic efficiency of plants).

[0049] The establishment of the fish growth model includes collecting fish image data to cover as many fish species as possible, and including different environmental scenes, different postures and angles of fish, different growth stages, etc., to ensure that the model can accurately identify in various situations. The source of these image data can be the database of scientific research institutions, the surveillance video of fish farming enterprises, and the photos of fish taken by photography enthusiasts. For the collected fish images, it is necessary to accurately label them, and the labeling content can include information such as the type of fish, gender (if there are obvious distinguishing features), age range, etc. In addition to convolutional neural networks (CNN), deep CNN models such as ResNet50 can also be used for training; ResNet50 solves the gradient disappearance problem in the deep network training process by introducing residual blocks. Each residual block contains a main path (performing conventional convolution operations) and a shortcut path (directly passing the input to the output, and the two are added to form the final output). This structure helps the direct flow of information and maintains the performance of the deep network. When building a fish recognition model based on ResNet50, a series of operations need to be performed, such as data enhancement (applying rotation, flipping, scaling, color transformation and other techniques to increase sample diversity and prevent overfitting), removing the fully connected layer of the original model and replacing it with a new fully connected layer adapted to the fish recognition task (the number of output nodes is equal to the number of fish species), freezing most of the convolutional layers in the initial training stage and only fine-tuning the newly added fully connected layers to speed up convergence, etc.

[0050] The following further describes the network architecture and training process of the fish recognition model, which can meet the dual-task requirements of growth stage classification and disease state recognition. The dual-branch multi-task learning model (DB-MTL) is improved, ConvNeXt-L is used as a shared feature extractor, and the dynamic sparse attention mechanism (DSA) is combined to improve the sensitivity to fish texture. Growth stage branch: Add Non-local Blocks to capture the global morphological characteristics of the fish body (such as body length / fatness); Disease state recognition branch: Use YOLOv8-P2 structure to output disease type + lesion location (Bounding Box). The gradient reversal layer (GRL) is used to separate growth-related features from disease features to avoid interference between tasks. The BiFPN structure is inserted into the backbone network to enhance the detection ability of small lesions (such as white spot disease). Use deep separable convolution to replace some standard convolutions to reduce the model size.

[0051] S14, triggering an early warning mechanism and generating a control instruction based on the analysis result, dynamically adjusting the operating status of the corresponding physical equipment in the fish-vegetable symbiosis system according to the control instruction, and realizing remote collaborative management of fish farming, plant growth and equipment operation.

[0052] Furthermore, in step S14, triggering the early warning mechanism according to the analysis result includes: The target abnormal area is marked with a red floating window in the fish-vegetable symbiosis digital twin platform, and the twin models of the corresponding fish schools, hydroponic plants and / or physical equipment are displayed as red holographic images.

[0053] Further, in step S14, dynamically adjusting the operating state of the corresponding physical device in the aquaponics system according to the control instruction includes: S14-1, when the analysis result indicates that the dissolved oxygen concentration should be adjusted, starting the aerator and adjusting the power; S14-2, when the analysis result indicates that the water quality parameters need to be adjusted, the microfiltration machine is started for circulating filtration, the ultraviolet sterilizer is turned on, and the water pump is started and the flow rate is adjusted; S14-3, when the analysis result indicates to adjust the lighting, adjust the intensity of the supplementary light and the spectrum to the wavelength band required by the plants; S14-4, when the analysis result indicates that the temperature should be adjusted, adjusting the opening and closing angle of the ventilation window; S14-5, when the analysis result indicates that the carbon dioxide concentration should be adjusted, start ventilation equipment or supplement the air source; S14-6, when the analysis result indicates that the pH value of the nutrient solution should be adjusted, the nutrient solution mixing device is started and the mixing ratio is adjusted.

[0054] In this embodiment, based on the above analysis results, an early warning is triggered and a control instruction is generated to remotely control the operating status of the physical equipment. It is based on the PLC controller, generates control instructions according to the output of the digital twin platform, and dynamically adjusts the operating status of the physical equipment, including water pumps, aerators, LED lighting equipment, temperature control equipment, nutrient solution mixing devices, etc. If the analysis results indicate that oxygen should be added to the fish pond, an oxygenation command is generated and sent to the aerator of the physical equipment until the received data shows that there is enough oxygen and no need to increase it. Then the digital twin platform generates a stop-cultivation command and sends it to the physical equipment; similarly, when the vegetables are not illuminated enough, a command to turn on the plant fill light will be issued and sent to the fill light of the physical equipment until the received data shows that no illumination is needed. Then a command to stop the plant fill light is issued and sent to the fill light of the physical equipment.

[0055] At the same time, users can remotely operate physical devices on the corresponding twin model of the digital twin platform according to their needs. The trigger warning marks the target abnormal area with a red floating window, and displays the corresponding twin model of the fish school, hydroponic plants and / or physical equipment in a red holographic image.

[0056] For example, when the analysis result indicates that the temperature of the corresponding vegetable rack is too high, a red floating window will be marked on the vegetable rack for early warning, and a control instruction will be generated to adjust the operating status of the corresponding physical equipment; the preset step interface that needs to be operated can also be displayed, including whether to open the ventilation window, so that the user can manually check the operation options of the corresponding physical equipment to issue an opening command to all corresponding ventilation windows. When the corresponding vegetable rack is not illuminated enough, a red floating window warning prompt will appear on the vegetable rack, and a control instruction will be generated to adjust the operating status of the corresponding physical equipment; the preset step interface that needs to be operated can also be displayed, including whether to turn on the plant fill light, so that the user can manually check the operation options of the corresponding physical equipment to issue an opening command to the corresponding plant fill light. When the fish pond is insufficient in oxygen, a red floating window warning prompt will appear on the fish pond, and a control instruction will be generated to adjust the operating status of the corresponding physical equipment; the preset step interface that needs to be operated can also be displayed, including whether to turn on the aerator, so that the user can manually check the operation options of the corresponding physical equipment to issue an opening command to the corresponding fish pond aerator. When the water quality monitoring of the fish pond indicates abnormality, a red floating window warning prompt will appear in the fish pond, and a control instruction will be generated to adjust the operating status of the corresponding physical equipment; it can also display the preset step interface that currently needs to be operated, including whether to turn on ultraviolet sterilization, whether to turn on the microfiltration machine, and whether to turn on the circulating water pump, so that the user can manually check the operation options of the corresponding physical equipment, and issue a start command to the corresponding fish pond aerator ultraviolet sterilizer and circulating water pump.

[0057] Reference Figure 4 Shown is a structural schematic diagram of a remote management device for fish and vegetable symbiosis based on digital twins provided by an embodiment of the present invention.

[0058] In this embodiment, the device 20 includes: The model building unit 21 is used to obtain physical parameter data of the physical fish pond and the physical plant shed in the aquaponics system, and create twin models corresponding to the fish school, fish pond, hydroponic plants, plant shed and physical equipment of the aquaponics system based on the physical parameter data to obtain a digital twin platform for aquaponics; The data acquisition unit 22 is used to obtain fish farming parameters, plant growth parameters, and equipment operation parameters collected by sensors and cameras deployed in the aquaponics system in real time, and upload them to the cloud server; A data analysis unit 23 is used to analyze the fish farming parameters, plant growth parameters and equipment operation parameters through the aquaponics digital twin platform to obtain analysis results; The remote management unit 24 is used to trigger the early warning mechanism and generate control instructions according to the analysis results, and dynamically adjust the operating status of the corresponding physical equipment in the fish-vegetable symbiosis system according to the control instructions to achieve remote collaborative management of fish farming, plant growth and equipment operation.

[0059] Each unit module of the device 20 can respectively execute the corresponding steps in the above method embodiment, so each unit module will not be described in detail here. Please refer to the description of the above corresponding steps for details.

[0060] The embodiment of the present invention further provides a remote management device for fish and vegetable symbiosis based on digital twins, the device comprising the remote management device for fish and vegetable symbiosis based on digital twins as described above, wherein the remote management device for fish and vegetable symbiosis based on digital twins can be used Figure 4 The structure of the embodiment can be executed accordingly. Figure 1 The technical solution of the method embodiment shown has similar implementation principles and technical effects. For details, please refer to the relevant records in the above embodiments, which will not be repeated here.

[0061] The device includes: a mobile phone, a digital camera, a tablet computer, or other devices with a camera function, or a device with an image processing function, or a device with an image display function. The device may include components such as a memory, a processor, an input unit, a display unit, and a power supply.

[0062] Among them, the memory can be used to store software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as an image playback function, etc.), etc.; the data storage area may store data created according to the use of the device, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory may also include a memory controller to provide the processor and the input unit with access to the memory.

[0063] The input unit can be used to receive input digital or character or image information, and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control. Specifically, the input unit of this embodiment includes not only a camera, but also a touch-sensitive surface (such as a touch display) and other input devices.

[0064] The display unit can be used to display information input by the user or information provided to the user and various graphical user interfaces of the device, which can be composed of graphics, text, icons, videos and any combination thereof. The display unit may include a display panel, and optionally, the display panel may be configured in the form of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc. Further, the touch-sensitive surface may cover the display panel, and when the touch-sensitive surface detects a touch operation on or near it, it is transmitted to the processor to determine the type of touch event, and then the processor provides a corresponding visual output on the display panel according to the type of touch event.

[0065] The embodiment of the present invention further provides a computer-readable storage medium, which may be a computer-readable storage medium included in the memory in the above embodiment; or a computer-readable storage medium that exists independently and is not installed in a device. The computer-readable storage medium stores at least one instruction, which is loaded and executed by a processor to implement Figure 1 The remote management method of aquaponics based on digital twins is shown. The computer-readable storage medium may be a read-only memory, a disk or an optical disk, etc.

[0066] The embodiment of the present invention also provides a computer program product, including a computer program / instruction, which is loaded and executed by a processor to implement Figure 1 A remote management method of aquaponics based on digital twin is shown.

[0067] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device embodiment, equipment embodiment and storage medium embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0068] Furthermore, in this document, the terms "comprises," "comprising," or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device that includes the element.

[0069] The above description shows and describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the invention, through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not depart from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A remote management method for fish and vegetable symbiosis based on digital twins, characterized in that: The method comprises: Obtain physical parameter data of a physical fish pond and a physical plant shed in the aquaponics system, and create twin models corresponding to the fish school, fish pond, hydroponic plants, plant shed, and physical equipment of the aquaponics system based on the physical parameter data to obtain a digital twin platform for aquaponics; Real-time acquisition of fish farming parameters, plant growth parameters, and equipment operation parameters collected by sensors and cameras deployed in the aquaponics system, and upload to a cloud server; The fish-vegetable symbiosis digital twin platform performs analysis based on the fish culture parameters, plant growth parameters, and equipment operation parameters to obtain analysis results; According to the analysis results, an early warning mechanism is triggered and a control instruction is generated. According to the control instruction, the operating status of the corresponding physical equipment in the fish-vegetable symbiosis system is dynamically adjusted to realize remote collaborative management of fish farming, plant growth and equipment operation.

2. According to claim 1, a remote management method of fish and vegetable symbiosis based on digital twins is characterized in that: The physical parameter data include: The structural layout parameters of the physical fish pond, including the type of fish pond, layered design, size and shape of the fish pond, and the location of the fish pond; The layout parameters of the physical plant shed include the type of planting rack, the size and shape of the planting rack, and the location of the planting rack; Device information of physical devices, including device type, model, number, installation location, and device operating parameters.

3. A remote management method of aquaponics based on digital twins according to claim 2, characterized in that: The method of creating a twin model corresponding to a school of fish, a fish pond, a hydroponic plant, a plant shed, and physical equipment of the aquaponics system based on the physical parameter data includes: A three-dimensional model is constructed based on the structural layout parameters of the physical fish pond, and the environmental parameters of the fish pond are mapped to obtain a twin model of the fish pond including the fish school and the fish pond; A three-dimensional model is constructed based on the layout parameters of the physical plant shed, and the plant environment parameters are mapped to obtain a twin model of the plant shed including hydroponic plants and planting racks; Create virtual entities for physical equipment including sensors, cameras, water pumps, aerators, ultraviolet sterilizers, microfiltration machines, fill lights, and nutrient solution mixing devices, and synchronize the device information of the physical equipment to obtain a device twin model.

4. According to claim 1, a remote management method of fish and vegetable symbiosis based on digital twins is characterized in that: The fish breeding parameters include fish pond water temperature, dissolved oxygen concentration, water quality parameters, pH value, and fish image data; the plant growth parameters include plant shed temperature, humidity, light intensity, carbon dioxide concentration, nutrient solution pH value, and plant image data; the equipment operation parameters include water pump flow, aerator power, ultraviolet sterilizer status, microfiltration machine working cycle, fill light spectrum parameters, ventilation window opening and closing angle, and nutrient solution mixing ratio.

5. The remote management method of aquaponics based on digital twin according to claim 1, characterized in that: The fish-vegetable symbiosis digital twin platform performs analysis based on the fish farming parameters, plant growth parameters, and equipment operation parameters, including: When the fish pond environmental parameters meet the preset growth environment range, the fish health status of fish in different fish ponds is analyzed and the growth trend of fish is predicted based on the fish image data combined with the feeding amount, wherein the fish pond environmental parameters include fish pond water temperature, dissolved oxygen concentration, water quality parameters, and pH value; When the plant environmental parameters meet the preset growth environment range, the plant growth status of hydroponic plants in different planting racks is analyzed and the plant growth trend and yield are predicted based on the plant image data combined with the biological characteristics of the plants, wherein the plant environmental parameters include plant shed temperature, humidity, light intensity, carbon dioxide concentration, and nutrient solution pH value; When there is abnormal growth in the health status of fish or plant growth, the historical disease database is called for matching to obtain a treatment plan; The operating parameters of the equipment are analyzed to detect whether the physical equipment has equipment abnormalities, obtain an abnormality treatment plan, and use the treatment plan and / or the abnormality treatment plan as the analysis result.

6. The remote management method of aquaponics based on digital twin according to claim 1, characterized in that: The triggering of the early warning mechanism according to the analysis result includes: The target abnormal area is marked with a red floating window in the fish-vegetable symbiosis digital twin platform, and the twin models of the corresponding fish schools, hydroponic plants and / or physical equipment are displayed as red holographic images.

7. The remote management method of aquaponics based on digital twin according to claim 1, characterized in that: The dynamically adjusting the operating state of the corresponding physical equipment in the aquaponics system according to the control instruction includes: When the analysis result indicates that the dissolved oxygen concentration should be adjusted, the aerator is started and the power is adjusted; When the analysis result indicates that the water quality parameters need to be adjusted, the microfiltration machine is started for circulating filtration, the ultraviolet sterilizer is turned on, and the water pump is started and the flow rate is adjusted; When the analysis result indicates to adjust the lighting, adjust the intensity of the supplementary light and the spectrum to the wavelength band required by the plants; When the analysis result indicates that the temperature should be adjusted, adjusting the opening and closing angle of the ventilation window; When the analysis result indicates that the carbon dioxide concentration should be adjusted, a ventilation device or a supplementary air source is activated; When the analysis result indicates that the pH value of the nutrient solution should be adjusted, the nutrient solution mixing device is started and the mixing ratio is adjusted.

8. A remote management device for fish and vegetable symbiosis based on digital twin, characterized in that: The device comprises: A model building unit, used to obtain physical parameter data of a physical fish pond and a physical plant shed in the aquaponics system, and create twin models corresponding to fish schools, fish ponds, hydroponic plants, plant sheds, and physical equipment in the aquaponics system based on the physical parameter data to obtain a digital twin platform for aquaponics; A data acquisition unit, used to acquire fish farming parameters, plant growth parameters, and equipment operation parameters collected by sensors and cameras deployed in the aquaponics system in real time, and upload them to a cloud server; A data analysis unit, configured to analyze the fish farming parameters, plant growth parameters, and equipment operation parameters through the aquaponics digital twin platform to obtain analysis results; The remote management unit is used to trigger the early warning mechanism and generate control instructions according to the analysis results, dynamically adjust the operating status of the corresponding physical equipment in the fish-vegetable symbiosis system according to the control instructions, and realize remote collaborative management of fish farming, plant growth and equipment operation.

9. A remote management device for fish and vegetable symbiosis based on digital twin, characterized in that: It includes a processor, a memory, and a computer program stored in the memory, wherein the computer program is executed by the processor to implement the steps of a remote management method of aquaponics based on digital twins as described in any one of claims 1 to 7.

10. A computer program product, characterized in that It includes a computer program / instruction, which, when executed by a processor, implements the steps of a remote management method of aquaponics based on digital twins as described in any one of claims 1 to 7.

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