Shared aquaculture digital twin system and construction method thereof

Abstract

The invention discloses a shared aquaculture digital twin system and a construction method thereof.The system takes a shared service platform as a center node and scene applications as peripheral nodes to form a star structure of twin applications, and the shared service platform is based on a server cluster and comprises an initialization creation module and a runtime sharing module; each scene application consists of a local server, an aquaculture physical unit, a group of sensors and an execution mechanism; the construction method comprises the steps of developing a shared service platform, initializing and creating a digital twinning scene application based on the shared service platform and according to personalized requirements, acquiring personalized data of the digital twinning scene application and developing a personalized calculation model, and remotely registering the personalized data, the model and the service resources of the digital twinning scene application. And sharing digital twinning scene application resources. Based on the twinning system and the construction method, rapid creation of the digital twinning personalized scene application of aquaculture and resource sharing among different scene applications can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent fishery, and particularly relates to a shared aquaculture digital twin system and a construction method thereof. Background Art

[0002] Aquaculture is a process of artificially producing fish, shellfish and other seedlings by using natural water surfaces or artificial ponds, and carrying out planned artificial production through feed feeding and management. It involves various physical elements such as aquaculture facilities, aquaculture environment, material inputs during the aquaculture process, aquaculture objects (such as fish and shrimp), aquaculture containers (such as ponds and land-based barrels), aquaculture water bodies, feeds, medicaments, and water discharges. These elements vary according to different scenario applications. In a specific scenario, through the optimized management of these physical elements, the maximization of aquaculture goals can be achieved, such as increasing the total output (weight) of aquaculture products, improving the quality of aquaculture objects (such as meat quality), and reducing input costs and environmental pollution (such as optimizing water quality). However, due to the diversity of scenarios and the complex interaction relationships among various physical elements, it is difficult to achieve the optimal results only relying on traditional experience for management. Therefore, it is necessary to rely on information technology for calculation, simulation and prediction.

[0003] In recent years, digital twin technology has developed rapidly in multiple fields, especially in intelligent manufacturing and urban management, and remarkable results have been achieved. The core concept of digital twin is to use information technology to sense the entities in the physical world through sensor technology and map them into digital models, forming virtual entities of digital twins. In the virtual digital world, through data analysis, model calculation, simulation and other means, the complex relationships and dynamic changes of physical entities can be deeply understood and predicted, and then through optimizing management strategies, feedback is sent to the physical entities, ultimately achieving precise control and optimization of the physical world.

[0004] Aquaculture is a typical field that can utilize digital twin calculation for process control. The aquaculture digital twin system uses sensors and modeling technology to map the physical entities in the aquaculture process into digital models, and understands the process dynamics through model calculation and simulation analysis. Furthermore, through simulation and prediction, the inputs and management strategies in the aquaculture process are optimized. At the same time, the optimized strategies can send instructions to the execution agencies (such as feeding machines, aerators, water purification equipment, etc.) to adjust the state of the physical entities, thereby achieving the optimization of decision-making goals.

[0005] At present, there are some research cases of digital twins applied to aquaculture, but the overall progress is lagging behind, especially considering the cost and time expenditure of system construction, its application in the field of aquaculture still needs more efforts. The existing technology has the following shortcomings: 1) The lack of a digital twin infrastructure with a shared mode results in low efficiency in the personalized creation of scenario application instances and high cost of twin application development; 2) The lack of resource sharing mechanisms such as data and computing models for scenario applications results in huge development costs, but the collected application data and the developed computing model results can only serve individual scenarios and cannot be shared in similar applications, resulting in low resource utilization; 3) The lack of aquaculture digital twin application data, models and service standards makes it impossible to give full play to the personalized characteristics of aquaculture digital twin sharing mechanisms and applications and realize flexible resource sharing configuration.

[0006] The construction of aquaculture digital twin systems involves a variety of data and models. From a data perspective, the system requires professional data on different farming modes (such as pond farming, land-based barrel farming, and recirculating water farming) and varieties (such as different fish and shrimp species), as well as management data, environmental data, growth data, input-output data, and other data. These data are crucial for optimizing farming management. In addition, the system also relies on professional models (such as growth models, nutritional health models, water quality models, and feed demand models) to simulate and analyze farming effects under different management scenarios. The construction of the above models requires a lot of data and domain knowledge, but they are usually based on some historical data or experience. Although the formed models have overall adaptability, due to differences in infrastructure conditions, farming objects, and farming environments under different farming units, it is obvious that the common model parameters need to be fine-tuned to reflect the differences in this farming instance. Therefore, it is necessary not only to collect a large amount of data from local farming units, but also to obtain shared data from other farming units with similar attributes to explore the potential value of key data. At the same time, the optimization and iteration of the computational model itself also requires a lot of data support. Taking into account the cost of model development, in a digital twin scenario application, the developed model can also be provided to other scenario applications in the form of services for sharing, that is, sharing can also be achieved in model calculations.

[0007] Considering the differences in aquaculture facilities, breeding objects, breeding environment, material input, etc., in order to give full play to the management advantages, aquaculture uses breeding units (such as a pond, a factory breeding barrel, etc.) as management units, and optimizes management according to the specific needs of the breeding units. The aquaculture digital twin system provides an important technical means for the above management optimization goals. Considering the commonality and diversity of aquaculture scenarios and breeding units, how to quickly and efficiently build a digital twin system that meets the application characteristics of each breeding unit, while giving full play to the shared value of resources of different breeding units or digital twin systems, is a problem that needs to be solved in the digital transformation of aquaculture. Summary of the Invention

[0008] In view of the deficiencies of the prior art, the present invention provides a shared aquaculture digital twin system and a construction method thereof. By summarizing and modeling common parts and supplementing personalized content, a sharing mechanism is designed to achieve intercommunication and resource sharing among different scenario applications. The information of the digital twin can be shared across scenario applications, and the calculation accuracy and result reliability of the model are improved through information sharing.

[0009] The shared aquaculture digital twin system includes a shared service platform and several aquaculture digital twin scenario applications created according to the requirements of scenario applications. Specifically, the shared service platform is the central node, and the aquaculture digital twin scenario applications are the peripheral nodes, and the peripheral nodes are respectively connected to the central node to form a star structure.

[0010] Furthermore, the shared service platform is based on a multi-server cluster with two or more servers sharing disk storage, and provides high-availability service support for the initialization creation and runtime resource sharing of each aquaculture digital twin scenario application with a floating virtual IP address. Each aquaculture digital twin scenario application consists of a local server, an aquaculture physical unit, a group of sensors and actuators. One aquaculture digital twin scenario application corresponds to a cultivation unit including a specific cultivation mode, cultivation variety and cultivation environment.

[0011] Furthermore, each server in the shared service platform cluster includes two modules: initialization creation and runtime sharing. The initialization creation module consists of three types of service interfaces: basic data support, basic model support and basic service support. It is responsible for distributing common resources such as basic data, basic models and basic services from the shared service platform to the aquaculture digital twin scenario application when initializing and creating the aquaculture digital twin scenario application, and providing creation support services for it. The runtime sharing module consists of a scenario registration service, a data sharing service and a calculation model sharing service, which provides service registration and retrieval of resources such as personalized data and personalized calculation models for the aquaculture digital twin scenario application, and realizes resource sharing during the runtime of different aquaculture digital twin scenario applications.

[0012] Furthermore, the basic data in the initialization creation module provides operation management configuration data such as the infrastructure, cultivation environment, cultivation variety, and feed elements for the operation of the shared aquaculture digital twin system, as well as demonstration sample data to support the operation of the system. The basic models cover statistical analysis models, knowledge-driven professional models, data-driven machine learning models, large models driven by massive data, 3D data models and visualization, simulation and prediction models. The basic services provide sensor perception data interfaces, output interfaces for device control, and application service interfaces for external request access.

[0013] Furthermore, the local server for the aquaculture digital twin scenario application serves as the operating platform for the aquaculture digital twin scenario application, which is embodied as a set of computer hardware and software systems. It accommodates the common resources of the aquaculture digital twin scenario application obtained from the shared service platform during initialization creation, as well as the personalized data, models, and services of the custom-developed scenario applications. It is the digital twin of the aquaculture physical unit; each aquaculture physical unit is the physical object of aquaculture operations, corresponding to physical attributes such as aquaculture facilities, aquaculture objects, aquaculture environment, and aquaculture feed; sensors and actuators are connected to the aquaculture physical unit and the local server. The sensors are used to sense and collect multi-modal data such as numerical indicators, images, and videos related to the growth, environment, and production materials of the aquaculture physical unit. Under the precise calculation of the computational model stored in the local server, the actuators manage and optimize the feed feeding and aquaculture environment attributes of the aquaculture physical unit through switch control.

[0014] Furthermore, the shared aquaculture digital twin system is constructed based on the designed data standards, computational model standards, and service standards. The above three types of standards are specifically defined through the designed json normalization schema documents. Among them, the data standard schema document defines the necessary data attributes of the aquaculture digital twin entities, including the detailed attribute lists of aquaculture infrastructure, aquaculture environment, aquaculture varieties, production material input and output elements, and each element; the schema document of the computational model standard defines the model specifications for the spatio-temporal dynamic calculation of the digital twin virtual entities. The necessary attributes include the computational model name, type, input and output lists; the schema document of the service standard defines the naming of the external service interfaces and service parameters for the interaction between the shared service platform and the aquaculture digital twin scenario application. The aquaculture digital twin is described through the schema documents of data, computational models, and services, realizing the remote registration of local resources on the shared service platform, and the cross-scenario application sharing of data and computational models by using the shared service platform as an intermediary to call the remote service interfaces of other scenario applications.

[0015] The construction method of the shared aquaculture digital twin system includes the following steps: Step 1, develop the shared service platform; Step 2, according to the personalized requirements of the aquaculture digital twin scenario application, clone the relevant data, computational models, and service interfaces from the shared service platform to complete the initialization creation of the aquaculture digital twin scenario application; Step 2.1, develop the scenario application initialization command-line tool createLocalCopy on the shared service platform and define the personalized parameter template localcopy.template; Step 2.2: Modify localcopy.template according to the actual requirements of the aquaculture digital twin scenario application and save it on the local server of the aquaculture digital twin scenario application; Step 2.3: Copy createLocalCopy from the shared service platform to the local server, use the modified localcopy.template as the parameter list, and specify the IP address and service port of the remote shared service platform. Execute the scenario application creation tool createLocalCopy to complete the initialization creation of the aquaculture digital twin scenario application; Step 2.4: Repeat steps 2.2 - 2.3 to complete the initialization creation of multiple aquaculture digital twin scenario applications, and configure the web application environment, personalized data collection, and personalized models; Step 3: According to the personalized requirements of the aquaculture digital twin scenario application, configure the corresponding types and quantities of sensor hardware devices, connect them to the aquaculture digital twin scenario application to achieve personalized data collection, and develop personalized calculation models; Step 4: Upload the description information of the local data, calculation models, and service interfaces of the aquaculture digital twin scenario application to the shared service platform by defining a schema document for service registration of the aquaculture digital twin scenario application; Step 5: Any other aquaculture digital twin scenario applications can retrieve services on the shared service platform. According to the retrieved service interface definition, they can obtain shared personalized data, calculation models, and resource interfaces to achieve resource sharing across scenario applications.

[0016] Furthermore, in step 1, develop two modules for the initialization creation and runtime sharing of the shared service platform by writing service programs, set the service port of the shared service platform, and use database design to store basic data, basic models, and basic service descriptions. The basic data includes at least a list of common aquaculture facilities, a list of aquaculture environment parameters, a list of aquaculture species, and a list of aquaculture material inputs. The basic models include at least growth models, feed demand models, nitrogen and phosphorus emission models, nutritional health models, and water environment change models for common aquaculture species. The basic services include at least data access services for general sensors, model invocation services, simulation and prediction services, and data statistics and summary services.

[0017] Further, the createLocalCopy tool in step 2.1 is used for the initialization creation of the aquaculture digital twin scenario application. Executing the createLocalCopy tool can create the file directory structure of the local aquaculture digital twin scenario application, configure the storage access path and password of the relational database, and perform necessary environment checks, and configure the relevant data, models, and services of the shared service platform into the database system of the local application. The localcopy.template file is a template file placed on the shared service platform, and its content includes infrastructure, aquaculture environment, aquaculture varieties, feed types, output elements, and system configuration data related to the above topics, as well as model and service definition list information.

[0018] Further, each aquaculture digital twin scenario application created in the initialization in step 2.4 provides web service support, configures a web server, and configures personalized data collection and personalized models under the support of the web server to realize the registration, sharing, and management of personalized data, models, and services of the aquaculture digital twin scenario application on the shared service platform.

[0019] Further, in step 4, the aquaculture digital twin scenario application registers services by defining a json schema document and uploading it to the shared service platform. After completing the service registration, the sensor data, personalized calculation model, and services for accessing these data and models of the aquaculture digital twin scenario application can be opened to other aquaculture digital twin scenario applications through the shared service platform.

[0020] Further, in step 5, any aquaculture digital twin scenario application can query and list the personalized data and calculation model services of the registered aquaculture digital twin scenario applications from the shared service platform, and then according to the service json schema document specification, read the specific content of the service interface supported by the json schema document, including service endpoints, service names, versions, model lists on which the service depends, inputs, and outputs, and obtain the corresponding data or calculation model results by providing specific parameters according to the json schema document, so as to maximize the resource utilization rate in the digital twin scenario application.

[0021] Compared with the prior art, the present invention has the following advantages: 1) The present invention supports the rapid creation of scenario applications. Based on the aquaculture digital twin application standard system (data standard, model standard, service standard), a shared aquaculture digital twin system is designed. By constructing a shared service platform, and then based on the shared service platform and the scenario application parameter template, relevant data, calculation models, and service interfaces are cloned from the shared service platform to each aquaculture digital twin scenario application, realizing the rapid creation of personalized scenario applications for aquaculture digital twins.

[0022] 2) The present invention supports the personalized resource expansion and sharing of scenario applications. For data and computing models that cannot be satisfied by the shared service platform, local resource expansion can be performed after initializing and creating a scenario application. At the same time, it can be registered through the service interface of the shared service platform to share local resources for access by other scenario applications, or data and computing model services of other registered scenario applications can be retrieved and obtained in the shared service platform, opening up the resource sharing channel between personalized scenario applications of aquaculture digital twins. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 It is a schematic structural diagram of the shared aquaculture digital twin system of the present invention.

[0025] Figure 2 It is a schematic structural diagram of the shared service platform of the present invention.

[0026] Figure 3 It is a working principle diagram of the initialization creation module and the runtime sharing module of the shared service platform of the present invention.

[0027] Figure 4 It is a server deployment and network topology relationship diagram of an embodiment of the present invention.

[0028] Figure 5 It is a flowchart of the method for constructing the shared aquaculture digital twin system of the present invention.

[0029] Figure 6 It is a structural diagram of the service interface mode document description in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the drawings and embodiments in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0031] Embodiment 1 As Figure 1As shown in the figure, an embodiment of the present invention provides a shared aquaculture digital twin system, which consists of a shared service platform and several aquaculture digital twin scenario applications created according to different scenario application requirements. Specifically, the shared service platform is the central node, and the aquaculture digital twin scenario applications are the peripheral nodes. The peripheral nodes are respectively connected to the central node to form a star structure. The shared service platform is based on a multi-server cluster of two or more servers sharing disk storage, and provides high-availability service support for the initialization creation and runtime resource sharing of each aquaculture digital twin scenario application with a floating virtual IP address. Each aquaculture digital twin scenario application consists of a local server, an aquaculture physical unit, a group of sensors, and actuators. An aquaculture digital twin scenario application corresponds to a cultivation unit that includes a specific cultivation mode (such as traditional pond cultivation or intensive factory cultivation), cultivation varieties, and cultivation environment. Each cultivation unit corresponds to the physical unit of the aquaculture digital twin scenario application, including cultivation facilities, cultivation objects, water environment, and feed. The local server serves as the operating platform of the aquaculture digital twin scenario application, which is embodied as a set of computer software and hardware systems, accommodating the common resources of the aquaculture digital twin scenario application obtained from the shared service platform during initialization creation, as well as the personalized data, models, and services of the customized developed scenario application. It is the digital twin of the aquaculture physical unit. The sensors and actuators are connected to the aquaculture physical unit and the digital twin (local server). The sensors are used to sense and collect multi-modal data of numerical indicators, images, and videos related to the growth, environment, and production materials of the aquaculture physical unit, such as sensors that collect data on water quality, feeding behavior, and feed intake based on the principles of sound, light, and electricity. The actuators, under the precise calculation of the computing model stored in the local server, realize the management and optimization of feed feeding and aquaculture environment attributes of the aquaculture physical unit through switch control, such as feeders, aerators, and medicated feed mixers.

[0032] As Figure 2As shown in the figure, each server in the shared service platform cluster includes two modules: initialization creation and runtime sharing. The initialization creation module consists of three types of service interfaces: basic data support, basic model support, and basic service support. It is responsible for distributing common resources such as basic data, basic models, and basic services from the shared service platform to the aquaculture digital twin scenario application when initializing and creating an aquaculture digital twin scenario application instance, providing necessary data, model, and service support for it. Among them, the basic data provides operation management configuration data such as the infrastructure, aquaculture environment, aquaculture varieties, and feed elements for the operation of the shared aquaculture digital twin system, as well as demonstration sample data to support the system operation; the basic models cover statistical analysis models, knowledge-driven professional models, data-driven machine learning models, large models driven by massive data, 3D data models and visualization, simulation and prediction models; the basic services provide sensor perception data interfaces, output interfaces for device control, and application service interfaces for external request access, see Figure 3 . The runtime sharing module consists of a scenario registration service, a data sharing service, and a computing model sharing service, providing service registration and retrieval of resources such as personalized data and personalized computing models for the aquaculture digital twin scenario application, and realizing the sharing of resources (data, computing models) during the operation of different aquaculture digital twin scenario applications.

[0033] The shared aquaculture digital twin system is built based on the designed data standards, computing model standards, and service standards. The above three types of standards are all specifically defined through designed json normalization schema documents. Among them, the data standard schema document defines the necessary data attributes of the aquaculture digital twin entity, including the detailed attribute lists of aquaculture infrastructure, aquaculture environment, aquaculture varieties, production material input and output elements, and each element; the schema document of the computing model standard defines the model specifications for the spatio-temporal dynamic calculation of the digital twin virtual entity, and the necessary attributes include the computing model name, type, input and output lists; the schema document of the service standard defines the naming of the external service interface and service parameters for the interaction between the shared service platform and the aquaculture digital twin scenario application. The aquaculture digital twin scenario application instance realizes the remote registration of local resources in the shared service platform through the description of the data, computing model, and service schema documents, and realizes the cross-scenario application sharing of data and computing models by using the shared service platform as an intermediary to call the remote service interfaces of other scenario applications.

[0034] Such as Figure 4As shown in the figure, this embodiment uses four servers to build a shared aquaculture digital twin system. Among them, two servers (192.168.4.10 and 192.168.4.11) are used for the shared service platform. A dual-machine cluster configuration and IP address drift are adopted, and the shared virtual IP address (192.168.4.12) is used to keep the shared service platform in a highly available state all the time, and the two servers share disks. The other two servers (192.168.4.122 and 192.168.4.123) are respectively used as the servers for two aquaculture digital twin scenario application instances. The shared service platform and the two aquaculture digital twin scenario applications form a star structure centered on the former, and the network access connectivity is configured well.

[0035] Embodiment 2 As Figure 5 As shown in the figure, an embodiment of the present invention provides a method for building a shared aquaculture digital twin system, which is used to implement the shared aquaculture digital twin system described in Embodiment 1. The method for building a shared aquaculture digital twin system includes five steps: developing a shared service platform, initializing multiple aquaculture digital twin scenario applications based on the shared service platform and according to personalized requirements, collecting personalized data and developing calculation models for different aquaculture digital twin scenario applications, registering personalized resource services for different aquaculture digital twin scenario applications, and sharing resources among different aquaculture digital twin scenario applications. The specific operations of each step are as follows: Step 1: Develop a shared service platform.

[0036] Use Python to write service programs to complete the development of two modules for initializing and running the shared service platform. In this embodiment, the service port of the shared service platform is set to 5050, and a relational database is used to design and store basic data, basic models, and basic service descriptions. The basic data includes common aquaculture facility lists, aquaculture environment parameter lists, aquaculture variety lists, aquaculture material input lists, etc. The basic models include growth models, feed demand models, nitrogen and phosphorus emission models, nutritional health models, water environment change models, etc. for common aquaculture varieties. The basic services include data access services for general sensors, model call services, simulation and prediction services, data statistics and summary services, etc. In this embodiment, a status check command-line tool testPlatformStatus is developed to check the running status of the shared service platform on the current server.

[0037] Step 2: According to the personalized requirements of the aquaculture digital twin scenario application, clone relevant data, calculation models, and service interfaces from the shared service platform to complete the initialization creation of the aquaculture digital twin scenario application.

[0038] This embodiment takes the creation of a growth prediction digital twin scenario application on server 192.168.4.122 and the creation of a water quality prediction digital twin scenario application on server 192.168.4.123 as examples to demonstrate the initialization creation process of aquaculture digital twin scenario applications. In practical applications, digital twin applications with more than two scenario applications (such as feed demand prediction, health quality (disease) prediction, feed feeding control, water quality regulation, immune enhancement regulation, etc.) can be created according to the same principle.

[0039] First, develop a scenario application initialization command-line tool createLocalCopy on the shared service platform and define a personalized parameter template localcopy.template.

[0040] The createLocalCopy tool can be distributed to each aquaculture digital twin scenario application server for the initialization creation of scenario applications. Executing the createLocalCopy tool can create the file directory structure of the local aquaculture digital twin scenario application, configure the storage access path and password of the relational database, and perform necessary environment checks, and configure relevant data, models, and services of the shared service platform into the local application's database system. The localcopy.template file is a template file placed on the shared service platform. Each aquaculture digital twin scenario application can modify the content according to its actual needs and clone the corresponding data, models, and services from the shared platform according to the modified content. The content of the localcopy.template file includes corresponding data such as infrastructure, aquaculture environment, aquaculture varieties, feed types, output elements, etc., as well as a list of model and service definition information.

[0041] In the parameter template file localcopy.template defined in this embodiment, the data part defines that the infrastructure of this scenario uses a pond as the aquaculture unit. According to the application requirements of this scenario, three types of aquaculture environment data need to be collected: water temperature, dissolved oxygen (DO), and fish activity. The aquaculture variety is Yellowcartfish, the type of feed is Tongwei 157, and the output factors are nitrogen (N) and phosphorus (P) emissions. The data part also attaches system configuration data related to the above data topics. For example, after selecting the aquaculture variety, various configuration requirements such as its corresponding growth stage division, nutritional requirements, stocking density, water temperature requirements, and feed nutrient composition are automatically attached to the configuration items, which can provide parameters for subsequent model calculations. The model part defines a growth model, a feeding requirement model, a water quality model, and a virtual simulation model. The growth model uses the classical bioenergetics model (thermal growth coefficient, tgc_model), the feeding requirement model uses the method of quadrat sampling and regression analysis (quadrat_regression), the aquaculture water quality model uses the nutrient recycle and balance model (nutrient_recycle_balanced), and the virtual simulation model uses individual interaction. In this way, each individual in the aquaculture unit can be regarded as an independent simulation object for behavior prediction and simulation in digital twin applications. The service part defines obtaining the feeding intensity, water quality, control of the feed dispenser, and control of the aerator, and defines the input and output for each service. According to the requirements, the above content can be modified and extended to provide more basic data, models, and service support.

[0042] Then, modify localcopy.template according to the actual requirements of the digital twin scenario application in aquaculture and save it on the local server.

[0043] According to the personalization of the digital twin scenario application for aquaculture in the local server 192.168.4.122, modify the data, models, and services in localcopy.template to create a scenario application that meets the personalized requirements. For example, for this digital twin scenario application for aquaculture, the main focus is on optimizing the growth process of the aquaculture objects. Since the water source of the aquaculture facilities is rich, water can be changed regularly, and there is no need to pay too much attention to water quality issues. Therefore, the local scenario application requirements can be customized, and the dissolved oxygen (do) in the aquaculture environment parameters of the basic data and the water quality model can be removed from the parameter template file. The modified localcopy.template file records the specific data, models, and services to be cloned from the shared platform for the local scenario application instance.

[0044] Finally, copy createLocalCopy from the shared service platform to the local server, use the modified localcopy.template as the parameter list, and specify the IP address 192.168.4.12 and service port 5050 of the remote shared service platform. Execute the scenario application creation tool createLocalCopy to complete the initialization creation of the digital twin scenario application for aquaculture.

[0045] Since the IP address of the shared service platform is a floating virtual IP, and its physical address can point to 192.168.4.11 or 192.168.4.10 according to the actual situation, the offline of any physical server will not affect the service function of the shared service platform, ensuring the high availability of the services provided as the central node.

[0046] When a new digital twin scenario application needs to be created, considering that the key goal of the scenario application requirements is to meet water quality standards, on the server 192.168.4.123, follow the same process as above, modify the parameter template file localcopy.template, remove the fish activity monitoring data (fish_activity) and feeding requirement (feed_requirement) models, while retaining the water quality environment indicators (temperature and do) and water quality model in the basic data. Deploy the modified parameter template file on this server and run the createLocalCopy tool to complete the creation of the second scenario application.

[0047] On the scenario application servers at 192.168.4.122 and 192.168.4.123, configure web access services respectively, and configure their respective local service directories into the access directories of Nginx to provide a visualization management tool based on web services for the digital twin applications of the local scenario and the remote sharing of local data and computing models.

[0048] By default, the aquaculture digital twin scenario applications created through the shared service platform all provide the visualization display of the aquaculture units and the function of configuring personalized data collection parameters for the aquaculture units, and dock with the actual sensor data service interfaces of the aquaculture units to obtain various personalized data such as dynamic data, aquaculture models, aquaculture varieties, feed varieties, etc., for the dynamic display of digital twin applications and the simulation prediction aiming at promoting the growth process, optimizing feed feeding, reducing water quality pollution, etc.

[0049] Step 3: According to the personalized requirements of the aquaculture digital twin scenario application, configure the corresponding types and quantities of sensor hardware devices, connect them to the aquaculture digital twin scenario application to achieve personalized data collection, and develop personalized computing models.

[0050] On the servers at 192.168.4.122 and 192.168.4.123, by configuring the parameters of the sensor data interface service, access the personalized data required in their respective scenario applications to connect sensor data for local applications. Connect video sensor, material balance sensor, and feeding intensity sensor data on 192.168.4.122 so as to be able to sense the body length, size, feed consumption, feeding intensity, etc. of fish in real time. Connect video sensor and water quality sensor on 192.168.4.123 to collect data such as dissolved oxygen, ammonia salt, and water temperature of water quality. Configure feed feeding control strategies and water quality control strategies for two different aquaculture digital twin scenario applications respectively, so as to automatically trigger the feed feeding system and water quality aeration control system on the basis of the sensor-sensed data, and realize the automation of feed feeding and water quality control.

[0051] On the server 192.168.4.122, considering that the general model of the shared service platform cannot meet the personalized requirements of local scenario applications, a personalized calculation model is developed by adjusting the model parameters, including a growth calculation model and a growth prediction calculation model for the later stage. The length of the fish body is identified through a video sensor, and then the weight is calculated using the growth model. The calculation results are stored locally. Based on historical weight data and data such as feed feeding and aquaculture environment, the growth prediction calculation model is used to predict the weight of the fish at a future time. On the server 192.168.4.123, a personalized calculation model for water quality prediction is developed. By collecting sensor data on dissolved oxygen concentration and nitrite concentration in the water quality, the water quality changes in the future are simulated and predicted.

[0052] Step 4: The description information of the local data, calculation model, and service interface of the aquaculture digital twin scenario application is uploaded to the shared service platform by defining a schema document for service registration of the aquaculture digital twin scenario application.

[0053] The aquaculture digital twin scenario application performs service registration by defining a json schema document and uploading it to the shared service platform. After completing the service registration, the sensor data, personalized calculation model, and services for accessing these data and models of the aquaculture digital twin scenario application can be opened to other aquaculture digital twin scenario applications through the shared service platform. In the configured json schema document, a shared data (or calculation model) test case will be automatically created to provide legal verification of the configuration result of the schema document.

[0054] On the server 192.168.4.122, by configuring the json schema document, the sensor data collected in this scenario application and the growth prediction calculation model can be opened for use by other aquaculture digital twin scenario applications (such as 192.168.4.123).

[0055] Step 5: Any other aquaculture digital twin scenario application can retrieve the shared personalized data, calculation model, and resource interface according to the service interface definition retrieved through service retrieval on the shared service platform, realizing resource sharing across scenario applications.

[0056] Taking the data and computing model resource sharing registered through services in 192.168.4.122 as an example, obtain the shared data and computing model services in the 192.168.4.123 scenario application. 192.168.4.123 can query and list the personalized data and computing model services of the aquaculture digital twin scenario application that have been registered from the shared service platform, and then according to the json schema document specification of the service, read the specific content of the service interface supported by the json schema document. Among them, the service items (services) include service endpoint (endpoint, mandatory), service name (servicename, mandatory), version (version, optional), list of models on which the service depends (modelList, optional), input (input, mandatory), output (output, optional), such as Figure 6 . By providing specific parameters according to the json schema document, the corresponding data or computing model results can be obtained from the relevant scenario applications, so that the data content missing in the isolated scenario applications can be supplemented, and the maximization of resource utilization rate in the digital twin scenario applications can be achieved.

[0057] In specific implementation, the method proposed by the technical solution of the present invention can be automatically run by those skilled in the art using computer software technology. The system device for implementing the method, such as a computer-readable storage medium storing the corresponding computer program of the technical solution of the present invention and a computer device including running the corresponding computer program, should also be within the protection scope of the present invention.

[0058] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Those skilled in the art of the present invention can make various modifications or supplements to the described specific embodiments or use similar ways to replace them, but will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. A shared aquaculture digital twin system, characterized in that: It consists of a shared service platform and multiple aquaculture digital twin scenario applications, with the shared service platform as the central node and the aquaculture digital twin scenario application as the peripheral node. Each peripheral node is connected to the central node to form a star structure; the shared service platform is based on a multi-server cluster with two or more shared disk storage, and uses a floating virtual IP address to provide high-availability service support for the initialization creation and runtime resource sharing of each aquaculture digital twin scenario application; each aquaculture digital twin scenario application consists of a local server, an aquaculture physical unit, a group of sensors and actuators, inherits necessary resources from the shared service platform, and supports the development of personalized data and personalized computing models.

2. A shared aquaculture digital twin system according to claim 1, characterized in that: Each server in the shared service platform cluster includes two modules: initialization creation and runtime sharing. The initialization creation module consists of three types of service interfaces: basic data support, basic model support and basic service support. It is responsible for distributing common resources such as basic data, basic models and basic services from the shared service platform to the aquaculture digital twin scenario application when initializing the creation of the aquaculture digital twin scenario application, and providing creation support services for the aquaculture digital twin scenario application; the runtime sharing module consists of scene registration service, data sharing service and computing model sharing service, and provides personalized data and personalized computing model service registration and retrieval for the aquaculture digital twin scenario application, so as to realize resource sharing during the runtime of different aquaculture digital twin scenario applications.

3. A shared aquaculture digital twin system as claimed in claim 2, characterized in that: The basic data in the initialization creation module provides the infrastructure, breeding environment, breeding varieties, feed element operation and management configuration data for the shared aquaculture digital twin system, as well as display sample data to support system operation; the basic models cover statistical analysis models, knowledge-driven professional models, data-driven machine learning models, large models driven by massive data, three-dimensional data models and visualization, simulation and prediction models; the basic services provide sensor perception data interfaces, equipment control output interfaces, and application service interfaces for external access requests.

4. A shared aquaculture digital twin system according to claim 1, characterized in that: The local server of the aquaculture digital twin scenario application serves as the operating platform of the aquaculture digital twin scenario application, which is embodied as a set of computer software and hardware systems. It accommodates the common resources of the aquaculture digital twin scenario application obtained from the shared service platform during initialization and creation, and the customized personalized data, models, and services of the scenario application. It is the digital twin of the aquaculture physical unit; each aquaculture physical unit is a physical object of aquaculture operations, corresponding to the physical properties of aquaculture facilities, aquaculture objects, aquaculture environment, and aquaculture feed; sensors and actuators connect the aquaculture physical unit and the local server. The sensors are used to sense and collect numerical indicators, images, and video multimodal data of growth, environment, and production materials related to the aquaculture physical unit. The actuators achieve management optimization of feed feeding and aquaculture environment properties of the aquaculture physical unit through switch control under the precise calculation of the computing model stored in the local server.

5. A shared aquaculture digital twin system according to claim 1, characterized in that: The shared aquaculture digital twin system is built based on the designed data standards, computing model standards and service standards. The above three types of standards are specifically defined by designing json normalized schema documents; among them, the data standard schema document defines the necessary data attributes of aquaculture digital twin entities, including aquaculture infrastructure, farming environment, farming varieties, production material input and output elements and detailed attribute lists of each element; the schema document of the computing model standard defines the model specifications for the spatiotemporal dynamic calculation of digital twin virtual entities. The necessary attributes include the computing model name, type, input and output list; the schema document of the service standard defines the naming and service parameters of the external service interface for the interaction between the shared service platform and the aquaculture digital twin scenario application; the aquaculture digital twin realizes the remote registration of local resources on the shared service platform through the schema document description of data, computing model and service, and realizes the cross-scenario application sharing of data and computing model by calling the remote service interface of other scenario applications with the shared service platform as the intermediary.

6. A method for constructing a shared aquaculture digital twin system, used to implement the shared aquaculture digital twin system according to claim 1, characterized in that: The following steps are involved: Step 1: Develop a shared service platform; Step 2: According to the personalized needs of the aquaculture digital twin scenario application, clone the relevant data, calculation model and service interface from the shared service platform to complete the initialization creation of the aquaculture digital twin scenario application; Step 3: According to the personalized needs of the aquaculture digital twin scenario application, configure the corresponding types and quantities of sensor hardware devices, connect them to the aquaculture digital twin scenario application to realize personalized data collection, and develop a personalized computing model; Step 4: Upload the local data, computing model and service interface description information of the aquaculture digital twin scenario application to the shared service platform by defining the model document to register the service of the aquaculture digital twin scenario application; Step 5: Any other aquaculture digital twin scenario application can obtain shared personalized data, computing models and resource interfaces according to the retrieved service interface definition by performing service retrieval on the shared service platform, thereby realizing resource sharing across scenario applications.

7. A method for constructing a shared aquaculture digital twin system according to claim 6, characterized in that: In step 1, write a service program to complete the development of the two modules of initialization creation and runtime sharing of the shared service platform, set the service port of the shared service platform, and use the database to design and store basic data, basic models and basic service descriptions. The basic data at least includes a list of common breeding facilities, a list of breeding environment parameters, a list of breeding varieties, and a list of breeding material inputs. The basic model at least includes a growth model for common breeding varieties, a feed demand model, a nitrogen and phosphorus emission model, a nutrition and health model, and a water environment change model. The basic service at least includes data access services for general sensors, model call services, simulation and prediction services, and data statistical summary services.

8. A method for constructing a shared aquaculture digital twin system according to claim 6, characterized in that: Step 2 includes the following steps: Step 2.1: Develop the scenario application initialization command line tool createLocalCopy on the shared service platform and define the personalized parameter template localcopy.template; The createLocalCopy tool is used to initialize and create the aquaculture digital twin scenario application. Executing the createLocalCopy tool can create the file directory structure of the local aquaculture digital twin scenario application, configure the storage access path and password of the relational database, and perform necessary environmental checks to configure the relevant data, models, and services of the shared service platform to the database system of the local application; the localcopy.template file is a template file placed on the shared service platform, and its content includes infrastructure, aquaculture environment, aquaculture species, feed type, output factors, and system configuration data related to the above topics, as well as model and service definition list information; Step 2.2, modify localcopy.template according to the actual needs of the aquaculture digital twin scenario application, and save it on the local server of the aquaculture digital twin scenario application; Each aquaculture digital twin scenario application modifies the data, models, and services in localcopy.template according to its actual needs. The modified localcopy.template file records the specific data, models, and services to be cloned from the shared platform for the local scenario application. Step 2.3, copy createLocalCopy from the shared service platform to the local server, use the modified localcopy.template as the parameter list, specify the IP address and service port of the remote shared service platform, execute the scenario application creation tool createLocalCopy, and complete the initialization creation of the aquaculture digital twin scenario application; Step 2.4, repeat steps 2.2-2.3 to complete the initialization creation of multiple aquaculture digital twin scenario applications, and configure the web application environment and personalized data collection and personalized models; Each aquaculture digital twin scenario application initially created provides web service support, configures a web server, and configures personalized data collection and personalized models under the support of the web server to register, share and manage personalized data, models and services of aquaculture digital twin scenario applications on the shared service platform.

9. A method for constructing a shared aquaculture digital twin system according to claim 6, characterized in that: In step 4, the aquaculture digital twin scenario application defines a json schema document and uploads it to the shared service platform for service registration. After completing the service registration, the sensor data, personalized computing model, and services for accessing these data and models of the aquaculture digital twin scenario application can be opened to other aquaculture digital twin scenario applications through the shared service platform.

10. A method for constructing a shared aquaculture digital twin system according to claim 6, characterized in that: In step 5, any aquaculture digital twin scenario application can query and list the personalized data and computing model services of the registered aquaculture digital twin scenario applications from the shared service platform, and then read the specific content of the service interface supported by the json schema document according to the json schema document specification definition of the service, including the service endpoint, service name, version, service dependent model list, input, and output, and obtain the corresponding data or computing model results according to the specific parameters provided in the json schema document, thereby maximizing the resource utilization in the digital twin scenario application.

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