Remote control method of fish passing facility

Abstract

The invention provides a remote control method of a fish passing facility, and relates to the technical field of Internet of Things. The method comprises the following steps: acquiring real-time monitoring data of a plurality of water body environments through a plurality of data acquisition subsystems in an Internet of Things system, then storing the real-time monitoring data to a preset database through a plurality of edge computing nodes in the Internet of Things system, and processing the real-time monitoring data through a cloud server in the Internet of Things system, according to the method, the target device with the fault and the target fault type of the target device are determined, and then the target device is remotely controlled through the target edge computing node corresponding to the target device, so that the target device can execute the fault repair operation, and fault prediction and remote control of each device are realized through the method provided by the invention.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a remote control method for a fish passing facility. Background Art

[0002] The automated and intelligent systems of fish-passing facilities mainly include fish-attracting devices, intelligent identification technology, fish passage monitoring systems, and remote monitoring and management systems. The above systems improve the operating efficiency of fish-passing facilities and reduce labor.

[0003] At present, existing technologies can realize remote monitoring and management of fish-passing facilities through the Internet or dedicated networks, collect and analyze the operation data of fish-passing facilities, provide decision-making support for managers, and help optimize the design and operation strategies of fish-passing facilities. However, these solutions usually do not have the ability of real-time monitoring and remote operation, nor do they have the advantages of artificial intelligence deep learning technology, nor do they have the characteristics of iterative evolution, and may have limitations in accuracy and efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a remote control method for fish passing facilities in view of the above-mentioned deficiencies in the prior art, so as to achieve fault prediction and remote control of various devices.

[0005] To achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0006] In a first aspect, an embodiment of the present application provides a remote control method for a fish passage facility, which is applied to an Internet of Things system, and the method includes:

[0007] The multiple data acquisition subsystems in the Internet of Things system are used to respectively acquire real-time monitoring data of multiple water environments, wherein the real-time monitoring data of each water environment includes: real-time status data of each device in the fish passing facility in each water environment, and real-time operation records of each device;

[0008] Using the edge computing node set for each water environment in the Internet of Things system, the real-time status data of each device and the real-time operation record of each device are stored in a preset database;

[0009] Using the cloud server in the Internet of Things system to obtain the real-time status data of each device and the real-time operation record of each device from the preset database;

[0010] Using the cloud server, based on the pre-trained fault prediction model, the real-time status data of each device and the real-time operation record of each device are processed to generate the failure probability of each device for each preset fault type;

[0011] Using the cloud server, based on the failure probabilities of the various devices for the various preset failure types, determining a target failure type existing in a target device;

[0012] The cloud server is used to remotely control the target device to perform a fault repair operation through a target edge computing node corresponding to the target device.

[0013] In an optional implementation, the using the cloud server to remotely control the target device to perform a fault repair operation through a target edge computing node corresponding to the target device includes:

[0014] Using the cloud server to generate a repair operation command for the target device;

[0015] The cloud server sends a broadcast message of the repair operation command based on a preset adaptive network protocol;

[0016] If the intelligent gateway corresponding to the target edge computing node detects the broadcast message of the repair operation command, the target device is remotely controlled through the target edge computing node to perform the corresponding fault repair operation based on the repair operation command.

[0017] In an optional embodiment, the method further comprises:

[0018] The cloud server generates a fault repair report for the target device based on a prediction result of the target fault type existing in the target device and a fault repair result of the target device, wherein the fault repair report includes: preventive inspection and maintenance suggestions for the target device for the target fault type.

[0019] In an optional embodiment, the real-time monitoring data of each water environment further includes: real-time water monitoring data of each water environment and real-time water monitoring images in each water environment;

[0020] The method further comprises:

[0021] Using the edge computing node set for each water environment in the Internet of Things system, the real-time water monitoring data of each water environment is analyzed to obtain the real-time water change data of each water environment;

[0022] Using the edge computing node set for each water environment in the Internet of Things system, fish analysis is performed on the real-time water monitoring image of each water environment to obtain real-time fish passing data of each water environment;

[0023] Using the edge computing node set for each water environment in the Internet of Things system, the real-time water change data of each water environment and the real-time fish passing data of each water environment are stored in the preset database;

[0024] The cloud server is used to obtain the real-time water body change data of each water body environment and the real-time fish passing data of each water body environment from the preset database;

[0025] The cloud server is used to train a preset initial fish passing prediction model based on the real-time water body change data of the multiple water body environments and the real-time fish passing data of the multiple water body environments, so as to obtain the fish passing prediction models of the watersheds corresponding to the multiple water body environments.

[0026] In an optional embodiment, the edge computing node set for each water environment in the Internet of Things system is used to perform fish analysis on the real-time water monitoring image of each water environment to obtain real-time fish passing data of each water environment, including:

[0027] Using the edge computing node set for each water environment in the Internet of Things system, based on a preset fish recognition model, fish recognition is performed on the real-time water monitoring image of each water environment to obtain the real-time fish type, real-time fish size and real-time number of fish passing through each water environment;

[0028] Using the edge computing node set for each water environment in the Internet of Things system, based on a preset fish behavior analysis model, fish behavior recognition is performed on the real-time water monitoring image of each water environment to obtain the real-time fish passing behavior of each water environment;

[0029] The real-time fish passing data of each water environment includes: the real-time fish passing type, the real-time fish passing size, the real-time number of fish passing and the real-time fish passing behavior.

[0030] In an optional embodiment, the method further comprises:

[0031] By using the edge computing nodes set for each water environment in the Internet of Things system, the operating parameters of each device in the fish passing facility in each water environment are adjusted in real time based on the real-time water change data of each water environment.

[0032] In an optional embodiment, the method further comprises:

[0033] The data gateway set for each water environment in the Internet of Things system is used, and a preset adaptive network protocol is used to transmit the real-time monitoring data of each water environment to the edge computing node set for each water environment.

[0034] In an optional embodiment, the method further comprises:

[0035] The cloud server is used to generate a virtual water environment of each water environment and a three-dimensional visualization image of virtual fish objects in the virtual water environment based on the real-time water change data of each water environment and the real-time fish passing data of each water environment.

[0036] In an optional embodiment, the method further comprises:

[0037] The cloud server is used to generate a virtual water environment simulating each water environment and a visualization image of the virtual fish passing facilities simulating operation in the virtual water environment based on the real-time status data of each device and the real-time operation records of each device.

[0038] In an optional embodiment, the method further comprises:

[0039] Using the cloud server, based on the historical status data of each device and the historical operation records of each device, a virtual water environment simulating each water environment and a simulation training scene of a virtual fish passing facility in the virtual water environment are generated;

[0040] In response to a simulation training operation on a target virtual device in the simulation training scenario, the operating state of the target virtual device in the simulation training scenario is controlled to be adjusted, and the virtual water environment is adjusted.

[0041] In a second aspect, the present application also provides a remote control device for a fish passing facility, which is applied to an Internet of Things system, and the device includes:

[0042] An acquisition module, used to respectively acquire real-time monitoring data of multiple water environments using multiple data acquisition subsystems in the Internet of Things system, wherein the real-time monitoring data of each water environment includes: real-time status data of each device in the fish passing facility in each water environment, and real-time operation records of each device;

[0043] A storage module, used to use the edge computing node set for each water environment in the Internet of Things system to store the real-time status data of each device and the real-time operation record of each device in a preset database;

[0044] The acquisition module is further used to acquire the real-time status data of each device and the real-time operation record of each device from the preset database using the cloud server in the Internet of Things system;

[0045] A processing module, configured to process the real-time status data of each device and the real-time operation record of each device using the cloud server based on a pre-trained fault prediction model, and generate a fault probability of each device for each preset fault type;

[0046] A determination module, configured to determine, using the cloud server, a target fault type existing in a target device based on the fault probabilities of the respective devices for the respective preset fault types;

[0047] The control module is used to remotely control the target device to perform a fault repair operation by using the cloud server through a target edge computing node corresponding to the target device.

[0048] In the third aspect, an embodiment of the present application also provides a cloud server, comprising: a processor, a storage medium and a bus, wherein the storage medium stores program instructions executable by the processor, and when the cloud server is running, the processor communicates with the storage medium through the bus, and the processor executes the program instructions to perform the steps of the remote control method for fish passing facilities as described in any one of the first aspects.

[0049] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the remote control method for a fish passage facility as described in any one of the first aspects are executed.

[0050] The beneficial effects of this application are:

[0051] An embodiment of the present application provides a remote control method for fish-passing facilities, which is applied to an Internet of Things system. The method includes: using multiple data acquisition subsystems in the Internet of Things system to respectively obtain real-time monitoring data of multiple water environments, the real-time monitoring data of each water environment including: real-time status data of each device in the fish-passing facilities in each water environment, and real-time operation records of each device; using an edge computing node set for each water environment in the Internet of Things system to store the real-time status data of each device and the real-time operation records of each device in a preset database; using a cloud server in the Internet of Things system to obtain the real-time status data of each device and the real-time operation records of each device from the preset database; using the cloud server to process the real-time status data of each device and the real-time operation records of each device based on a pre-trained fault prediction model to generate a failure probability of each device for each preset failure type; using the cloud server to determine the target failure type of the target device based on the failure probability of each device for each preset failure type; using the cloud server to remotely control the target device to perform a fault repair operation through the target edge computing node corresponding to the target device.

[0052] In the method provided by the present application, real-time monitoring data of multiple water environments are obtained through multiple data acquisition subsystems in the Internet of Things system, and then the real-time monitoring data are stored in a preset database through multiple edge computing nodes in the Internet of Things system. The real-time monitoring data is processed by the cloud server in the Internet of Things system to determine the target device with faults and the target fault type of the target device. Then, the target device is remotely controlled through the target edge computing node corresponding to the target device, so that the target device can perform fault repair operations. Through the method provided by the present application, fault prediction and remote control of each device are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 A schematic diagram of the structure of an Internet of Things system for a fish-passing facility provided in an embodiment of the present application;

[0055] Figure 2 One of the flow charts of a remote control method for a fish passing facility provided in an embodiment of the present application;

[0056] Figure 3 A second flow chart of a remote control method for a fish passage facility provided in an embodiment of the present application;

[0057] Figure 4 A third flow chart of a remote control method for a fish passage facility provided in an embodiment of the present application;

[0058] Figure 5 A fourth flow chart of a remote control method for a fish passage facility provided in an embodiment of the present application;

[0059] Figure 6 A fifth flow chart of a remote control method for a fish passage facility provided in an embodiment of the present application;

[0060] Figure 7 A schematic diagram of functional modules of a remote control device for a fish-passing facility provided in an embodiment of the present application;

[0061] Figure 8 A schematic diagram of a cloud server provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0062] 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 only part of the embodiments of the present invention, not all of the embodiments.

[0063] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0064] In the description of the present application, it should be noted that if the terms "upper", "lower", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, or is the orientation or position relationship in which the product of the application is usually placed when used. It is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application.

[0065] In addition, the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0066] It should be noted that, in the absence of conflict, the features in the embodiments of the present application may be combined with each other.

[0067] In order to realize fault prediction and remote control of each device in the fish-passing facility based on the Internet of Things system of the fish-passing facility, an embodiment of the present application provides a remote control method for the fish-passing facility, in which the real-time status data of each device and the real-time operation record of each device are processed by the cloud server in the Internet of Things, and the failure probability of each device for each preset fault type is generated, so as to determine the target fault type existing in the target device, and through the target edge computing node corresponding to the target device, the target device is remotely controlled to perform fault repair operations, thereby realizing fault prediction of the target device and remote control of the target device.

[0068] Since the remote control method of a fish passage facility provided in the embodiment of the present application is applied to an Internet of Things system, the Internet of Things system is first introduced. Figure 1 This is a schematic diagram of the structure of an Internet of Things system for fish passing facilities provided in an embodiment of the present application. Figure 1 As shown in Figure 1, the IoT system includes: multiple data acquisition subsystems, multiple edge computing nodes, and cloud servers.

[0069] A data acquisition subsystem is set up in the system, which is used to collect real-time monitoring data in the corresponding water environment. The real-time monitoring data includes real-time status data of each device in the fish passing facilities in each water environment, as well as real-time operation records of each device.

[0070] An edge computing node is also set up in each water environment. The edge computing node is communicated with the data acquisition subsystem. The edge computing node is used to receive the real-time status data of each device in the water environment transmitted by the data acquisition subsystem, as well as the real-time operation records of each device, and store the real-time status data and real-time operation records of each device in a preset database.

[0071] The cloud server is connected to the edge computing nodes set up in each water environment to obtain the real-time status data and real-time operation records of each device, predict the failure of each device, determine the faulty device, and remotely control the faulty device to perform fault repair operations.

[0072] The remote control method for the fish passing facility provided in the embodiment of the present application is explained in detail below through specific examples in conjunction with the accompanying drawings. Figure 2 This is one of the flow charts of a remote control method for a fish passage facility provided in an embodiment of the present application. Figure 2 As shown, the method includes:

[0073] S101. Use multiple data acquisition subsystems in the Internet of Things system to respectively obtain real-time monitoring data of multiple water environments.

[0074] In this embodiment, the real-time monitoring data of each water environment includes: the real-time status data of each device in the fish passing facilities in each water environment, and the real-time operation record of each device.

[0075] Among them, since a data acquisition subsystem is set in each water environment, each data acquisition subsystem is used to collect real-time detection data of each water environment. Specifically, each data acquisition subsystem includes: equipment status monitoring sensors installed at preset key positions of each equipment in the fish passing facilities of the water environment. The equipment status monitoring sensors include: temperature sensors, vibration sensors, pressure sensors, etc., which are used to monitor the real-time status data of each equipment, such as temperature, vibration, pressure, etc. The real-time status data of each equipment is: the status data collected by the equipment status monitoring sensors installed at the preset key positions of each equipment, and the real-time operation records of each equipment are the real-time operation status of each equipment, that is, the real-time operation records, operation parameters, operation logs, etc. of each equipment.

[0076] S102, using the edge computing nodes set for each water environment in the Internet of Things system, the real-time status data of each device and the real-time operation records of each device are stored in a preset database.

[0077] S103, using the cloud server in the Internet of Things system to obtain the real-time status data of each device and the real-time operation record of each device from a preset database.

[0078] Specifically, since the data acquisition subsystem is communicatively connected with the edge computing node, the data acquisition subsystem can transmit the collected real-time status data of each device and the real-time operation records of each device to the edge computing node, so that the edge computing node performs data format standardization processing on the received real-time status data and real-time operation records of each device. Since the edge computing node is communicatively connected with the cloud server, the edge computing node can store the standardized real-time status data and real-time operation records of each device in the preset database of the cloud server.

[0079] The cloud server then obtains the real-time status data of each device and the real-time operation records of each device from the preset database.

[0080] S104. Using a cloud server, based on a pre-trained fault prediction model, the real-time status data of each device and the real-time operation record of each device are processed to generate the failure probability of each device for each preset fault type.

[0081] Specifically, a cloud server is used to perform data cleaning and format conversion on the real-time status data of each device and the real-time operation record of each device, and then feature extraction is performed to obtain the real-time status data of each device and the features of the real-time operation record.

[0082] The historical status data and historical operation records of each device constitute the historical data set. The pre-trained fault prediction model is a fault prediction model obtained by training the historical data set using a recurrent neural network. The cloud server adds the real-time status data and real-time operation records of each device to the historical data set, and forms a real-time data set with the historical status data and historical operation records of each device, so that the pre-trained fault prediction model is trained based on the real-time data set to obtain the failure probability of each device for each preset fault type.

[0083] S105 . Using a cloud server, determine a target fault type existing in a target device based on the fault probability of each device for each preset fault type.

[0084] Specifically, the maximum failure probability is determined from the failure probabilities of the various devices for various preset failure types, a target device is determined from the various devices according to the maximum failure probability, and a target failure type of the target device is determined.

[0085] It should be noted that after determining the target fault type existing in the target device, early warning notification information is generated according to the target fault type existing in the target device and sent to the device management personnel.

[0086] S106. Use the cloud server to remotely control the target device to perform fault repair operations through the target edge computing node corresponding to the target device.

[0087] Among them, after the cloud server determines that the target device has the target fault type, it sends a remote control instruction to the target edge computing node corresponding to the target device, so that the target edge computing node controls the target device according to the remote control instruction, so that the target device performs the fault repair operation and completes the fault repair of the target device.

[0088] In summary, an embodiment of the present application provides a remote control method for fish-passing facilities, which is applied to an Internet of Things system. The method includes: using multiple data acquisition subsystems in the Internet of Things system to respectively obtain real-time monitoring data of multiple water environments, the real-time monitoring data of each water environment includes: real-time status data of each device in the fish-passing facility in each water environment, and real-time operation records of each device; using an edge computing node set for each water environment in the Internet of Things system to store the real-time status data of each device and the real-time operation records of each device in a preset database; using a cloud server in the Internet of Things system to obtain the real-time status data of each device and the real-time operation records of each device from the preset database; using the cloud server to process the real-time status data of each device and the real-time operation records of each device based on a pre-trained fault prediction model, and generate a failure probability of each device for each preset fault type; using the cloud server to determine the target fault type of the target device based on the failure probability of each device for each preset fault type; using the cloud server to remotely control the target device through the target edge computing node corresponding to the target device to perform a fault repair operation.

[0089] In the method provided by the present application, real-time monitoring data of multiple water environments are obtained through multiple data acquisition subsystems in the Internet of Things system, and then the real-time monitoring data are stored in a preset database through multiple edge computing nodes in the Internet of Things system. The real-time monitoring data is processed by the cloud server in the Internet of Things system to determine the target device with faults and the target fault type of the target device. Then, the target device is remotely controlled through the target edge computing node corresponding to the target device, so that the target device can perform fault repair operations. Through the method provided by the present application, fault prediction and remote control of each device are achieved.

[0090] Based on the remote control method for a fish passage facility provided in the above embodiment, the embodiment of the present application also provides another possible implementation of the remote control method for a fish passage facility. Figure 3 The second flow chart of a remote control method for a fish passage facility provided in an embodiment of the present application. Figure 3 As shown, a cloud server is used to remotely control the target device through the target edge computing node corresponding to the target device to perform fault repair operations, including:

[0091] S201: Generate a repair operation command for a target device using a cloud server.

[0092] In this embodiment, the cloud server generates repair operation instructions for the target device according to the target fault type existing in the target device. The repair operation instructions may include: shutting down the target device, restarting the target device, adjusting the operating parameters of the target device, switching the backup system of the target device, etc.

[0093] S202: Using the cloud server to issue a broadcast message of a repair operation command based on a preset adaptive network protocol.

[0094] The preset adaptive network protocol is a lightweight publish / subscribe message transport protocol (Message Queuing Telemetry Transport, MQTT), and a cloud server is used to send a broadcast message for a repair operation command based on the preset adaptive network protocol.

[0095] S203: If the intelligent gateway corresponding to the target edge computing node detects the broadcast message of the repair operation command, the target device is remotely controlled through the target edge computing node to perform the corresponding fault repair operation based on the repair operation command.

[0096] Specifically, the Internet of Things system includes multiple edge computing nodes. The intelligent gateway of each edge computing node monitors the broadcast message in real time and determines whether the monitored broadcast message is for each edge computing node.

[0097] If the intelligent gateway corresponding to the target edge computing node detects the broadcast message of the repair operation command and determines that the broadcast message is for the target device in the target edge computing node, it sends the received instruction to the cloud server.

[0098] Since each edge computing node is provided with a programmable logic controller (PLC) for controlling each device, the programmable controller in the target edge computing node controls the target device according to the repair operation command, so that the target device performs the corresponding fault repair operation.

[0099] For example, taking the target fault as the water pump in the fish-passing facility A, the cloud server obtains the real-time status data and real-time operation records of the water pump, such as temperature data, vibration data, etc. The cloud server processes the real-time status data and real-time operation records of the water pump based on the pre-trained fault prediction model, generates the failure probability of the water pump for each preset fault type, and determines the target fault type of the water pump, and generates a repair operation command for the water pump. If the repair operation command for the water pump is a restart command, a broadcast message of the restart command for the water pump is issued based on the preset adaptive network protocol. If the smart gateway corresponding to the edge computing node A corresponding to the fish-passing facility A detects the broadcast message of the restart command for the water pump, the edge computing node A controls the water pump to restart, thereby realizing the fault repair of the water pump.

[0100] In the method provided in the embodiment of the present application, a cloud server is used to generate a repair operation command for a target device, and the cloud server is used to issue a broadcast message of the repair operation command based on a preset adaptive network protocol. If the intelligent gateway corresponding to the target edge computing node detects the broadcast message of the repair operation command, the target device is remotely controlled through the target edge computing node to perform the corresponding fault repair operation based on the repair operation command, thereby realizing remote control of the target device by the cloud server, timely repairing the fault of the target device, and ensuring the normal operation of the fish passing facility where the target device is located.

[0101] The embodiment of the present application also provides another possible implementation of a remote control method for a fish passage facility. The method further includes:

[0102] A cloud server is used to generate a fault repair report for the target device based on the prediction results of the target fault type existing in the target device and the fault repair results of the target device. The fault repair report includes: preventive inspection and maintenance suggestions for the target device for the target fault type.

[0103] In this embodiment, a fault repair report for the target device is generated based on the prediction results of the target fault type existing in the target device and the fault repair results of the target device, so that the equipment management personnel can perform regular inspections and preset maintenance on the target device based on the fault repair report.

[0104] The cloud server can also generate a repair log for the target device based on the prediction results of the target fault type existing in the target device and the fault repair results of the target device, and record the target fault type, time of occurrence, repair operation, etc. of the target fault.

[0105] The embodiment of the present application also provides another possible implementation of the remote control method of the fish passing facility. The real-time monitoring data of each water environment also includes: real-time water monitoring data of each water environment and real-time water monitoring images in each water environment. Among them, each data acquisition subsystem in the Internet of Things system includes: water body detection sensors installed at each preset position of the corresponding fish passing facility, such as water quality sensors, flow rate sensors, image sensors, sonar sensors, etc.

[0106] Specifically, a water quality sensor is installed at a preset position of the fish-passing facility to monitor changes in water temperature. The water quality sensor may use an insitu Troll500 waterproof digital temperature water quality multi-parameter sensor; a flow rate sensor is installed within multiple preset ranges of the fish-passing facility, such as the entrance and exit of the fish-passing facility channel, or at the vertical seam of the vertical seam fishway, to monitor the water flow velocity within each preset range. The flow rate sensor may use a Doppler flow rate sensor; an image sensor is installed at the vertical seam of the fish-passing facility to capture water images or water videos through a camera. The water images or water videos record scenes of fish passing by. The image sensor may use various waterproof underwater video acquisition devices; a sonar sensor is installed at a preset position of the fish-passing facility. It is generally suitable for use in water environments with poor water quality or turbid water. It is used to detect the position and number of fish using sound waves. The sonar sensor may use a Blueprint Oculus M1200d sonar sensor.

[0107] The real-time water body monitoring data of each water body environment is the monitoring data collected in real time by water quality sensors, flow rate sensors and sonar sensors, and the real-time water body monitoring image in each water body environment is the monitoring image collected in real time by image sensors.

[0108] Figure 4 The third flow chart of a remote control method for a fish passage facility provided in an embodiment of the present application. Figure 4 As shown, the method also includes:

[0109] S301. Use the edge computing nodes set for each water environment in the Internet of Things system to analyze the real-time water monitoring data of each water environment to obtain real-time water change data of each water environment.

[0110] In this embodiment, the edge computing node set up for each water environment in the Internet of Things system obtains the real-time water body monitoring data of each water body environment transmitted by the corresponding data acquisition subsystem, performs data analysis, and determines the real-time water body change data of each water body environment, wherein the real-time water body change data includes: water flow velocity change data, water temperature change data, etc.

[0111] At the same time, the data format of the real-time water monitoring data of each water environment is standardized to obtain a data format that complies with the requirements of the cloud server.

[0112] S302: Use the edge computing nodes set for each water environment in the Internet of Things system to perform fish analysis on the real-time water monitoring images of each water environment to obtain real-time fish passing data for each water environment.

[0113] Specifically, the edge computing node set up for each water environment in the Internet of Things system obtains the real-time water body monitoring image of each water body environment transmitted by the corresponding data acquisition subsystem, performs fish analysis, determines the real-time fish passing data of each water body environment, and filters out key frames containing fish images from the real-time water body monitoring image.

[0114] S303, using the edge computing node set for each water environment in the Internet of Things system, the real-time water change data of each water environment and the real-time fish passing data of each water environment are stored in a preset database.

[0115] S304, using a cloud server to obtain real-time water body change data of each water body environment and real-time fish passing data of each water body environment from a preset database.

[0116] S305. Using a cloud server, a preset initial fish passing prediction model is trained based on real-time water body change data of multiple water body environments and real-time fish passing data of multiple water body environments to obtain fish passing prediction models for river basins corresponding to multiple water body environments.

[0117] Specifically, the cloud server uses a distributed database system (MongoDB) to store structured data, i.e., real-time water change data of multiple water environments, and uses an object storage system (Amazon S3) to store large-scale unstructured data, i.e., real-time fish passing data of multiple water environments.

[0118] A cloud server is used to preprocess the real-time water change data of each water environment and the real-time fish passing data of each water environment, including extracting features from the real-time water change data of each water environment and the real-time fish passing data of each water environment, including water flow velocity, water quality, water level, fish behavior, etc., and then using ETL (Extract, Transform, Load) tools to clean, transform and load the feature data.

[0119] Based on the real-time water body change data of each water body environment and the real-time fish passing data of each water body environment, the preset initial fish passing prediction model is trained to obtain the fish passing prediction model corresponding to each water body environment. The fish passing prediction model corresponding to each water body environment can predict the relationship between the fish passage rate and water body changes of each water body environment.

[0120] A cloud server is used to integrate the real-time water body change data and the real-time fish passing data of multiple water body environments. Then, a preset initial fish passing prediction model is trained based on the integrated real-time water body change data and the real-time fish passing data to obtain a fish passing prediction model for the river basins corresponding to the multiple water body environments. The fish passing prediction model for the river basins corresponding to the multiple water body environments can predict the relationship between the fish passage rate and water body changes in the entire river basin.

[0121] Specifically, when training a fish passing prediction model for a watershed corresponding to an environment, first, key frames containing fish are extracted from real-time fish passing data of multiple water environments, and the key frames are annotated with fish behaviors to expand the data set. Finally, the expanded training data set is used to train a preset initial fish passing prediction model to obtain fish passing prediction models for watersheds corresponding to multiple water environments.

[0122] In the method provided in the embodiment of the present application, an edge computing node set for each water environment in the Internet of Things system is used to perform data analysis on the real-time water monitoring data of each water environment to obtain real-time water change data of each water environment; an edge computing node set for each water environment in the Internet of Things system is used to perform fish analysis on the real-time water monitoring image of each water environment to obtain real-time fish passing data of each water environment; an edge computing node set for each water environment in the Internet of Things system is used to store the real-time water change data of each water environment and the real-time fish passing data of each water environment in a preset database; a cloud server is used to obtain the real-time water change data of each water environment and the real-time fish passing data of each water environment from the preset database; a cloud server is used to train a preset initial fish passing prediction model based on the real-time water change data of multiple water environments and the real-time fish passing data of multiple water environments to obtain fish passing prediction models for the corresponding river basins of multiple water environments.

[0123] After the edge computing node set up for each water environment in the Internet of Things system receives the real-time water body monitoring data and real-time water body monitoring images of each water body environment, it quickly performs preliminary analysis and pre-processing, which can reduce delays and improve the response speed of the Internet of Things system. The cloud server in the Internet of Things system can obtain the fish passing prediction model for the corresponding river basins of multiple water environments based on the real-time water body change data and real-time fish passing data of multiple water environments, thereby predicting the relationship between fish passing and water environment changes in the entire river basin.

[0124] The embodiment of the present application also provides another possible implementation of the remote control method of the fish passing facility. Figure 5 The fourth flow chart of a remote control method for a fish passage facility provided in an embodiment of the present application. Figure 5 As shown, the edge computing nodes set for each water environment in the IoT system are used to analyze the real-time water monitoring images of each water environment to obtain real-time fish passing data for each water environment, including:

[0125] S401. Using the edge computing nodes set for each water environment in the Internet of Things system, based on a preset fish recognition model, fish recognition is performed on the real-time water monitoring images of each water environment to obtain the real-time type of fish, real-time size of fish, and real-time number of fish passing through each water environment.

[0126] S402. Using the edge computing nodes set for each water environment in the Internet of Things system, based on a preset fish behavior analysis model, fish behavior recognition is performed on the real-time water monitoring images of each water environment to obtain the real-time fish passing behavior of each water environment.

[0127] The real-time fish passing data of each water environment includes: real-time fish passing type, real-time fish passing size, real-time fish passing number and real-time fish passing behavior.

[0128] In this embodiment, an edge computing node set for each water environment in the Internet of Things system is used to filter out key frame images containing fish based on a preset fish recognition model, and the type of fish, that is, the species of fish, such as carp, crucian carp, etc., is identified from each key frame image containing fish, and the size of each fish and the number of fish in each image are identified.

[0129] According to the preset fish behavior analysis model, fish behavior recognition is performed on the real-time water monitoring images of each water environment to determine the fish passing behavior, that is, to determine whether each fish in each image is swimming from bottom to top or from top to bottom.

[0130] The real-time fish passing data of each water environment includes: real-time fish passing type, real-time fish passing size, real-time fish passing number and real-time fish passing behavior, as well as key frame images containing fish.

[0131] The method of the present application adopts an edge computing node set for each water environment in the Internet of Things system, and based on a preset fish recognition model, performs fish recognition on the real-time water monitoring image of each water environment to obtain the real-time fish type, real-time fish size and real-time fish number of each water environment; adopts an edge computing node set for each water environment in the Internet of Things system, and based on a preset fish behavior analysis model, performs fish behavior recognition on the real-time water monitoring image of each water environment to obtain the real-time fish behavior of each water environment; the real-time fish data of each water environment includes: real-time fish type, real-time fish size, real-time fish number and real-time fish behavior. The real-time water monitoring image of each water environment is processed in advance by the edge computing node to obtain the real-time fish data of each water environment, without the need for a cloud server to process the real-time water monitoring image of each water environment, thereby improving the response speed of the Internet of Things system.

[0132] The embodiment of the present application also provides another possible implementation of a remote control method for a fish passage facility. The method further includes:

[0133] By using edge computing nodes set up for each water environment in the Internet of Things system, the operating parameters of each equipment in the fish passing facilities in each water environment are adjusted in real time based on the real-time water change data of each water environment.

[0134] In this embodiment, the edge computing node set for each water environment in the Internet of Things system is used to perform data analysis on the real-time water monitoring data of each water environment to obtain the real-time water change data of each water environment. If the real-time water change data is not within the preset range, the operating parameters of each device in the fish passing facility in each water environment are adjusted in real time. For example, if the real-time water data monitored by the flow velocity sensor is analyzed and it is determined that the water flow velocity is changing faster and faster and exceeds the preset range of the water flow, the edge computing node sends an instruction to adjust the operating speed of the water pump to optimize the water flow conditions.

[0135] The embodiment of the present application also provides another possible implementation of a remote control method for a fish passage facility. The method further includes:

[0136] The data gateway set up for each water environment in the Internet of Things system is used, and the preset adaptive network protocol is adopted to transmit the real-time monitoring data of each water environment to the edge computing node set up for each water environment.

[0137] In this embodiment, the data gateway in the Internet of Things system performs preliminary processing on the real-time water body monitoring data of each water body environment and the real-time water body monitoring image in each water body environment, including: data cleaning, format conversion and compression, and then transmits the preliminarily processed real-time water body monitoring data of each water body environment and the real-time water body monitoring image in each water body environment to the corresponding edge computing node through the data gateway using a preset adaptive network protocol.

[0138] Among them, Adaptive Network Protocol is a communication protocol that can dynamically adjust data transmission strategies according to network conditions. Its main purpose is to maintain the stability and efficiency of data transmission in an unstable network environment. These protocols usually combine dynamic routing selection, congestion control, error detection and correction and other technologies to ensure the reliability and efficiency of data transmission.

[0139] This embodiment adopts a method of using multiple network protocols in combination. For text-type data, i.e., real-time water monitoring data, a lightweight publish / subscribe message transmission protocol MQTT (Message Queuing Telemetry Transport) is used. The working principle is: the MQTT protocol manages message transmission between clients through a message broker (Broker). Clients (such as sensors and edge computing nodes) dynamically choose to connect to different message brokers according to network conditions, thereby optimizing the data transmission path. MQTT supports different quality of service (QoS) levels, allowing clients to select the appropriate QoS level according to the current network conditions. For example, when the network conditions are good, QoS2 (guaranteeing that the message arrives and arrives only once) can be used; when the network conditions are poor, QoS0 (best effort transmission, no guarantee of arrival) can be used. The MQTT protocol detects and corrects transmission errors through an acknowledgment mechanism (Acknowledgement) between the client and the agent. When the client sends a message, it will wait for the agent's confirmation. If the confirmation is not received, the client will retransmit the message. The data acquisition subsystem transmits the real-time water monitoring data to the edge computing node through the MQTT protocol. The edge computing node selects the appropriate QoS level according to the network conditions to ensure the reliable transmission of real-time water monitoring data. After receiving the real-time water monitoring data, the edge computing node immediately performs real-time data processing and analysis.

[0140] For video stream data, i.e. real-time water monitoring images, the network control protocol RTSP (Real-Time Streaming Protocol) for controlling media streams is used for real-time transmission. In order to ensure the transmission quality of video streams in unstable network environments, adaptive network protocols can be combined to optimize data transmission. The bit rate of the video stream is dynamically adjusted according to the network conditions. For example, when the bandwidth is sufficient, high-definition (HD) video is transmitted; when the bandwidth is limited, the video resolution and bit rate are automatically reduced to ensure the continuity and stability of the video stream. The data acquisition subsystem transmits real-time water monitoring images to the edge computing node through the RTSP protocol.

[0141] The embodiment of the present application also provides another possible implementation of a remote control method for a fish passage facility. The method further includes:

[0142] A cloud server is used to generate a three-dimensional visualization image of a virtual water environment of each water environment and virtual fish objects in the virtual water environment based on the real-time water change data of each water environment and the real-time fish passing data of each water environment.

[0143] In this embodiment, the cloud server uses augmented reality AR technology and virtual reality VR technology to simulate and display the real-time water body change data of each water body environment and the real-time fish passing data of each water body environment, and generates a three-dimensional visualization image of a virtual water environment of each water body environment and virtual fish objects in the virtual water environment. The fish behavior of the virtual fish objects under different real-time water body change data can be intuitively displayed in the three-dimensional visualization image, so that equipment managers can intuitively view the operation of the fish passing facilities.

[0144] The embodiment of the present application also provides another possible implementation of a remote control method for a fish passage facility. The method further includes:

[0145] Using a cloud server, a virtual water environment simulating each water environment and a visualization image of the virtual fish passing facilities simulating operation in the virtual water environment are generated based on the real-time status data of each device and the real-time operation records of each device.

[0146] In this embodiment, the cloud server uses augmented reality AR technology and virtual reality VR technology to simulate and display the real-time status data of each device and the real-time operation records of each device, generate a virtual water environment that simulates each water environment, and a visualization image of the virtual fish passing facilities simulated in the virtual water environment. The device parameters, maintenance reports, fault information, etc. of each device can be intuitively displayed in the visualization image, so that equipment managers can intuitively view the fault information of each device and perform timely maintenance.

[0147] It should be noted that the visualized image of the virtual fish-passing facility can be displayed superimposed on the three-dimensional visualized image of the virtual fish object.

[0148] The embodiment of the present application also provides another possible implementation of the remote control method of the fish passing facility. Figure 6 FIG5 is a flowchart of a remote control method for a fish passage facility provided in an embodiment of the present application. Figure 6 As shown, the method also includes:

[0149] S501. Using a cloud server, based on the historical status data of each device and the historical operation records of each device, a virtual water environment simulating each water environment and a simulation training scene of a virtual fish passing facility in the virtual water environment are generated.

[0150] S502: In response to a simulation training operation on a target virtual device in a simulation training scene, control the operation state of the target virtual device in the simulation training scene to adjust, and adjust the virtual water environment.

[0151] In this embodiment, the cloud server uses virtual reality VR technology to simulate and display the historical status data of each device and the historical operation records of each device, generate a virtual water environment that simulates each water environment, and a simulated training scene of virtual fish passing facilities in the virtual water environment, respond to the simulated training operation of the target virtual device in the simulated training scene, control the operation state of the target virtual device in the simulated training scene to adjust, and adjust the virtual water environment. For example, the operating parameters of the water pump are adjusted based on the simulated training scene, and the behavior of fish in the virtual water environment is also adjusted accordingly. If the water flow speed is reduced by controlling the water pump, the number of fish passing through is relatively reduced, or the number of fish upstream is relatively reduced, so that equipment managers can be trained to operate various devices in different water environments.

[0152] The following is a corresponding explanation of the remote control device and electronic device for executing the fish passing facility provided by any of the above embodiments of the present application. Its specific implementation process and the technical effects produced are the same as those of the corresponding method embodiments mentioned above. For the sake of brief description, the parts not mentioned in this embodiment can refer to the corresponding contents in the method embodiments.

[0153] Figure 7 This is a functional module diagram of a remote control device for a fish passage facility provided in an embodiment of the present application. Figure 7 As shown, the remote control device 100 of the fish passing facility includes:

[0154] The acquisition module 110 is used to respectively acquire real-time monitoring data of multiple water environments using multiple data acquisition subsystems in the Internet of Things system, and the real-time monitoring data of each water environment includes: real-time status data of each device in the fish passing facility in each water environment, and real-time operation records of each device;

[0155] The storage module 120 is used to use the edge computing node set for each water environment in the Internet of Things system to store the real-time status data of each device and the real-time operation record of each device in a preset database;

[0156] The acquisition module 110 is also used to obtain the real-time status data of each device and the real-time operation record of each device from a preset database using a cloud server in the Internet of Things system;

[0157] The processing module 130 is used to process the real-time status data of each device and the real-time operation record of each device using a cloud server based on a pre-trained fault prediction model, and generate a fault probability of each device for each preset fault type;

[0158] A determination module 140 is used to determine the target fault type of the target device based on the fault probability of each device for each preset fault type using a cloud server;

[0159] The control module 150 is used to remotely control the target device to perform fault repair operations through the target edge computing node corresponding to the target device using the cloud server.

[0160] Optionally, the control module 150 is also used to use a cloud server to generate a repair operation command for the target device; use the cloud server to issue a broadcast message of the repair operation command based on a preset adaptive network protocol; if the intelligent gateway corresponding to the target edge computing node monitors the broadcast message of the repair operation command, the target device is remotely controlled through the target edge computing node to perform a corresponding fault repair operation based on the repair operation command.

[0161] Optionally, the remote control device 100 for the fish passing facility further comprises:

[0162] The generation module is used to generate a fault repair report for the target device based on the prediction results of the target fault type existing in the target device and the fault repair results of the target device using the cloud server. The fault repair report includes: preventive inspection and maintenance suggestions for the target device for the target fault type.

[0163] Optionally, the remote control device 100 for the fish passing facility further comprises:

[0164] The analysis module is used to use the edge computing nodes set for each water environment in the Internet of Things system to perform data analysis on the real-time water monitoring data of each water environment to obtain real-time water change data of each water environment; use the edge computing nodes set for each water environment in the Internet of Things system to perform fish analysis on the real-time water monitoring images of each water environment to obtain real-time fish passing data of each water environment;

[0165] The storage module 120 is also used to use the edge computing node set for each water environment in the Internet of Things system to store the real-time water change data of each water environment and the real-time fish passing data of each water environment into a preset database;

[0166] The acquisition module 110 is also used to acquire the real-time water body change data of each water body environment and the real-time fish passing data of each water body environment from a preset database using a cloud server;

[0167] The training module is used to use a cloud server to train a preset initial fish passing prediction model based on real-time water body change data of multiple water body environments and real-time fish passing data of multiple water body environments, so as to obtain fish passing prediction models for river basins corresponding to multiple water body environments.

[0168] Optionally, the analysis module is also used to use the edge computing node set for each water body environment in the Internet of Things system to perform fish identification on the real-time water body monitoring image of each water body environment based on a preset fish identification model, so as to obtain the real-time type of fish passing through, the real-time size of fish passing through, and the real-time number of fish passing through each water body environment; use the edge computing node set for each water body environment in the Internet of Things system to perform fish behavior identification on the real-time water body monitoring image of each water body environment based on a preset fish behavior analysis model, so as to obtain the real-time fish passing behavior of each water body environment; the real-time fish passing data of each water body environment includes: real-time type of fish passing, real-time size of fish passing, real-time number of fish passing, and real-time fish passing behavior.

[0169] Optionally, the remote control device 100 for the fish passing facility further comprises:

[0170] The adjustment module is used to use the edge computing nodes set for each water environment in the Internet of Things system to adjust the operating parameters of each equipment in the fish passing facilities in each water environment in real time based on the real-time water change data of each water environment.

[0171] Optionally, the remote control device 100 for the fish passing facility further comprises:

[0172] The transmission module is used to use the data gateway set up for each water environment in the Internet of Things system, and adopt a preset adaptive network protocol to transmit the real-time monitoring data of each water environment to the edge computing node set up for each water environment.

[0173] Optionally, the generation module is also used to use a cloud server to generate a three-dimensional visualization image of a virtual water environment for each water environment and virtual fish objects in the virtual water environment based on real-time water change data of each water environment and real-time fish passing data of each water environment.

[0174] Optionally, the generation module is also used to use a cloud server to generate a virtual water environment simulating each water environment, as well as a visualization image of the virtual fish passing facilities simulated in the virtual water environment based on the real-time status data of each device and the real-time operation records of each device.

[0175] Optionally, the generating module is further used to generate, using a cloud server, a virtual water environment simulating each water environment, and a simulated training scene of a virtual fish passing facility in the virtual water environment based on historical status data of each device and historical operation records of each device;

[0176] The adjustment module is also used to respond to the simulation training operation of the target virtual device in the simulation training scene, control the operation state of the target virtual device in the simulation training scene to adjust, and adjust the virtual water environment.

[0177] The above-mentioned device is used to execute the method provided by the aforementioned embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0178] The above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASICs), or one or more microprocessors, or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0179] Figure 8 The following is a schematic diagram of a cloud server provided in an embodiment of the present application, which can be used for remote control of fish passing facilities. Figure 8 As shown, the cloud server 200 includes: a processor 210 , a storage medium 220 , and a bus 230 .

[0180] The storage medium 220 stores machine-readable instructions executable by the processor 210. When the cloud server is running, the processor 210 communicates with the storage medium 220 via the bus 230, and the processor 210 executes the machine-readable instructions to perform the steps of the above method embodiment. The specific implementation method and technical effect are similar and will not be repeated here.

[0181] Optionally, the present application further provides a storage medium 220, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method embodiment are executed. The specific implementation method and technical effect are similar and will not be repeated here.

[0182] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0183] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0185] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (English: processor) to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (English: Read-Only Memory, abbreviated: ROM), random access memory (English: Random Access Memory, abbreviated: RAM), disk or optical disk and other media that can store program codes.

[0186] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A remote control method for a fish passing facility, characterized in that: Applied to an Internet of Things system, the method comprises: The multiple data acquisition subsystems in the Internet of Things system are used to respectively acquire real-time monitoring data of multiple water environments, wherein the real-time monitoring data of each water environment includes: real-time status data of each device in the fish passing facility in each water environment, and real-time operation records of each device; Using the edge computing node set for each water environment in the Internet of Things system, the real-time status data of each device and the real-time operation record of each device are stored in a preset database; Using the cloud server in the Internet of Things system to obtain the real-time status data of each device and the real-time operation record of each device from the preset database; Using the cloud server, based on the pre-trained fault prediction model, the real-time status data of each device and the real-time operation record of each device are processed to generate the failure probability of each device for each preset fault type; Using the cloud server, based on the failure probabilities of the various devices for the various preset failure types, determining a target failure type existing in a target device; The cloud server is used to remotely control the target device to perform a fault repair operation through a target edge computing node corresponding to the target device.

2. The method according to claim 1, characterized in that The adopting the cloud server to remotely control the target device to perform a fault repair operation through a target edge computing node corresponding to the target device includes: Using the cloud server to generate a repair operation command for the target device; The cloud server sends a broadcast message of the repair operation command based on a preset adaptive network protocol; If the intelligent gateway corresponding to the target edge computing node detects the broadcast message of the repair operation command, the target device is remotely controlled through the target edge computing node to perform the corresponding fault repair operation based on the repair operation command.

3. The method according to claim 1, characterized in that The method further comprises: The cloud server generates a fault repair report for the target device based on a prediction result of the target fault type existing in the target device and a fault repair result of the target device, wherein the fault repair report includes: preventive inspection and maintenance suggestions for the target device for the target fault type.

4. The method according to claim 1, characterized in that: The real-time monitoring data of each water environment also includes: real-time water monitoring data of each water environment and real-time water monitoring images in each water environment; The method further comprises: Using the edge computing node set for each water environment in the Internet of Things system, the real-time water monitoring data of each water environment is analyzed to obtain the real-time water change data of each water environment; Using the edge computing node set for each water environment in the Internet of Things system, fish analysis is performed on the real-time water monitoring image of each water environment to obtain real-time fish passing data of each water environment; Using the edge computing node set for each water environment in the Internet of Things system, the real-time water change data of each water environment and the real-time fish passing data of each water environment are stored in the preset database; The cloud server is used to obtain the real-time water body change data of each water body environment and the real-time fish passing data of each water body environment from the preset database; The cloud server is used to train a preset initial fish passing prediction model based on the real-time water body change data of the multiple water body environments and the real-time fish passing data of the multiple water body environments, so as to obtain the fish passing prediction models of the watersheds corresponding to the multiple water body environments.

5. The method according to claim 4, characterized in that The edge computing node set for each water environment in the Internet of Things system is used to perform fish analysis on the real-time water monitoring image of each water environment to obtain real-time fish passing data of each water environment, including: Using the edge computing node set for each water environment in the Internet of Things system, based on a preset fish recognition model, fish recognition is performed on the real-time water monitoring image of each water environment to obtain the real-time fish type, real-time fish size and real-time number of fish passing through each water environment; Using the edge computing node set for each water environment in the Internet of Things system, based on a preset fish behavior analysis model, fish behavior recognition is performed on the real-time water monitoring image of each water environment to obtain the real-time fish passing behavior of each water environment; The real-time fish passing data of each water environment includes: the real-time fish passing type, the real-time fish passing size, the real-time number of fish passing and the real-time fish passing behavior.

6. The method according to claim 4, characterized in that The method further comprises: By using the edge computing nodes set for each water environment in the Internet of Things system, the operating parameters of each device in the fish passing facility in each water environment are adjusted in real time based on the real-time water change data of each water environment.

7. The method according to claim 1, characterized in that The method further comprises: The data gateway set for each water environment in the Internet of Things system is used, and a preset adaptive network protocol is used to transmit the real-time monitoring data of each water environment to the edge computing node set for each water environment.

8. The method according to claim 1, characterized in that The method further comprises: The cloud server is used to generate a virtual water environment of each water environment and a three-dimensional visualization image of virtual fish objects in the virtual water environment based on the real-time water change data of each water environment and the real-time fish passing data of each water environment.

9. The method according to claim 1, characterized in that: The method further comprises: The cloud server is used to generate a virtual water environment simulating each water environment and a visualization image of the virtual fish passing facilities simulating operation in the virtual water environment based on the real-time status data of each device and the real-time operation records of each device.

10. The method according to claim 1, characterized in that The method further comprises: Using the cloud server, based on the historical status data of each device and the historical operation records of each device, a virtual water environment simulating each water environment and a simulation training scene of a virtual fish passing facility in the virtual water environment are generated; In response to a simulation training operation on a target virtual device in the simulation training scenario, the operating state of the target virtual device in the simulation training scenario is controlled to be adjusted, and the virtual water environment is adjusted.

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