Marine ranch water quality management method and system, electronic equipment and storage medium

Abstract

The invention provides a marine ranch water quality management method and system, electronic equipment and a storage medium, and the method comprises the steps that a water quality monitoring device, an edge computing terminal and a water quality adjusting device are arranged on a navigation mark in a marine ranch in advance, and the edge computing terminal obtains water quality parameter data of the marine ranch at a plurality of continuous moments; the water quality parameter data is collected through a water quality monitoring device, and if the water quality parameter predicted value exceeds a preset water quality normal range, a target adjusting amount is determined based on the water quality parameter predicted value; the water quality adjusting device adjusts the water quality according to the target adjusting quantity; near-end water quality prediction and water quality deterioration accident treatment can be completed at the navigation mark, the defect that water quality data cannot be monitored in real time and water quality deterioration accidents cannot be treated in time due to the fact that the time interval of Beidou satellite data transmission is long is overcome, and ecological risks and economic losses are effectively reduced.

Description

Technical Field

[0001] This application relates to the technical field of water quality regulation, and particularly to a water quality management method, system, electronic device and storage medium for a marine ranch. Background Art

[0002] As an important type of navigation aid equipment, the basic functions of a navigation mark include ship positioning, navigation or for other special purposes. With the improvement of shipping management requirements and the development of technology, the functions of navigation marks have become increasingly perfect, evolving from basic navigation marks to multi-functional navigation marks. In addition to the basic functions, they also include functions such as three-dimensional ship monitoring, water pollution monitoring, early warning and auxiliary decision-making, historical information query, and trend analysis and prediction. A marine ranch is a new way of utilizing and managing marine resources. By carrying out artificial intervention and management in a specific sea area, it promotes the healthy development of the marine ecosystem and the sustainable utilization of resources. It combines multiple functions such as ecological protection, resource aquaculture, environmental restoration, and economic development, aiming to create a multi-functional and sustainable marine ecological economic zone.

[0003] Currently, the water quality data collected by the navigation mark terminal is mainly sent to the navigation mark telemetry and remote control system through the Beidou satellite system to achieve water quality monitoring and management of the marine ranch. Due to the long time interval of data transmission by the Beidou satellite, the management efficiency of this remote management method is low, and it is impossible to monitor water quality data in real time and handle water quality deterioration accidents in a timely manner, which is likely to further increase ecological risks and expand economic losses. Summary of the Invention

[0004] In view of the above-mentioned disadvantages of the prior art, this application provides a water quality management method, system, electronic device and storage medium for a marine ranch to solve the technical problems of low management efficiency of the above-mentioned remote water quality management method for marine ranches, inability to monitor water quality data in real time and handle water quality deterioration accidents in a timely manner, which is likely to further increase ecological risks and expand economic losses.

[0005] This application provides a water quality management method for a marine ranch. The method includes: pre-setting a water quality monitoring device, an edge computing terminal and a water quality regulating device on the navigation marks in the marine ranch; the edge computing terminal obtains the water quality parameter data of the marine ranch at multiple consecutive moments, and performs water quality prediction based on the water quality parameter data to obtain the predicted value of the water quality parameters of the marine ranch, where the water quality parameter data is collected by the water quality monitoring device; if the predicted value of the water quality parameters exceeds the preset normal water quality range, a target adjustment amount is determined based on the predicted value of the water quality parameters, so that the water quality regulating device adjusts the water quality according to the target adjustment amount.

[0006] In an embodiment of the present application, a water quality monitoring device, an edge computing terminal, and a water quality adjustment device are pre-set on the buoys in the ocean ranch, including: dividing a plurality of buoys in the ocean ranch according to a preset quantity to obtain different buoy groups; setting the corresponding water quality monitoring device and the water quality adjustment device on each buoy in each buoy group; determining a main buoy from each buoy group, and setting the corresponding edge computing terminal on the main buoy in each buoy group; for the same buoy group, establishing communication between the edge computing terminal and each of the water quality monitoring devices and each of the water quality adjustment devices respectively.

[0007] In an embodiment of the present application, before predicting the water quality according to the water quality parameter data, the method includes: obtaining the historical water quality parameter data of the ocean ranch at a plurality of consecutive historical moments, and dividing the historical water quality parameter data into a training data set and a test data set in chronological order, wherein the moments of the training data set are earlier than the moments of the test data set; training an initial time series prediction model based on the training data set to obtain a water quality parameter prediction result, and comparing the water quality parameter prediction result with the test data set to obtain a prediction accuracy rate and a prediction loss rate; performing iterative calculation on the time series prediction model according to the prediction accuracy rate and the prediction loss rate until the new prediction accuracy rate and the new prediction loss rate reach a preset threshold, to obtain a trained time series prediction model, so as to input the water quality parameter data into the trained time series prediction model for water quality prediction to obtain the water quality parameter prediction value.

[0008] In an embodiment of the present application, determining a target adjustment amount based on the water quality parameter prediction value includes: when the water quality parameter prediction value is within a preset water quality warning range, taking a preset adjustment amount as the target adjustment amount, wherein the water quality normal range and the water quality warning range are adjacent intervals to each other, and the common endpoint of the water quality normal range and the water quality warning range is a preset warning value.

[0009] In an embodiment of the present application, determining a target adjustment amount based on the water quality parameter prediction value includes: when the water quality parameter prediction value is within a preset water quality alarm range, obtaining the real-time water quality parameter data of the ocean ranch through the water quality monitoring device, and obtaining the operation state data of the water quality adjustment device through the water quality adjustment device, wherein the water quality warning range and the water quality alarm range are adjacent intervals to each other, the common endpoint of the water quality warning range and the water quality alarm range is a preset alarm value, and the degree of water quality deterioration represented by the alarm value is higher than the degree of water quality deterioration represented by the warning value; performing reinforcement learning based on the real-time water quality parameter data and the operation state data to obtain an optimal solution as the target adjustment amount.

[0010] In an embodiment of the present application, reinforcement learning based on the real-time water quality parameter data and the operation status data includes: aiming at the water quality parameters within the normal water quality range, and calculating the minimum power consumption and the shortest time required to achieve the target according to the real-time water quality parameter data and the operation status data to form a preliminary plan; executing the preliminary plan and observing the corresponding results of executing the preliminary plan; using the power consumption of the water quality adjustment device and the adjustment time required to achieve the target as a reward function, and calculating the reward value according to the observed results; if the reward value is greater than the preset reward value, iterating the preliminary plan according to the reward value to form a new plan until the new reward value is equal to the preset reward value to obtain the optimal plan.

[0011] In an embodiment of the present application, if the predicted water quality parameter value exceeds the normal water quality range, the method further includes: the edge computing terminal reporting an early warning signal or an alarm signal to the beacon telemetry and remote control system, where the early warning signal is generated based on the predicted water quality parameter value triggering within the water quality early warning range, the alarm signal is generated based on the predicted water quality parameter value triggering within the water quality alarm range, and the edge computing terminal communicates with the beacon telemetry and remote control system through the Beidou satellite system.

[0012] In an embodiment of the present application, there is also provided a marine ranch water quality management system, which includes a water quality monitoring device, an edge computing terminal, and a water quality adjustment device disposed on a beacon in the marine ranch; the water quality monitoring device is used to collect the water quality parameter data of the marine ranch; the edge computing terminal is used to perform water quality prediction based on the water quality parameter data at multiple consecutive moments to obtain the predicted water quality parameter value of the marine ranch, and if the predicted water quality parameter value exceeds the preset normal water quality range, determining the target adjustment amount based on the predicted water quality parameter value; the water quality adjustment device is used to adjust the water quality according to the target adjustment amount.

[0013] In an embodiment of the present application, there is also provided an electronic device, which includes: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enabling the electronic device to implement the marine ranch water quality management method as described above.

[0014] In an embodiment of the present application, there is also provided a computer-readable storage medium, on which a computer program is stored, when the computer program is executed by a processor of a computer, enabling the computer to execute the marine ranch water quality management method as described above.

[0015] Advantages of the present invention: The present invention provides a method, system, electronic device and storage medium for water quality management in a marine ranch. By installing a water quality monitoring device, an edge computing terminal and a water quality adjustment device on a navigation buoy, real-time monitoring of the water quality in the marine ranch is achieved, and early prediction of water quality deterioration is carried out through edge computing. When the water quality deteriorates, the target adjustment amount is determined based on the predicted value of the water quality parameter, and adjustment is carried out through the water quality adjustment device. Near-end water quality prediction and treatment of water quality deterioration accidents are completed at the navigation buoy, making up for the shortcomings that the Beidou satellite transmission data has a long time interval and cannot monitor water quality data in real time and handle water quality deterioration accidents in time, effectively reducing ecological risks and economic losses. Through this application, new application scenarios of navigation buoys in the field of marine ranch can also be expanded, new businesses of navigation buoys can be developed, which is of great significance for the high-quality development of the marine industry.

[0016] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic diagram of the implementation environment of a method for water quality management in a marine ranch shown in an exemplary embodiment of this application;

[0018] Figure 2 is a flowchart of a method for water quality management in a marine ranch shown in an exemplary embodiment of this application;

[0019] FIG. 3(a) is an iterative curve graph of the prediction accuracy rate shown in a specific embodiment of this application;

[0020] FIG. 3(b) is an iterative curve graph of the prediction loss rate shown in a specific embodiment of this application;

[0021] Figure 4 is a schematic diagram of reinforcement learning shown in a specific embodiment of this application;

[0022] Figure 5 is a schematic diagram of a cloud-edge collaborative network shown in a specific embodiment of this application;

[0023] Figure 6 is a block diagram of a system for water quality management in a marine ranch shown in an exemplary embodiment of this application;

[0024] Figure 7 is a working flowchart of a system for water quality management in a marine ranch shown in a specific embodiment of this application;

[0025] Figure 8 is a schematic structural diagram of an electronic device shown in an exemplary embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The following describes the implementation modes of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through different specific implementation modes. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0027] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. Therefore, only the components related to the present application are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and proportion of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0028] It should be noted that in the present application, "first", "second", etc. are only used to distinguish similar objects, and are not intended to limit the order or sequence of similar objects. The described "including", "having", etc. are deformations, indicating that the scope covered by the subject of the word excludes the examples shown by the word, but is not exclusive.

[0029] It can be understood that the various numerical numbers, step numbers, etc. recorded in the present application are for the convenience of description and are not used to limit the scope of the present application. The size of the labels in the present application does not mean the order of execution. The execution order of each process should be determined by its function and internal logic.

[0030] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0031] Embodiments of the present application respectively propose a method for managing the water quality of a marine ranch, a system for managing the water quality of a marine ranch, an electronic device, a computer-readable storage medium, and a computer program product. These embodiments will be described in detail below.

[0032] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the implementation environment of a method for managing the water quality of a marine ranch shown in an exemplary embodiment of the present application.

[0033] As shown in Figure 1As shown in the figure, the implementation environment may include a water quality monitoring device 110, an edge computing terminal 120, and a water quality regulation device 130. Among them, the water quality monitoring device 110 may include a water quality monitoring sensor for monitoring at least one of water temperature, dissolved oxygen, pH value, salinity, etc. The edge computing terminal 120 may be at least one of a microcomputer, an embedded computer, a neural network computer, etc. The water quality regulation device 130 may be at least one of a heater, a cooler, an aerator, a pH regulator, a salt adding water pump, a fresh water pump, etc., and no restrictions are imposed here. The water quality monitoring device 110, the edge computing terminal 120, and the water quality regulation device 130 can be used to implement local water quality management in the marine ranch.

[0034] Schematically, the water quality monitoring device 110, the edge computing terminal 120, and the water quality regulation device 130 are pre-set on the navigation buoy in the marine ranch; the edge computing terminal 120 acquires the water quality parameter data of the marine ranch at multiple consecutive moments, and performs water quality prediction based on the water quality parameter data to obtain the predicted value of the water quality parameters of the marine ranch. Among them, the water quality parameter data is collected by the water quality monitoring device 110; if the predicted value of the water quality parameters exceeds the preset normal water quality range, the target adjustment amount is determined based on the predicted value of the water quality parameters, so that the water quality regulation device 130 adjusts the water quality according to the target adjustment amount. It can be seen that the technical solution of the embodiment of the present application can achieve proximal water quality prediction and water quality deterioration accident handling at the navigation buoy, making up for the disadvantages that the Beidou satellite transmission data has a long time interval and cannot monitor water quality data in real time and handle water quality deterioration accidents in time, effectively reducing ecological risks and economic losses. Through the present application, new application scenarios of the navigation buoy in the field of marine ranch can also be expanded, new services of the navigation buoy can be developed, which is of great significance to the high-quality development of the marine industry.

[0035] It should be noted that the marine ranch water quality management method provided by the embodiment of the present application is generally specifically executed by the water quality monitoring device 110, the edge computing terminal 120, and the water quality regulation device 130.

[0036] Please refer to Figure 2 , Figure 2 is a flowchart of a marine ranch water quality management method shown in an exemplary embodiment of the present application. This marine ranch water quality management method can be applied to Figure 1 the implementation environment shown in the figure, and is specifically executed by the water quality monitoring device 110, the edge computing terminal 120, and the water quality regulation device 130 in this implementation environment. It should be understood that this marine ranch water quality management method can also be applicable to other exemplary implementation environments and be specifically executed by devices in other implementation environments. This embodiment does not limit the implementation environment applicable to this marine ranch water quality management method. Such as Figure 2As shown, in an exemplary embodiment, the marine ranch water quality management method includes at least steps S210 to S230, which are described in detail as follows:

[0037] Step S210: pre-install a water quality monitoring device, an edge computing terminal, and a water quality regulating device on the navigation buoy in the marine ranch.

[0038] Navigational aids, or aids to navigation, are visual, acoustic, and radio navigational aids installed to assist ships in safe, economical, and convenient navigation. They are a crucial component of the safety and security systems for major maritime passages and port hubs. Marine ranch navigational aids are typically used to maintain the stable and safe operation of the ranch, alert passing ships, and prevent them from straying into the ranch area.

[0039] In one embodiment of the present application, based on the warning buoys previously deployed in the marine ranch, a water quality monitoring device for monitoring water quality parameters can be added to the buoy, and an intelligent edge computing terminal (i.e., edge computing terminal) and a water quality regulating device can be installed at the same time. The water quality monitoring device, the water quality regulating device and the intelligent edge computing terminal are formed into an efficient local area network. The water quality monitoring device can transmit the collected water quality parameter data to the intelligent edge computing terminal in real time for calculation, processing and analysis, and provide a predicted value of the water quality parameters in the future. When the water quality deterioration is predicted, the water quality is regulated through the water quality regulating device to realize local water quality management of the marine ranch.

[0040] In principle, the water quality monitoring device includes but is not limited to water quality monitoring sensors for monitoring water quality parameters such as water temperature, dissolved oxygen, pH value, and salinity. The intelligent edge computing terminal can be a small embedded computer system, which can be composed of multiple processors and have certain GPU computing capabilities. The water quality adjustment device can be at least one of a heater, a refrigerator, an aerator, a pH regulator, a salt water pump, a fresh water pump, etc.

[0041] In a specific embodiment of the present application, based on the relevant requirements of marine ranch aquaculture and in combination with specific aquaculture species, the key water quality parameters (i.e., water quality parameters) of the aquaculture sea area can be determined, such as water temperature, dissolved oxygen, salinity, pH value, etc. Different aquaculture species have different corresponding key water quality parameters. After determining the key water quality parameters, through investigation of the aquaculture sea area, it is determined that the key water quality parameters change significantly over time, and the changes in the parameter values will affect the quality and reproduction efficiency of the aquaculture species, and finally the key water quality parameters that need to be monitored in real time are determined. Then, a water quality monitoring device that needs to monitor the determined key water quality parameters is added to the original navigation mark, and an intelligent edge computing terminal and a water quality regulating device are installed at the same time.

[0042] In an embodiment of the present application, a water quality monitoring device, an edge computing terminal, and a water quality regulation device are pre - set on the buoys in the marine ranch, including: dividing a plurality of buoys in the marine ranch according to a preset quantity to obtain different buoy groups; setting corresponding water quality monitoring devices and water quality regulation devices on each buoy in each buoy group; determining a main buoy from each buoy group, and setting a corresponding edge computing terminal on the main buoy in each buoy group; for the same buoy group, establishing communications between the edge computing terminal and each water quality monitoring device and each water quality regulation device respectively.

[0043] In this embodiment, corresponding water quality monitoring devices and water quality regulation devices can be installed on each buoy, and about ten buoys in the same water area can form a buoy group. Each buoy group is configured with an intelligent edge computing terminal. The water quality monitoring devices and water quality regulation devices within each buoy group form a local network with the intelligent edge computing terminal, and the intelligent edge computing terminal within each buoy group completes the processing of water quality parameter data at each buoy within the same buoy group.

[0044] Step S220: The edge computing terminal acquires water quality parameter data of the marine ranch at multiple consecutive moments, and performs water quality prediction based on the water quality parameter data to obtain a water quality parameter prediction value of the marine ranch.

[0045] In an embodiment of the present application, the water quality parameter data is collected by the water quality monitoring device, including at least one of water temperature, dissolved oxygen, salinity, pH value, etc. The water quality monitoring device collects the water quality parameter data of the marine ranch in real - time and transmits it to the edge computing terminal in real - time. After the edge computing terminal acquires the water quality parameter data at multiple consecutive moments, it performs water quality prediction based on the water quality parameter data at multiple consecutive moments. Among them, the edge computing terminal can use a model for water quality prediction or use other methods for water quality prediction, which is not limited here.

[0046] In an embodiment of the present application, before performing water quality prediction based on the water quality parameter data, the method includes: acquiring historical water quality parameter data of the marine ranch at multiple consecutive historical moments, and dividing the historical water quality parameter data into a training data set and a test data set in chronological order, where the moments of the training data set are earlier than those of the test data set; training an initial time - series prediction model based on the training data set to obtain a water quality parameter prediction result, and comparing the water quality parameter prediction result with the test data set to obtain a prediction accuracy rate and a prediction loss rate; performing iterative calculation on the time - series prediction model according to the prediction accuracy rate and the prediction loss rate until the new prediction accuracy rate and the new prediction loss rate reach a preset threshold, obtaining a trained time - series prediction model, and inputting the water quality parameter data into the trained time - series prediction model for water quality prediction to obtain a water quality parameter prediction value.

[0047] In this embodiment, an artificial intelligence time series prediction algorithm can be used to establish a model as the initial time series prediction model. Then, historical data of water quality parameters at historical times is obtained, such as 10,000 data information in a continuous time period. The first 9,000 are taken as training set data, and the last 1,000 are taken as test set data. Then, the one-dimensional data is converted into a tensor data type. Then, data preprocessing is performed through a multi-layer perceptron and an activation function. Then, according to its core algorithm, the last 1,000 data are predicted based on the first 9,000 historical data. The predicted last 1,000 data are compared with the actual 1,000 data, and the prediction accuracy rate and prediction loss rate are analyzed. After multiple iterative calculations, the prediction accuracy rate and prediction loss rate finally converge to a stable value, i.e., a preset threshold, and the iteration is completed to obtain the trained time series prediction model.

[0048] Please refer to FIG. 3. FIG. 3(a) is an iterative curve graph of the prediction accuracy rate shown in a specific embodiment of the present application, and FIG. 3(b) is an iterative curve graph of the prediction loss rate shown in a specific embodiment of the present application. As shown in FIG. 3, at the beginning, the prediction is not accurate and the error is large. After multiple iterative calculations, the prediction accuracy is further improved and finally stabilized at a certain value. It can be obtained from the calculation results that the final prediction accuracy rate is above 90%.

[0049] The input data of the time series prediction model can be single-point prediction and multi-point prediction. Single-point prediction means inputting the data of a water quality parameter to predict this water quality parameter, such as the data information of water temperature, dissolved oxygen, pH value, and salinity per second on the time axis. For example, the unit of water temperature is degrees Celsius, the unit of dissolved oxygen is mg / L, the pH value has no unit, and its value range is from 0 to 14. The salinity is also a ratio and has no unit. Generally, one-thousandth is a unit, that is, one gram of salt is contained in one kilogram of seawater. Multi-point prediction is predicting a certain water quality parameter based on the data of several water quality parameters. Which specific prediction method to adopt can be determined according to the specific scenario and water quality parameters, and no limitation is made here.

[0050] Schematically, the artificial intelligence time series prediction algorithm adopted in the present application can be the Informer algorithm or the Transformer algorithm, and no limitation is made here. Taking the Informer algorithm as an example, the core of its improvement lies in introducing a matrix composed of a query quantity Q, a key vector K, and a value vector V and the KL divergence.

[0051] The matrix formula is as follows:

[0052]

[0053] Among them, q i 、k i 、v i represent the i-th row in the QKV matrix, k(qi , k j ) is an asymmetric exponential kernel function p(k j | q i ) performs a weighted sum on the V matrix.

[0054] The Kullback-Leibler divergence, also known as relative entropy, is a method for describing the difference between two probability distributions P and Q. It is asymmetric, and the formula is as follows:

[0055]

[0056] In Equations (2) and (3), P is the attention probability distribution, Q is the uniform probability distribution, and X is the data set.

[0057] Substitute the attention probability distribution formula and the uniform probability distribution formula into the KL formula to obtain the following formula:

[0058]

[0059] Among them, the first term is the log-sum-exp of the inner product of q i and all keys, and the second term is the arithmetic mean of the inner product of q i and all keys.

[0060] Step S230, if the predicted value of the water quality parameter exceeds the preset normal range of water quality, then determine the target adjustment amount based on the predicted value of the water quality parameter, so that the water quality adjustment device adjusts the water quality according to the target adjustment amount.

[0061] In an embodiment of the present application, the normal water quality range is determined based on a preset warning value. Different water quality parameters have different warning values. The warning value of the same water quality parameter can be one or two. Taking salinity as an example, when the warning value corresponding to salinity is one, correspondingly, the maximum or minimum value of the normal water quality range corresponding to salinity is determined by the warning value corresponding to salinity. When the warning value corresponding to salinity is two, that is, the upper limit and the lower limit of the warning value, correspondingly, the maximum value of the normal water quality range corresponding to salinity is determined by the upper limit of the warning value corresponding to salinity, and the minimum value of the normal water quality range corresponding to salinity is determined by the lower limit of the warning value corresponding to salinity. Different water quality deterioration ranges can be pre-divided, and an adjustment amount is set for each water quality deterioration range. After the edge computing terminal determines that the predicted value of the water quality parameter exceeds the normal water quality range, the water quality deterioration range where the predicted value of the water quality parameter is located is determined, and the adjustment amount corresponding to the water quality deterioration range is used as the target adjustment amount to control the water quality adjustment device to perform water quality adjustment according to the target adjustment amount until the new predicted value of the water quality parameter is within the normal water quality range.

[0062] In an embodiment of the present application, determining the target adjustment amount based on the predicted value of the water quality parameter includes: when the predicted value of the water quality parameter is within the preset water quality warning range, using the preset adjustment amount as the target adjustment amount, where the normal water quality range and the water quality warning range are adjacent intervals, and the common endpoint of the normal water quality range and the water quality warning range is the preset warning value.

[0063] In an embodiment of the present application, determining the target adjustment amount based on the predicted value of the water quality parameter includes: when the predicted value of the water quality parameter is within the preset water quality alarm range, obtaining the real-time data of the water quality parameters of the marine ranch through the water quality monitoring device, and obtaining the operation state data of the water quality adjustment device through the water quality adjustment device, where the water quality warning range and the water quality alarm range are adjacent intervals, and the common endpoint of the water quality warning range and the water quality alarm range is the preset alarm value, and the degree of water quality deterioration represented by the alarm value is higher than the degree of water quality deterioration represented by the warning value; performing reinforcement learning based on the real-time data of the water quality parameters and the operation state data to obtain the optimal solution as the target adjustment amount.

[0064] It can be understood that the number of alarm values set corresponding to the water quality parameter is the same as the number of warning values set corresponding to the water quality parameter.

[0065] In this embodiment, a small adjustment amount can be preset as the preset adjustment amount, such as 10% of the maximum adjustment amount of the water quality adjustment device. When the predicted value of the water quality parameter is within the water quality warning range, it indicates that the degree of water quality deterioration is relatively light, that is, it has reached the degree of water quality deterioration represented by the warning value, but has not reached the degree of water quality deterioration represented by the alarm value. The preset adjustment amount can be used as the target adjustment amount for a small amount of water quality adjustment, such as adjusting the power, oxygenation amount, heating or cooling amount, etc.

[0066] When the predicted value of the water quality parameter is within the water quality warning range, it indicates that the small-scale water quality adjustment of the water quality adjustment device is ineffective, and the degree of water quality deterioration is relatively serious, that is, it has reached the degree of water quality deterioration indicated by the warning value. At this time, the target adjustment amount can be determined by reinforcement learning based on the real-time data of the water quality parameters in the marine ranch and the operation status data of the water quality adjustment device, so as to improve the amplitude and accuracy of water quality adjustment. Among them, the operation status data of the water quality adjustment device can include at least one of the remaining power, the maximum water output of the water pump, the maximum power of the heater and the cooler, etc. The reinforcement learning can adopt the Markov decision process or other reinforcement learning methods, which are not limited here. Taking the Markov decision process as an example, when an intelligent agent completes a certain task, it first interacts with the surrounding environment through actions. Under the action of the actions and the environment, the intelligent agent will generate a new state, and at the same time the environment will give an immediate reward (that is, the reward value); this cycle continues, and the intelligent agent and the environment continuously interact to generate a lot of data. The reinforcement learning algorithm uses the generated data to modify its own action strategy, then interacts with the environment to generate new data, and uses the new data to further improve its behavior. After several iterative learning, the intelligent agent can finally learn the optimal strategy to complete the corresponding task.

[0067] In an embodiment of the present application, the reinforcement learning based on the real-time data of the water quality parameters and the operation status data includes: taking the water quality parameter within the normal water quality range as the target, and calculating the minimum power and the shortest time required to reach the target according to the real-time data of the water quality parameters and the operation status data to form a preliminary plan; executing the preliminary plan and observing the corresponding result; taking the power consumption of the water quality adjustment device and the adjustment time required to reach the target as the reward function, and calculating the reward value according to the observed result; if the reward value is greater than the preset reward value, then iteratively optimize the preliminary plan according to the reward value to form a new plan until the new reward value is equal to the preset reward value to obtain the optimal plan.

[0068] In this embodiment, the preset reward value can be 0 or other values, which are not limited here. The water quality parameter within the normal water quality range can be taken as the target, the real-time data of the water quality parameters and the operation status data of the water quality adjustment device such as the remaining power of the water quality adjustment device and the adjustable maximum range can be taken as the state, and the adjustment amount of the water quality adjustment device can be taken as the action plan (that is, the action). Through the Markov decision process for reinforcement learning, the optimal solution of the water quality deterioration control plan (that is, the optimal plan) is obtained as the target adjustment amount. Please refer to Figure 4 , Figure 4 is the schematic diagram of reinforcement learning shown in a specific embodiment of the present application. As Figure 4 shown, the intelligent agent is to complete solution A for water quality deterioration at a certain moment t t, based on the data of water quality deterioration (i.e., real-time water quality parameter data) and the operating status data of the water quality adjustment device (such as remaining power, maximum water output of the water pump, maximum power of the heater and cooler, etc.), calculate the minimum power required to adjust the water quality parameters within the normal water quality range and the minimum time required to adjust the water quality. On the basis of the planning scheme A t , optimize the scheme, and plan a new scheme A at time t+1 t+1 , compared with route A at time t t , obtain a reward value R t+1 , where the reward value is obtained from a reward function based on the power consumption of the water quality adjustment device and the time required to adjust the water quality parameters within the normal water quality range (i.e., adjustment time). In this way, the loop continues to optimize the water quality adjustment scheme. When the reward value is no longer positive, for example, the reward value is equal to 0, the optimal water quality adjustment scheme (i.e., the optimal scheme) is obtained.

[0069] In an embodiment of the present application, if the predicted value of the water quality parameter exceeds the normal water quality range, the method further includes: the edge computing terminal reports a warning signal or an alarm signal to the beacon telemetry and remote control system, where the warning signal is generated based on the predicted value of the water quality parameter triggering within the water quality warning range, and the alarm signal is generated based on the predicted value of the water quality parameter triggering within the water quality alarm range. The edge computing terminal communicates with the beacon telemetry and remote control system through the Beidou satellite system.

[0070] Generally, the signal transmitted from the intelligent edge computing terminal on the beacon to the beacon telemetry and remote control system through the Beidou satellite system has a long transmission distance, a narrow transmission bandwidth, a limited amount of data transmitted each time, and the transmission frequency cannot be too high.

[0071] In this embodiment, it can be transmitted once an hour, and only the warning signal and the alarm signal are transmitted, without transmitting the water quality parameter data. While realizing the local water quality prediction and water quality adjustment of the beacon terminal, it can report the warning signal or alarm signal of water quality deterioration to the beacon telemetry and remote control system to prompt relevant personnel, and can also effectively avoid the problem of equipment failure caused by excessive satellite load. Schematically, the information can be transmitted to the beacon telemetry and remote control system through the SIM card on the beacon, and the warning signal and alarm signal of the water quality parameters in the marine ranch are updated every hour.

[0072] Please refer to Figure 5 , Figure 5 is a schematic diagram of the cloud-edge collaborative network shown in a specific embodiment of the present application, as Figure 5As shown in the figure, in the cloud-edge collaborative network, the cloud refers to the total controller of the beacon telemetry and remote control system, i.e., the cloud end, and the edge refers to the terminal, i.e., the water quality monitoring device, water quality adjustment device, and intelligent edge computing terminal on the beacon. Data is exchanged between the cloud end and the terminal through the Beidou satellite system. In this specific embodiment, relying on the water quality monitoring device and intelligent edge computing terminal of the beacon terminal, the nearby water quality prediction is completed. Relying on the water quality adjustment device and intelligent edge computing terminal of the beacon terminal, the nearby water quality adjustment is completed. And the intelligent edge computing terminal uploads the warning signal or alarm signal to the cloud end through the Beidou satellite system to complete the closed-loop of the task.

[0073] Please refer to Figure 6 , Figure 6 which is a block diagram of a water quality management system for a marine ranch shown in an exemplary embodiment of the present application. This system can be applied to Figure 1 the implementation environment shown in the figure. This system can also be applicable to other exemplary implementation environments, and the present embodiment does not limit the implementation environment applicable to this system. As Figure 6 shown, this exemplary water quality management system for a marine ranch includes a water quality monitoring device 110, an edge computing terminal 120, and a water quality adjustment device 130 provided on a beacon in the marine ranch; the water quality monitoring device 110 is used to collect water quality parameter data of the marine ranch; the edge computing terminal 120 is used to perform water quality prediction based on water quality parameter data at multiple consecutive moments to obtain a predicted value of the water quality parameter of the marine ranch. If the predicted value of the water quality parameter exceeds the preset normal water quality range, a target adjustment amount is determined based on the predicted value of the water quality parameter; the water quality adjustment device 130 is used to adjust the water quality according to the target adjustment amount.

[0074] In an embodiment of the present application, this water quality management system for a marine ranch further includes a beacon telemetry and remote control system and a Beidou satellite system. The edge computing terminal 120 communicates with the beacon telemetry and remote control system through the Beidou satellite system to report a warning signal to the beacon telemetry and remote control system when the predicted value of the water quality parameter is within the water quality warning range, and report an alarm signal to the beacon telemetry and remote control system when the predicted value of the water quality parameter is within the water quality alarm range.

[0075] Please refer to Figure 7 , Figure 7 which is a working flow chart of the water quality management system for a marine ranch shown in a specific embodiment of the present application. As Figure 7 shown, the working process of this water quality management system for a marine ranch is as follows:

[0076] 1. The water quality monitoring device collects real-time water quality parameter data and transmits it to the intelligent edge computing terminal, and the intelligent edge computing terminal makes a prediction based on the water quality parameter data at continuous times;

[0077] 2. When the intelligent edge computing terminal predicts that the water quality parameter is about to reach the warning value, that is, predicts that the water quality parameter is about to exceed the normal range of water quality, start the water quality adjustment device to perform a small-scale water quality adjustment according to the preset adjustment amount;

[0078] 3. If the water quality parameter is far from the warning value and the intelligent edge computing terminal predicts that the water quality parameter will not exceed the warning value, it is displayed as normal; if the water quality parameter exceeds the warning value and continues to deteriorate, but is less than the alarm value, that is, the predicted value of the water quality parameter is within the water quality warning range, continue to perform a small-scale adjustment;

[0079] 4. If the water quality parameter improves and the intelligent edge computing terminal predicts that the water quality parameter is lower than the warning value, it is displayed as normal; if the water quality parameter continues to deteriorate and its value reaches and exceeds the alarm value, that is, the predicted value of the water quality parameter is within the water quality alarm range, start the reinforcement learning algorithm to give the best solution so that the water quality adjustment device performs a large-scale adjustment according to the best solution;

[0080] 5. When the water quality parameter gradually returns to normal and its value is lower than the alarm value but higher than the warning value, that is, the predicted value of the water quality parameter is within the water quality warning range, turn off the reinforcement learning algorithm, and start the water quality adjustment device to perform a small-scale water quality adjustment according to the preset adjustment amount until the water quality parameter is lower than the warning value, that is, until the predicted value of the water quality parameter is within the normal range of water quality.

[0081] Figure 7 For the detailed process of the specific embodiment shown, please refer to the descriptions in the foregoing embodiments, and details will not be repeated here. The technical solution of this specific embodiment is based on the beacon telemetry and remote control system, the Beidou satellite system, and the beacon terminal (including the water quality monitoring device, the edge computing terminal, and the water quality adjustment device), sets the warning value and the alarm value of the water quality parameter, and forms a remote transmission network for the warning signal and the alarm signal of the marine ranch. Receive the real-time warning signal and alarm signal of the marine ranch in the duty room. On the basis of making full use of the transmission function of the traditional beacon system, an edge computing function is added. The reliability of its system is guaranteed, and the software upgrade and transformation are simple, and the upgrade cost is low. Moreover, by the beacon terminal, the water quality parameter data is monitored in real time, predicted and adjusted, and self-learning and self-strategy optimization are carried out on the edge side of the marine ranch, that is, the intelligent edge computing terminal, to achieve near-end calculation and near-end processing at the terminal, so that its water quality parameter reaches a reasonable numerical range, ensuring the normal development and reproduction of its aquaculture organisms, closing the entire set of systems, and without the need for manual active intervention, so as to expand the application scenario of the beacon in the ocean on the premise of maintaining the safety of the marine ranch. In addition, by transmitting the warning signal and the alarm signal to the beacon telemetry and remote control system through the Beidou satellite system, it can effectively avoid the problem of equipment failure caused by excessive satellite load.

[0082] It should be noted that the above-mentioned marine ranch water quality management system and the above-mentioned marine ranch water quality management method belong to the same concept. The specific ways in which each module and unit perform operations have been described in detail in the method embodiments, and will not be repeated here. In practical applications, the above-mentioned marine ranch water quality management system can, as needed, allocate the above functions to different functional modules, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above. This is not limited here either.

[0083] This embodiment also provides an electronic device, including: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the marine ranch water quality management method provided in each of the above embodiments.

[0084] Please refer to Figure 8 , Figure 8 which is a schematic structural diagram of an electronic device shown in an exemplary embodiment of the present application. It should be noted that Figure 8 the electronic device 800 shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0085] As Figure 8 shown, the electronic device 800 includes a processor 801, a memory 802, and a communication bus 803; the communication bus 803 is used to connect the processor 801 and the memory 802; the processor 801 is used to execute the computer program stored in the memory 802 to implement one or more of the methods as in the above embodiments.

[0086] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor of the computer, the computer is caused to execute the marine ranch water quality management method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist separately and not be assembled into the electronic device.

[0087] This embodiment also provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to execute the marine ranch water quality management method provided in each of the above embodiments.

[0088] The electronic device provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication therebetween. The memory is used to store computer programs, the communication interface is used for communication, and the processor and the transceiver are used to run the computer programs so that the electronic device executes each step of the above method.

[0089] In this embodiment, the memory may include a Random Access Memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0090] The above-mentioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0091] For the computer-readable storage medium in this embodiment, those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to computer programs. The foregoing computer program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: ROM (Read Only Memory), RAM (Random Access Memory), magnetic disk, or optical disk and other media that can store program codes.

[0092] The above embodiments only exemplarily illustrate the principles and effects of the present application, rather than limiting the present application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present application should still be covered by the claims of the present application.

Claims

1. A method for water quality management in a marine ranch, characterized in that, The method includes: Pre-setting a water quality monitoring device, an edge computing terminal, and a water quality adjustment device on a navigation mark in a marine ranch; The edge computing terminal acquires water quality parameter data of the marine ranch at multiple consecutive moments, and performs water quality prediction based on the water quality parameter data to obtain a predicted value of the water quality parameters of the marine ranch, wherein the water quality parameter data is collected by the water quality monitoring device; If the predicted value of the water quality parameters exceeds a preset normal water quality range, a target adjustment amount is determined based on the predicted value of the water quality parameters, so that the water quality adjustment device adjusts the water quality according to the target adjustment amount.

2. The water quality management method for the marine ranch according to claim 1, wherein Pre-setting a water quality monitoring device, an edge computing terminal, and a water quality adjustment device on a navigation mark in a marine ranch includes: Dividing multiple navigation marks in the marine ranch according to a preset quantity to obtain different navigation mark groups; Setting the corresponding water quality monitoring device and the water quality adjustment device on each navigation mark in each navigation mark group; Determining a main navigation mark from each navigation mark group, and setting the corresponding edge computing terminal on the main navigation mark in each navigation mark group; For the same navigation mark group, establishing communication between the edge computing terminal and each of the water quality monitoring devices and each of the water quality adjustment devices.

3. The method for managing the water quality of a marine ranch according to claim 1, characterized in that, Before performing water quality prediction based on the water quality parameter data, the method includes: Acquiring historical water quality parameter data of the marine ranch at multiple consecutive historical moments, and dividing the historical water quality parameter data into a training data set and a test data set in chronological order, wherein the moments of the training data set are earlier than the moments of the test data set; Training an initial time series prediction model based on the training data set to obtain a water quality parameter prediction result, and comparing the water quality parameter prediction result with the test data set to obtain a prediction accuracy rate and a prediction loss rate; Performing iterative calculation on the time series prediction model according to the prediction accuracy rate and the prediction loss rate until a new prediction accuracy rate and a new prediction loss rate reach a preset threshold, obtaining a trained time series prediction model, and inputting the water quality parameter data into the trained time series prediction model to perform water quality prediction to obtain the predicted value of the water quality parameters.

4. The method for managing the water quality of a marine ranch according to claim 1, characterized in that, Determining a target adjustment amount based on the predicted value of the water quality parameters includes: When the predicted value of the water quality parameters is within a preset water quality warning range, taking the preset adjustment amount as the target adjustment amount, wherein the normal water quality range and the water quality warning range are adjacent intervals to each other, and the common end point of the normal water quality range and the water quality warning range is a preset warning value.

5. The method for managing the water quality of a marine ranch according to claim 4, characterized in that Determining a target adjustment amount based on the predicted value of the water quality parameters includes: When the predicted value of the water quality parameter is within the preset water quality warning range, obtain the real-time data of the water quality parameter of the marine ranch through the water quality monitoring device, and obtain the operation status data of the water quality regulation device through the water quality regulation device, wherein the water quality warning range and the water quality warning range are adjacent intervals to each other, and the common endpoint of the water quality warning range and the water quality warning range is the preset warning value, and the degree of water quality deterioration represented by the warning value is higher than the degree of water quality deterioration represented by the warning value; Perform reinforcement learning based on the real-time water quality parameter data and the operation status data to obtain an optimal solution as the target adjustment amount.

6. The method for managing the water quality of a marine ranch according to claim 5, characterized in that, Performing reinforcement learning based on the real-time water quality parameter data and the operation status data includes: Taking the water quality parameter within the normal water quality range as the target, and calculating the minimum power and the shortest time required to reach the target according to the real-time water quality parameter data and the operation status data to form a preliminary solution; Execute the preliminary solution and observe the result corresponding to the execution of the preliminary solution; Taking the power consumption of the water quality regulation device and the adjustment time required to reach the target as the reward function, and calculating the reward value according to the observed result; If the reward value is greater than the preset reward value, iterate the preliminary solution according to the reward value to form a new solution until the new reward value is equal to the preset reward value to obtain the optimal solution.

7. The method for managing the water quality of a marine ranch according to claim 5, characterized in that, If the predicted value of the water quality parameter exceeds the normal water quality range, the method further includes: The edge computing terminal reports a warning signal or an alarm signal to the beacon telemetry and remote control system, wherein the warning signal is generated based on the predicted value of the water quality parameter within the water quality warning range, the alarm signal is generated based on the predicted value of the water quality parameter within the water quality warning range, and the edge computing terminal communicates with the beacon telemetry and remote control system through the Beidou satellite system.

8. An aquaculture water quality management system, characterized in that, The system includes a water quality monitoring device, an edge computing terminal and a water quality regulation device arranged on a beacon in a marine ranch; The water quality monitoring device is used to collect the water quality parameter data of the marine ranch; The edge computing terminal is used to perform water quality prediction based on the water quality parameter data at multiple consecutive moments to obtain the predicted value of the water quality parameter of the marine ranch. If the predicted value of the water quality parameter exceeds the preset normal water quality range, determine the target adjustment amount based on the predicted value of the water quality parameter; The water quality regulation device is used to adjust the water quality according to the target adjustment amount.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, which when executed by the one or more processors, cause the electronic device to implement the marine ranch water quality management method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer program is stored thereon, which when executed by a processor of a computer, causes the computer to execute the marine ranch water quality management method according to any one of claims 1-7.

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