Waterwheel aerator control system and method

Abstract

The invention relates to the technical field of aquaculture, in particular to a waterwheel aerator control system and method. Water body data information corresponding to each water area after bait is added every time is collected, then the water body data information of the multiple water areas is input into a control model to train the control model, the control model continuously learns the corresponding relation between the water body data information and the water area states, and the trained control model is obtained; finally, real-time water body information of the water area is collected, and the working mode of the waterwheel aerator is adjusted in combination with the real-time water body information. The water area is clustered through the control model in combination with the water body data information of the water area, so that the state of the water area is judged, and the working mode of the waterwheel aerator is adjusted when the water area is in the abnormal state; according to the method and the device, the labor cost is effectively reduced, the purpose of adaptively adjusting the working mode of the waterwheel type aerator according to the water body data information is achieved, and the energy consumption of the waterwheel type aerator is saved.

Description

Technical Field

[0001] The present invention relates to the technical field of aquaculture, and in particular to a waterwheel-type aerator control system and method. Background Art

[0002] A waterwheel aerator is a device that increases the dissolved oxygen in the water by driving the impeller to rotate through an electric motor. When working, the waterwheel aerator uses the electric motor as the power to drive the impeller to rotate. The impeller blades are partially or completely immersed in the water. During the rotation process, the blades hit the water surface at high speed, stirring up splashes, further dissolving a large amount of air to form dissolved oxygen, and then bringing the oxygen into the water. At the same time, a strong force is generated, which, on the one hand, presses the surface water to the bottom of the pool, and on the other hand, pushes the water to make the water flow and quickly diffuse the dissolved oxygen.

[0003] Chinese patent publication number CN118244661A discloses an aerator gear adjustment system, method and aerator, including a power circuit and a control unit, the power circuit is used to power the control unit, and the control unit is used to: when the aerator is operating in the first gear, if the power circuit is disconnected and connected within a preset time, the aerator is controlled to switch to the third gear; when the aerator is operating in the third gear, if the power circuit is disconnected and connected within a preset time, the aerator is controlled to switch to the second gear; when the aerator is operating in the second gear, if the power circuit is disconnected and connected within a preset time, the aerator is controlled to switch back to the first gear. However, in the prior art, it is difficult to adjust the working mode of the aerator in combination with real-time water information in the water area, resulting in the aerator being in a high-frequency operation state for a long time, increasing production costs. Summary of the Invention

[0004] The present invention aims to solve the problems existing in the background technology and propose a waterwheel-type aerator control system and method.

[0005] The technical solution of the present invention:

[0006] In one aspect, the present application provides a waterwheel-type aerator control system, comprising:

[0007] Waterwheel aerator;

[0008] Data collection component; collecting water body data information through the data collection component;

[0009] Control component; the control component is in communication with the data acquisition component, and the control component adjusts the working mode of the waterwheel aerator in real time according to the water body data information.

[0010] Preferably, the waterwheel-type aerator includes a motor, and the data acquisition component includes a plurality of sensors.

[0011] On the other hand, the present application also provides a waterwheel-type aerator control method, which is applied to the waterwheel-type aerator, comprising:

[0012] Add bait multiple times to multiple water areas and collect water body data information corresponding to each water area after each bait addition;

[0013] Create control models;

[0014] Inputting water body data information of multiple water areas into the control model to train the control model, so that the control model continuously learns the corresponding relationship between the water body data information and the water area state, and obtains a trained control model;

[0015] Collect real-time water body information of the water area and adjust the working mode of the waterwheel aerator based on the real-time water body information.

[0016] Preferably, adding bait multiple times to multiple water areas and collecting water body data information corresponding to each water area after each bait addition includes:

[0017] Create a water body data table;

[0018] Setting acquisition parameters; the acquisition parameters include an acquisition cycle and multiple acquisition nodes;

[0019] Bait is placed in multiple water areas before each collection node, and water body data information of each water area after the bait is placed is collected, and all collected water body data information is put into a water body data table; the water body data information includes water temperature, water pressure, pH value and dissolved oxygen concentration of the water body;

[0020] For each water area, the water state of the water area is determined based on the water body data information of the water area.

[0021] Preferably, the water body data information of the plurality of water areas is respectively input into the control model to train the control model, so that the control model continuously learns the corresponding relationship between the water body data information and the water area state, and obtains the trained control model, including:

[0022] All water body data information is divided into training set and test set according to random proportions;

[0023] The training set is input into the control model, so that the control model continuously learns the corresponding relationship between water body data information and water state, and obtains a trained control model;

[0024] The test set is input into the trained control model to verify whether the trained control model is trained.

[0025] Preferably, the training set is input into the control model so that the control model continuously learns the corresponding relationship between the water body data information and the water state, and a trained control model is obtained, including:

[0026] For each water area in the training set, obtain each water area and the water body data information corresponding to the water area, establish a coupling relationship between the water body data information and the water area state, and use the water body data information, the water area state, and the coupling relationship between the water body data information and the water area state as a training sample, thereby obtaining multiple training samples;

[0027] Multiple training samples are sequentially input into the control model, so that the control model continuously learns the corresponding relationship between water body data information and water state;

[0028] Multiple water areas are clustered to obtain multiple target central water areas, and abnormal water areas are screened out based on the water status of the target central water areas.

[0029] Preferably, clustering multiple water areas to obtain multiple target central water areas, and screening out abnormal water areas based on the water status of the target central water areas, including:

[0030] Randomly select K water areas from the water body data information, and record the selected K water areas as the initial central water areas;

[0031] Based on the water body data information, the distances from other water areas to the initial central water area are calculated, and each water area is divided into the initial central water area closest to it, obtaining K water area clusters;

[0032] Set the iteration threshold;

[0033] For each water area cluster, calculate the distance between each water area in the cluster and the initial central water area, and record the point corresponding to the average value of the distance as the central water area of ​​the cluster;

[0034] The iteration times of the water area cluster are counted until the iteration times of the water area cluster are equal to the iteration times threshold, and the iteration is stopped. The central water area obtained in the last iteration is recorded as the target central water area, and K target central water areas are obtained.

[0035] Preferably, clustering the multiple water areas to obtain multiple target central water areas, and screening out abnormal water areas based on the water status of the target central water areas, further comprises:

[0036] Obtain a target central water area and adjacent water areas of the target central water area;

[0037] Count the number of abnormal water conditions in the target central water area and adjacent water areas during the collection period;

[0038] Set an abnormal number threshold and determine whether the number of abnormal water conditions in the target central water area and the adjacent water areas during the collection period is greater than or equal to the abnormal number threshold;

[0039] If the number of abnormal water conditions in the target central water area and the adjacent water areas during the acquisition period is greater than or equal to the abnormal number threshold, the target central water area and the water cluster corresponding to the target central water area are recorded as abnormal water areas;

[0040] Return to obtain a target central water area and the adjacent water areas of the target central water area until all target central water areas are selected, and multiple abnormal water areas are obtained.

[0041] Preferably, collecting real-time water body information and adjusting the working mode of the waterwheel-type aerator in combination with the real-time water body information include:

[0042] Obtain real-time water body information of real-time water areas;

[0043] Input the real-time water body information into the trained control model, and use the trained control model to determine whether the real-time water area belongs to an abnormal water area;

[0044] If the real-time water area is an abnormal water area, adjusting the acquisition parameters for the real-time water area to obtain multiple real-time water body information;

[0045] Adjust the working mode of the waterwheel aerator according to real-time water body information.

[0046] Preferably, adjusting the working mode of the aerator according to real-time water body information includes:

[0047] Set water body information threshold;

[0048] Input the real-time water body information into the trained control model to determine the relationship between the real-time water body information and the water body information threshold;

[0049] The working mode of the waterwheel aerator is adjusted according to the relationship between the real-time water body information and the water body information threshold.

[0050] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0051] By adding bait multiple times to multiple water areas, collecting water body data information corresponding to each water area after each bait addition, and then creating a control model, and inputting the water body data information of multiple water areas into the control model to train the control model, so that the control model continuously learns the correspondence between the water body data information and the water area status, and obtains the trained control model, and finally collects the real-time water body information of the water area, and adjusts the working mode of the waterwheel aerator in combination with the real-time water body information. The present application clusters the water area through the control model in combination with the water body data information of the water area, thereby judging the status of the water area, and adjusting the working mode of the waterwheel aerator when the water area is in an abnormal state, helping the water area to return to normal. The present application effectively reduces labor costs, achieves the purpose of adaptively adjusting the working mode of the waterwheel aerator according to water body data information, and saves energy consumption of the waterwheel aerator. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a structural diagram of a waterwheel-type aerator control system proposed by the present invention;

[0053] Figure 2 A schematic flow chart of a waterwheel-type aerator control method proposed in the present invention;

[0054] Description of the drawings: 100, waterwheel-type aerator; 101, motor; 200, data acquisition component;

[0055] 201. Sensor; 300. Control component. DETAILED DESCRIPTION

[0056] Example 1, as Figure 1 As shown, the present invention proposes a waterwheel aerator 100 control system, which includes a waterwheel aerator 100, a data acquisition component 200 and a control component 300. The data acquisition component 200 collects water body data information, and the control component 300 is communicated with the data acquisition component 200. The control component 300 adjusts the working mode of the waterwheel aerator 100 in real time according to the water body data information.

[0057] In the present invention, water body data information of multiple water areas is collected through the data acquisition component 200, and all the collected water body data information is transmitted to the control component 300. The control component 300 judges the current state of the water area based on the water body data information, and adjusts the working mode of the waterwheel-type aerator 100 in time when the water area state is abnormal, so that the water area state returns to normal, thereby improving the efficiency of aquaculture in the water area.

[0058] In an optional embodiment, the waterwheel-type aerator 100 includes a motor 101 , and the data acquisition component 200 includes a plurality of sensors 201 .

[0059] It should be noted that adjusting the working mode of the waterwheel aerator 100 is actually to adjust the speed of the motor 101 through the control component 300. For example, in the normal mode, the motor 101 of the waterwheel aerator 100 runs at a medium speed, while in the high-intensity mode, the motor 101 of the waterwheel aerator 100 runs at a high speed.

[0060] The data acquisition component 200 of the waterwheel-type aerator 100 control system includes multiple different types of sensors 201, which are used to collect different water body data information. The sensor 201 types include dissolved oxygen sensor 201, environmental parameter sensor 201 and pH sensor 201. The dissolved oxygen sensor 201 is used to collect the dissolved oxygen concentration in the surface and bottom layers of the water body, the environmental parameter sensor 201 is used to collect environmental data such as water temperature and pressure, and the pH sensor 201 is used to collect the real-time pH value of the water body.

[0061] like Figure 2 As shown, the present invention proposes a waterwheel type aerator control method, comprising:

[0062] S100, adding bait multiple times to multiple water areas, and collecting water body data information corresponding to each water area after each bait addition;

[0063] S200, creating a control model;

[0064] S300, inputting water body data information of multiple water areas into a control model to train the control model, so that the control model continuously learns the corresponding relationship between the water body data information and the water area state, thereby obtaining a trained control model;

[0065] S400, collects real-time water body information of the water area, and adjusts the working mode of the waterwheel aerator based on the real-time water body information.

[0066] It should be noted that by adding bait multiple times to multiple water areas, the water body data information corresponding to each water area after each bait addition is collected, and then a control model is created, and the water body data information of multiple water areas are input into the control model to train the control model, so that the control model continuously learns the correspondence between the water body data information and the water area state, and obtains the trained control model. Finally, the real-time water body information of the water area is collected, and the working mode of the waterwheel aerator is adjusted in combination with the real-time water body information. The present application clusters the water area through the control model in combination with the water body data information of the water area, thereby judging the state of the water area, and adjusting the working mode of the waterwheel aerator when the water area is in an abnormal state to help the water area return to normal. The present application effectively reduces labor costs, achieves the purpose of adaptively adjusting the working mode of the waterwheel aerator according to water body data information, and saves energy consumption of the waterwheel aerator.

[0067] In an optional embodiment, the step S100 includes:

[0068] S110, creating a water body data table;

[0069] S120, setting acquisition parameters; the acquisition parameters include an acquisition cycle and a plurality of acquisition nodes;

[0070] S130, placing bait in multiple water areas before each collection node, and collecting water body data information of each water area after the bait is placed, and storing all collected water body data information into a water body data table; the water body data information includes water temperature, water pressure, pH value, and dissolved oxygen concentration of the water body;

[0071] S140: For each water area, determine the water state of the water area based on the water body data information of the water area.

[0072] It should be noted that since this application is mainly used for waterwheel-type aerators in aquaculture, when collecting data, what needs to be collected is the water body data information of multiple water areas after bait is released, so that the control component can adjust the working mode of the waterwheel-type aerator in combination with the water body data information to improve aquaculture efficiency and safety.

[0073] The water state is an evaluation standard for the overall environment of the water area. The better the water state, the better the overall environment of the water area, that is, the dissolved oxygen concentration of the water area is moderate, and the water temperature, pressure, and pH value are all in a state of efficient aquaculture. If the water state of a water area is poor, it proves that the water area is not suitable for aquaculture, and the aquaculture efficiency in the water area is low. The correspondence between the water state and water body data information is shown in Table-1 Water State Table.

[0074] Table-1

[0075] Water status Dissolved oxygen concentration water temperature pressure … Poor 2 mg / L < or > 12 mg / L 15 °C < or > 35 °C 6.5 < or > 8.5 … generally 2mg / L-12mg / L 15℃-35℃ 6.5-8.5 … excellent 5mg / L-12mg / L 22℃-30℃ 7-8 …

[0076] In an optional embodiment, the step S300 includes:

[0077] S310, dividing all water body data information into a training set and a test set according to a random ratio;

[0078] Specifically, the division ratio of the training set should be larger than that of the test set to ensure that there are sufficient training samples in the training set and to ensure the training reliability of the control model;

[0079] S320, inputting the training set into the control model so that the control model continuously learns the corresponding relationship between the water body data information and the water body state, thereby obtaining a trained control model;

[0080] S330, inputting the test set into the trained control model to verify whether the trained control model is trained;

[0081] Specifically, when verifying whether the trained control model is trained, the response speed and accuracy of the trained control model may be used as judgment criteria.

[0082] It should be noted that the water body data information of multiple water areas and the water state of each water area are obtained above, which are used as training samples for the control model. By inputting multiple training samples into the control model in sequence, the control model is continuously learning. The trained control model finally obtained can automatically judge the real-time water state of the water area based on the input water body data information, thereby facilitating the control component to adjust the working mode of the waterwheel aerator according to the water state.

[0083] In an optional embodiment, the step S320 includes:

[0084] S321, for each water area in the training set, respectively obtain each water area and the water body data information corresponding to the water area, establish a coupling relationship between the water body data information and the water area state, and use the water body data information, the water area state, and the coupling relationship between the water body data information and the water area state as a training sample, thereby obtaining multiple training samples;

[0085] S322, sequentially inputting a plurality of training samples into the control model so that the control model continuously learns the corresponding relationship between the water body data information and the water body state;

[0086] S323, clustering the multiple water areas to obtain multiple target central water areas, and screening out abnormal water areas based on the water status of the target central water areas.

[0087] It should be noted that.

[0088] In an optional embodiment, the step S323 includes:

[0089] S323-1, randomly selecting K water areas from the water body data information, and recording the selected K water areas as initial central water areas;

[0090] S323-2, based on the water body data information, calculate the distances from other water areas to the initial central water area, and divide each water area into the water area closest to the initial central water area, to obtain K water area clusters;

[0091] S232-3, set the iteration number threshold;

[0092] S323-4, for each water area cluster, calculate the distance between each water area in the cluster and the initial central water area, and record the point corresponding to the average value of the distance as the central water area of ​​the cluster;

[0093] S323-5, counting the number of iterations of the water area cluster, stopping the iteration when the number of iterations of the water area cluster equals the iteration threshold, and recording the central water area obtained in the last iteration as the target central water area, obtaining K target central water areas.

[0094] It should be noted that the core idea of ​​this application is to divide all water areas into K water area clusters by adopting the K-means clustering algorithm, so that each water area belongs to its nearest cluster center, thereby minimizing the differences within the water area cluster. Then, the multiple target center water areas obtained by K-means clustering can represent the water areas corresponding to K different water body data information.

[0095] In an optional embodiment, the step S323 further includes:

[0096] S323-6, obtaining a target central water area and adjacent water areas of the target central water area;

[0097] S323-7, counting the number of abnormal water conditions in the target central water area and adjacent water areas during the collection period;

[0098] S323-8, setting an abnormality threshold, and determining whether the number of abnormal water conditions in the target central water area and the adjacent water areas during the acquisition period is greater than or equal to the abnormality threshold;

[0099] S323-9, if the number of abnormal water conditions in the target central water area and the adjacent water areas during the acquisition period is greater than or equal to the abnormal number threshold, the target central water area and the water cluster corresponding to the target central water area are recorded as abnormal water areas;

[0100] S323-10, returning to obtain a target central water area and adjacent water areas of the target central water area, until all target central water areas are selected, and multiple abnormal water areas are obtained.

[0101] It should be noted that, through the aforementioned embodiment, K target center water areas are obtained, and these K target center water areas can represent water areas corresponding to K different water body data information. Then, on this basis, these K target center water areas are judged a second time, and water areas in abnormal states are screened out from the K target center water areas, thereby completing the information connection of water body data information-target center water areas-abnormal water areas, and realizing the correspondence between water body data information and abnormal water areas, so that the trained control model can directly judge whether the water area is an abnormal water area based on the input water body data information, and adjust the waterwheel-type aerator in the abnormal water area, so that the abnormal water area is restored to normal water area.

[0102] Since adjacent water areas are often connected, the water body data information of adjacent water areas is also relatively close. Therefore, in order to avoid errors in the collection of water body data information due to unexpected situations, the number of abnormalities in adjacent water areas is also included in the reference range of abnormal water areas during calculation, thereby improving the reliability of the judgment of abnormal water areas.

[0103] In an optional embodiment, the step S400 includes:

[0104] S410, obtaining real-time water body information of the real-time water area;

[0105] S420, inputting the real-time water body information into the trained control model, and determining whether the real-time water area is an abnormal water area through the trained control model;

[0106] S430, if the real-time water area is an abnormal water area, adjusting the acquisition parameters for the real-time water area to obtain multiple real-time water body information;

[0107] Specifically, if the real-time water area does not belong to an abnormal water area, there is no need to adjust the working mode of the waterwheel-type aerator in the real-time water area;

[0108] S440, adjusting the working mode of the waterwheel-type aerator according to the real-time water body information.

[0109] It should be noted that it is only necessary to input the real-time water body information of the real-time water area into the trained control model, and the real-time water area can be clustered based on the real-time water body information through the trained control model, so as to divide the real-time water area into the closest water area cluster. If the central water area of ​​the closest water area cluster of the real-time water area is an abnormal water area, then it means that the real-time water area is also an abnormal water area.

[0110] After determining that the real-time water area is an abnormal water area, it is necessary to adjust the collection parameters corresponding to the real-time water area, that is, increase the collection frequency and / or extend the collection period, so as to obtain more real-time water body information of the real-time water area, so as to facilitate the control system to adjust the working mode of the waterwheel-type aerator in the real-time water area, so that the real-time water area can gradually return to normal water area.

[0111] In an optional embodiment, the step S440 includes:

[0112] S441, setting water body information threshold;

[0113] S442, inputting the real-time water body information into the trained control model to determine the relationship between the real-time water body information and the water body information threshold;

[0114] S443, adjusting the operating mode of the waterwheel aerator according to the relationship between the real-time water body information and the water body information threshold;

[0115] Specifically, the working modes of the waterwheel aerator include normal mode, high-intensity mode, shutdown mode, sleep mode and water change mode, and the water change mode is turned on once every set time;

[0116] The S443 includes the following steps:

[0117] K100, select water temperature data;

[0118] K110, determining whether the water temperature data information is greater than or equal to the water temperature information threshold;

[0119] K120, if the water temperature data information is less than the water temperature information threshold, adjust the working mode of the aerator to the normal mode;

[0120] K130, select dissolved oxygen concentration data information;

[0121] K140, determines whether the dissolved oxygen concentration data information is greater than or equal to the dissolved oxygen concentration information threshold;

[0122] K150, if the dissolved oxygen concentration data information is greater than or equal to the dissolved oxygen concentration information threshold, the working mode of the aerator is adjusted to the sleep mode;

[0123] Furthermore, in addition to the water temperature data information and the dissolved oxygen concentration information, step S443 can also judge water body data such as pressure data information.

[0124] It should be noted that after determining that the real-time water area is an abnormal water area, the real-time water body information is combined to determine which water body data of the real-time water area has an error, and the motor speed of the waterwheel-type aerator is adjusted to gradually restore the real-time water area to normal water area.

[0125] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A waterwheel aerator control system, characterized in that: include: Waterwheel aerator; Data collection components; Collecting water body data information through the data acquisition component; Control components; The control component is in communication with the data acquisition component, and the working mode of the waterwheel aerator is adjusted in real time according to water body data information through the control component.

2. A waterwheel-type aerator control system according to claim 1, characterized in that: The waterwheel-type aerator includes a motor, and the data acquisition component includes a plurality of sensors.

3. A waterwheel aerator control method, applied to a waterwheel aerator, characterized in that: include: Add bait multiple times to multiple water areas and collect water body data information corresponding to each water area after each bait addition; Create control models; Inputting water body data information of multiple water areas into the control model to train the control model, so that the control model continuously learns the corresponding relationship between the water body data information and the water area state, and obtains a trained control model; Collect real-time water body information of the water area and adjust the working mode of the waterwheel aerator based on the real-time water body information.

4. A waterwheel type aerator control method according to claim 3, characterized in that: Add bait multiple times to multiple water areas and collect water body data information corresponding to each water area after each bait addition, including: Create a water body data table; Setting acquisition parameters; the acquisition parameters include an acquisition cycle and multiple acquisition nodes; Bait is placed in multiple water areas before each collection node, and water body data information of each water area after the bait is placed is collected, and all collected water body data information is put into a water body data table; the water body data information includes water temperature, water pressure, pH value and dissolved oxygen concentration of the water body; For each water area, the water state of the water area is determined based on the water body data information of the water area.

5. A waterwheel type aerator control method according to claim 4, characterized in that: The water body data information of multiple water areas is respectively input into the control model to train the control model, so that the control model continuously learns the corresponding relationship between the water body data information and the water area state, and obtains the trained control model, including: All water body data information is divided into training set and test set according to random proportions; The training set is input into the control model, so that the control model continuously learns the corresponding relationship between water body data information and water state, and obtains a trained control model; The test set is input into the trained control model to verify whether the trained control model is trained.

6. A waterwheel type aerator control method according to claim 5, characterized in that: The training set is input into the control model, so that the control model continuously learns the corresponding relationship between water body data information and water body status, and obtains the trained control model, including: For each water area in the training set, obtain each water area and the water body data information corresponding to the water area, establish a coupling relationship between the water body data information and the water area state, and use the water body data information, the water area state, and the coupling relationship between the water body data information and the water area state as a training sample, thereby obtaining multiple training samples; Multiple training samples are sequentially input into the control model, so that the control model continuously learns the corresponding relationship between water body data information and water state; Multiple water areas are clustered to obtain multiple target central water areas, and abnormal water areas are screened out based on the water status of the target central water areas.

7. A waterwheel type aerator control method according to claim 6, characterized in that: Cluster multiple water areas to obtain multiple target central water areas, and screen out abnormal water areas based on the water status of the target central water areas, including: Randomly select K water areas from the water body data information, and record the selected K water areas as the initial central water areas; Based on the water body data information, the distances from other water areas to the initial central water area are calculated, and each water area is divided into the initial central water area closest to it, obtaining K water area clusters; Set the iteration threshold; For each water area cluster, calculate the distance between each water area in the cluster and the initial central water area, and record the point corresponding to the average value of the distance as the central water area of ​​the cluster; The iteration times of the water area cluster are counted until the iteration times of the water area cluster are equal to the iteration times threshold, and the iteration is stopped. The central water area obtained in the last iteration is recorded as the target central water area, and K target central water areas are obtained.

8. A waterwheel type aerator control method according to claim 7, characterized in that: Clustering multiple water areas to obtain multiple target central water areas, and screening out abnormal water areas based on the water status of the target central water areas, also includes: Obtain a target central water area and adjacent water areas of the target central water area; Count the number of abnormal water conditions in the target central water area and adjacent water areas during the collection period; Set an abnormal number threshold and determine whether the number of abnormal water conditions in the target central water area and the adjacent water areas during the collection period is greater than or equal to the abnormal number threshold; If the number of abnormal water conditions in the target central water area and the adjacent water areas during the acquisition period is greater than or equal to the abnormal number threshold, the target central water area and the water cluster corresponding to the target central water area are recorded as abnormal water areas; Return to obtain a target central water area and the adjacent water areas of the target central water area until all target central water areas are selected, and multiple abnormal water areas are obtained.

9. A waterwheel type aerator control method according to claim 8, characterized in that: Collect real-time water body information and adjust the working mode of the waterwheel aerator based on the real-time water body information, including: Obtain real-time water body information of real-time water areas; Input the real-time water body information into the trained control model, and use the trained control model to determine whether the real-time water area belongs to an abnormal water area; If the real-time water area is an abnormal water area, adjusting the acquisition parameters for the real-time water area to obtain multiple real-time water body information; Adjust the working mode of the waterwheel aerator according to real-time water body information.

10. A waterwheel type aerator control method according to claim 9, characterized in that: Adjust the aerator's operating mode based on real-time water information, including: Set water body information threshold; Input the real-time water body information into the trained control model to determine the relationship between the real-time water body information and the water body information threshold; The working mode of the waterwheel aerator is adjusted according to the relationship between the real-time water body information and the water body information threshold.

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