Clustering cooperative control system for aquaculture equipment based on Internet of Things technology

Abstract

The invention relates to the field of aquaculture, is used for solving the problems that in the traditional water quality monitoring process, water area monitoring comprehensiveness is insufficient, and equipment clusters cannot be automatically coordinated and controlled according to water quality distribution, and particularly relates to an aquaculture equipment clustering cooperative control system based on the Internet of Things technology. Comprising a water body information acquisition module, a real-time analysis module, a migration analysis module, a distribution gradient analysis module and a coordination control module, through linkage of the water body information acquisition module and the real-time analysis module, key parameters of a water area can be dynamically monitored, regional monitoring of aquaculture water is realized by combining water body region division, health degree change of each water body region is tracked for a long time through the migration analysis module, a water quality deterioration trend can be identified, and a pollution migration risk can be pre-warned in advance; therefore, the defect that a single-point monitoring mode in a traditional monitoring scheme is insufficient in comprehensive monitoring capability of the water area is overcome, and meanwhile, a control equipment cluster of the aquaculture water area can be automatically adjusted through the cooperative control module.

Description

Technical Field

[0001] The present invention relates to the field of aquaculture, and in particular to a clustered collaborative control system for aquaculture equipment based on Internet of Things technology. Background Art

[0002] As the domestic aquaculture industry develops towards high density and high yield, the electrification, technology and intelligence of aquaculture technology are inevitable. Weather, temperature, light, wind speed, oxygenation equipment and other factors affect the growth and safety of aquaculture. Deterioration of aquaculture water quality and hypoxia will directly induce hypoxia of aquatic products, disease outbreaks and even mass death. In order to solve the above problems, the current aquaculture system has gradually developed in the direction of intelligence. Through online monitoring of water quality data, such as the detection of dissolved oxygen values, and setting upper and lower limits of dissolved oxygen in various waters such as fish ponds, the aerator is automatically started when the dissolved oxygen is lower than the set lower limit, and the aerator is automatically stopped when the dissolved oxygen is higher than the set upper limit, avoiding hypoxia and blind oxygenation, and maintaining the appropriate dissolved oxygen content in the water with the least amount of electricity.

[0003] At present, the existing patent application CN201811212099.6 discloses a technical solution, which monitors the aquaculture waters through a monitoring device to obtain relevant information of the aquaculture waters, thereby realizing an alarm function. However, the invention can only realize the monitoring function of the waters, and cannot automatically adjust the waters according to the monitoring results, so that the water adjustment still relies on manual operation, and it is impossible to realize automatic cluster management of aquaculture equipment. At the same time, when monitoring the waters, the solution adopts a single collection point for monitoring, and can only use a single point static sample as the basis, and cannot further analyze the dynamic characteristics of the waters and the distribution of water parameters, resulting in the water monitoring process being not detailed enough, which reduces the accuracy of the aquaculture monitoring process;

[0004] In response to the above technical problems, this application proposes a solution. Summary of the Invention

[0005] The present invention can dynamically monitor key parameters such as dissolved oxygen, water pollution, and temperature through the linkage of the water body information acquisition module and the real-time analysis module. Combined with the regional division capability of the distribution gradient analysis module, it can realize the zoning monitoring of aquaculture water bodies, and track the changes in the health of each water body area over a long period of time through the migration analysis module. It can identify the trend of water quality deterioration and give early warning of pollution migration risks, thereby changing the shortcomings of the single-point monitoring method in the traditional monitoring scheme that is insufficient in comprehensive monitoring capabilities of water areas. At the same time, it can automatically adjust the control equipment cluster of aquaculture water areas through the collaborative control module to achieve the effect of automatic equipment control and closed-loop intervention, solving the problems of insufficient comprehensive water area monitoring in the traditional water quality monitoring process and the inability to automatically coordinate and control the equipment cluster according to the water quality distribution, and proposes a clustered collaborative control system for aquaculture equipment based on the Internet of Things technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The clustered collaborative control system for aquaculture equipment based on Internet of Things technology includes a water body information collection module, a real-time analysis module, a migration analysis module, a distribution gradient analysis module, and a coordination control module. The water body information collection module is used to collect water parameters related to aquaculture in the water body and send the collected water parameters to the real-time analysis module.

[0008] After the real-time analysis module obtains the water body parameters, it analyzes the water body parameters and obtains the health of the water body according to the analysis results;

[0009] The distribution gradient analysis module divides the aquaculture water body into regional gradients according to the water body parameters, obtains multiple water body analysis areas, and obtains the independent health level of each water body analysis area;

[0010] After the migration analysis module obtains the independent health level of each water body analysis area through the distribution gradient analysis module, it continuously records the independent health level of the water body obtained at different times, and performs a comprehensive analysis based on the independent health level to obtain the water body migration situation;

[0011] The coordinated control module obtains the water body migration situation and independent health level, performs fusion analysis, generates a coordinated control signal matrix, and coordinates the aquaculture equipment cluster through the coordinated control signal matrix.

[0012] As a preferred embodiment of the present invention, the water parameters collected by the water information collection module include dissolved oxygen content, water temperature and water harmful substance content;

[0013] When collecting water parameters, the water body information collection module sets multiple groups of collection equipment at different positions in the water body to collect water parameters in different areas. The positions of the collection equipment set by the water body information collection module in the water body are obtained through the distribution gradient analysis module.

[0014] As a preferred embodiment of the present invention, after obtaining the water body parameters, the real-time analysis module calibrates the dissolved oxygen content, water body temperature and water body harmful substance content in the water body parameters, and generates a water body qualified signal or a water body abnormal signal according to the calibration results, wherein the water body abnormal signal includes oxygen abnormality, temperature abnormality and pollution abnormality. The real-time analysis module uses the water body abnormal signal or water body qualified signal as the water body health level.

[0015] As a preferred embodiment of the present invention, the method for calibrating water parameters by the real-time analysis module is:

[0016] The real-time analysis module obtains the collection location of the water body parameters, and obtains the breeding requirements of the breeding products according to the collection location of the water body parameters, the breeding requirements including the suitable oxygen content range and the suitable temperature;

[0017] The real-time analysis module obtains the dissolved oxygen content in the water parameters and compares the dissolved oxygen content with the set oxygen content range. If the dissolved oxygen content is within the oxygen content range, an oxygen normal signal is generated. If the dissolved oxygen content is not within the oxygen content range, the difference between the dissolved oxygen content and the oxygen content range is determined to generate an oxygen deficiency signal or an oxygen excess signal, and the specific data of the difference is recorded as an oxygen abnormality indicator.

[0018] The real-time analysis module obtains the water temperature from the water parameters, and compares the water temperature with the appropriate temperature to obtain a normal temperature signal, a low temperature signal, or a high temperature signal, and records the temperature difference as a temperature anomaly indicator;

[0019] The real-time analysis module compares the content of harmful substances in the water body with the set threshold value. If the content of harmful substances in the water body exceeds the set threshold value, it is recorded as a pollution abnormality, and the content of harmful substances in the water body exceeding the set threshold value is recorded as a pollution index.

[0020] As a preferred embodiment of the present invention, the distribution gradient analysis module divides the water body parameters into regional gradients according to preset water body specifications, wherein the water body specifications include water body depth and water area;

[0021] The distribution gradient analysis module divides the water body depth into a depth zone at every preset depth, divides the water area into projection zones using a matrix grid, forms multiple three-dimensional zones by combining the depth zones and projection zones, and records each three-dimensional zone as an independent water body analysis zone.

[0022] As a preferred embodiment of the present invention, the distribution gradient analysis module sends each independent water body analysis area to the water body information collection module. When setting up the collection equipment, the water body information collection module sets up a group of collection equipment in each water body analysis area;

[0023] The distribution gradient analysis module obtains the water health levels of different areas through the real-time analysis module and records them as independent health levels.

[0024] As a preferred embodiment of the present invention, after first obtaining the independent health level, the migration analysis module constructs a three-dimensional model using the multiple three-dimensional regions divided by the distribution gradient analysis module, and records the independent health level in each three-dimensional region;

[0025] After subsequently obtaining the independent health levels, the migration analysis module constructs a new three-dimensional model and records the independent health levels in each three-dimensional region;

[0026] The migration analysis module compares the two groups of three-dimensional models, obtains the difference in independent health levels in the two groups of three-dimensional models based on the comparison results, and records the difference as migration data.

[0027] As a preferred embodiment of the present invention, the step of comparing the three-dimensional model by the migration analysis module is:

[0028] A1: Mark all areas with abnormal water signals in the two sets of 3D models and classify different abnormal water signal areas into hypoxic areas, hyperoxic areas, low temperature areas, high temperature areas, and polluted areas;

[0029] A2: Compare the same type of areas in the two sets of 3D models to obtain the position changes of the same type of abnormal areas;

[0030] A3: Record the changed area as the migration direction, record the number of changed areas as the migration scale, and record the migration direction and migration scale as the migration data.

[0031] As a preferred embodiment of the present invention, after the coordinated control module obtains the migration data, it generates a circulation intensity control through the migration data, generates an oxygen supplement control signal through the number of oxygen anomalies in the independent health level, generates a purification control signal through the number of pollution anomalies in the independent health level, and generates a temperature control signal through the number of temperature anomalies in the independent health level.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. In the present invention, through the linkage of the water information acquisition module and the real-time analysis module, key parameters such as dissolved oxygen, water pollution, and temperature can be dynamically monitored. Combined with the regional division capability of the distribution gradient analysis module, regional monitoring of aquaculture water bodies can be realized, thereby changing the shortcomings of the single-point monitoring method in traditional monitoring schemes that are insufficient for comprehensive monitoring of water areas, and avoiding inaccurate monitoring caused by uneven distribution of water quality in water areas.

[0034] 2. In the present invention, the migration analysis module is used to track the changes in the health of each water area over a long period of time, which can identify the trend of water quality deterioration and provide early warning of pollution migration risks. At the same time, the collaborative control module is used to automatically adjust the control equipment cluster of aquaculture waters to achieve the effect of automatic equipment control and closed-loop intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0036] Figure 1 is a system block diagram of the present invention;

[0037] Figure 2 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0038] The following is a clear and complete description of the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] Example 1:

[0040] See also Figure 1 - Figure 2 As shown, the clustered collaborative control system for aquaculture equipment based on the Internet of Things technology includes a water body information collection module, a real-time analysis module, a migration analysis module, a distribution gradient analysis module and a coordination control module. The water body information collection module is used to collect water parameters related to aquaculture in the water body. The water body parameters collected by the water body information collection module include dissolved oxygen content, water body temperature and water body harmful substance content. The water body information collection module sends the collected water body parameters to the real-time analysis module;

[0041] When collecting water parameters, the water body information collection module sets multiple sets of collection devices at different locations in the water body to collect water parameters in different areas. The locations of the collection devices set by the water body information collection module in the water body are obtained through the distribution gradient analysis module.

[0042] When the distribution gradient analysis module divides the water body parameters into regional gradients, it divides them according to the preset water body specifications, where the water body specifications include water body depth and water area;

[0043] The distribution gradient analysis module divides the water depth into a depth zone at every preset depth, divides the water area into projection zones using a matrix grid, and forms multiple three-dimensional zones by combining the depth zones and projection zones. Each three-dimensional zone is recorded as an independent water analysis zone.

[0044] The distribution gradient analysis module sends each independent water body analysis area to the water body information collection module. When setting up the collection equipment, the water body information collection module sets up a group of collection equipment in each water body analysis area;

[0045] After the real-time analysis module obtains the water parameters, it calibrates the dissolved oxygen content, water temperature and harmful substance content in the water parameters, and generates a water quality signal or a water abnormality signal based on the calibration results. The water abnormality signal includes oxygen abnormality, temperature abnormality and pollution abnormality. The real-time analysis module uses the water abnormality signal or water quality signal as the health level of the water body.

[0046] The method for calibrating water parameters by the real-time analysis module is:

[0047] The real-time analysis module obtains the collection location of water parameters and obtains the breeding requirements of the aquaculture products based on the collection location of the water parameters. The breeding requirements include the suitable oxygen content range and the suitable temperature;

[0048] The real-time analysis module obtains the dissolved oxygen content in the water parameters and compares the dissolved oxygen content with the set oxygen content range. If the dissolved oxygen content is within the oxygen content range, a normal oxygen signal is generated. If the dissolved oxygen content is not within the oxygen content range, the difference between the dissolved oxygen content and the oxygen content range is determined. If the dissolved oxygen content is greater than the oxygen content range, an overoxygen signal is generated. If the dissolved oxygen content is less than the oxygen content range, an hypoxia signal is generated. At the same time, the specific data of the difference is recorded as an oxygen abnormality indicator.

[0049] The real-time analysis module obtains the water temperature from the water parameters and compares the water temperature with the suitable temperature. If the water temperature is within the suitable temperature range, a normal temperature signal is obtained. If the water temperature is not outside the suitable temperature range, the difference between the water temperature and the suitable temperature range is determined. If the difference is negative, a low temperature signal is generated. If the difference is positive, a high temperature signal is generated. At the same time, the temperature difference is recorded as a temperature anomaly indicator.

[0050] The real-time analysis module compares the content of harmful substances in the water body with the set threshold. If the content of harmful substances in the water body exceeds the set threshold, it will be recorded as a pollution abnormality, and the content of harmful substances in the water body that exceeds the set threshold will be recorded as a pollution index.

[0051] After the distribution gradient analysis module obtains the water health level of different areas through the real-time analysis module, the water health level of each water analysis area is recorded as an independent health level, and the independent health level is sent through the network, so that managers can intuitively understand the water quality conditions of the aquaculture area.

[0052] Example 2:

[0053] See also Figure 1 - Figure 2 As shown, after the migration analysis module obtains the independent health level for the first time through the distribution gradient analysis module, it constructs a three-dimensional model through the multiple three-dimensional regions divided by the distribution gradient analysis module and records the independent health level in each three-dimensional region;

[0054] The migration analysis module continuously records the independent health of the water body at different times. After obtaining the independent health, it constructs a new three-dimensional model and records the independent health in each three-dimensional area.

[0055] The migration analysis module compares the two sets of three-dimensional models, obtains the difference in independent health levels in the two sets of three-dimensional models based on the comparison results, and records the difference as migration data;

[0056] The steps for comparing 3D models in the migration analysis module are as follows:

[0057] A1: Mark all areas with abnormal water signals in the two sets of 3D models and classify different abnormal water signal areas into hypoxic areas, hyperoxic areas, low temperature areas, high temperature areas, and polluted areas;

[0058] A2: Compare the same types of areas in the two sets of 3D models to obtain the position changes of the same type of abnormal areas, such as the changes in the hypoxic areas in the two sets of 3D models, so as to understand the changes in the hypoxia situation in different three-dimensional areas of the aquaculture waters;

[0059] A3: Record the changed area as the migration direction, record the number of changed areas as the migration scale, and record the migration direction and migration scale as the migration data.

[0060] After obtaining the migration data, the coordination control module generates a circulation intensity control based on the migration scale and migration direction in the migration data. The circulation control intensity is positively correlated with the migration scale. At the same time, the water area circulation direction is controlled according to the migration direction, so that the water area circulates in the opposite direction of the migration direction, avoiding the acceleration of the migration speed in abnormal water areas.

[0061] The oxygen supply control signal is generated by the abnormal amount of oxygen in the independent health level. The specific control logic is as follows: a circulation flow direction is created according to the water circulation direction to obtain the oxygen supply flow direction. The oxygen supply flow direction is used to oxygenate the hypoxic area. For the hyperoxic area, the dissolved oxygen is dispersed through the water circulation.

[0062] Generate a purification control signal through the number of pollution anomalies in the independent health level. The higher the number of pollution anomalies, the higher the purification power control and the faster the cycle speed.

[0063] The temperature control signal is generated by the number of temperature anomalies in the independent health level, and the temperature flow direction is calculated according to the circulation direction of the water area. The water flowing to the low temperature area is heated up, and the water flowing to the high temperature area is cooled down. The operating power of the temperature control equipment is used to adjust the area with temperature anomalies.

[0064] The coordinated control module uses the circulation intensity control, oxygen supply control signal, purification control signal and temperature control signal as a coordinated control signal matrix, and coordinates the breeding equipment cluster through the coordinated control signal matrix.

[0065] Thresholds, preset values, and preset ranges are set for comparative analysis of results to determine quality. The values ​​are determined based on a combination of large-scale model analysis of sample data and manual experience, and can also be adjusted appropriately based on seasonal or common-sense factors.

[0066] The settings of weight ratio coefficients, influencing factors, etc. are assigned specific values ​​according to the influence of each parameter on the result, which ultimately reflects the impact on the result. They are also set and entered into storage through a combination of large-scale model analysis of sample data and manual experience. Appropriate adjustments can also be made based on seasonal or common-sense influencing conditions.

[0067] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A clustered collaborative control system for aquaculture equipment based on Internet of Things technology, characterized by: It includes a water body information collection module, a real-time analysis module, a migration analysis module, a distribution gradient analysis module and a coordination control module. The water body information collection module is used to collect water parameters related to aquaculture in the water body and send the collected water parameters to the real-time analysis module; After the real-time analysis module obtains the water body parameters, it analyzes the water body parameters and obtains the health of the water body according to the analysis results; The distribution gradient analysis module divides the aquaculture water body into regional gradients according to the water body parameters, obtains multiple water body analysis areas, and obtains the independent health level of each water body analysis area; After the migration analysis module obtains the independent health level of each water body analysis area through the distribution gradient analysis module, it continuously records the independent health level of the water body obtained at different times, and performs a comprehensive analysis based on the independent health level to obtain the water body migration situation; The coordinated control module obtains the water body migration situation and independent health level, performs fusion analysis, generates a coordinated control signal matrix, and coordinates the aquaculture equipment cluster through the coordinated control signal matrix.

2. The aquaculture equipment cluster collaborative control system based on Internet of Things technology according to claim 1 is characterized in that: The water parameters collected by the water information collection module include dissolved oxygen content, water temperature and water harmful substance content; When collecting water parameters, the water body information collection module sets multiple groups of collection equipment at different positions in the water body to collect water parameters in different areas. The positions of the collection equipment set by the water body information collection module in the water body are obtained through the distribution gradient analysis module.

3. The aquaculture equipment cluster collaborative control system based on Internet of Things technology according to claim 1 is characterized in that: After obtaining the water body parameters, the real-time analysis module calibrates the dissolved oxygen content, water temperature and harmful substance content in the water body parameters, and generates a water body qualified signal or a water body abnormal signal based on the calibration results, wherein the water body abnormal signal includes oxygen abnormality, temperature abnormality and pollution abnormality. The real-time analysis module uses the water body abnormal signal or water body qualified signal as the water body health level.

4. The aquaculture equipment cluster collaborative control system based on Internet of Things technology according to claim 3 is characterized in that: The method for calibrating water parameters by the real-time analysis module is: The real-time analysis module obtains the collection location of the water body parameters, and obtains the breeding requirements of the breeding products according to the collection location of the water body parameters, the breeding requirements including the suitable oxygen content range and the suitable temperature; The real-time analysis module obtains the dissolved oxygen content in the water parameters and compares the dissolved oxygen content with the set oxygen content range. If the dissolved oxygen content is within the oxygen content range, an oxygen normal signal is generated. If the dissolved oxygen content is not within the oxygen content range, the difference between the dissolved oxygen content and the oxygen content range is determined to generate an oxygen deficiency signal or an oxygen excess signal, and the specific data of the difference is recorded as an oxygen abnormality indicator. The real-time analysis module obtains the water temperature from the water parameters, and compares the water temperature with the appropriate temperature to obtain a normal temperature signal, a low temperature signal, or a high temperature signal, and records the temperature difference as a temperature anomaly indicator; The real-time analysis module compares the content of harmful substances in the water body with the set threshold value. If the content of harmful substances in the water body exceeds the set threshold value, it is recorded as a pollution abnormality, and the content of harmful substances in the water body exceeding the set threshold value is recorded as a pollution index.

5. The aquaculture equipment cluster collaborative control system based on Internet of Things technology according to claim 1 is characterized in that: When the distribution gradient analysis module divides the water body parameters into regional gradients, the division is performed according to preset water body specifications, wherein the water body specifications include water body depth and water area; The distribution gradient analysis module divides the water body depth into a depth zone at every preset depth, divides the water area into projection zones using a matrix grid, forms multiple three-dimensional zones by combining the depth zones and projection zones, and records each three-dimensional zone as an independent water body analysis zone.

6. The aquaculture equipment cluster collaborative control system based on Internet of Things technology according to claim 1 is characterized in that: The distribution gradient analysis module sends each independent water body analysis area to the water body information collection module. When setting up the collection equipment, the water body information collection module sets up a group of collection equipment in each water body analysis area; The distribution gradient analysis module obtains the water health levels of different areas through the real-time analysis module and records them as independent health levels.

7. The aquaculture equipment cluster collaborative control system based on Internet of Things technology according to claim 1 is characterized in that: After first obtaining the independent health level, the migration analysis module constructs a three-dimensional model using the multiple three-dimensional regions divided by the distribution gradient analysis module, and records the independent health level in each three-dimensional region; After subsequently obtaining the independent health levels, the migration analysis module constructs a new three-dimensional model and records the independent health levels in each three-dimensional region; The migration analysis module compares the two groups of three-dimensional models, obtains the difference in independent health levels in the two groups of three-dimensional models based on the comparison results, and records the difference as migration data.

8. The aquaculture equipment cluster collaborative control system based on Internet of Things technology according to claim 1 is characterized in that: The steps of comparing the three-dimensional model with the migration analysis module are as follows: A1: Mark all areas with abnormal water signals in the two sets of 3D models and classify different abnormal water signal areas into hypoxic areas, hyperoxic areas, low temperature areas, high temperature areas, and polluted areas; A2: Compare the same type of areas in the two sets of 3D models to obtain the position changes of the same type of abnormal areas; A3: Record the changed area as the migration direction, record the number of changed areas as the migration scale, and record the migration direction and migration scale as the migration data.

9. The aquaculture equipment cluster collaborative control system based on Internet of Things technology according to claim 1, characterized in that: After obtaining the migration data, the coordination control module generates a circulation intensity control signal based on the migration data, generates an oxygen supplement control signal based on the number of oxygen anomalies in the independent health level, generates a purification control signal based on the number of pollution anomalies in the independent health level, and generates a temperature control signal based on the number of temperature anomalies in the independent health level.

Images