CN111487386A - Automatic detection method for water quality parameters of large-area river crab culture pond - Google Patents
Automatic detection method for water quality parameters of large-area river crab culture pond Download PDFInfo
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Abstract
The invention discloses an automatic detection method for water quality parameters of a large-area river crab culture pond. According to the method, a water level, water quality and water temperature sensor is carried by an automatic navigation ship of the river crab culture pond to acquire parameters such as dissolved oxygen, pH value and water temperature of each grid bottom layer and a middle layer of the whole pond and pond water level, and a water level and water temperature parameter database of each grid water level and water temperature of the whole pond of the river crab culture pond at a limited time point is established by combining sampling time and GPS/Beidou navigation system information of the automatic navigation ship; a water level, water quality and water temperature sensor is arranged at a specific point of the bottom layer of the pond, and a long-term historical database of water level, water quality and water temperature parameters of the specific point of the river crab culture pond is established by combining longitude and latitude information of the specific point. And estimating the current global water level, water quality and water temperature parameters of the river crab culture pond by combining the global water level, water quality and water temperature parameter database of the river crab culture pond at the limited time points and a neural network fitting method according to the current parameter data of each specific detection point to form a dynamic information distribution map of the water quality, the water level and the water temperature of each grid of the pond global.
Description
Technical Field
The invention relates to an automatic detection method of water quality parameters of a large-area river crab culture pond, in particular to real-time detection of water temperature and water quality parameters.
Background
The river crab contains rich protein and trace elements, has high nutritive value and delicious taste, and the requirement of people on the river crab is gradually increased, so that the river crab breeding industry is rapidly developed. However, the traditional culture mode lacks scientific guidance, pursues yield and economic benefit, and is easy to cause over-capacity and high-density culture and unreasonable bait casting, pesticide application and fertilizer application, so that the culture water quality is seriously polluted, and the growth environment of aquatic products is greatly deteriorated. The coverage rate of the crab pond aquatic weeds is high, the underwater environment is complex, the illumination of the upper layer is strong, more phytoplankton is provided, the oxygen production of the lower layer is less, the oxygen consumption is large, and in addition, the upper water layer and the lower water layer are not easy to convect due to the layering of the water body. In ponds without artificial oxygenation, the first to be anoxic is the bottom water. The river crabs are used as benthos and have strict requirements on water temperature and water quality, so the water quality detection of river crab culture is particularly important. At present, most monitoring systems are fixed measuring systems, water quality conditions can be accurately measured through multi-point measurement, but the cost is high, and common farmers cannot accept the water quality conditions. The unmanned ship technology is mostly used for surveying and mapping, monitoring and the like of scientific research and is rarely used for agriculture; the river crabs have large breeding water surface in China, have strong territorial awareness, cannot move in a large range, can only forage in the area near the river crabs, are not uniformly distributed in density, and cannot efficiently monitor the whole large water area in real time by the mobile detection ship.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an automatic detection method for water quality parameters of a large-area river crab culture pond, which comprises the following steps:
(a) carrying water level, water quality and water temperature sensors by using an automatic navigation ship to realize the acquisition of water quality and water temperature parameters of all grid bottom layers (about 20cm from the bottom of the pond), middle layers (about 50cm from the bottom of the pond) and crab pond water level values of the river crab culture pond universe for multiple times, and establishing a water level and water temperature parameter database of the universe of the river crab culture pond at a limited time point by combining sampling time and longitude and latitude information of a GPS/Beidou navigation system of the automatic navigation ship;
(b) setting sensors for water quality, water temperature and water level (only 1 point) at a bottom layer (about 20cm from the bottom of the pond) and a middle layer (about 50cm underwater) above 3 points (the distance between each point is more than or equal to 30m, and the dissolved oxygen content is high, medium and low) in the river crab culture pond, detecting the dissolved oxygen content, the pH value, the water temperature, the water level and other parameters, and establishing a long-term historical database of water quality and water temperature parameters of specific point water levels of the river crab culture pond by combining sampling time and longitude and latitude information of the points;
(c) and estimating the current global water level, water quality and water temperature parameters of the river crab culture pond by a neural network fitting method according to the current specific point water level, water quality and water temperature parameters and a long-term historical database of the water quality and water temperature parameters of the specific point water level, the water quality and the water temperature parameters of the global water level, the limited time point and the global time point of the limited time point of the river crab culture pond, and forming a dynamic information distribution map of the water quality, the water level and the water temperature of.
Further, the step a specifically includes:
step1, dividing the whole pond into m × n grids, preferably taking the area of each grid equal, taking the side length of the grid 7-10m usually, taking the center position of each grid as a detection point to detect the water level, the water quality and the water temperature parameters, detecting the water quality and the water temperature parameters for 2 times according to the original path, and recording corresponding parameters and detection time;
step2, in order to overcome the influence of dynamic change of water quality and temperature parameters of the river crab culture pond, collecting the water quality and temperature parameters each time after the water falls into the mountains in the sun, so that photosynthesis of water plants in the pond is stopped, the content of dissolved oxygen in the water body of the pond is in a monotonous reduction process, and river crabs usually eat at night;
step3, detecting the global water quality parameters of the river crab culture pond at the early stage of river crab culture for not less than 2 times per month and at the later stage of river crab culture for not less than 1 time per month, wherein the parameters at least comprise dissolved oxygen content, water quality PH value, water temperature and water level;
and Step4, establishing a global gridding water level, water quality and water temperature parameter database of the river crab culture pond at a limited time point by combining sampling time and longitude and latitude information of a GPS/Beidou navigation system.
Further, the detection of the dissolved oxygen content needs to be carried out in a state that the water body is not disturbed, and water quality parameters of different depths need to be detected, and the detection steps are as follows:
step1, when approaching the detection point, the water quality automatic detection ship closes the power, approaches the detection point by inertia, and inserts 2 rods into the bottom of the pool through the transmission mechanism on the ship to realize automatic parking.
Step2, various water quality sensors are placed at proper depths in the water body of the pond through a lifting system at the front part of the ship body. In order to realize water quality detection of the bottom layer (20 cm away from the bottom of the pond), a weight and a floating block are tied at a proper position of a bottom layer water quality sensor through a soft rope, the distance between the water quality sensor and the weight is 20cm, the distance between the weight and the floating block is smaller than the depth of water at the shallowest part of the pond, and the buoyancy of the floating block is smaller than the gravity of the weight in water and larger than the gravity of the water quality sensor in water. When water quality detection is carried out, the weight is positioned at the bottom of the pond, the soft rope between the water quality sensor and the floating block is in a tensioning state, and the distance between the water quality sensor and the weight is 20cm, so that water temperature, pH value and dissolved oxygen of the bottom layer (20 cm away from the bottom of the pond) of the pond are detected.
And Step3, tying a floating block at a proper position of the middle-layer water quality sensor through a soft rope, wherein the distance between the water quality sensor and the floating block is 50cm, and the buoyancy of the floating block is greater than the gravity of the water quality sensor in water. When water quality detection is carried out, the soft rope between the water quality sensor and the floating block is in a tensioning state, and the water quality sensor is positioned at the depth of 50cm under water, so that water temperature, pH value and dissolved oxygen in the middle layer of the pond can be detected.
Further, in the step (b), sensors are required to be arranged in 3 areas with different dissolved oxygen contents of more than or equal to 30m away in the river crab culture pond, parameters are accurately collected, and a water quality and water temperature parameter historical database of a specific point water level is established.
Further, the step (c) specifically includes:
step3.1. the aquatic weeds not only can provide a hidden place for molting crabs to avoid mutual killing, but also can absorb and utilize ammonia nitrogen in water, adjust the pH value of the water body, and influence the dissolved oxygen of the water body through photosynthesis and respiration. According to the change trend of the two detection results at the same point in the global water level water quality and water temperature parameter database at the limited time point, the change trend is matched with the water quality parameter change trend at the corresponding time period of the specific point water level water quality and water temperature parameter long-term historical database of the river crab culture pond, and whether each grid water quality parameter detection point belongs to a water grass area or a water grass-free area is determined, so that the grid water quality parameter detection points are bound with the corresponding specific point water level water quality and water temperature parameter long-term historical database.
And Step3.2, estimating the water quality and water temperature parameters of the global water level of the current river crab culture pond by a neural network fitting method according to the water quality and water temperature parameters of the current water level of two specific points.
And Step3.3, establishing a BP neural network. Taking the water temperature (T), pH value, Dissolved Oxygen (DO) and oxidation-reduction potential (ORP) of the same time period as a data unit DnIn actual production, each water quality parameter is gradually changed, and the quantity of the parameter at a certain moment is greatly related to historical data of the parameter. In order to make the neural network predict according to the historical data of the water quality parameters, taking m as 9, taking 10 continuous groups of historical data as input, taking the data at the following time as output, and the functional expression of the neural network for predicting is as follows:
Dn+1＝F(Dn，Dn-1，Dn-2，…Dn-m)
wherein, F (D) is a prediction mapping from the water quality data at the time n to the water quality data at the time n +1 generated by the neural network, DnIs the value of the water quality parameter set at time n.
Step4 training of neural network. The number of nodes of the hidden layer determines the fitting ability of the neural network, and generally, the more the number of nodes of the hidden layer is, the stronger the fitting ability of the neural network is. Determining the number of nodes of the hidden layer by reference to an empirical formula:
l is the number of hidden layer nodes, b is the number of input layer nodes, c is the number of output layer nodes, a is the empirical constant normalization processing:
x' is the value after normalization, x is the original value before normalization, xminIs the smallest value in the data, xmaxIs the largest value in the data. The neural network is ensured to have enough input sensitivity and good fitting property to the sample, and adverse effects caused by different magnitude of the number of the factors are reduced. And setting a training set, training times, learning rate and the like, training and finishing fitting.
In the process of river crab cultivation, when the dissolved oxygen in a cultivation water area is more than 5 mg/L, the food intake of river crabs reaches an optimal value, when the dissolved oxygen in the cultivation water area is reduced to 4 mg/L, the food intake of river crabs is reduced by 13%, when the dissolved oxygen in the cultivation water area is reduced to 2 mg/L, the food intake of the river crabs is reduced by 54%, the growth is stopped, and a 'floating head' phenomenon begins to appear.
Because of this, compared with the prior art, the invention has the following beneficial effects: according to the invention, the water quality sensors are erected at fixed points at the bottom of water in different areas and combined with the automatic navigation ship carrying the water quality sensors, so that the cost is reduced, and the detection efficiency and real-time performance are improved; establishing the correlation between the water level, water quality and water temperature parameters of all network water levels of the river crab culture pond universe and the water quality and water temperature parameters of specific point water levels through a neural network, and estimating the water level, water quality and water temperature parameters of all grid water levels of the pond universe according to the water quality and water temperature parameters of the current specific point water levels; carrying a water quality detection structure by an automatic navigation ship, acquiring water quality parameters of different depth levels of each network of the pond universe, and forming a dynamic water quality, water temperature and water level information distribution map of each grid area by combining positioning information of a GPS/Beidou navigation system; and estimating the living environment of the underwater river crabs by combining the water quality parameters of all grids of the universe, monitoring the water quality, the water level and the water temperature of all grids of the river crab culture pond, providing data reference for scientific and reasonable culture of the river crabs and guiding correct oxygenation, fertilization, bait casting and pesticide spraying.
Drawings
FIG. 1 is a water quality automatic detection workboat platform;
FIG. 2 is a schematic view of the operation of the automatic water quality detecting ship;
FIG. 3 is a schematic view of a detection structure of the automatic water quality detection ship; FIG. 3(a) is a water quality test of a water bottom concave topography; (b) the water quality detection of the water bottom convex terrain is carried out; FIG. 3(c) water quality detection at 50cm underwater;
FIG. 4 is a schematic diagram of a neural network topology for water quality in river crab cultivation;
Detailed Description
The following further describes embodiments of the present invention with reference to the schematic drawings.
The invention provides an automatic detection method for water quality parameters of a large-area river crab culture pond. Is used for solving the problem of automatic detection of the water quality of the bottom water area of the crab pond in the river crab culture process. The method is particularly characterized in that a correlation between each grid water level water quality and water temperature parameter of the river crab culture pond universe and a specific point water level water quality and water temperature parameter is established through a neural network by combining a pond universe limited time point water level water temperature and water quality parameter database and a specific point water level water temperature and water quality parameter long-term historical database, and the water level water temperature and water quality parameter of the culture pond universe is estimated according to the current pond specific point water level water quality and water temperature parameter. And establishing a water quality and water temperature dynamic information distribution map by adopting the underwater water quality sensor and the longitude and latitude information of the GPS coordinates. The installation and the embodiment of the device are explained in detail below.
1. Installation of equipment
FIG. 1 is a simplified diagram of a platform of an automatic water quality detection operation ship, wherein reference numeral 1 is a navigation box of the detection ship, and a low-precision GPS navigation device with the precision of 2m and an azimuth sensor are arranged in the navigation box, and the GPS can ensure that the positioning precision is within the range of 2m under the condition of relatively low cost; the azimuth sensor is used for obtaining the current movement direction and realizing the steering selection and control of the automatic operation ship. And the reference number 3 is a driving paddle wheel, and the automatic water quality detection ship adopts a paddle wheel driving mode. The paddle wheel is distributed on two sides of the ship floating body, and the horizontal positions are the same. The reference numeral 4 is a main control box which receives the position angle information transmitted by the navigation box and controls the paddle wheel after processing and operation; after reaching the designated detection point, the water quality automatic detection ship closes the power, approaches the detection point by inertia, and inserts 2 rods into the bottom of the pool through a transmission mechanism (11) on the ship to realize automatic mooring. And then various water quality sensors are placed into the appropriate depth of the pond water body through a lifting system (reference numbers 6 and 7) at the front part of the ship body, and detection is completed.
1.1 Underwater detection sensor
In order to realize water quality detection of the bottom layer (20 cm away from the bottom of the pond), a weight and a floating block are tied at a proper position of a bottom layer water quality sensor through a soft rope, the distance between the water quality sensor and the weight is 20cm, the distance between the weight and the floating block is smaller than the depth of water at the shallowest part of the pond, and the buoyancy of the floating block is smaller than the gravity of the weight in water and larger than the gravity of the water quality sensor in water. When the water quality detection is carried out, the weight is positioned at the bottom of the pond, the soft rope between the water quality sensor and the floating block is in a tensioning state, and the distance between the water quality sensor and the weight is 20cm, so that the water quality detection of the bottom layer (20 cm away from the bottom of the pond) of the pond is realized. FIG. 3(a) and (b) show water quality measurements of a water bottom uneven topography.
FFloating body＝ρgvFloating body
GDetection of＜FFloating body＜GLead (II)
1.2 Water middle layer detecting sensor
The floating block is tied on the proper position of the middle-layer water quality sensor through the soft rope, the distance between the water quality sensor and the floating block is 50cm, and the buoyancy of the floating block is larger than the gravity of the water quality sensor in water. When water quality detection is carried out, the soft rope between the water quality sensor and the floating block is in a tensioning state, and the water quality sensor is positioned at the depth of 50cm under water. FIG. 3(c) Water quality test at 50cm underwater.
FFloating body＞GDetection of
2 establishing a global water level, water quality and water temperature parameter database of the river crab culture pond at a limited time point
And an automatic navigation ship is adopted to carry water level, water quality and water temperature sensors to realize the acquisition of water quality and water temperature parameters of the global bottom layer (about 20cm away from the bottom of the pond), the central layer (about 50cm away from the bottom of the pond) and the water level value of the crab pond. Detecting the global water quality parameters of the river crab culture pond at least 2 times per month in the early stage of river crab culture and at least 1 time per month in the later stage of culture, wherein the parameters at least comprise dissolved oxygen content, water quality PH value, water temperature and water level change. In order to overcome the influence of dynamic change of water quality and temperature parameters of the river crab culture pond, the water quality and temperature parameters are collected after the sun falls on the mountains every time, so that the photosynthesis of water plants in the pond is stopped, the dissolved oxygen content in the water body of the pond is in a monotonous reduction process, and the river crabs usually eat the water at night. The pond universe is divided into m-n grids, the area of each grid is preferably equal, the side length of each grid is 7-10m generally, the central position of each grid is used for detecting water temperature, water quality and water level parameters, the water quality, water temperature and water level parameters are collected according to the path of an automatic detection ship shown in a graph 2, the detection is carried out for 1 time, the average value of 2 detection parameter results is obtained, the longitude and latitude and time information of the automatic detection ship in the water quality and water temperature collection process are recorded, and a universe grid water level, water quality and water temperature parameter database of the river crab culture pond is established.
3 establishing a long-term historical database of water quality and temperature parameters of specific point water level of the river crab culture pond
Setting sensors for water quality, water temperature and water level (only 1 point) above a bottom layer (about 20cm away from the bottom of the pond) at 3 points (the distance between each point is more than or equal to 30m, and the dissolved oxygen content is high, medium and low) in the river crab culture pond, detecting the dissolved oxygen content, the pH value, the water temperature, the water level and other parameters, and establishing a long-term historical database of water quality and water temperature parameters of the water level at a specific point in the river crab culture pond by combining longitude and latitude information of the points;
in figure 2, the small red flags are the places for placing the water quality and temperature sensors at specific points of the water level of the bottom layer of the river crab culture pond. M1, M2, M3 and M4 are coordinate points of the region for placing the river crab culture pond.
4 establishing a BP neural network of the river crab culture water quality
Taking the water temperature (T), pH value, Dissolved Oxygen (DO) and oxidation-reduction potential (ORP) of the same time period as a data unit DnIn actual production, each water quality parameter is gradually changed, and the quantity of the parameter at a certain moment is greatly related to historical data of the parameter. In order to make the neural network predict according to the historical data of the water quality parameters, taking m as 9, taking 10 continuous groups of historical data as input, taking the data at the following time as output, and the functional expression of the neural network for predicting is as follows:
Dn+1＝F(Dn，Dn-1，Dn-2，…Dn-m)
wherein, F (D) is a prediction mapping from the water quality data at the time n to the water quality data at the time n +1 generated by the neural network, DnIs the value of the water quality parameter set at time n. FIG. 4 is a topology diagram of a BP neural network for water quality of river crab cultivation.
The number of nodes of the hidden layer determines the fitting ability of the neural network, and generally, the more the number of nodes of the hidden layer is, the stronger the fitting ability of the neural network is. Determining the number of nodes of the hidden layer by reference to an empirical formula:
l is the number of hidden layer nodes, b is the number of input layer nodes, c is the number of output layer nodes, a is the empirical constant normalization processing:
x' is the value after normalization, x is the original value before normalization, xminIs the smallest value in the data, xmaxIs the largest value in the data. The neural network is ensured to have enough input sensitivity and good fitting property to the sample, and adverse effects caused by different magnitude of the number of the factors are reduced. 33 continuously sampled from Internet of things application demonstration base for river crab cultivation in Jintan JiangsuAnd 6 samples are used as a training set, the training times are set to be 100, the learning rate is set to be 0.1, and the like, training is carried out, and fitting is completed.
In conclusion, the invention provides an automatic detection method for water quality parameters of a large-area river crab culture pond. According to the method, a water level, water quality and water temperature sensor is carried by an automatic navigation ship of the river crab culture pond to acquire parameters such as dissolved oxygen, pH value and water temperature of each grid bottom layer and a middle layer of the whole pond and pond water level, and a water level, water quality and water temperature parameter database of each grid water level and water temperature of the whole pond of the river crab culture pond is established by combining the GPS/Beidou navigation system information of the automatic navigation ship; a water level, water quality and water temperature sensor is arranged at a specific point of the bottom layer of the pond, and a long-term historical database of water level, water quality and water temperature parameters of the specific point of the river crab culture pond is established by combining longitude and latitude information of the specific point. According to the parameter data of each current fixed detection point, the water quality and the water temperature parameters of the global water level and the water temperature of the river crab culture pond are estimated by combining a global water level and water quality parameter database of the river crab culture pond through a neural network fitting method, and a dynamic water quality level and water temperature information distribution diagram of each grid of the pond global is formed by combining positioning information of a GPS/Beidou navigation system.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (5)
1. An automatic detection method for water quality parameters of a large-area river crab culture pond is characterized by comprising the following steps:
(a) carrying water level, water quality and water temperature sensors by using an automatic navigation ship to realize the acquisition of water temperature parameters of water quality of bottom layers and middle layers of grids and water level values of the river crab culture pond universe for multiple times, and establishing a universe water level and water temperature parameter database of the river crab culture pond universe at a limited time point by combining sampling time and longitude and latitude information of a GPS/Beidou navigation system of the automatic navigation ship;
(b) water quality, water temperature and water level sensors are arranged on the bottom layer and the middle layer above 3 points in the river crab culture pond, the dissolved oxygen content is high, medium and low, the dissolved oxygen content, the pH value, the water temperature and the water level parameters are detected, and a long-term historical database of the water quality and the water temperature parameters of the water level at a specific point in the river crab culture pond is established by combining the sampling time and the longitude and latitude information of the points;
(c) and (3) estimating the current water quality and water temperature parameters of the global water level of the river crab aquaculture pond by a neural network fitting method according to the current water quality and water temperature parameters of the specific point water level and the global water level and water temperature parameters of the limited time point by combining the long-term historical database of the water quality and water temperature parameters of the specific point water level of the river crab aquaculture pond and the global water level and water temperature parameters of the limited time point, and forming a dynamic information distribution map of the water quality.
2. The automatic detection method for the water quality parameters of the large-area river crab culture pond according to claim 1, which is characterized in that: the step a specifically comprises the following steps:
step1, dividing the whole pond into m × n grids, preferably taking the area of each grid equal, taking the side length of the grid 7-10m usually, taking the center position of each grid as a detection point to detect the water level, the water quality and the water temperature parameters, detecting the water quality and the water temperature parameters for 2 times according to the original path, and recording corresponding parameters and detection time;
step2, in order to overcome the influence of dynamic change of water quality and temperature parameters of the river crab culture pond, the collection of the water quality and temperature parameters is carried out after the sun falls on the mountain each time, so that the photosynthesis of the pond waterweeds is stopped, the dissolved oxygen content in the pond water body is in a monotonous reduction process, and the method is also based on the fact that the river crabs usually eat at night;
step3, detecting the global water quality parameters of the river crab culture pond at the early stage of river crab culture for not less than 2 times per month and at the later stage of river crab culture for not less than 1 time per month, wherein the parameters at least comprise dissolved oxygen content, water quality PH value, water temperature and water level;
and Step4, establishing a global gridding water level, water quality and water temperature parameter database of the river crab culture pond at a limited time point by combining sampling time and longitude and latitude information of a GPS/Beidou navigation system.
3. The automatic detection method for the water quality parameters of the large-area river crab culture pond according to claim 1, wherein the detection of the dissolved oxygen content needs to be performed in a state that the water body is not disturbed, and the water quality parameters at different depths need to be detected, and the detection steps are as follows:
step2.1, when approaching a detection point, automatically detecting the closing power of the ship for water quality, approaching the detection point by means of inertia, and inserting 2 rods into the bottom of the pool through a transmission mechanism on the ship to realize automatic mooring;
step2.2, putting various water quality sensors into a proper depth of a pond water body through a lifting system at the front part of a ship body, tying a heavy object and a floating block at a proper position of the water quality sensor at the bottom layer through a soft rope, wherein the distance between the water quality sensor and the heavy object is 20cm, the distance between the heavy object and the floating block is smaller than the depth of the water at the shallowest part of the pond, the buoyancy of the floating block is smaller than the gravity of the heavy object in the water and larger than the gravity of the water quality sensor in the water, when water quality detection is carried out, the heavy object is positioned at the bottom of the pond, the soft rope between the water quality sensor and the floating block is in a tensioning state, and the distance between the water quality sensor and the heavy object is 20 cm;
step2.3, tying a floating block on a proper position of the middle-layer water quality sensor through a soft rope, wherein the distance between the water quality sensor and the floating block is 50cm, the buoyancy of the floating block is larger than the gravity of the water quality sensor in water, when water quality detection is carried out, the soft rope between the water quality sensor and the floating block is in a tensioning state, and the water quality sensor is at the depth of 50cm underwater, so that the water quality detection of the water temperature, the pH value and dissolved oxygen in the middle layer of the pond is realized.
4. The automatic detection method for the water quality parameters of the large-area river crab culture pond according to claim 1, wherein in the step (b), sensors are required to be arranged in 3 points with different dissolved oxygen contents of more than or equal to 30m away in the river crab culture pond, parameters are accurately collected, and a historical database of water quality and water temperature parameters of specific points is established.
5. The method for automatically detecting the water quality parameters of the large-area river crab culture pond according to claim 1, wherein the step (c) specifically comprises the following steps:
step3.1, according to the change trend of the two detection results of the same point in the global water level water quality and water temperature parameter database at the limited time points, matching the change trend of the water quality parameter at the specific point water level water quality and water temperature parameter at the long term history database at the corresponding time period of the river crab culture pond, determining whether each grid water quality parameter detection point belongs to a water grass area or a water grass-free area, and binding the grid water quality parameter detection points with the corresponding long term history database of the water quality and water temperature parameters at the specific point water level;
step3.2, estimating the water quality and water temperature parameters of the global water level of the current river crab culture pond by a neural network fitting method according to the water quality and water temperature parameters of the current water level of two specific points;
step3.3 establishing a BP neural network, and taking the water temperature T, pH value, the dissolved oxygen DO and the oxidation-reduction potential ORP in the same time period as a data unit DnIn actual production, each water quality parameter is gradually changed, and the quantity of the parameter at a certain moment is greatly related to historical data of the parameter. In order to make the neural network predict according to the historical data of the water quality parameters, continuous 10 groups of historical data are used as input, the data at the following moment is used as output, and the function expression of the neural network for predicting is as follows:
Dn+1＝F(Dn，Dn-1，Dn-2，…Dn-m)
wherein, taking m as 9, F () is the water quality number at n time generated by the neural networkAccording to a predictive mapping of the water quality data at the time n +1, DnIs the value of the water quality parameter group at the time n;
step4, training the neural network, wherein the number of nodes of the hidden layer determines the fitting ability of the neural network, and generally, the more the number of nodes of the hidden layer, the stronger the fitting ability of the neural network. Determining the number of nodes of the hidden layer by reference to an empirical formula:
l is the number of hidden layer nodes, b is the number of input layer nodes, c is the number of output layer nodes, a is an empirical constant, normalization processing:
x' is the value after normalization, x is the original value before normalization, xminIs the smallest value in the data, xmaxIs the largest value in the data. The neural network is ensured to have enough input sensitivity and good fitting property to the sample, adverse effects caused by different magnitude of the number of the factors are reduced, a training set, training times, learning rate and the like are set, training is carried out, and fitting is completed.
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