CN111985670A - Aquaculture monitoring method and device and storage medium - Google Patents
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Abstract
The embodiment of the invention provides an aquaculture monitoring method, an aquaculture monitoring device and a storage medium. The method comprises the following steps: collecting water quality parameter data and life information data of cultured products; analyzing the collected water quality parameter data by using a first water quality prediction model to obtain a prediction result of water quality parameter change; acquiring the activity index of the cultured product by using the acquired life information data of the cultured product; and determining whether to send out early warning information of easy disease occurrence or not by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product. In the embodiment of the invention, the change trend of the collected water quality parameters is predicted, meanwhile, the activity index of the cultured product is combined to jointly evaluate whether the cultured product has the risk of easily occurring diseases or not, and early warning information is sent out when the risk is determined to exist, so that the relevant cultured personnel can conveniently perform corresponding disease prevention treatment in advance, and thus, the large-scale outbreak of the diseases of the cultured product can be effectively prevented in time.
Description
Technical Field
The invention relates to the technical field of aquaculture, in particular to an aquaculture monitoring method, an aquaculture monitoring device and a storage medium.
Background
The aquaculture industry is one of the important industries in agriculture in China. The traditional aquaculture technology is still relatively extensive, and the supporting technologies such as disease prevention and control in the aquaculture process are relatively poor, so that the aquaculture yield and the economic benefit are not high. Therefore, in order to meet the expanding development requirements of the aquaculture industry, a method capable of effectively preventing large-scale outbreaks of diseases of aquaculture products in time is urgently needed.
Disclosure of Invention
In order to solve the existing technical problems, the embodiment of the invention provides an aquaculture monitoring method, an aquaculture monitoring device and a storage medium, which can provide timely and effective early warning for large-scale outbreak of diseases of aquaculture products, so that relevant aquaculture personnel can conveniently perform corresponding disease prevention treatment.
The embodiment of the invention provides an aquaculture monitoring method, which comprises the following steps:
collecting water quality parameter data and life information data of cultured products;
analyzing the collected water quality parameter data by using a first water quality prediction model to obtain a prediction result of water quality parameter change; acquiring the activity index of the cultured product by using the acquired life information data of the cultured product;
and determining whether to send out early warning information of easy disease occurrence or not by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product.
In the above scheme, the analyzing the collected water quality parameter data by using the first water quality prediction model to obtain the prediction result of the water quality parameter change includes:
analyzing the collected water quality parameter data by adopting a differential integration Moving Integrated Moving Average (ARIMA) model and a Radial Basis Function (RBF) neural network model to obtain a prediction result of the water quality parameter change.
In the above scheme, the analyzing the collected water quality parameter data by using the ARIMA model and the RBF neural network model to obtain the prediction result of the water quality parameter change includes:
dividing the collected water quality parameter data into a linear part and a nonlinear part;
predicting the linear part by adopting an ARIMA model to obtain a prediction result of the linear part;
predicting the nonlinear part by adopting an RBF neural network model to obtain a prediction result of the nonlinear part;
and obtaining a prediction result of the water quality parameter change according to the prediction result of the linear part and the prediction result of the nonlinear part.
In the above scheme, the determining whether to send out early warning information of easy disease by using the obtained prediction result of water quality parameter change and the activity index of the cultured product includes:
And determining whether to send out early warning information of the easy-to-send diseases or not by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product and combining the data in the reference database of the easy-to-send diseases.
In the above scheme, the method further comprises:
obtaining a plurality of water samples according to a preset rule; the water quality parameters of the water samples are different;
collecting life information data of the cultured products in each water sample to obtain a corresponding relation between the change of the water quality parameters and the life information data of the cultured products;
and establishing the disease-prone reference database by using the corresponding relation between the obtained change of the water quality parameters and the life information data of the cultured products.
In the above scheme, the determining whether to send out early warning information of easy disease occurrence by using the obtained prediction result of water quality parameter change and the activity index of the cultured product and combining the data in the reference database of easy disease occurrence includes:
comparing the prediction result of the water quality parameter change with a water quality parameter threshold value to obtain a first comparison result; comparing the activity index of the cultured product with an activity index threshold value to obtain a second comparison result; the water quality parameter threshold and the activity index threshold are obtained by utilizing the corresponding relation between the change of the water quality parameter in the disease-prone reference database and the life information data of the cultured product;
And when the first comparison result meets a first preset condition and/or the second comparison result meets a second preset condition, determining to send out early warning information of easy disease sending.
In the above scheme, the method further comprises:
and when the early warning information is determined to be sent, sending the early warning information through a WEB (World Wide Web) terminal and/or a mobile terminal Application program (APP).
An embodiment of the present invention further provides an aquaculture monitoring device, including:
the acquisition unit is used for acquiring water quality parameter data and life information data of the cultured products;
the first determining unit is used for analyzing the collected water quality parameter data by using the first water quality prediction model to obtain a prediction result of water quality parameter change; acquiring the activity index of the cultured product by using the acquired life information data of the cultured product;
and the second determining unit is used for determining whether to send out early warning information of diseases easily sent out or not by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product.
An embodiment of the present invention further provides an aquaculture monitoring device, including: a processor and a memory for storing a computer program capable of running on the processor;
Wherein the processor is configured to implement the steps of any of the above methods when executing the computer program.
An embodiment of the present invention further provides a storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of any one of the above methods.
The embodiment of the invention provides an aquaculture monitoring method, an aquaculture monitoring device and a storage medium. The method comprises the following steps: collecting water quality parameter data and life information data of cultured products; analyzing the collected water quality parameter data by using a first water quality prediction model to obtain a prediction result of water quality parameter change; acquiring the activity index of the cultured product by using the acquired life information data of the cultured product; and determining whether to send out early warning information of easy disease occurrence or not by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product. In the embodiment of the invention, the change trend of the water quality parameters is predicted by the collected water quality parameter data, and whether the cultured products have the risk of easily occurring diseases is jointly evaluated by combining the activity indexes of the cultured products so as to judge whether to send out early warning information, so that the relevant cultured personnel can conveniently perform corresponding disease prevention treatment in advance, and thus, the large-scale outbreak of the diseases of the cultured products can be effectively prevented in time.
Drawings
FIG. 1 is a first schematic flow chart of an implementation of an aquaculture monitoring method according to an embodiment of the present invention;
FIG. 2 is a schematic view of a second implementation flow of an aquaculture monitoring method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an aquaculture monitoring system according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the WEB side of an aquaculture monitoring system according to an embodiment of the invention;
FIG. 5 is a schematic structural diagram of a mobile terminal APP of an aquaculture monitoring system according to an embodiment of the present invention;
FIG. 6 is a flow chart of a method for implementing an aquaculture monitoring system according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of an aquaculture monitoring apparatus according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a hardware structure of an aquaculture monitoring device according to an embodiment of the invention.
Detailed Description
So that the manner in which the features and aspects of the embodiments of the present invention can be understood in detail, a more particular description of the embodiments of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings.
The embodiment of the invention provides an aquaculture monitoring method. FIG. 1 is a schematic view of an implementation process of an aquaculture monitoring method according to an embodiment of the invention. As shown in fig. 1, the method comprises the steps of:
Step S101, collecting water quality parameter data and life information data of a culture product;
step S102, analyzing the collected water quality parameter data by using a first water quality prediction model to obtain a prediction result of water quality parameter change; acquiring the activity index of the cultured product by using the acquired life information data of the cultured product;
and S103, determining whether to send out early warning information of easy disease occurrence by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product.
In practical applications, the water quality parameters in aquaculture can include various parameters, such as water temperature, dissolved oxygen (dissolved oxygen), PH, illumination duration, ammonia nitrogen content, nitrite content, sulfide content, etc., and these water quality parameters may change at any time within 24 hours of a day. Here, in step S101, the water quality parameter data may include water temperature data, PH value data, dissolved oxygen data, and the like. The life information data of the cultured products can comprise growth conditions, activity conditions, life rule characteristics and the like of the cultured products.
In one embodiment, water quality parameter data may be collected by a water quality sensor; and life information data of the cultured products can be acquired through the underwater video acquisition equipment. When the water temperature monitoring device is applied specifically, water temperature data are monitored in real time by a water temperature sensor; monitoring the pH value by using a pH sensor; and detecting the dissolved oxygen data in real time by adopting a dissolved oxygen sensor. The growth condition, the activity condition, the life rule characteristics and the like of the cultured products can be obtained by all-weather nondestructive observation and recording of the underwater camera.
In step S102, the first water quality prediction model may be one or a mixture of ARIMA model, T-S fuzzy neural network model, and RBF neural network model.
In an embodiment, the collected water quality parameter data may be subjected to noise reduction, and then the water quality parameter data subjected to noise reduction is analyzed by using the first water quality prediction model, so as to obtain a prediction result of water quality parameter change. In practical application, the collected water quality parameter data can be screened according to a preset rule to obtain the water quality parameter data after noise reduction treatment. Here, the preset rule refers to some integrity constraints which must be satisfied and are set in advance for the water quality parameter data, for example, the water quality parameter data cannot be null, the water quality parameter data must be within a measuring range acquired by a sensor, and the like, and the preset rule can be adjusted according to actual needs.
In one embodiment, for the collected water quality parameter data, analyzing the collected water quality parameter data by using an ARIMA model and an RBF neural network model to obtain a prediction result of water quality parameter change, the concrete implementation steps are as follows:
Step a, dividing collected water quality parameter data into a linear part and a nonlinear part;
in practical application, the collected water quality parameter data is divided into a linear part and a nonlinear part at time t, and the collected water quality parameter data can be expressed by equation (1).
Yt＝Lt+Nt (1)
Wherein, YtRepresenting acquired water quality parameter data, LtLinear part, N, representing acquired water quality parameter datatRepresents the non-linear portion of the acquired water quality parameter data.
B, predicting the linear part by adopting an ARIMA model to obtain a prediction result of the linear part;
here, in actual use, the ARIMA model is used for parametersLinear part of data LtThe prediction is carried out in such a way that,
is a linear part L of the water quality parameter datatThe prediction result at the time t, the difference value between the actual value of the linear part of the water quality parameter data which is actually acquired and the ARIMA model prediction, then
Can be represented by the formula (2).
Wherein the content of the first and second substances,
is a linear part L of the water quality parameter datatPrediction of the result at time t, LtLinear part representing acquired water quality parameter data, etIs the difference between the actual value of the linear part of the actually acquired water quality parameter data and the ARIMA model prediction.
Further, taking into account the complex irregularity relationship in the actually acquired water quality parameter data, etCan be represented by formula (3).
et＝g(et-1,et-2,…,et-n)+△t (3)
Wherein e istThe difference value between the actual value of the linear part of the actually acquired water quality parameter data and the ARIMA model prediction is obtained; g (e)t-1,et-2,…,et-n) Represents the correlation between the error value at a certain time and the error value at the previous time, and deltatIndicating a random error.
Here, { et-1,et-2,…,et-nDenotes the prediction residual sequence, which obeys a normal distribution with zero mean and invariant variance.
Water quality parameter data can be obtained by substituting formula (3) for formula (2)Linear part LtPredicted result at time t
Step c, predicting the nonlinear part by using an RBF neural network model to obtain a prediction result of the nonlinear part;
in practical application, the RBF neural network model is adopted to carry out nonlinear part N on water quality parameter datatPredicting to obtain the nonlinear part N of the water quality parameter datatPredicted result at time t
And d, obtaining a prediction result of the water quality parameter change according to the prediction result of the linear part and the prediction result of the nonlinear part.
In practical application, the result of prediction using the combined prediction model fusing the ARIMA model and the RBF neural network model can be expressed by equation (4).
Here, after step d is completed, a result of predicting a change in the water quality parameter is obtained.
In aquaculture, the collected water quality parameter data is not purely linear, but not purely nonlinear. Therefore, the data of the nonlinear part in the water quality parameter data can be effectively predicted by applying the RBF neural network model, and the data of the linear part in the water quality parameter data can be effectively predicted by applying the ARIMA model, so that the complex structure in the water quality parameter data can be effectively predicted. The advantages of RBF neural network nonlinear mapping and the advantages of ARIMA model linear mapping are utilized, the advantages are combined to realize advantage complementation, the self defect caused by using any prediction model independently is overcome, and the change trend of water quality parameter data is effectively predicted and analyzed.
In step S102, the activity index of the aquaculture product is used to evaluate the current vitality of the aquaculture product. In practical application, when the activity index is in a median value (which can be set according to requirements, such as 0.3-0.8), the vitality of the cultured product is strong; whereas, when the activity index is on both sides (e.g., < 0.3 or > 0.8), the vitality of the cultured product is weaker. In practical application, the number of activities of the cultured product in a preset time can be recorded through an underwater camera, or whether obvious disease characteristics (such as various pathological spots) exist on the body surface of the cultured product or not can be compared with the median condition to obtain the evaluation value of the activity index of the cultured product.
The water quality parameter condition is closely related to the growth state of the cultured product, and the life information data of the cultured product directly reflects whether the cultured product is in a disease state or not. Therefore, in step S103, whether to send out early warning information of easy disease can be determined by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product. In practical application, the determination method may be to compare the obtained prediction result of the change of each water quality parameter (such as water temperature, dissolved oxygen, PH value, etc.) and the activity index of the cultured product with corresponding thresholds (which may be a range) respectively to obtain at least two comparison results, and when at least one comparison result exceeds the threshold, send out early warning information of easy disease occurrence. The determination mode can also be that the obtained prediction results of the changes of the water quality parameters and the change rate of the activity index of the aquaculture product are respectively compared with corresponding threshold values to obtain at least two comparison results, and when at least one comparison result exceeds the corresponding threshold value (which can be a range), disease-prone early warning information is sent out.
For example, the fishery water quality standard specifies that the pH value of the aquaculture water body is 6.5-8.5, and when the pH value is lower than 6.5, the pH value of fish blood is reduced, the oxygen carrying function of hemoglobin is disturbed, so that the tissues of fish bodies are anoxic, and the fish show the symptom of anoxia; at too high a pH, ionic NH 4+Conversion to molecular ammonia NH3The toxicity is increased, the water body is strong alkaline, gill tissues of fishes are corroded, respiratory disorders are caused, and the fishes are suffocated in severe cases. Here, when the water quality is obtainedIn the parameters, the prediction result of the change of the PH value in a preset time period (such as 3 days in the future) is beyond the range of 6.5-8.5, or the activity index (for example, 0.85) of the fish is beyond the normal range (0.5-0.8), and the early warning information is determined to be sent out.
In practical application, the medium for sending the early warning information can provide various choices according to the practical situation of related personnel.
Based on this, in an embodiment, when it is determined that the early warning information needs to be sent, the early warning information is sent through the WEB terminal and/or the mobile terminal APP.
In practical application, after the relevant personnel receive the early warning information, corresponding disease prevention treatment is generally carried out in advance.
Based on the information, in one embodiment, the disease prevention suggestion information of the related cultured products is pushed according to the sent early warning information; specifically, disease prevention suggestion information of related culture products can be pushed through a WEB terminal and/or a mobile terminal APP.
The aquaculture monitoring method provided by the embodiment of the invention comprises the steps of collecting water quality parameter data and life information data of aquaculture products; analyzing the collected water quality parameter data by using a first water quality prediction model to obtain a prediction result of water quality parameter change; acquiring the activity index of the cultured product by using the acquired life information data of the cultured product; and determining whether to send out early warning information of easy disease occurrence or not by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product. In the embodiment of the invention, the change trend of the water quality parameters is predicted by the collected water quality parameter data, and whether the cultured products have the risk of easily occurring diseases is jointly evaluated by combining the activity indexes of the cultured products so as to judge whether to send out early warning information, so that the relevant cultured personnel can conveniently perform corresponding disease prevention treatment in advance, and thus, the large-scale outbreak of the diseases of the cultured products can be effectively prevented in time.
During actual application, the preset conditions can be formulated by referring to a reference database consisting of life information data of the cultured products corresponding to a large amount of water quality parameter changes, so that the accuracy and the scientificity of early warning are improved.
Based on this, the embodiment of the present invention further provides another aquaculture monitoring method, as shown in fig. 2, including the following steps:
step S201, establishing a reference database of easy disease occurrence;
step S202, collecting water quality parameter data and life information data of a culture product;
step S203, analyzing the collected water quality parameter data by using a first water quality prediction model to obtain a prediction result of water quality parameter change; acquiring the activity index of the cultured product by using the acquired life information data of the cultured product;
and S204, determining whether to send out early warning information of the easy-to-send diseases or not by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product and combining the data in the reference database of the easy-to-send diseases.
Here, in step S201, a disease-prone reference database needs to be established in advance, and the steps implemented in the disease-prone reference database are as follows:
a, obtaining a plurality of water samples according to a preset rule, wherein the water quality parameters of the water samples are different;
Here, the obtaining of the plurality of water samples according to the preset rule may be a plurality of water samples obtained by a natural change of an actual culture water body for a period of time (e.g., 1 year, etc.); obtaining a plurality of water samples according to the preset rule can also be a plurality of water samples obtained by manually interfering the water samples according to the water quality parameter matching set by related researchers. A plurality of water samples obtained according to natural change are closer to the actual culture condition; and the period of a plurality of water samples obtained according to the artificial interference is shorter and has more pertinence.
B, collecting life information data of the cultured products in each water sample to obtain a corresponding relation between the change of the water quality parameters and the life information data of the cultured products;
here, the life information data of the aquaculture products in each water sample needs to be collected to obtain the corresponding relation between the change of the water quality parameters and the life information data of the aquaculture products. In practical application, the whole process of the change of a certain parameter in a water sample can be tracked, and the life information data of the cultured product in the whole process is recorded, so that the corresponding relation between the water quality parameter and the life information data of the cultured product is obtained.
And c, establishing the disease-prone reference database by using the corresponding relation between the obtained change of the water quality parameters and the life information data of the cultured products.
Here, the establishment of the disease-prone reference database is completed.
Steps S202 and S203 are the same as steps S101 and S102, and are not described again here.
In step S204, whether to send out the early warning information of easy disease occurrence may be further determined by combining the data in the reference database of easy disease occurrence.
In one embodiment, one case of determining in conjunction with the data in the disease-prone reference database is:
comparing the prediction result of the water quality parameter change with a water quality parameter threshold value to obtain a first comparison result; comparing the activity index of the cultured product with an activity index threshold value to obtain a second comparison result; the water quality parameter threshold and the activity index threshold are obtained by utilizing the corresponding relation between the change of the water quality parameter in the disease-prone reference database and the life information data of the cultured product;
and when the first comparison result meets a first preset condition and/or the second comparison result meets a second preset condition, determining to send out early warning information of easy disease sending. Here, the first preset condition represents a water quality parameter of the breeding product prone to diseases, and the second preset condition represents an activity index of the breeding product prone to diseases.
Here, a range of the water quality parameter of the cultured product disease susceptibility closer to the actual situation and a range of the activity index of the cultured product disease susceptibility closer to the actual situation are obtained from the relationship between the change of the water quality parameter in the database and the life information data of the cultured product. In practical applications, for example, the relationship between the change of the water quality parameter in the database and the life information data of the cultured product may be a change process corresponding to the life information data of the cultured product when a single variable (e.g., PH value) of a certain water sample parameter changes in a wider range (e.g., 6 to 9, wider than the above-mentioned 6.5 to 8.5). Thus, a more accurate disease-prone pH threshold (e.g., 6.6, 8.4) for a aquaculture product can be determined based on the activity index of the aquaculture product. In practical application, for another example, the relationship between the change of the water quality parameter in the database and the life information data of the cultured product may be a change process corresponding to the life information data of the cultured product when a single variable or a plurality of variables of a certain water sample parameter are recorded, at this time, a plurality of activity index thresholds exist, and a more accurate activity index threshold is obtained by averaging a plurality of activity index thresholds.
Obviously, the disease incidence range determined according to a large amount of data in the database is closer to the actual condition and more accurate than the traditional disease incidence range recommended according to the experience of previous generation breeding personnel or textbooks. Therefore, the accuracy of disease early warning can be further improved.
In practical application, the determination method may be to compare the obtained prediction result of the change of each water quality parameter (such as water temperature, dissolved oxygen, PH value, etc.) and the obtained activity index of the cultured product with the range of the water quality parameter and the range of the activity index obtained from the database, respectively, to obtain at least two comparison results, and when at least one comparison result exceeds a threshold value, send out early warning information of easy disease occurrence. The determination mode can also be that the obtained prediction results of the changes of the water quality parameters and the change rate of the activity index of the aquaculture product are respectively compared with the range of the water quality parameters and the change rate of the activity index obtained from the database to obtain at least two comparison results, and when at least one comparison result exceeds a corresponding threshold (can be a range), disease-prone early warning information is sent out.
In the embodiment of the invention, the change trend of the collected water quality parameters is predicted, the activity index of the cultured product is considered, and meanwhile, the risk of the disease susceptibility of the cultured product is jointly evaluated by referring to the disease susceptibility reference database so as to judge whether to send out early warning information. The reference of the database is introduced, so that the accuracy of disease early warning is improved.
The present invention will be described in detail with reference to specific examples.
In an application embodiment, an aquaculture monitoring system based on the Internet of things is provided by combining with practical application conditions. The monitoring system is specifically realized according to the aquaculture monitoring method, namely disease monitoring of aquaculture fishes is realized through two aspects, on the first hand, a water quality parameter prediction model is used for predicting water quality parameter change, and fish diseases possibly caused when water quality parameters are greatly changed are predicted; secondly, acquiring the growth condition and the water environment of the sample fish in the whole life cycle and the diseased condition of the fish when external factors change according to the mode of carrying out underwater 24-hour video monitoring on the fish; and finally, analyzing and evaluating the fishes by utilizing the information of the two aspects to obtain whether the aquaculture fishes are at risk of disease susceptibility.
Here, as shown in fig. 3, the internet of things-based aquaculture monitoring system architecture comprises: a data storage layer, a data access layer, a business logic layer, and an application presentation layer, wherein,
and the data storage layer is used for storing the collected water quality parameter data and the fish life information data. In practical application, water quality parameter data are acquired through various water quality information sensors, and fish life information data are acquired through 24-hour monitoring videos of an underwater camera.
And the data access layer is used for realizing communication between the data storage layer and the service logic layer. In practical application, the bus (such as RS 232) and the wireless network technology (such as ZigBee) are utilized to realize communication among various water quality information sensors, underwater cameras and service logic layer data transmission interfaces, namely, data of the water quality information sensors and the underwater cameras are sent to the service logic layer data transmission interfaces, and control information of the service logic layer data transmission interfaces is returned to the water quality information sensors and the underwater cameras so as to control operation of the water quality information sensors and the underwater cameras.
The service logic layer is used for realizing two-aspect service; in particular, in a first aspect, interaction between a data access layer and an application representation layer; in a second aspect, the prediction of changes in water quality parameters and the assessment of susceptibility to disease. In practical application, for the first aspect, the user management, the data interface management, the system data setting, the basic information management, and other modules are used for implementation, as shown in fig. 3. For the second aspect, the combined water quality prediction model fusing the ARIMA model and the RBF neural network model is adopted to predict the water quality; the method adopts the determination mode of the disease susceptibility to evaluate whether the disease susceptibility exists or not.
And the application presentation layer is used for displaying the early warning information through a WEB end and/or a mobile terminal APP and displaying the early warning information to related personnel. In practical application, the structure of the WEB side system of the aquaculture monitoring system is shown in FIG. 4, and the WEB side system is mainly divided into four modules: user management, equipment management, early warning/prevention and disease management, wherein corresponding functional parts of each module are shown in fig. 4, wherein the user management comprises user names, contact ways, registration dates, login times, data record extraction, remarks extraction and the like; the equipment management comprises equipment name, management personnel, installation position, equipment state, equipment operation, reporting record and the like; the alarm/prevention comprises a culture area, a water quality environment, fry quantity, mark quantity, abnormal number, reporting records and the like; the disease management includes disease name, disease category, disease manifestation, head to record and remark, etc.
In practical application, the structure of the mobile terminal APP system of the aquaculture monitoring system is as shown in FIG. 5, and the APP can provide a convenient information interaction page for related personnel through registration authentication; and checking the growth condition of the cultured fishes, connecting a system background, acquiring prediction information, and judging the health condition of the fishes and information of some corresponding diseases possibly caused according to the prediction information. Here, the design of the mobile terminal APP corresponds to each functional module of the WEB side.
It should be noted that, 24 hours of monitoring camera under water is adopted in aquaculture monitored control system based on thing networking, can realize all-round real-time collection through arranging a plurality of waterproof cameras, and a series of video acquisition supervisory equipment, uploads the video image who gathers to the PC end, does benefit to the observation of fish growth situation, the activity condition to and the observation of the disease that some characterizations are obvious. Meanwhile, video materials and data information can be stored in the database, and the database is connected with the WEB end and the mobile terminal APP, so that the video materials and the data information can be conveniently extracted, checked, analyzed, recorded and the like at any time.
Based on the structure of the system, the aquaculture monitoring system based on the internet of things in the embodiment of the application is realized as shown in fig. 6 and comprises a wireless sensor network group part, a method and model research part and an intelligent monitoring and control system part. The wireless sensor network group part is mainly used for data acquisition, pretreatment and analysis, and comprises a technology for acquiring specific data (water temperature data, PH value data and dissolved oxygen data) of water quality parameter data, a technology for pretreating the acquired data, and a technology for analyzing action mechanisms of all water quality parameters and influence factors of all water quality parameters; the method and the model research part are mainly used for information modeling and comprise the following steps: establishing a water quality prediction model (one or a mixture of several models in an ARIMA model, a RBF neural network model and a T-S fuzzy neural network model), and predicting various water quality parameters; the intelligent monitoring and control system part is mainly used for information modeling and system application and optimization, and comprises the realization of various functional modules (an intelligent analysis and prediction early warning software module, a database realization module and a background service logic module) and the realization of an application display end (web foreground software and mobile terminal software). Here, the intelligent monitoring and control system portion may provide data support and optimized direction indication for the methodology and model study portion.
According to the aquaculture monitoring system based on the Internet of things, the designed intelligent monitoring and control system is used for storing the related data into the database, so that the analysis is assisted, and the accurate early warning of the susceptibility of fish diseases is realized; meanwhile, the WEB section and the mobile terminal can be connected, and useful information is displayed to related personnel.
In order to implement the method according to the embodiment of the present invention, an aquaculture monitoring apparatus is further provided according to the embodiment of the present invention, fig. 7 is a diagram illustrating a structure of the apparatus according to the embodiment of the present invention, and as shown in fig. 7, the apparatus 700 includes:
the acquisition unit 701 acquires water quality parameter data and life information data of a culture product;
a first determining unit 702, which analyzes the collected water quality parameter data by using a first water quality prediction model to obtain a prediction result of water quality parameter change; acquiring the activity index of the cultured product by using the acquired life information data of the cultured product;
the second determining unit 703 determines whether to send out early warning information for easy disease occurrence by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product.
In an embodiment, the first determining unit 702 is specifically configured to:
And analyzing the collected water quality parameter data by adopting an ARIMA model and a RBF neural network model to obtain a prediction result of the water quality parameter change.
In an embodiment, the first determining unit 702 is specifically configured to:
dividing the collected water quality parameter data into a linear part and a nonlinear part;
predicting the linear part by adopting an ARIMA model to obtain a prediction result of the linear part;
predicting the nonlinear part by adopting an RBF neural network model to obtain a prediction result of the nonlinear part;
and obtaining a prediction result of the water quality parameter change according to the prediction result of the linear part and the prediction result of the nonlinear part.
In an embodiment, the second determining unit 703 is specifically configured to:
and determining whether to send out early warning information of the easy-to-send diseases or not by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product and combining the data in the reference database of the easy-to-send diseases.
In one embodiment, the apparatus further comprises: a setup unit configured to:
obtaining a plurality of water samples according to a preset rule; the water quality parameters of the water samples are different;
collecting life information data of the cultured products in each water sample to obtain a corresponding relation between the change of the water quality parameters and the life information data of the cultured products;
And establishing the disease-prone reference database by using the corresponding relation between the obtained change of the water quality parameters and the life information data of the cultured products.
In an embodiment, the second determining unit 703 is specifically configured to:
comparing the prediction result of the water quality parameter change with a water quality parameter threshold value to obtain a first comparison result; comparing the activity index of the cultured product with an activity index threshold value to obtain a second comparison result; the water quality parameter threshold and the activity index threshold are obtained by utilizing the corresponding relation between the change of the water quality parameter in the disease-prone reference database and the life information data of the cultured product;
and when the first comparison result meets a first preset condition and/or the second comparison result meets a second preset condition, determining to send out early warning information of easy disease sending.
In an embodiment, the apparatus 700 further includes a sending unit, configured to: and when the early warning information is determined to be sent, sending the early warning information through the WEB terminal and/or the mobile terminal APP.
In practical application, the acquisition unit 701, the first determination unit 702, the second determination unit 703, the establishing unit and the sending unit can be realized by a processor in the aquaculture monitoring device.
It should be noted that: in the aquaculture monitoring device provided in the above embodiment, when monitoring the susceptibility of diseases of aquaculture products, only the division of the program modules is taken as an example, and in practical applications, the processing distribution can be completed by different program modules according to needs, that is, the internal structure of the device is divided into different program modules to complete all or part of the above-described processing. In addition, the aquaculture monitoring device provided by the embodiment and the aquaculture monitoring method embodiment belong to the same concept, and the specific implementation process is described in the method embodiment and is not described again.
Based on the hardware implementation of the program modules, and in order to implement the method according to the embodiment of the present invention, an aquaculture monitoring apparatus according to the embodiment of the present invention is provided, as shown in fig. 8, where the apparatus 800 includes: a processor 801 and a memory 802 for storing computer programs capable of running on the processor, wherein:
the processor 801 is configured to execute the method provided by one or more of the above technical solutions.
In practice, as shown in FIG. 8, the various components of the apparatus 800 are coupled together by a bus system 803. It is understood that the bus system 803 is used to enable communications among the components. The bus system 803 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration the various buses are labeled as bus system 803 in figure 8.
In an exemplary embodiment, embodiments of the present invention also provide a storage medium, in particular a computer readable storage medium, such as the memory 802 comprising a computer program executable by the processor 801 of the aquaculture monitoring apparatus 800 to perform the steps of the aforementioned method. The computer-readable storage medium may be a magnetic random access Memory (FRAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM), among other memories.
It should be noted that: it should be noted that: "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
In addition, the technical solutions described in the embodiments of the present invention may be arbitrarily combined without conflict.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (10)
1. An aquaculture monitoring method, said method comprising:
collecting water quality parameter data and life information data of cultured products;
analyzing the collected water quality parameter data by using a first water quality prediction model to obtain a prediction result of water quality parameter change; acquiring the activity index of the cultured product by using the acquired life information data of the cultured product;
and determining whether to send out early warning information of easy disease occurrence or not by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product.
2. The method of claim 1, wherein analyzing the collected water quality parameter data using the first water quality prediction model to obtain a prediction of water quality parameter changes comprises:
and analyzing the collected water quality parameter data by adopting a difference integration moving average autoregressive (ARIMA) model and a Radial Basis Function (RBF) neural network model to obtain a prediction result of the water quality parameter change.
3. The method as claimed in claim 2, wherein the analyzing the collected water quality parameter data by using the ARIMA model and the RBF neural network model to obtain the prediction result of the water quality parameter change comprises:
Dividing the collected water quality parameter data into a linear part and a nonlinear part;
predicting the linear part by adopting an ARIMA model to obtain a prediction result of the linear part;
predicting the nonlinear part by adopting an RBF neural network model to obtain a prediction result of the nonlinear part;
and obtaining a prediction result of the water quality parameter change according to the prediction result of the linear part and the prediction result of the nonlinear part.
4. The method of claim 1, wherein the step of determining whether to send out early warning information of the disease susceptibility by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product comprises the following steps:
and determining whether to send out early warning information of the easy-to-send diseases or not by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product and combining the data in the reference database of the easy-to-send diseases.
5. The method of claim 4, further comprising:
obtaining a plurality of water samples according to a preset rule; the water quality parameters of the water samples are different;
collecting life information data of the cultured products in each water sample to obtain a corresponding relation between the change of the water quality parameters and the life information data of the cultured products;
And establishing the disease-prone reference database by using the corresponding relation between the obtained change of the water quality parameters and the life information data of the cultured products.
6. The method of claim 4, wherein the step of determining whether to send out early warning information of the susceptibility to diseases by using the obtained prediction result of the change of the water quality parameters and the activity index of the cultured products and combining the data in the reference database of the susceptibility to diseases comprises the following steps:
comparing the prediction result of the water quality parameter change with a water quality parameter threshold value to obtain a first comparison result; comparing the activity index of the cultured product with an activity index threshold value to obtain a second comparison result; the water quality parameter threshold and the activity index threshold are obtained by utilizing the corresponding relation between the change of the water quality parameter in the disease-prone reference database and the life information data of the cultured product;
and when the first comparison result meets a first preset condition and/or the second comparison result meets a second preset condition, determining to send out early warning information of easy disease sending.
7. The method of claim 1, further comprising:
and when the early warning information is determined to be sent, sending the early warning information through an internet WEB terminal and/or a mobile terminal application program APP.
8. An aquaculture monitoring apparatus, said apparatus comprising:
the acquisition unit is used for acquiring water quality parameter data and life information data of the cultured products;
the first determining unit is used for analyzing the collected water quality parameter data by using the first water quality prediction model to obtain a prediction result of water quality parameter change; acquiring the activity index of the cultured product by using the acquired life information data of the cultured product;
and the second determining unit is used for determining whether to send out early warning information of diseases easily sent out or not by using the obtained prediction result of the water quality parameter change and the activity index of the cultured product.
9. An aquaculture monitoring apparatus, comprising: a processor and a memory for storing a computer program capable of running on the processor;
wherein the processor is adapted to perform the steps of the method of any one of claims 1 to 7 when running the computer program.
10. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, performing the steps of the method of any one of claims 1 to 7.
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