Health monitoring method, device and system based on aquatic animal excrements

Abstract

The invention provides a health monitoring method, device and system based on aquatic animal sequestration, and belongs to the technical field of data processing technology and aquatic product healthy cultivation, and the method comprises the steps: obtaining a physical feature parameter vector of to-be-monitored aquatic animal sequestration, the physical characteristic parameter vector is determined by at least one of particle size distribution information, hydrodynamic parameters, dissolution characteristics, color characteristics and unit time increment of the aquatic animal excrements to be monitored; respectively inputting the physical characteristic parameter vector obtained at each monitoring moment into a health monitoring model to obtain instantaneous health state information output by the model; forming a time sequence by a plurality of pieces of instantaneous health state information, performing feature analysis on the time sequence, and determining a final health state; and continuously monitoring, analyzing and displaying the health state of the aquatic animal to be monitored. The invention further provides a device and a system which are used for better implementing the method to obtain a more effective health monitoring result and improve the accuracy of aquatic animal health monitoring.

Description

Technical Field

[0001] The present application relates to the fields of data processing technology and healthy aquaculture technology, and in particular to a health monitoring method, device and system based on aquatic animal excrement. Background Art

[0002] Aquaculture health monitoring and presumptive disease diagnosis are key technologies for healthy aquaculture, but the current development of aquatic health monitoring technology and equipment is insufficient, particularly in the area of ​​in-situ monitoring. Presumptive diagnosis involves visual observation and correlation of animal symptoms such as abnormal behavior, stress, scale loss, bleeding, and injury using microscopes, drones, and machine learning methods based on water quality spectroscopy. Existing health monitoring and presumptive disease diagnosis methods provide important insights for healthy aquaculture, but they often rely on transient features and lack long-term monitoring information. Furthermore, for aquatic animals, the onset of these symptoms indicates that the animal has developed a significant disease, resulting in a certain lag. While water quality-based health prediction methods incorporate long-term monitoring information, they suffer from poor correlation and the high cost of water quality sensors. Furthermore, given the wide variety and complex nature of aquatic diseases, the incorporation of new features is necessary to enhance the effectiveness of health monitoring.

[0003] Furthermore, the health status of organisms can currently be monitored through the excreta of humans and livestock (pigs, chickens, cattle, and pandas), and there are already practices using excreta for health monitoring and testing. However, aquatic animal excreta is produced and dissolved underwater, and its characteristics differ from those of humans, livestock, and poultry (pigs, chickens, cattle, and pandas). Sampling is difficult, and testing requires high timeliness. Therefore, the aforementioned technology and equipment cannot be directly applied to aquatic animals. Using the same detection methods for terrestrial excreta as for aquatic animal health monitoring would result in reduced accuracy and even render monitoring impossible. Furthermore, existing methods often rely on ex situ analysis of animal excreta, either through odor analysis or spectral characteristics, resulting in high instrument costs and unsuitable for large-scale promotion and application.

[0004] Based on the problems of the hysteresis of the above methods and the adaptability of the devices, how to introduce new characteristic indicators to more accurately achieve effective correlation of health status and develop effective aquaculture health monitoring devices and methods remains to be solved. Summary of the Invention

[0005] The present application provides a health monitoring method, device and system based on aquatic animal excrement, which is used to make up for the problem of insufficient characteristic indicators and low accuracy of in-situ monitoring of aquatic animal health, and improve the accuracy of aquatic animal health monitoring.

[0006] The present application provides a health monitoring method based on aquatic animal excrement, comprising: Step S1, obtaining a physical characteristic parameter vector of the aquatic animal excrement to be monitored, wherein the physical characteristic parameter vector is determined by at least one of the characteristics of the aquatic animal excrement to be monitored, such as particle size distribution information, hydrodynamic parameters, solubility characteristics, color characteristics, and unit time increment; Step S2: Inputting the physical characteristic parameter vectors obtained at each monitoring moment into a health monitoring model to obtain instantaneous health status information output by the health monitoring model. The health monitoring model is pre-constructed based on the physical characteristic parameter vectors and their labels under the feeding conditions of the aquatic animal species to be monitored; Step S3, combining multiple instantaneous health status information into a time series, and performing feature analysis on the time series to determine the final health status; Step S4: continuously monitor, analyze and display the health status of the aquatic animals to be monitored.

[0007] The health monitoring model in step S2 is pre-built, which includes step S201, constructing a labeled data set for aquatic animal health monitoring. The physical characteristic parameter vector of the aquatic animal excrement to be monitored is predetermined by the monitored physical characteristic parameter vector, and its index composition includes at least one of the hydrodynamic parameters such as suspension time or sedimentation velocity, color characteristics including the number and proportion of colors, transparency and other information, and derivative indicators such as color quality, and its representation is as follows: X i =[ x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ] (i=6). Further, according to the sample information X i and its disease labels Constructing a reference set s ijk , which constitutes the basic case database S of cross-regional long time series.

[0008] ; in, The information of aquatic animal excrement samples collected at the i-th farm at the j-th site at the k-th time (growth stage) X i =[ x 1 ,x2 ,x 3 ,x 4 ,x 5 ,x 6 Specifically, i represents the index of the breeding area location, j represents the deployment index of the device in the area, and k represents the index of the time series (t1 to t k corresponding to the full growth stage), If the main parameter features of the inferred data set are arranged according to the importance of discrimination, at least two of PSD, suspension time, and decomposition time may be included. It includes information such as disease type p and disease stage q, where p is the disease type such as bacterial enteritis, especially gastrointestinal diseases; q is the stage of good health, incubation period, early stage, middle stage and late stage. Therefore, S can contain at least i×j×k×size(p)×size(q) reference sets, where size() represents the number of different elements in the type. Specially, It can be just two elements, normal and abnormal.

[0009] Step S202: Complete the model training based on the selected model, accuracy requirements and labeled data set. First, a disease type and its stage label Q in the labeled data set is used. bq The data is divided into a training set and a validation set. To cope with complex scenarios involving multiple diseases, feature expansion is performed based on composite features, such as x1x2, x3x4, x5x6, ... or x1+x2, x3+x4, x5+x6... or x1x1, x2x2, ... and sin(x1), sin(x2)... The cases are used to form a pre-trained health monitoring model. Based on the validity of the data, the health monitoring model adopts a multi-model path, including three categories: deep learning model, case-based reasoning model, and exponential model. Based on the selected model type and combined with the modeling process, the corresponding health monitoring model is generated.

[0010] The health monitoring model includes but is not limited to machine learning methods such as deep learning models, including: a model trained based on machine learning methods such as deep learning, using sample information and disease labels in a reference library as training data to train a model pre-determined according to the current scenario, and obtaining a health monitoring model after the training is completed.

[0011] The health monitoring model includes an index generation method, which includes: processing the physical characteristic parameter vector and each parameter in the composite characteristic of the aquatic animal excrement to be monitored in combination with water quality parameters, thereby correcting the physical characteristic parameter vector and obtaining an instantaneous health index by processing it using a specified function, as follows: Q i =f(P i ,φ ij , w i )=f(x1,x2,x3,x4,x5,x6)φ ij w i ; Q= ; in, x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 The estimated vector P representing the model input i , is illustrative and is not limited to the six basic physical features. It can also include processed composite features. Based on the content of the above case, this equation is established. The case content is input to obtain the resulting instantaneous health index, which includes the instantaneous health index and the corresponding disease name p-onset stage q. The corresponding expression is numerical value + label feature 1 + label feature 2. The calculation is mainly based on the numerical value. T represents the threshold of the Q index, which is used to determine the attribution of the label. φ ij represents the correction vector based on water quality; w i is the parameter estimation vector for the equation, i = 1, 2, 3, 4, 5, 6…; thus, a health index function for a specific disease type or stage is established. Through this model, the corresponding instantaneous health information Q can be obtained, and the disease type and stage information corresponding to the exponential equation can be obtained.

[0012] The health monitoring model includes a case analysis method, including: judging similarity, membership and difference in sequence, and determining the instantaneous health status according to the threshold. Specifically, (1) determining the similarity information between the physical characteristic parameter vector and the reference physical characteristic parameter vector of each reference disease; and comparing the similarity with the largest value in the similarity information with the preset similarity threshold; if the similarity with the largest value is greater than the preset similarity threshold, then determining the reference disease corresponding to the largest similarity as the instantaneous health status information of the aquatic animal to be monitored; (2) if the similarity with the largest value in the similarity information is less than or equal to the preset similarity threshold, then determining the membership information between the physical characteristic parameter vector and the reference physical characteristic parameter vector of each reference disease; and according to the membership information If the maximum membership degree is greater than the preset membership degree threshold, the reference disease corresponding to the maximum membership degree is determined as the instantaneous health status information of the aquatic animal to be monitored, and the health status of the aquatic animal to be monitored is determined; (3) If the maximum membership degree is less than or equal to the preset membership degree threshold, the deviation value information between the physical characteristic parameter vector and the reference physical characteristic parameter vector of each reference disease is determined; based on the deviation value information, the reference disease corresponding to the minimum deviation value in the deviation value information is determined as the instantaneous health status information of the aquatic animal to be monitored, and the health status of the aquatic animal to be monitored is determined.

[0013] According to a health monitoring method based on aquatic animal excrement provided by the present application, the step S1 of obtaining the physical characteristic parameter vector of the excrement of the aquatic animal to be monitored includes: step S11, collecting multiple excrement images of the aquatic animal to be monitored; step S12, performing target recognition based on each of the excrement images to obtain identification information; step S13, calculating and determining the physical characteristic parameter vector of the excrement of the aquatic animal to be monitored based on the identification information.

[0014] According to the health monitoring method based on aquatic animal excrement provided in this application, step S2 involves inputting the physical characteristic parameter vectors acquired at each monitoring moment into a health monitoring model to obtain instantaneous health status information. The method includes: step S21, acquiring and processing the physical characteristics of the aquatic animal excrement to be monitored; step S22, inputting the monitored characteristic vectors into the health monitoring model to obtain the instantaneous health status; and step S23, regularly updating and maintaining the model and reference set.

[0015] According to the health monitoring method based on aquatic animal excrement provided by the present application, the step S3 comprises forming a time series of multiple instantaneous health status information and performing feature analysis to determine the final health status, including: step S31, based on the time series composed of instantaneous (short-term) health status information, generating a time series feature set of instantaneous health status estimation results [Q1, Q2, Q3, Q4, ..., Q n ]; Step S32, use Mode (Q) to identify the instantaneous health state time series feature set Q to represent the final recognition result. Specifically, F C =Mode(Q)=Mode(F c1 :F cn ), where Mode() means taking the result of a certain value exceeding the threshold value for consecutive times as the output, F Ci Denotes the inferred set P i The instantaneous result, Q represents the instantaneous result of the sample F c1 to F cn The temporal feature set composed of , Fc represents their recognition results. ,For the result labels p, q, the corresponding disease type and stage are ,determined.

[0016] According to a health monitoring method based on aquatic animal excrement provided by the present application, the health status of the aquatic animals to be monitored is continuously monitored and analyzed and displayed in step S4, including: step S41, reviewing the accuracy of the judgment result, and performing early warning or specific area decision analysis in combination with the location information of the deployed device; step S42, repeating the above steps to achieve early monitoring and early warning of aquatic animal diseases.

[0017] The present application also provides a health monitoring device based on aquatic animal excrement, including a feces collection device, a feature information acquisition unit, a visual information collection device, a processing unit, a position information acquisition unit and a computer program stored on the processing unit. When the processing unit executes the computer program, it implements any of the above-mentioned health monitoring methods based on aquatic animal excrement.

[0018] The present application also provides a health monitoring system based on aquatic animal excrement, including a monitoring device, a cloud platform, an analysis unit, a display unit and a computer program stored on the analysis unit. When the analysis unit executes the computer program, it implements any of the above-mentioned health monitoring methods based on aquatic animal excrement.

[0019] The present application also provides a medium, which is a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements any of the above-mentioned health monitoring methods based on aquatic animal excrement.

[0020] The health monitoring method, device, and system based on aquatic animal excrement provided in this application obtain physical characteristic parameter vectors of the excrement of the aquatic animal to be monitored, wherein the physical characteristic parameter vectors are determined by at least one of the characteristics of the excrement of the aquatic animal to be monitored, such as particle size distribution information, hydrodynamic parameters, solubility characteristics, color characteristics, and unit time increment; the physical characteristic parameter vectors obtained at each monitoring moment are respectively input into the health monitoring model to obtain instantaneous health status information output by the model; multiple instantaneous health status information are combined into a time series, and feature analysis is performed on them to determine the final health status; and the health status of the aquatic animal to be monitored is continuously monitored and analyzed and displayed. This application uses computer vision technology to obtain the physical characteristics of aquatic animal excrement and adds new indicator features. At the same time, since underwater characteristic parameters that are more consistent with aquatic animals are used to establish annotated data sets and perform machine learning and other modeling, in-situ monitoring can be carried out and the instantaneous health status information of the aquatic animals to be monitored can be determined based on the physical characteristic parameter vector obtained at the monitoring time. Then, the time series composed of multiple instantaneous health status information can be analyzed to determine the final health status, thereby realizing the classification and identification of various diseases, and obtaining more accurate monitoring results, which can improve the accuracy of health monitoring of aquatic animals. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 This is a flow chart of the health monitoring method based on aquatic animal excrement provided in this application.

[0023] Figure 2 This is a detailed flow chart of the health monitoring method based on aquatic animal excrement provided in this application.

[0024] Figure 3 This is a schematic structural diagram of a health monitoring device based on aquatic animal excrement provided in this application.

[0025] Figure 4 This is a schematic diagram of the structure of the health monitoring system based on aquatic animal excrement provided in this application. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0027] It should be noted that, in the description of this application, the terms "comprise," "include," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or device comprising a list of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, article, or device. Without further limitation, an element defined by the phrase "comprises a..." does not preclude the presence of additional identical elements in the process, method, article, or device comprising the element. Terms such as "upper" and "lower" indicate positions or location relationships based on those shown in the accompanying drawings and are intended solely for ease of description and simplification of the description. They are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed, or operate in a specific orientation, and are therefore not to be construed as limiting this application. Unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly, for example, to mean a fixed connection, a detachable connection, or an integral connection; a mechanical connection or an electrical connection; a direct connection or an indirect connection through an intermediate medium; or internal communication between two elements. For those skilled in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0028] The terms "first," "second," and the like in this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that such terms are interchangeable where appropriate, so that embodiments of this application can be implemented in an order other than that illustrated or described herein. Furthermore, the terms "first," "second," and the like generally distinguish objects of a class and do not limit the number of objects; for example, the first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the connected objects.

[0029] It should be noted that all actions of acquiring signals, information or data in this application are carried out in compliance with the corresponding data protection laws and policies of the country where they are located, and with the authorization given by the owner of the corresponding device. Among them, the number of characteristic indicators of the reference set and the presumption set corresponds, and the reference set is predetermined. The standard reference set, basic reference set, and reference set library in the following embodiments all refer to reference sets, which are different names in different processes; monitoring data sets, health presumption sets, and presumption data sets all refer to presumption sets, which are different names in different stages.

[0030] The following combination Figures 1-4 The present invention describes a method, device and system for monitoring the health of aquatic animals based on their excrement.

[0031] Figure 1 This is a flow chart of the health monitoring method based on aquatic animal excrement provided by this application, such as Figure 1 As shown, the method includes the following steps S1 to S4.

[0032] Step S1, obtaining a physical characteristic parameter vector of the aquatic animal excrement to be monitored, wherein the physical characteristic parameter vector is determined by at least one of particle size distribution information, hydrodynamic parameters, solubility characteristics, color characteristics, and unit time increment of the aquatic animal excrement to be monitored; Step S2: Inputting the physical characteristic parameter vectors obtained at each monitoring moment into a health monitoring model to obtain instantaneous health status information output by the health monitoring model. The health monitoring model is pre-constructed based on the physical characteristic parameter vectors and their labels under the feeding conditions of the aquatic animal species to be monitored; Step S3, combining multiple instantaneous health status information into a time series, and performing feature analysis on the time series to determine the final health status; Step S4: continuously monitor, analyze and display the health status of the aquatic animals to be monitored.

[0033] Furthermore, the obtained health status can be sent to management personnel as reference information so that management personnel can formulate breeding plans based on the reference information. Health levels can also be determined based on the health status so that management personnel can formulate breeding plans based on the health levels.

[0034] Reference Figure 2 , Figure 2 This is a detailed flow chart of the health monitoring method based on aquatic animal excrement provided by this application. This application may include a model construction and maintenance part, in which the model and standard reference data set are pre-built and regularly maintained.

[0035] In step S1, a physical characteristic parameter vector of the aquatic animal excrement to be monitored is obtained. The physical characteristic parameter vector is determined by at least one of the characteristics of the aquatic animal excrement to be monitored, such as particle size distribution information, hydrodynamic parameters, solubility characteristics, color characteristics, and unit time increment. Specifically, obtaining the physical characteristic parameter vector of the aquatic animal excrement to be monitored includes: Step S11, collecting multiple excrement images of the aquatic animals to be monitored; Step S12, performing target recognition based on each excrement image to obtain recognition information; Step S13: Calculate and determine the physical characteristic parameter vector of the aquatic animal excrement to be monitored based on the identification information.

[0036] It should be noted that the obtained inference set indicators are predetermined based on the physical feature parameter vector, and the indicator composition includes but is not limited to hydrodynamic parameters such as suspension time or sedimentation velocity, color characteristics including information such as color quantity and ratio, transparency, and other information, as well as derivative indicators such as color quality, and includes at least one of the physical features. Specifically, each parameter in the above-mentioned physical feature parameter vector X can be defined as x i , i is a positive integer, which only represents the index of the feature index, and there is no order of precedence. The following are six typical physical features that are easy to obtain based on computer vision technology: Here, x1 represents the particle size distribution of aquatic animal excrement, such as fish feces. This refers to the proportion of particles within different size ranges (by mass or surface area). Particle size distribution can be expressed as Particle Size Distribution (PSD). When disease occurs, excrement may develop abnormal shapes, such as being excessively long, curled, or ill-formed. It also reflects the toughness and fracture uniformity of aquatic animal excrement, such as the distribution of various particle sizes or the proportion of each component within a batch of fresh fish feces at a specific time. Computer vision can be used to capture excrement images, identify the surface area and diameter of each component, and calculate their distribution.

[0037] x2 is a hydrodynamic characteristic parameter, specifically the suspension time t1 of the excrement. The change in the suspension time of fish feces and other excrement is evaluated through image processing technology such as deep learning, and this value reflects the change in its density value. Specifically, it is characterized by the time it appears in a fixed part of the visual information collection field of view, that is, t=L / v s Wherein, L is the length of a fixed part in the visual field of the acquisition device, which is a fixed value; v s is the sedimentation velocity of excreta, which is related to the water body and the density of excreta, and is determined by the specific actual excreta, so it can reflect the suspension characteristics. Specifically, in the formula In the equation, the acceleration g under gravity and the liquid density ρ L and liquid viscosity μ L (i.e., the viscosity of water) is generally a relatively fixed value, while the density of fish feces and other excrement ρ p and particle diameter D p It is a dynamic variable, which can reflect the hydrodynamic characteristics of the corresponding excreta.

[0038] x3 represents the dissolution characteristics of fish excrement and other waste products in water. Specifically, it can be the time t2 required for a fixed percentage of the waste product to decompose or dissolve in still or open water. This value reflects physical characteristics such as the structural density or viscosity of the fish excrement. Specifically, the time it takes for the waste product, such as fish excrement, to dissolve completely and then partially disappear in water, as monitored by machine vision and other technologies, such as the time it takes for a certain percentage of the waste product to dissolve, such as 30%, 50%, or 80%. This value reflects the dissolution characteristics.

[0039] x4 is the characteristic of the change in the mass of fish feces and other excrement, specifically the amount of new excrement V produced per unit biomass per unit time, that is, the mass of fish feces and other excrement under a fixed monitoring time scale and other conditions, which reflects the absorption and conversion rate of nutrients. Specifically, the number of specified pixel indicators V is obtained through machine vision and other technologies. fcr = Increment in the number of identified pixels * density / time. This metric is obtained by default using a standardized feeding method and a fixed monitoring time scale.

[0040] x5 is the color index 1 in the color feature, and its color is analyzed using the image to reflect the color composition, such as the red, green, and blue values ​​in the RGB color system.

[0041] x6 is the color index 2 in the color feature, which uses the image to analyze its color to reflect the quality of the color, for example, it may include brightness, hue, saturation, etc.

[0042] In the model building and maintenance steps of this application, images of the aquatic animal excrement to be monitored are collected using a visual information acquisition device, etc., and the characteristic information obtained from the excrement images is processed to determine the aforementioned physical characteristic parameter vectors of the aquatic animal excrement to be monitored, i.e., the basic estimated data set. Correspondingly, in the health monitoring application steps, the physical characteristic parameter vectors of the aquatic animal excrement to be monitored are obtained as the estimated data set.

[0043] Then, the physical features are processed, and several key features or all of the features are selected to obtain a feature set. This embodiment does not limit the indicators described above, and at least one of them can be selected as a separate indicator. The number of items selected is based on achieving a certain degree of accuracy in identifying the disease type and stage. Specifically, the selection of key features can be achieved through model optimization-related methods. In addition, the acquisition of physical features is relatively easy compared to chemical features, etc. Selecting the above-mentioned physical features with clear disease relevance makes the health monitoring model more interpretable and effective.

[0044] Understandably, to cope with scenarios involving a wide variety of monitored diseases and to enhance the rigor of inference and computation, it is possible to consider using composite features of the aforementioned features as inputs to health monitoring models, such as training set inputs consisting of composite features such as x1x2, x3x4, x5x6, ... or x1+x2, x3+x4, x5+x6, ... or x1x1, x2x2, ... and sin(x1), sin(x2), .... These features are constructed using basic operations between basic features, increasing the number of features without excessively increasing the computational complexity of the model.

[0045] It is understandable that the present application can also optimize the monitoring process based on the time scale T and form an inferred data set P when the conditions are met. i , thus completing the collection and processing of the estimated set information. Among them, the estimated data set P i =[x 1n , x 2n , x 3n , x 4n , x 5n , x 6n ], which represents the 6×n data matrix composed of 6 eigenvalues ​​collected n times at the monitoring point p at time i; T=nt represents the length of the monitoring time window, n is the number of collections, and t represents the sampling interval, which is used to improve the measurement accuracy, that is, P i =1 / n*∑p in =1 / n*∑[x 1n ,x 2n ,x 3n ,x 4n ,x 5n ,x 6n ]=[x1,x2,x3,x4,x5,x6].

[0046] Specifically, the present application can deploy the health monitoring device to obtain the physical and chemical characteristic parameters of the excrement (feces) of aquatic animals at the monitoring point during the monitoring period. First, through image acquisition devices set at multiple locations, such as underwater cameras or imaging sensors, images or videos are collected based on a pre-set sampling interval t and time window length T. The specific acquisition time can be carried out for a period of time after the feeding is completed, and the period of time is the time when excrement is concentratedly produced after concentrated feeding. Then, multiple excrement images of the aquatic animals to be monitored can be obtained, or a video of the excrement of the aquatic animals to be monitored can be obtained, and then multiple frames of excrement images can be determined from the excrement video. Furthermore, target recognition is performed on each excrement image separately, and feature information of each excrement image is obtained. The present application can perform feature recognition through artificial intelligence or other available methods. This application is not specifically limited here and only describes specific features. Finally, analysis is performed based on the time interval between the recognition information to obtain the physical characteristic parameter vector X of the excrement of the aquatic animals to be monitored. i =[x1,x2,x3,x4,x5,x6], which is the physical characteristic parameter vector of the aquatic animal excrement, and can also be referred to as physical characteristics or physical characteristic parameter vector.

[0047] It is understood that the physical characteristic parameter vector of aquatic animal excrement may include, but is not limited to, particle size distribution information, hydrodynamic parameters, solubility characteristics, color characteristics, and unit increment of the excrement. In this application, the physical characteristic parameter vector of aquatic animal excrement includes at least one of the following: particle size distribution information, hydrodynamic parameters, solubility characteristics, color characteristics, and unit increment of the aquatic animal excrement to be monitored, but is not limited to the above physical characteristics.

[0048] Step S2: Input the physical characteristic parameter vectors obtained at each monitoring moment into the health monitoring model to obtain instantaneous health status information output by the health monitoring model. The health monitoring model is pre-established based on the labeled data set.

[0049] The construction of the pre-built health monitoring model is divided into steps S201 and S202. In step S201, based on the features in step S1, monitoring features are obtained. The features are also used for updating and maintaining the reference set.

[0050] It should be noted that the health monitoring model is pre-established and continuously updated based on the case set in the case set library. The reference case set library is constructed based on the feeding conditions and physical characteristic parameter vectors of the aquatic animals to be monitored, such as the species, to construct the health monitoring model.

[0051] Specifically, this application first obtains a reference case set and constructs a reference case set library. The reference set contains the estimated feature vector P iand its corresponding "disease type name-onset stage" two label information, which is represented by " The calculations in steps S3 and S4 are mainly based on numerical values, and labels are used for judgment. The form of the inference set is represented by the part of the variable in the reference set, that is, P i Then, the dataset P containing the labels i Forming basic reference cases ijk , and finally construct the reference set case library S.

[0052] Sample information of this application dataset X i and its disease labels Reference sets can also be formed s ijk The cross-regional long time series sample information S is as follows: in, The information of aquatic animal excrement samples collected at the i-th farm at the j-th site at the k-th time (growth stage) is " X i = [ x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ]”; Therefore, there are at least i×j×k reference sets. The main parameters of the inferred data set are arranged according to the importance of the discrimination, including at least two of PSD, suspension time, and decomposition time. This includes information such as the disease type p and the disease stage q. p represents the disease type, such as bacterial enteritis, particularly gastrointestinal diseases, and q represents the stages of good health, incubation period, early onset, mid-stage, and late onset. To address unforeseen disease types, p can also be an unknown or a combination of multiple disease types.

[0053] Preferably, the reference set can be a time series, which is represented by the above values ​​corresponding to a period of time, then the estimated data set P i It also corresponds to the value of the time series, such as when there is only one indicator in the physical feature vector. Preferably, the data set includes the corresponding location information and the physical feature information acquisition at the location.

[0054] Step S202 involves pre-training, updating, and maintaining the model based on the data. A health monitoring model is pre-built based on the physical characteristic parameter vectors and labels of the aquatic animals under breeding conditions, such as the species of the monitored animals. Specifically, the reference set under a certain label in the estimated reference set library S can be divided into training, validation, and test sets based on breeding conditions, thereby implementing the pre-built health monitoring model.

[0055] It should be noted that, in order to better illustrate this embodiment, the quality of the data set and the effectiveness of the model are combined to construct complex models such as pattern recognition, deep learning models, and models based on case analysis. Figure 2 Path 1, Path 2 and Path 3 are shown, but not limited to three models. They can be described as Path 1 model, Path 2 model and Path 3 model below. Among them, Path 1 model can be a machine learning model such as deep learning, and the deep learning model can output the instantaneous health status estimation result Q i Path 2 is to determine the Q index and output the instantaneous health status estimation result Q i Path 3 model is a case analysis-based method that determines similarity, membership, and difference in turn, and outputs the results in combination with thresholds. First, it is determined whether similarity determination can be performed. If so, the instantaneous health status estimation result Q is output. i If not, further determine whether to perform membership determination. If so, output the instantaneous health status estimation result Q i ; Otherwise, calculate the difference and output the instantaneous health status estimation result Q i Therefore, all three paths can achieve the generation of instantaneous health status Q.

[0056] Specifically, the physical characteristic parameter vectors obtained at each monitoring moment are input into the health monitoring model to obtain the instantaneous health status information output by the health monitoring model, including: Step S21, obtaining the physical characteristics of the aquatic animal excrement to be monitored and processing the characteristics; Step S22: input the monitored feature vector into the health monitoring model to obtain the instantaneous health status; specifically, the real-time monitored aquatic animal excrement feature data set, i.e., the estimated feature set P i Input the pre-trained model to get the instantaneous health status at the corresponding moment Q i (Right now , containing information of p and q); S23, regularly update and maintain the model and reference set.

[0057] Based on the integration of acquired feature sets and case information, the reference set is regularly updated and maintained to improve the richness of the data in the reference set S and enable monitoring of multiple types of diseases. At the same time, based on the updated data set, the aforementioned multiple models are regularly updated and maintained.

[0058] Step S3, perform feature analysis on the time series composed of multiple instantaneous health status information to determine the final health status. Specifically, step S31, based on the instantaneous health status estimation result, generate the instantaneous health status estimation result time series feature set [Q1, Q2, Q3, Q4, ..., Q n ]. Further, based on step S32, Mode(Q) is used to discriminate the instantaneous health state time series feature set Q to represent the final recognition result. Specifically, F C =Mode(Q)=Mode(F c1 :F cn ), where Mode() means taking the result of a certain value exceeding the threshold value for consecutive times as the output, F Ci Denotes the inferred set P i The instantaneous result, Q represents the instantaneous result of the sample F c1 to F cn The time series feature set composed of , Fc represents their recognition results. Based on the result labels p and q information, the final result is obtained. Then, the final health status is output through this step.

[0059] Step S4: continuously monitor, analyze and display the health status of the aquatic animals to be monitored.

[0060] Among them, step S41 continuously monitors and analyzes the health status of the monitored aquatic animals. Specifically, the accuracy of the judgment results is reviewed, and the model library and reference set can be updated based on the accuracy. Optionally, early warning or specific area decision analysis is performed in combination with the location information of the deployed device. Step S42 repeats the above steps to achieve early monitoring and early warning of aquatic animal diseases. Specifically, repeat steps S1-S4 to achieve early monitoring of aquatic animal diseases, and at the same time complete the inference result verification, model data update and maintenance and system analysis during execution.

[0061] The aquatic animal excrement health monitoring method provided in the embodiments of the present application is primarily based on the aquatic animal excrement health monitoring device. Considering that leftover bait and excrement, such as feces, are concentrated at different times, the present application focuses on monitoring feces, so that the monitoring timing of the aquatic animal excrement health monitoring device is mainly concentrated some time after feeding. This effectively avoids confusion between leftover bait and feces information. The present application relies on a health monitoring device that includes an underwater camera or imaging sensor (video and image) module to identify potential problems and take corrective measures. In addition, feeding can be adjusted based on the monitoring results, but the primary purpose is to conduct health monitoring and make presumptive diagnoses of various diseases.

[0062] It should be noted that by conducting in-situ monitoring and abnormality warnings based on the physical characteristics of aquatic animal excrement (feces), and by associating labels with various known diseases, it is possible to presumptively diagnose broad categories of diseases and provide early diagnosis and warning of unknown diseases by detecting abnormalities in these characteristics. Although certain physical characteristics of feces and other excrement, such as color, are related to feed ingredients and the fish body, continuous abnormalities or deviations from normal values ​​indicate the development of a related disease. The aquatic animals described in this application are illustrated using fish as an example, such as fish in land-based aquaculture facilities and fish farmed in facilities such as gravity cages in modern marine ranches. While fish are used as an example to illustrate aquatic animal health monitoring in this application, the method of this application is not limited to fish health monitoring and can also be applied to aquatic animals such as shrimp, crabs, shellfish, and reptiles.

[0063] Specifically, with respect to the pre-built model, based on step S202, it specifically includes: Obtain sample information and disease labels of the sample aquatic animals, wherein the sample information is a vector of sample physical characteristic parameters under fixed breeding conditions including species. The disease label may include the disease type p and the stage q in which it is located. The disease name is a common and easily occurring disease, such as "enteritis" and other digestive system-related diseases, and the stage may include "good health, incubation period, early stage of onset, middle stage of onset and late stage of onset". It is understandable that if the sample aquatic animal does not have a disease, its disease type is healthy or none, and the corresponding disease stage is "good health".

[0064] Example 1. Reference Figure 2 Health monitoring model path 1 is used for complex scenarios such as multiple diseases at multiple stages. This embodiment is trained using machine learning methods such as deep learning. Sample information and disease labels in the reference library are used as training data to train a model pre-determined for the current scenario. After training, a health monitoring model is obtained.

[0065] Taking the monitoring of two specified diseases and their stages as an example, the disease label Q pq One-hot encoding is performed, and the good health stage, incubation stage, early stage, middle stage and late stage of disease type 1 are represented as [1,0,0,0,0,0,0,0,0,0], [0,1,0,0,0,0,0,0,0,0], [0,0,1,0,0,0,0,0,0,0,0], [0,0,1,0,0,0,0,0,0,0], [0,0,0,1,0,0,0,0,0,0], [0,0,0,0,1,0,0,0,0,0,0], [0,0,0,0,1,0,0,0,0,0], and the encoding of the five stages of disease type 2 is obtained similarly. The structure of the deep learning model is based on common convolutional neural networks such as LeNet-5 and AlexNet, with the feature set P being inferred. i Or the image corresponding to the feature is used as input, and a combination of convolutional layers and pooling layers is used in the hidden layer to extract features, using functions such as ReLU as activation functions, and using Dropout to suppress overfitting; the Softmax function is used as the activation function of the output neuron in the output layer. The output layer has a total of 10 nodes, representing two disease types and their stages, and if the value of the node is 1, the result of the network recognition is the disease type and stage corresponding to the node. Further, after setting the learning rate and training method, the training is started, and after the training is completed, a deep learning model for identifying the two disease types and their stages is obtained, which is used to realize the monitoring of the two disease types and their stages. In addition, a multi-classification model can be established, combined with the presumed case set P i The physical characteristics of the disease are classified to determine the stage of disease and other information.

[0066] It is understandable that each feeding condition in this application corresponds to a health monitoring model, and feeding conditions include feeding species, growth stage, feed formula, etc. The method system includes multiple health monitoring models, and further, can include health monitoring models corresponding to multiple feeding information.

[0067] Therefore, under the condition that the species and other feeding information of the aquatic animals to be monitored remain unchanged, this application obtains the physical characteristic parameter vectors of the excrement of the aquatic animals to be monitored at multiple moments during the monitoring period, and then inputs the physical characteristic parameter vectors into the trained deep learning model, analyzes them through the health monitoring model, and obtains the instantaneous health status information of the aquatic animals to be monitored. Among them, the instantaneous health status information Q i Including possible disease types p. In one embodiment, the instantaneous health status information can also include the disease stage q. This allows for classification of multiple disease types and stages based on machine learning methods such as deep learning. The present application is beneficial in that disease monitoring labels are divided into disease type and disease stage, enabling detailed monitoring of disease type and disease stage.

[0068] Example 2. Reference Figure 2 In the health monitoring model path 2, the classification judgment of a disease type or a disease stage is performed. The present application can also monitor the physical characteristic parameter vector of aquatic animal excrement, that is, the estimated feature set P i Each parameter x in i , combined with water quality parameters, and thus the physical characteristic parameter vector is corrected, the corresponding instantaneous health index Q can be obtained through this function, as follows: Q i =f(P i ,φ ij , w i )=f(x1,x2,x3,x4,x5,x6)φ ij w i ; Q= ; in, x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 The indicators representing the model are schematic and are not limited to the six basic features. They also include processed composite features. This equation is established based on the content cases in the above case library. The input result is the instantaneous health index, which includes the instantaneous health index and two labels, "corresponding disease name p-onset stage q", where the corresponding expression is "value + label feature 1 + label feature 2". The calculation is mainly carried out around the numerical part. T represents the threshold of the Q index, which is used to determine the attribution of the label. Specifically, this model path is mainly used for the monitoring of a certain known disease, that is, the Q index function of each disease corresponds to a set of w i For example, taking Q as “1, label 1, label 2” and multiple different reference cases as input, a set of targeted parameters [w i ](i=1,2,3,4,5,6), which is w i The parameter estimation of φ is used to establish a health index function for a specific disease type or stage. The output value of this function is in the range of [0,1], which is the predicted probability value. ij It represents the correction matrix of physical characteristics based on water quality. It is an i×j dimensional transformation matrix, where i corresponds to the index i of the eigenvector and j represents the index of the water quality parameter. Its calculation can be approximately expressed as (Among them, O i , O 0i and B iThey are all j-dimensional, corresponding to j water quality parameters such as water temperature, pH, dissolved oxygen, turbidity, etc., to achieve comprehensive correction based on multiple water qualities. At the same time, because the effects of water quality parameters on each physical characteristic xi are different, there are corresponding i correction processes). Indicates the water quality parameters at the real-time monitoring moment, represents the baseline water quality of the i-th vector feature, It represents the deviation value of the i-th vector characteristic water quality parameter, B i Represents the transformation matrix of the i-th vector feature. The corrections in this part are all specific parameters obtained through experimental methods.

[0069] Furthermore, the present application may compare the predicted probabilities corresponding to the types of diseases in the health status analysis results with probability thresholds pre-set based on actual needs, or compare the predicted probability with the preset probability threshold. If the predicted probability with the largest value is greater than or equal to the preset probability threshold (e.g., T ≥ 0.8), the disease type corresponding to the predicted probability is determined as the type of the instantaneous health status information of the aquatic animal to be monitored.

[0070] Furthermore, if the health status analysis results also include the stage of the disease, the predicted probabilities corresponding to the stage of each disease type in the health status analysis results can be compared with probability thresholds pre-set based on actual needs, or the predicted probability with the largest value among the predicted probabilities can be compared with a preset probability threshold. If the predicted probability with the largest value is greater than or equal to the preset probability threshold (e.g., T ≥ 0.8), the disease type and its stage corresponding to the predicted probability are determined as the disease type p and stage q of the instantaneous health status information of the aquatic animal to be monitored.

[0071] Example 3. Reference Figure 2 In the health monitoring model path 3, when the disease type is unknown or the first two health monitoring models cannot be implemented, case analysis methods such as case matching can be used to determine the occurrence of abnormal conditions. Therefore, in order to obtain the health status of the aquatic animals to be monitored, the present application can further determine the similarity information between the physical feature parameter vector and the reference physical feature parameter vector of each reference disease. The similarity information can include the inferred feature set P i The similarity between the reference physical characteristic parameter vectors of each disease in the reference case library. iThe similarity between each reference physical characteristic parameter vector and each reference disease can be the average or weighted average of the similarities between each parameter in the physical characteristic parameter vector and the corresponding parameter in the reference physical characteristic parameter vector of each reference disease. The similarity can be calculated using any method, such as Euclidean distance, Manhattan distance, or cosine similarity.

[0072] Specifically, after retrieving the second case that matches the first physical feature index, the similarity between each second case and the first case is further calculated to obtain the similarity value corresponding to each second case, wherein the physical feature description f of the first case can be defined m (m=1,2,3,…,6) and the second case f in the case library t,m The similarity function between the physical feature descriptions is: , t=1,2,...,6; Among them, max(fm,ft,m) means taking fm and f t,m The larger value of , according to the above formula, the similarity function between the first case Fx and each second case Ft can be defined as: ; Where SW(Fx, Ft) represents the value of the similarity function between the first case Fx and each second case Ft. λm is the weighting coefficient for each physical characteristic indicator. Its value is determined by the data corresponding to each physical indicator of the feces, that is, the relative importance of each physical indicator of the feces is determined, and it is usually determined by empirical trial and error. The maximum similarity between Fx and all cases Ft (t=1,2,…,n) in the reference set is: S max =Max{S W (F x ,F t )},t=1,2,…,n; Among them, S max represents the maximum similarity value among the similarity values ​​corresponding to the second cases, and SW(Fx, Ft) represents the value of the similarity function between the first case Fx and each second case Ft.

[0073] Furthermore, the similarity with the largest value in the similarity information is compared with a preset similarity threshold; If the maximum similarity value is greater than the preset similarity threshold T1, such as T1 = 0.9, the reference disease label Q corresponding to the maximum similarity value will be bq Determine the instantaneous health status information of the aquatic animals to be monitored.

[0074] After obtaining the similarity information, the present application may compare the similarity with the largest value in the similarity information with a preset similarity threshold, thereby determining the magnitude relationship between the similarity with the largest value in the similarity information and the preset similarity threshold.

[0075] Furthermore, if it is determined that the similarity with the largest value is greater than a preset similarity threshold, the reference disease corresponding to the similarity with the largest value is determined as the instantaneous health status information of the aquatic animal to be monitored.

[0076] Alternatively, the reference disease and its stage corresponding to the greatest numerical similarity are determined as the instantaneous health status information and the stage of the aquatic animal to be monitored.

[0077] After this application adopts underwater characteristic parameters that are more consistent with aquatic animals, if the health monitoring model cannot be constructed and applied, the health status of the aquatic animals to be monitored can be further determined by the similarity information between the reference physical characteristic parameter vectors and the reference diseases, thereby achieving monitoring results for unknown diseases and improving the accuracy of health monitoring of aquatic animals.

[0078] In this embodiment, after comparing the similarity with the largest value in the similarity information with the preset similarity threshold T1, the following steps are further included: If the similarity with the largest value in the similarity information is less than or equal to the preset similarity threshold, determining the membership information between the physical feature parameter vector and the reference physical feature parameter vector of each reference disease; Determine the health status of the aquatic animals to be monitored based on the membership information.

[0079] If the similarity with the largest value in the similarity information is still less than or equal to the preset similarity threshold, the health status of the aquatic animal to be monitored cannot be determined through similarity determination, and the membership information between the physical characteristic parameter vector and the reference physical characteristic parameter vector of each reference disease can be further determined, where the membership information can include the membership between the physical characteristic parameter vector and the reference physical characteristic parameter vector of each reference disease. The membership between the physical characteristic parameter vector and the reference physical characteristic parameter vector of each reference disease can be the average or weighted average of the memberships of each parameter in the physical characteristic parameter vector and the corresponding parameter of the reference physical characteristic parameter vector of each reference disease.

[0080] It should be noted that the calculation of the degree of membership can be achieved using the attribution degree method, and the specific calculation process is not specifically limited here.

[0081] After obtaining the membership information, the health status of the aquatic animal to be monitored can be determined by comparing the membership in the similarity information with the membership threshold T2 pre-set according to actual needs. In other words, after determining the type of disease, the onset stage of the disease can also be determined.

[0082] After the present application adopts underwater characteristic parameters that are more consistent with aquatic animals, if the health monitoring model analysis fails and the determination of the health status of the aquatic animals to be monitored by the similarity information between the reference physical characteristic parameter vector and the reference disease also fails, the health status of the aquatic animals to be monitored can be further determined by the membership information between the reference physical characteristic parameter vector and the reference disease, thereby obtaining more accurate monitoring results and improving the accuracy of health monitoring of aquatic animals.

[0083] In this embodiment, determining the health status of the aquatic animal to be monitored based on the membership information includes: Compare the membership with the largest value in the membership information with the preset membership threshold; If the membership degree with the largest value is greater than a preset membership threshold, the reference disease corresponding to the membership degree with the largest value is determined as the instantaneous health status information of the aquatic animal to be monitored.

[0084] After obtaining the membership information, the present application may compare the membership with the largest value in the membership information with a preset membership threshold, thereby determining the magnitude relationship between the membership with the largest value in the membership information and the preset membership threshold.

[0085] Furthermore, if it is determined that the membership with the largest value is greater than a preset membership threshold, the reference disease corresponding to the membership with the largest value is determined as the instantaneous health status information of the aquatic animal to be monitored.

[0086] Alternatively, the reference disease corresponding to the membership degree with the largest value and the stage it is in are determined as the instantaneous health status information and the stage of the aquatic animal to be monitored.

[0087] After the present application adopts underwater characteristic parameters that are more consistent with aquatic animals, if a health monitoring model cannot be constructed, and the health status of the aquatic animals to be monitored cannot be determined by the similarity information between the reference physical characteristic parameter vectors of the reference diseases, the health status of the aquatic animals to be monitored can be further determined by the membership information between the reference physical characteristic parameter vectors of the reference diseases, thereby obtaining more accurate monitoring results and improving the accuracy of health monitoring of aquatic animals.

[0088] In this embodiment, after comparing the membership with the largest value in the membership information with a preset membership threshold, the method further includes: If the maximum membership degree is less than or equal to the preset membership degree threshold, then determining the deviation value information between the physical characteristic parameter vector and the reference physical characteristic parameter vector of each reference disease; Based on the deviation value information, the health status of the aquatic animals to be monitored is determined.

[0089] If the present application determines that the membership with the largest value is still less than or equal to the preset membership threshold, it is determined that the membership judgment cannot be performed. In order to determine the health status of the aquatic animals to be monitored, the deviation value information between the physical characteristic parameter vector and the reference physical characteristic parameter vector of each reference disease can be further determined.

[0090] The deviation value information may include deviation values ​​between the physical characteristic parameter vector and the reference physical characteristic parameter vectors of each reference disease. The deviation values ​​between the physical characteristic parameter vector and the reference physical characteristic parameter vectors of each reference disease may be the average or weighted average of the deviation values ​​corresponding one-to-one between each parameter in the physical characteristic parameter vector and the corresponding parameter in the reference physical characteristic parameter vector of each reference disease.

[0091] It should be noted that the calculation of the deviation value can be achieved using methods such as absolute deviation, root mean square error, relative error or percentage error, and the specific calculation process is not specifically limited here.

[0092] After the deviation value information is obtained, the health status of the aquatic animal to be monitored can be determined by comparing the deviation values ​​in the deviation value information.

[0093] Furthermore, the health status of the aquatic animals to be monitored is determined based on the deviation value information, including: The reference disease corresponding to the smallest deviation value in the deviation value information is determined as the instantaneous health status information of the aquatic animal to be monitored.

[0094] Specifically, when determining the health status of the aquatic animal to be monitored based on the deviation value information, the present application can determine the size relationship between each deviation value in the deviation value information.

[0095] Furthermore, after obtaining the magnitude relationship between the deviation values ​​in the deviation value information, the reference disease corresponding to the deviation value with the smallest value in the deviation value information can be determined as the instantaneous health status information of the aquatic animal to be monitored.

[0096] Alternatively, the reference disease and its stage corresponding to the smallest deviation value in the deviation value information can be determined as the instantaneous health status information and the stage of the aquatic animal to be monitored.

[0097] This application adopts underwater characteristic parameters that are more consistent with aquatic animals and analyzes the estimated vector set P through case studies. iBy sequentially calculating the similarity, membership, and deviation described above, more accurate monitoring results can be obtained. Specifically, if the health monitoring model analysis fails, and if determining the health status of the aquatic animal to be monitored based on the similarity information between the physical characteristic parameter vectors in the reference case set of the reference disease also fails, and if further determining the health status of the aquatic animal to be monitored based on the membership information between the reference physical characteristic parameter vectors of the reference disease also fails, then determining the health status of the aquatic animal to be monitored based on the deviation information between the reference physical characteristic parameter vectors of the reference disease can yield more accurate monitoring results, thereby improving the accuracy of aquatic animal health monitoring.

[0098] It should be noted that, under the premise that the growth stage, feed composition and feeding frequency and other feeding parameters of aquatic animals are fixed, the body function and digestive capacity caused by their health status will directly affect the physical and chemical properties of feces. Therefore, the physical and chemical characteristic values ​​of group or individual feces and whether they are abnormal can to a certain extent represent their health status.

[0099] The benefit of this method is that it expands health monitoring, disease warning and early diagnosis from traditional warnings based on water quality, lesions, behavior and other characteristics to the evaluation of physical state characteristics of fecal excretions: First, by measuring the physical properties of excretions in the water in situ, the changes in information contained in the characteristics of certain diseases can be detected, thereby making an intuitive classification and judgment of the health status of intestinal-related diseases of aquatic animals such as fish at different stages, providing a low-cost and effective monitoring method and device for aquaculture management. Moreover, this method relies on health monitoring devices or is integrated with feces collection devices in aquaculture facilities, making it easier to conduct underwater in situ monitoring to further reduce costs. In addition, the described method can also be used to monitor and analyze the physical characteristics of aquatic animal excretions such as fish feces after they are removed from the water. At the same time, the physical characteristic vector of excretions can be further expanded to include relevant indicators such as mechanical properties and physical and chemical properties.

[0100] The benefit of this method is that the constructed method and its parameter settings are fine-tuned from model construction factors such as time and water quality environmental parameters, so as to correct its judgment results and achieve adaptive adjustment and optimization for different aquatic animals or different scenarios to further improve the accuracy of monitoring. There is no limitation on the specific correction and optimization methods. The water quality environment has the greatest impact on the physical characteristics of the excretions of aquatic animals. The model can be corrected in combination with water quality to correct the short-term effects of water quality parameters on the physical and chemical properties of excretions discharged from the body, such as according to the deviation correction coefficient between the real-time water quality parameters and the optimal water quality parameters. φ=fThe estimated values ​​of physical characteristics (temperature, conductivity, pH value, turbidity, ...) can be corrected; the accuracy of time series analysis can be enhanced by changing the length of the time scale T of statistical information; the site scale M of information collection can be increased to eliminate errors or occasional special outliers; the judgment results can also be reviewed, and the reference set can be updated and supplemented based on the correct cases of the estimated results, so as to realize the continuous updating of the system.

[0101] It should be noted that the health monitoring model is constructed using a multi-model approach to address the deficiencies and shortcomings of various models. In one specific embodiment, a standard fecal physical characteristic parameter library is first generated. This library consists of the aforementioned physical characteristic values ​​of fish feces corresponding to the early stages of disease infection. It primarily focuses on enabling early diagnosis of aquatic animal diseases. This library, referred to as a disease identification dataset, contains a label p (denoted by 1 and 0) indicating whether the disease is present and the incubation period (denoted by "01000") within the disease stage labels, such as "good health, incubation period, early, middle, or late stage of disease." This is used for pre-established model building and case-control comparisons. Based on this dataset library, a model library composed of the aforementioned health monitoring models is pre-established. Then, by inputting the acquired real-time fecal physical characteristics into this health model library, the instantaneous health status is determined. Furthermore, the output label q can be combined to diagnose the disease stage, such as the incubation period, early, middle, or late stage of a disease. Among them, generally in different situations, for monitoring and early warning of known disease types and disease stages, classification or analysis is carried out through machine learning models such as deep learning and fish feces index methods; for unknown diseases but with similar symptoms or cross-infection of multiple diseases, case analysis methods are used, such as analyzing the similarity, membership or deviation value of the basic reference set through physical characteristics to make a presumptive diagnosis.

[0102] Based on step S3, after completing the above multiple consecutive instantaneous health states Q i On this basis, the final state of the disease is determined, including: analyzing the time series characteristics formed by multiple instantaneous health status information to obtain more accurate health status monitoring values.

[0103] Specifically, the mode model Mode() is used to identify and output the final result. C =Mode(F c1 :F cn );Where:F Ci Denotes the health monitoring inference set P i The instantaneous result Q i , F c1 :F cnRepresents the instantaneous health status Q of any recent n times i :Q n , Fc represents the recognition result after time series feature analysis. Take 5 as the conditional threshold. If the instantaneous health state with the same label is generated 5 times in a row, the corresponding label Q will be output. pq , and obtain the final result, which is determined as the corresponding disease type p and stage q.

[0104] Based on step S4, steps S1 to S3 are repeated to implement health monitoring and analysis of aquatic animals. Furthermore, combining the monitoring dataset, structured visualization methods, and analysis systems for health monitoring results, a rich monitoring report can be generated on the aquatic animal disease situation in a specific region.

[0105] The benefit of this application lies in that the monitoring is carried out by using a physical characteristic parameter vector including at least one of the particle size distribution information, hydrodynamic parameters, solubility characteristics, color characteristics and unit increments of the excreta of the aquatic animals to be monitored. The selection of physical characteristics makes the model more interpretable. At the same time, the physical characteristics are conducive to in-situ measurement and are not affected by the open water environment, making them more suitable for health monitoring in aquaculture sites such as large water surfaces and marine ranches. In addition, based on a case set consisting of the physical characteristic parameter vectors and disease labels of the excreta of the aquatic animals to be monitored under feeding information conditions such as specific species, a health monitoring model is pre-constructed so that the physical characteristic parameter vectors obtained at each monitoring moment are input into the health monitoring model and instantaneous health status information is obtained; then, a feature analysis is performed on the time series composed of multiple instantaneous health status information to determine the final health status, and the health status of the aquatic animals to be monitored can be continuously monitored and analyzed and displayed. Because underwater characteristic parameters that are more consistent with aquatic animals are used and modeled, and the use of composite features increases the adaptability to complex scenarios of multi-disease monitoring, the final health status can be determined based on the physical characteristic parameter vectors obtained at multiple monitoring moments, thereby obtaining more accurate monitoring results and improving the accuracy of aquatic animal health monitoring.

[0106] The following describes the health monitoring device and system based on aquatic animal excrement provided in this application. The health monitoring device and system based on aquatic animal excrement described below and the steps of the health monitoring method based on aquatic animal excrement described above can be referenced to each other.

[0107] Figure 3 This is a schematic diagram of the structure of the health monitoring device based on aquatic animal excrement provided by this application. Figure 3The health monitoring device based on aquatic animal excrement described in this application may include a feces collection unit (including rotating parts such as cleaning, receiving tray and rotating disk), a feature information acquisition unit (including visual information acquisition parts), a position information acquisition unit (including a position monitoring module), a control unit and a processing unit and a computer program stored on the processing unit. When the processing unit executes the computer program, the above-mentioned health monitoring method based on aquatic animal excrement is implemented.

[0108] It should be noted that, in addition to acquiring and calculating the physical information of aquatic animal waste, such as fish feces, the device also includes location information collection and transmission capabilities, enabling communication with the system platform and generating analysis reports. The device is not limited to specific scenarios and can be used for aquaculture in factories, aquaculture vessels, or cages in open waters. The device can be submerged permanently or only for measurement. The specific mechanical structure and connection method are not specified.

[0109] In one embodiment, a health monitoring device for aquatic animal excrement can also include a controller, a guide plate, a rotating structure, a receiving tray, a rotating disk, and a cleaning brush. Optionally, the control unit includes sensing mechanisms for the device's operating components, such as weight sensors and angle sensors. This device is controlled using this method and related parameters, and its operation is similar to that of a rainfall monitoring device, with updated monitored samples and batches being released from the bottom end.

[0110] Figure 4 This is a schematic diagram of the structure of the health monitoring system based on aquatic animal excrement provided by this application. Figure 4 The aquatic animal excrement-based health monitoring system described in this application includes the monitoring device and a cloud platform (also referred to as a system platform). The cloud platform further includes an analysis unit (also referred to as an analysis module), a display unit (also referred to as a display module), and a computer program stored on the analysis unit. When the analysis unit executes the computer program, it implements the aforementioned aquatic animal excrement-based health monitoring method. Optionally, the aquatic animal excrement-based health monitoring system can also be connected to other IoT control devices, including equipment monitoring modules and intelligent feeding machines, to achieve consistent correlation and feedback control with aquaculture species and feeding information.

[0111] The monitoring device includes an equipment monitoring module. This module controls the image acquisition unit, the integrated handling unit (which implements functions such as device component speed, opening and closing, and self-cleaning based on sensor data), and information processing and transmission. The monitoring device primarily acquires monitoring information, including real-time excrement feature vectors, water quality correction information, and auxiliary information such as the monitoring time scale. The monitoring device also receives judgment sets, generates judgment results and monitoring location information, and transmits them to the system platform. The system platform can then transmit information such as reference sets and control instructions to the monitoring device.

[0112] The system platform includes a display module and an analysis module. The display module displays monitoring results and regional analysis, while the analysis module performs disease classification, regional characteristics analysis, outbreak scale analysis, and epidemic trend analysis. Optionally, the system platform can also issue control instructions to other IoT control devices such as smart feeders, and receive feeding parameters, operation instructions, and other feeding information related to smart feeders and other IoT control devices.

[0113] It should be noted that the aquatic animal excretion-based health monitoring system is used to display and analyze the occurrence and development of diseases. It analyzes and displays the "final determination result - location information," thereby enabling health monitoring and disease analysis of major aquaculture species and diseases in a specific area, and triggering an alarm when thresholds are exceeded. The threshold is any one of the disease stage descriptions (e.g., "healthy state," "incubation period," "early stage," "middle stage," or "late stage"), excluding health status.

[0114] It should be noted that the health monitoring system based on aquatic animal excrement is also responsible for collecting, updating and maintaining reference data sets from multiple parties to form a trusted data space to provide a more effective reference data set. The system makes collection, update and maintenance decisions based on the final judgment result. If the inference is successful, the physical feature vector and its result label at that moment are collected as a new reference data set, and the data of the inferred data set are updated to achieve self-maintenance of the system. The inference process can be completed in the health monitoring device or in the cloud platform or center. Optionally, the system can also perform case library S (containing reference data sets) s ijk ), final judgment result, location information, estimated data set P i It can also conduct two-way communication with monitoring indicator information of other IoT devices to achieve connection of data trust space, thereby realizing integration and data sharing with other IoT platforms.

[0115] In another aspect, the present application further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for health monitoring of aquatic animal excrement based on the above methods is implemented, and the method includes: Step S1, obtaining a physical characteristic parameter vector of the aquatic animal excrement to be monitored, wherein the physical characteristic parameter vector is determined by at least one of the characteristics of the aquatic animal excrement to be monitored, such as particle size distribution information, hydrodynamic parameters, solubility characteristics, color characteristics, and unit time increment; Step S2: Inputting the physical characteristic parameter vectors obtained at each monitoring moment into a health monitoring model to obtain instantaneous health status information output by the health monitoring model. The health monitoring model is pre-constructed based on the physical characteristic parameter vectors and their labels under the feeding conditions of the aquatic animal species to be monitored; Step S3, combining multiple instantaneous health status information into a time series, and performing feature analysis on the time series to determine the final health status; Step S4: continuously monitor, analyze and display the health status of the aquatic animals to be monitored.

[0116] The devices, systems, and media described above are merely illustrative. The units and mechanisms described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the unit modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0117] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus the necessary general-purpose hardware platform, or of course, hardware alone. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0118] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they may modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the various embodiments of this application.

Claims

1. A health monitoring method based on aquatic animal excrement, characterized in that: include: Step S1, obtaining a physical characteristic parameter vector of the aquatic animal excrement to be monitored, wherein the physical characteristic parameter vector is determined by at least one of particle size distribution information, hydrodynamic parameters, solubility characteristics, color characteristics, and unit time increment of the aquatic animal excrement to be monitored; Step S2: Inputting the physical characteristic parameter vectors obtained at each monitoring moment into a health monitoring model to obtain instantaneous health status information output by the health monitoring model. The health monitoring model is pre-constructed based on the physical characteristic parameter vectors and their labels under the feeding conditions of the aquatic animal species to be monitored; Step S3, combining multiple instantaneous health status information into a time series, and performing feature analysis on the time series to determine the final health status; Step S4: continuously monitor, analyze and display the health status of the aquatic animals to be monitored.

2. The health monitoring method based on aquatic animal excrement according to claim 1, characterized in that: The step of obtaining the physical characteristic parameter vector of the aquatic animal excrement to be monitored includes: Collect multiple images of excrement of aquatic animals to be monitored; Performing target recognition based on each of the excrement images to obtain recognition information; Determining the physical characteristic parameter vector of the aquatic animal excrement to be monitored based on the identification information; Among them, the indicators obtained are predetermined based on the monitored physical characteristic parameter vector, and the indicator composition includes hydrodynamic parameters, color characteristics and color quality; the hydrodynamic parameters include suspension time or sedimentation velocity; the color characteristics include color quantity, color ratio and transparency.

3. The health monitoring method based on aquatic animal excrement according to claim 1, characterized in that: The health monitoring model is pre-established based on the reference set library, used to generate instantaneous health status and perform regular updates and maintenance; Based on any disease label The reference dataset is divided into a training set and a validation set based on the data of the disease. In order to cope with the complex scenarios of multiple diseases, feature expansion based on composite features is proposed. A pre-trained health monitoring model is constructed based on the cases in the reference library; based on the validity of the data, the health monitoring model is a multi-model, including at least a deep learning model, a case-based reasoning model and an exponential model; The reference data sets and health monitoring models in the reference set library are regularly maintained and updated.

4. The health monitoring method based on aquatic animal excrement according to claim 3, characterized in that: The reference dataset is in the following form: From the dataset sample information X i and its disease labels Constructing a reference dataset s ijk , constituting the sample information S of the cross-region long time series: ； in, The information of aquatic animal excrement samples collected at the jth site at the kth time in the i-th farm: X i =[x1,x2,x3,x4,x5,x6]; in, There are at least i×j×k reference data sets. The parameters of the reference data sets are estimated to be arranged according to the importance of the discrimination, including at least two of PSD, suspension time, and decomposition time; disease label It includes disease type p and disease stage q, where p is bacterial enteritis and q includes good health, incubation period, early stage, middle stage and late stage. S contains at least i×j×k×size(p)×size(q) reference data sets; size() represents the number of different elements in the type. The elements corresponding to normal and abnormal.

5. The health monitoring method based on aquatic animal excrement according to claim 4, characterized in that: The method of inputting the physical characteristic parameter vectors obtained at each monitoring moment into the health monitoring model to obtain instantaneous health status information output by the model includes a machine learning model, and the machine learning model is a deep network model, including: Determine the validity of the data set formed by the physical feature parameter vectors obtained at each monitoring moment, process each physical feature vector, and obtain input features consistent with the pre-trained model; The processed features are input into the deep network model in the health monitoring model to obtain the instantaneous health status information Q i .

6. The health monitoring method based on aquatic animal excrement according to claim 4, characterized in that: The health monitoring model includes a method for generating an index, including: Each parameter in the physical characteristic parameter vector of the aquatic animal excrement to be monitored is processed in combination with the water quality parameters. The physical characteristic parameter vector is corrected and the instantaneous health index is obtained by processing it using a specified function. The processing process is as follows: Q i =f(P i ,φ ij ,w i )=f(x1,x2,x3,x4,x5,x6)φ ij ·w i ; Q= ； in, x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 The estimated vector P representing the input of the health monitoring model i , also including the processed composite features; based on the case content in the reference library, an equation is established, and the case content is input to obtain the resulting instantaneous health index, which includes the instantaneous health index and the corresponding disease name p-onset stage q two labels, where the corresponding expression is numerical value + label feature 1 + label feature 2; the calculation is performed around the numerical part, T represents the threshold value of the Q index, which is used to determine the attribution of the label; φ ij represents the correction vector based on water quality; w i is the parameter estimation vector of the equation, i=1,2,3,4,5,6…; thereby establishing a health index function for any specified disease type or stage; obtaining the corresponding instantaneous health information Q through the health monitoring model, and obtaining the disease type and onset stage information corresponding to the index equation.

7. The health monitoring method based on aquatic animal excrement according to claim 4, characterized in that: The health monitoring model includes a case-based judgment method, which judges similarity, membership and difference in sequence and determines instantaneous health status information based on thresholds, including: Determining similarity information between the physical characteristic parameter vector and reference physical characteristic parameter vectors of each reference disease; and comparing the similarity with the largest value in the similarity information with a preset similarity threshold; if the similarity with the largest value is greater than the preset similarity threshold, determining the reference disease corresponding to the largest similarity as the instantaneous health status information of the aquatic animal to be monitored; If the similarity with the largest value in the similarity information is less than or equal to the preset similarity threshold, membership information between the physical characteristic parameter vector and the reference physical characteristic parameter vectors of each reference disease is determined; and based on the membership information, if the membership with the largest value is greater than the preset membership threshold, the reference disease corresponding to the membership with the largest value is determined as the instantaneous health status information of the aquatic animal to be monitored, thereby determining the health status of the aquatic animal to be monitored; If the membership with the largest value is less than or equal to the preset membership threshold, the deviation value information between the physical characteristic parameter vector and the reference physical characteristic parameter vector of each reference disease is determined; based on the deviation value information, the reference disease corresponding to the deviation value with the smallest value in the deviation value information is determined as the instantaneous health status information of the aquatic animal to be monitored, and the health status of the aquatic animal to be monitored is determined.

8. The health monitoring method based on aquatic animal excrement according to claim 1, characterized in that: Perform feature analysis on the time series composed of multiple instantaneous health status information to determine the final health status, including: Based on the time series of instantaneous health status information, a time series feature set of instantaneous health status estimation results [Q1, Q2, Q3, Q4, ..., Q n ]; Mode(Q) is used to discriminate the instantaneous health state time series feature set Q to represent the final recognition result; Specifically, F C =Mode(Q)=Mode(F c1 :F cn ), where Mode() means taking the value where the number of consecutive times exceeds the threshold as the output, F Ci Denotes the inferred set P i The instantaneous result, Q represents the instantaneous result of the sample F c1 to F cn The temporal feature set composed of , Fc represents their recognition results; The result labels are determined to correspond to the disease type and stage.

9. A health monitoring device based on aquatic animal excrement, characterized in that: It includes a feces collection device, a feature information acquisition unit, a visual information collection device, a processing unit, a position information acquisition unit and a computer program stored on the processing unit. When the processing unit executes the computer program, it implements the health monitoring method based on aquatic animal excrement as described in any one of claims 1 to 8.

10. A health monitoring system based on aquatic animal excrement, characterized in that: It includes the health monitoring device, a cloud platform, an analysis unit, a display unit and a computer program stored on the analysis unit. When the analysis unit executes the computer program, it implements the health monitoring method based on aquatic animal excrement as described in any one of claims 1 to 8.

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