On-demand intelligent feeding management system and method based on aquaculture

Abstract

The invention relates to the technical field of aquaculture, in particular to an on-demand intelligent feeding management system and method based on aquaculture, and the system obtains the ingestion activeness of siniperca chuatsi in each time period in each density of culture environment through analysis according to ingestion state parameters of the siniperca chuatsi in each time period in each density of culture environment. And constructing a time period-ingestion activity degree curve under each density, so as to obtain the optimal feeding time period of the siniperca chuatsi under the breeding environment of each density, and analyzing to obtain the positive influence degree of the breeding environment of each density on the ingestion activity degree of the siniperca chuatsi, the ingestion behavior growth correlation value of the siniperca chuatsi and the ingestion activity degree of the siniperca chuatsi. The optimal feeding amount of siniperca chuatsi in the breeding environment of each density is determined, the improvement effect evaluation index of aquaculture is obtained according to the water quality evaluation coefficients and the breeding cost of the experimental breeding ponds of each density and the control breeding ponds of each density, and the improvement effect evaluation index is fed back, so that blind feeding is avoided, the feeding accuracy is improved, and the breeding environment is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of aquaculture, and specifically, it is an intelligent feeding management system and method based on aquaculture on demand. Background Art

[0002] As a precious freshwater aquaculture fish, Siniperca chuatsi occupies an important position in the aquaculture industry. Its meat is delicious and the market demand is large. However, the breeding of Siniperca chuatsi faces many challenges. The traditional feeding method is often based on empirical timed and quantitative feeding, lacking accuracy.

[0003] On the one hand, the nutritional requirements of Siniperca chuatsi vary significantly at different growth stages. The types and amounts of feed required in the juvenile stage and adult stage are different, and its feeding habits are greatly affected by environmental factors. On the other hand, overfeeding will lead to feed waste and water pollution, affecting the growth environment of Siniperca chuatsi and increasing the risk of diseases. Underfeeding, however, will limit its growth rate and reduce the breeding efficiency. Therefore, intelligent feeding on demand in aquaculture is of great significance for the breeding of Siniperca chuatsi.

[0004] For example, the existing Chinese patent with the application number 202411123800.2 discloses an intelligent management method and system for aquaculture. This solution constructs a three-dimensional model of aquatic products based on the images of the feeding points collected after feeding the feed, and places the three-dimensional model of aquatic products into the monitoring model. According to the images collected by the image acquisition devices set at various preset positions in the water area, the position of the three-dimensional model of aquatic products in the monitoring model is updated. Before each feeding, the three-dimensional model of aquatic products in the monitoring model is analyzed to generate a feeding plan, and the feeding amount and feeding position can be determined according to the actual quantity and distribution of aquatic products, avoiding feed waste and water pollution caused by overfeeding, and at the same time preventing underfeeding from affecting the growth of aquatic products.

[0005] However, the following problems exist in the above patent: When obtaining the three-dimensional model of aquatic products, this solution relies on the image acquisition device. However, if there are obstacles in the water body, such as a large amount of algae reproduction, suspended impurities in the water, or partial occlusion by aquaculture facilities, the image acquisition device cannot accurately collect the images of aquatic products, then the constructed three-dimensional model of aquatic products will have errors, thus affecting the subsequent analysis and the generation of the feeding plan.

[0006] As disclosed in the existing Chinese patent with the application number 201611023860.2, an intelligent fisherman's precise feeding management system is disclosed. This solution uses a feeding plan binding management module to select appropriate initial feeding strategies for different varieties cultured in different ponds and formulate a feeding plan for the entire breeding cycle. The plan binding binds the feeding plan strategy with the breeding cycle time as the dimension. The plan generation is based on the initial feeding plan, combined with the parameter requirements of different ponds and historical breeding situations, and dynamically generates the daily feeding plan through a feeding plan algorithm, considering factors such as the initial feeding strategy, historical feeding, and fish weight indicators.

[0007] However, there are the following problems in the above patent: First, although this solution considers the impact of the feeding machine switch response time on the feeding amount deviation and makes certain adjustments, it does not clearly mention the relationship between the feeding amount and the growth stage. In different growth stages of fish, their nutritional requirements and feeding habits may vary greatly. The lack of corresponding nutritional supply in different growth stages may lead to slow growth of the fish population, failure to reach the expected specifications and weight gain, and extension of the breeding cycle.

[0008] Second, this solution does not involve the analysis of the improvement of water quality parameters and breeding costs after adjusting the feeding. Different feeding strategies may require different water quality control measures. Without the analysis of water quality parameters, breeders cannot accurately adjust water quality indicators such as pH value and dissolved oxygen content according to the water quality requirements after feeding adjustment. At the same time, it is impossible to determine whether the feeding adjustment has brought cost optimization, which may cause waste of resources. Summary of the Invention

[0009] To overcome the shortcomings in the background technology, the embodiments of the present invention provide an on-demand intelligent feeding management system and method based on aquaculture, which can effectively solve the problems involved in the above background technology.

[0010] The object of the present invention can be achieved through the following technical solutions: The present invention provides an on-demand intelligent feeding management system based on aquaculture, including: a feeding signal detection module for monitoring the feeding state parameters of Siniperca chuatsi in each density breeding environment at each time period, and the feeding state parameters include the feeding sound frequency value, feeding sound intensity, and feeding sound duration.

[0011] A feeding state analysis module for analyzing the feeding activity of Siniperca chuatsi in each density breeding environment at each time period according to the feeding state parameters of Siniperca chuatsi in each density breeding environment at each time period.

[0012] A feeding time period analysis module for constructing a time period - feeding activity curve at each density to obtain the optimal feeding time period of Siniperca chuatsi in each density breeding environment.

[0013] The feeding amount analysis module is used to analyze the positive influence degree of each density aquaculture environment on the feeding activity of Siniperca chuatsi and the growth correlation value of the feeding behavior of Siniperca chuatsi, and determine the optimal feeding amount of Siniperca chuatsi under each density aquaculture environment.

[0014] The water quality parameter analysis module is used to obtain the water quality evaluation coefficients of each density experimental aquaculture pond and each density control aquaculture pond by detecting the water quality parameters at each time point of each density experimental aquaculture pond and each density control aquaculture pond. The water quality parameters include pH value, dissolved oxygen content, and ammonia nitrogen content.

[0015] The aquaculture cost analysis module is used to obtain the aquaculture costs of each density experimental aquaculture pond and each density control aquaculture pond during the aquaculture cycle.

[0016] The improvement effect analysis module is used to obtain the improvement effect evaluation index of aquaculture by comparing the water quality evaluation coefficients and aquaculture costs of each density experimental aquaculture pond and each density control aquaculture pond, and provide feedback on it.

[0017] The management database is used to store the standard feeding amounts corresponding to each growth stage of Siniperca chuatsi.

[0018] Preferably, the present invention provides an intelligent feeding management method on demand based on aquaculture. The specific steps of the management method are as follows: S1. Feeding signal detection: Monitor the feeding state parameters of Siniperca chuatsi at each time period in each density aquaculture environment. The feeding state parameters include the feeding sound frequency value, feeding sound intensity, and feeding sound duration.

[0019] S2. Feeding state analysis: Analyze the feeding activity of Siniperca chuatsi at each time period in each density aquaculture environment according to the feeding state parameters of Siniperca chuatsi at each time period in each density aquaculture environment.

[0020] S3. Feeding time period analysis: Construct a time period - feeding activity curve for each density, so as to obtain the optimal feeding time period of Siniperca chuatsi under each density aquaculture environment.

[0021] S4. Feeding amount analysis: Analyze the positive influence degree of each density aquaculture environment on the feeding activity of Siniperca chuatsi and the growth correlation value of the feeding behavior of Siniperca chuatsi, and determine the optimal feeding amount of Siniperca chuatsi under each density aquaculture environment.

[0022] S5. Water quality parameter analysis: Obtain the water quality evaluation coefficients of each density experimental aquaculture pond and each density control aquaculture pond by detecting the water quality parameters at each time point of each density experimental aquaculture pond and each density control aquaculture pond. The water quality parameters include pH value, dissolved oxygen content, and ammonia nitrogen content.

[0023] S6. Aquaculture cost analysis: Obtain the aquaculture costs of each density experimental aquaculture pond and each density control aquaculture pond during the aquaculture cycle.

[0024] S7. Analysis of improvement effect: By comparing the water quality evaluation coefficients and breeding costs of each density experimental breeding pond and each density control breeding pond, an improvement effect evaluation index for aquaculture is obtained and fed back.

[0025] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: First, according to the feeding state parameters of Siniperca chuatsi at each time period in each density breeding environment, the feeding activity of Siniperca chuatsi at each time period in each density breeding environment is analyzed, and then the time period - feeding activity curve under each density is constructed, so as to obtain the optimal feeding time period of Siniperca chuatsi in each density breeding environment. Feeding according to the natural feeding law of fish helps the normal growth and development of Siniperca chuatsi and reduces phenomena such as digestive problems or growth retardation caused by improper feeding.

[0026] Second, by analyzing the positive influence degree of each density breeding environment on the feeding activity of Siniperca chuatsi and the growth correlation value of the feeding behavior of Siniperca chuatsi, the optimal feeding amount of Siniperca chuatsi in each density breeding environment is determined. The accurate feeding amount can ensure that the fish obtain sufficient nutrition and at the same time will not cause waste of feed, thereby reducing the proportion of feed cost in the total breeding cost.

[0027] Third, according to the water quality evaluation coefficients and breeding costs of each density experimental breeding pond and each density control breeding pond, an improvement effect evaluation index for aquaculture is obtained and fed back, which helps to timely discover problems in the breeding process, so as to take corresponding improvement measures, continuously optimize the breeding mode, and improve the economic and environmental benefits of breeding. Description of the Drawings

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0029] Figure 1 It is a module connection diagram of an on - demand intelligent feeding management system based on aquaculture.

[0030] Figure 2 For Figure 1 The flowchart of the feeding signal detection module in

[0031] Figure 3 It is a flowchart of an on - demand intelligent feeding management method based on aquaculture. Detailed Embodiments

[0032] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] Please refer to Figure 1 As shown, an on-demand intelligent feeding management system based on aquaculture, the management system includes a feeding signal detection module, a feeding state analysis module, a feeding time period analysis module, a feeding amount analysis module, a water quality parameter analysis module, a breeding cost analysis module, an improvement effect analysis module, and a management database.

[0034] The management database is connected to the feeding state analysis module and the improvement effect analysis module. The feeding state analysis module is connected to the feeding signal detection module, the feeding time period analysis module, and the feeding amount analysis module. The improvement effect analysis module is connected to the water quality parameter analysis module and the breeding cost analysis module.

[0035] The feeding signal detection module is used to monitor the feeding state parameters of Siniperca chuatsi in each density breeding environment at each time period. The feeding state parameters include the feeding sound frequency value, the feeding sound intensity, and the feeding sound duration.

[0036] Please refer to Figure 2 As shown, the specific analysis method of the feeding signal detection module is as follows: A1. Different density breeding environments are obtained by stocking different numbers of Siniperca chuatsi fry in each breeding pond of the same volume, denoted as each density breeding environment, and corresponding feeding amounts are fed to each density breeding pond according to the preset feeding amount per unit fry; by comparing the feeding situations under different density breeding environments, the influence of breeding density on the feeding and other aspects of Siniperca chuatsi can be clarified, which helps to determine the most suitable breeding density to achieve the best breeding benefits.

[0037] A2. Select each time period according to a fixed duration, and continuously monitor the feeding sound signals of Siniperca chuatsi in each density breeding environment at each time period using a hydrophone. The time period during which the frequency of the feeding sound signal continuously appears within the set frequency range is recorded as a feeding event, and thus each feeding event in each density breeding environment at each time period is screened and determined; the time period during which the frequency of the feeding sound signal continuously appears within this range is defined as a feeding event, and this method can accurately identify the feeding activity period of the fish.

[0038] A3. By performing spectral analysis on the sound signals of each feeding event in each time period, the frequency points of each peak of each feeding sound signal are obtained, which are recorded as the main frequency values of the sound in each feeding event. And the feeding sound frequency values of each time period in each density aquaculture environment are obtained through mean calculation; An index based on the feeding sound frequency is established for the feeding situation of Siniperca chuatsi in different density aquaculture environments, which is convenient for subsequent comparison and analysis of the differences in feeding behaviors under different conditions.

[0039] A4. Calculate the average value of the sound intensity in each feeding event in each time period in each density aquaculture environment, and obtain the feeding sound intensity of each time period in each density aquaculture environment through mean calculation; It helps to explore the internal relationship between the feeding behavior and the aquaculture environment. According to the change of the feeding sound intensity, the aquaculture conditions can be adjusted more accurately, such as adjusting the aquaculture density, improving the water quality, etc., to maintain the good feeding state of Siniperca chuatsi.

[0040] A5. By determining the duration from the start to the end of each feeding event, the duration of each feeding event is obtained, and the feeding sound duration of each time period in each density aquaculture environment is obtained through mean calculation; Determining the duration of each feeding event and calculating the feeding sound duration of each time period in each density aquaculture environment further improves the description of the feeding behavior of Siniperca chuatsi and quantifies the feeding activity from the time dimension.

[0041] The feeding state analysis module is used to analyze the feeding activity of Siniperca chuatsi in each time period in each density aquaculture environment according to the feeding state parameters of Siniperca chuatsi in each density aquaculture environment.

[0042] The specific analysis method of the feeding state analysis module is: read the feeding sound frequency value, feeding sound intensity, and feeding sound duration of each time period in each density aquaculture environment, and record them as F im 、K im 、T im , i represents the number of the i-th density aquaculture environment, i = 1, 2,..., n, m represents the number of the m-th time period, m = 1, 2,..., q. Through the formula The feeding activity H of Siniperca chuatsi in each time period in each density aquaculture environment is obtained im , F 0 、K 0 、T 0 respectively represent the reference values of the set feeding sound frequency value, feeding sound intensity, and feeding sound duration, and φ 1 、φ 2 、φ 3Weight factors respectively representing a preset feeding sound frequency value, feeding sound intensity, and feeding duration of the feeding sound; it is possible to comprehensively and meticulously understand the feeding behavior characteristics of Siniperca chuatsi under various breeding conditions, which helps to improve the growth rate and breeding output of Siniperca chuatsi.

[0043] It should be noted that in a specific embodiment, φ 1 can be set to 0.4, φ 2 can be set to 0.4, φ 3 can be set to 0.2. The feeding sound frequency value may be closely related to the feeding enthusiasm and feeding speed of Siniperca chuatsi. In a high-density breeding environment, the competition among fish groups may prompt an increase in the feeding speed, and the frequency of the feeding sound will also increase accordingly. Moreover, within different time periods, the change in the feeding sound frequency can reflect the feeding activity pattern of Siniperca chuatsi at different times of the day. If the feeding sound frequency continuously remains at a high level during a certain time period, it indicates that this time period is a period with a high feeding activity; the feeding sound intensity can reflect the strength and investment degree when Siniperca chuatsi is feeding. In a breeding environment with a large density, the competition pressure may cause the fish to exert more force when feeding, generating a feeding sound with a greater intensity. A stronger feeding sound intensity may mean that the fish are scrambling for food more fiercely, which is also a manifestation of high feeding activity; the feeding sound duration can reflect the continuous feeding time of Siniperca chuatsi during one feeding process. Under different density breeding environments, the feeding sound duration will be different. In a low-density breeding environment, due to relatively abundant food, the fish may have a longer feeding sound duration, but this does not necessarily completely represent the highest feeding activity because there may be a situation where the fish eat slowly. Therefore, the weight corresponding to the feeding sound frequency value and the feeding sound intensity is higher.

[0044] A feeding time period analysis module, which is used to construct a time period - feeding activity curve for each density, so as to obtain the optimal feeding time period of Siniperca chuatsi under each density breeding environment.

[0045] The specific analysis method of the feeding time period analysis module is as follows: taking the time period as the abscissa and the feeding activity of Siniperca chuatsi as the ordinate, constructing a two-dimensional coordinate system, and then marking several points in the constructed two-dimensional coordinate system for the feeding activity of Siniperca chuatsi corresponding to each time period, forming a time period - feeding activity curve for each density. Obtain the time period corresponding to the peak value of the feeding activity from the time period - feeding activity curve for each density, and record it as the optimal feeding time period of Siniperca chuatsi under each density breeding environment; it can accurately determine the optimal feeding time period of Siniperca chuatsi under different breeding densities, and the fish body can more effectively utilize the nutrients in the feed, thereby improving the growth rate and enhancing the physique and immunity of the fish.

[0046] The specific analysis method for the positive influence degree of each density aquaculture environment on the feeding activity of Siniperca chuatsi is as follows: Read the feeding activity H of Siniperca chuatsi in each time period under each density aquaculture environment. im , through the formula Obtain the positive influence degree θ of each density aquaculture environment on the feeding activity of Siniperca chuatsi. i , n represents the number of density aquaculture environments, and q represents the number of time periods; Appropriate aquaculture density helps Siniperca chuatsi feed better, thereby promoting its growth and development. By optimizing the aquaculture density, the growth rate of fish can be increased, the incidence of diseases can be reduced, and finally the aquaculture yield and economic benefits can be increased.

[0047] When calculating the positive influence degree of each density aquaculture environment on the feeding activity of Siniperca chuatsi, first calculate the difference between the feeding activity of Siniperca chuatsi in each time period under each density aquaculture environment and the average value of the feeding activity of Siniperca chuatsi in each time period under each density aquaculture environment. This difference reflects the degree of deviation of each observation value (feeding activity under a specific density aquaculture environment and time period) from the average level, and it can be known in which cases the feeding activity is higher or lower than the average level. Take the average value of the above differences to comprehensively consider the overall deviation situation of all density aquaculture environments and time periods. Finally, use the natural logarithm function for transformation. The logarithmic function has the characteristic of converting product and ratio relationships into addition and subtraction relationships, which can help better fit the data.

[0048] The feeding amount analysis module is used to analyze the positive influence degree of each density aquaculture environment on the feeding activity of Siniperca chuatsi and the growth correlation value of the feeding behavior of Siniperca chuatsi, and determine the optimal feeding amount of Siniperca chuatsi under each density aquaculture environment.

[0049] The specific analysis method of the feeding amount analysis module is as follows: B1. Through the formula Obtain the growth correlation value ε of the feeding behavior of Siniperca chuatsi. respectively represent the weight factors of the preset feeding sound frequency value, feeding sound intensity, and feeding sound duration. Determine the growth stage of Siniperca chuatsi according to the preset range of the growth correlation value of the feeding behavior corresponding to each growth stage of Siniperca chuatsi. Extract the standard feeding amount corresponding to each growth stage of Siniperca chuatsi from the management database, and obtain the standard feeding amount corresponding to the growth stage of Siniperca chuatsi, denoted as σ; It helps to improve the health status of the fish body, promote its normal growth and development, and improve the quality of aquaculture products.

[0050] Perform normalization processing on the feeding sound intensity, feeding sound duration, and feeding sound frequency value, and convert these actually measured values into a proportional relationship relative to the reference standard by dividing them by their respective reference values. Enable measurement values of different units or magnitudes to be compared and calculated on the same scale. The feeding sound intensity and the feeding sound duration are positively correlated in the growth correlation value of the feeding behavior of Siniperca chuatsi, while the feeding sound frequency value is negatively correlated. By multiplying them with their corresponding weights respectively, the influences of various characteristics (intensity, duration, and frequency) of the feeding sound on the growth correlation value of the feeding behavior of Siniperca chuatsi are obtained. Finally, an average value is calculated to obtain a more representative growth correlation value of the feeding behavior of Siniperca chuatsi, and this average value can reflect an average level of the overall complex relationship between such feeding sound characteristics and the growth of the feeding behavior.

[0051] It should be noted that in a specific embodiment, can be set to 0.5, can be set to 0.3, can be set to 0.2. Juvenile Siniperca chuatsi grow rapidly and require a large amount of nutrients to support the development of various organs of the body. At this time, if the feeding sound frequency is high, it indicates that it is in an active feeding state and can meet the nutritional needs of rapid growth, which is an important manifestation of the healthy growth of juvenile fish. As Siniperca chuatsi gradually grows, its feeding frequency will gradually stabilize at a level suitable for the growth stage. The change in the feeding frequency can more intuitively reflect the transition process of Siniperca chuatsi from rapid growth in the juvenile stage to relatively stable growth in the adult stage; the feeding sound intensity can be used as a reference factor for judging the growth stage of Siniperca chuatsi. In the juvenile stage, the feeding sound intensity may be relatively weak because juvenile fish are small in size and limited in strength. As Siniperca chuatsi grows and develops, its physical functions increase, and the feeding sound intensity may increase. However, the feeding sound intensity is affected by various factors, such as the food competition pressure in the environment. Even Siniperca chuatsi in an immature growth stage may show a strong feeding sound intensity, so it cannot accurately reflect the growth stage like the feeding sound frequency; the role of the feeding sound duration in judging the growth stage of Siniperca chuatsi is relatively limited. A longer feeding sound duration may be due to low foraging efficiency or uneven food distribution of Siniperca chuatsi at a certain moment, rather than the essential characteristics of the growth stage. Therefore, the weight corresponding to the feeding sound frequency value is the highest, the weight corresponding to the feeding sound intensity is the second, and the weight corresponding to the feeding sound duration is lower.

[0052] B2. Substitute the positive influence degree θ of each density aquaculture environment on the feeding activity of Siniperca chuatsi i into the formula σ' i = θ i *κ + σ to obtain the optimal feeding amount σ' of Siniperca chuatsi under each density aquaculture environment i , where κ represents the proportionality constant between the preset positive influence degree of the feeding activity and the feeding amount; it helps to achieve efficient aquaculture under different aquaculture scales and densities and ensure the growth requirements of Siniperca chuatsi.

[0053] Multiply the positive influence degree of each density aquaculture environment on the feeding activity of Siniperca chuatsi by the proportionality constant of the feeding amount to obtain θ. i *κ can adjust the feeding amount according to the positive influence degree of the aquaculture environment on the feeding activity. Add the adjusted feeding amount based on the positive influence degree of the feeding activity to the standard feeding amount corresponding to the growth stage to obtain a feeding amount that comprehensively considers the influence of the aquaculture environment and the needs of the growth stage. The standard feeding amount corresponding to the growth stage of Siniperca chuatsi is a basic component of the final feeding amount, which reflects the basic nutritional needs of Siniperca chuatsi at different growth stages and, together with the adjusted feeding amount based on the positive influence degree of the feeding activity, constitutes the optimal feeding amount of Siniperca chuatsi under each density aquaculture environment.

[0054] A water quality parameter analysis module is used to obtain the water quality evaluation coefficients of each density experimental aquaculture pond and each density control aquaculture pond by detecting the water quality parameters of each density experimental aquaculture pond and each density control aquaculture pond at each time point. The water quality parameters include pH value, dissolved oxygen content, and ammonia nitrogen content.

[0055] The specific analysis method of the water quality parameter analysis module is as follows: C1. Set the aquaculture cycle according to the preset aquaculture duration, and select several time points at equal time intervals within the aquaculture cycle, denoted as each time point; based on fixed time points, the situations at different stages during the aquaculture process can be evaluated more comprehensively.

[0056] C2. Select several aquaculture ponds from each density aquaculture environment, and adjust the feeding parameters of the corresponding feeding equipment according to the optimal feeding time period and the optimal feeding amount under each density aquaculture environment within the aquaculture cycle. Denote the aquaculture ponds with adjusted feeding parameters as each density experimental aquaculture ponds, and keep the feeding parameters of the remaining aquaculture ponds unchanged, denoted as each density control aquaculture ponds; adjusting the feeding equipment of the experimental aquaculture ponds according to the optimal feeding time period and the feeding amount can achieve precise optimization of the feeding process, ensure the appropriate feeding amount is given at the most suitable time, and help improve the feeding efficiency and growth rate of Siniperca chuatsi.

[0057] C3. Use a water quality detector to detect the pH value, dissolved oxygen content, and ammonia nitrogen content of each density experimental aquaculture pond and each density control aquaculture pond at each time point, and then analyze to obtain the water quality evaluation coefficients of each density experimental aquaculture pond and each density control aquaculture pond, denoted as Precise feeding optimization can improve the feeding efficiency of Siniperca chuatsi, reduce feed waste, promote the growth of fish, help improve the aquaculture yield and quality, and thus improve the aquaculture benefit.

[0058] It should be noted that the specific analysis method for the water quality evaluation coefficients of each density experimental aquaculture pond and each density control aquaculture pond is as follows: C31. At each time point during the aquaculture cycle, a set amount of pond water samples are taken from each density experimental aquaculture pond and each density control aquaculture pond, and the pH values of each density experimental aquaculture pond and each density control aquaculture pond at each time point are obtained through a pH detector, denoted as PH ij and PH' ij , where j represents the number of the j-th time point, and j = 1, 2,..., g.

[0059] C32. By putting the probe of the dissolved oxygen meter into the pond water samples of each density experimental aquaculture pond and each density control aquaculture pond at each time point, the dissolved oxygen content of each density experimental aquaculture pond and each density control aquaculture pond at each time point is read, denoted as σ ij and σ' ij .

[0060] C33. At each time point, a set amount of water samples are taken from each density experimental aquaculture pond and each density control aquaculture pond and placed in a colorimetric tube. The potassium sodium tartrate solution and Nessler's reagent are added in sequence to measure the absorbance, and the ammonia nitrogen content of each density experimental aquaculture pond and each density control aquaculture pond at each time point is obtained according to the ammonia nitrogen content - absorbance standard curve, denoted as NH ij and NH' ij .

[0061] C34. Respectively, through the formula the water quality evaluation coefficients of each density experimental aquaculture pond and the water quality evaluation coefficients of each density control aquaculture pond w 1 and w 2 and w 3 respectively represent the preset weight factors of pH value, dissolved oxygen content, and ammonia nitrogen content. g represents the number of time points, and PH 0 represents the preset optimal pH value.

[0062] For the part related to the pH value in the numerator, first calculate the difference between the pH value of each density experimental aquaculture pond at each time point and the reference value, and then divide the reference value by this difference, which can reflect the impact of the deviation degree of the pH value from the reference value on the water quality evaluation. The closer the pH value is to the reference value, the greater the positive contribution to the water quality evaluation coefficient. At the same time, the dissolved oxygen content of each density experimental aquaculture pond at each time point is added to the numerator. The higher the dissolved oxygen content, the more favorable it is usually for the water quality evaluation, which will increase the value of the numerator and thus improve the water quality evaluation coefficient. The denominator uses the ammonia nitrogen content of each density control aquaculture pond at each time point Ammonia nitrogen is an important indicator of water pollution. A high ammonia nitrogen content usually indicates poor water quality. Finally, the average value is obtained, that is, the water quality evaluation coefficient of each density experimental aquaculture pond is obtained. Considering the situations of different density experimental aquaculture ponds, a relatively comprehensive evaluation coefficient that can reflect the overall water quality status is obtained.

[0063] It should be noted that in a specific embodiment, w 1 can be set to 0.4, w 2 can be set to 0.4, w 3 can be set to 0.2. For the organisms in the aquaculture pond, especially fish such as Siniperca chuatsi, the pH value has a direct and crucial impact on their physiological functions. Most aquatic organisms have a suitable pH value range. For example, the suitable pH value for Siniperca chuatsi is generally between 7 and 8.5. If the pH value is too high or too low, it will affect basic physiological processes such as the osmotic pressure regulation and respiration of the fish body; dissolved oxygen is a key factor for the survival of aquatic organisms. Fish take in dissolved oxygen from the water through their gills for respiration to maintain life activities. Siniperca chuatsi has certain requirements for dissolved oxygen. Generally speaking, the dissolved oxygen content in the aquaculture pond should be maintained at 5-8 mg / L, which is more appropriate. When the dissolved oxygen content is insufficient, the fish will show the phenomenon of floating heads, and in severe cases, it will lead to suffocation and death; ammonia nitrogen is an inevitable substance in the aquaculture pond, mainly derived from fish excrement, residual bait, etc. An appropriate amount of ammonia nitrogen can be utilized by microorganisms in the water and participate in the nitrogen cycle. However, when the ammonia nitrogen content is too high, it will be toxic to the aquaculture organisms; therefore, the corresponding weights of the pH value and the dissolved oxygen content are higher.

[0064] The aquaculture cost analysis module is used to obtain the aquaculture costs of each density experimental aquaculture pond and each density control aquaculture pond during the aquaculture cycle.

[0065] The specific analysis method of the aquaculture cost analysis module is as follows: Obtain the feed cost, water conditioning agent cost, and water and electricity cost of each density experimental aquaculture pond and each density control aquaculture pond during the aquaculture cycle. According to the setting, corresponding weights are assigned to the feed cost, water conditioning agent cost, and water and electricity cost respectively. By multiplying the feed cost, water conditioning agent cost, and water and electricity cost by their corresponding weights and accumulating them, the aquaculture costs of each density experimental aquaculture pond and each density control aquaculture pond are obtained, denoted as C i 、C' i ; which is convenient for intuitively comparing the cost differences between the experimental aquaculture ponds and control aquaculture ponds with different aquaculture densities.

[0066] The improvement effect analysis module is used to obtain the improvement effect evaluation index of aquaculture by comparing the water quality evaluation coefficients and aquaculture costs of each density experimental aquaculture pond and each density control aquaculture pond, and give feedback on it.

[0067] The specific analysis method of the improvement effect analysis module is as follows: compare the water quality evaluation coefficients and breeding costs of each density experimental breeding pond and each density control breeding pond respectively, and calculate the improvement effect evaluation index of aquaculture. ω 1 and ω 2 respectively represent the preset weight factors of the water quality evaluation coefficient and the breeding cost. n represents the number of density breeding environments. Compare the improvement effect evaluation index of aquaculture with the preset improvement effect evaluation index threshold. If the improvement effect evaluation index of aquaculture is greater than or equal to the preset improvement effect evaluation index threshold, it indicates that the improvement effect of aquaculture is qualified; otherwise, it indicates that the improvement effect of aquaculture is unqualified. Feed back the improvement effect of aquaculture to the system, and can specifically analyze whether it is a water quality management problem, a cost control problem or other aspects of the problem, so as to take effective improvement measures to promote the sustainable development of aquaculture.

[0068] In this part, select the water quality evaluation coefficients of the experimental breeding pond and the control breeding pond for comparison. The water quality evaluation coefficient of the experimental breeding pond is the one calculated previously, and the water quality evaluation coefficient of the control breeding pond is also calculated according to the same or similar standards. Divide the water quality evaluation coefficient of the experimental breeding pond by the water quality evaluation coefficient of the control breeding pond. If this ratio is greater than 1, it means that the water quality of the experimental breeding pond has improved compared with the control breeding pond; if it is less than 1, it means that the water quality may be inferior to that of the control breeding pond. In part, compare the breeding costs of the experimental breeding pond and the control breeding pond. If the breeding cost of the experimental breeding pond is lower than that of the control breeding pond, this ratio will be less than 1, which may mean that the experimental breeding pond is more economical in terms of breeding methods, management, etc. Finally, calculate the average value of these two parts to obtain the improvement effect evaluation index of aquaculture, which can combine the water quality improvement situation and the breeding cost situation, and avoid drawing one-sided conclusions by considering only one factor alone.

[0069] It should be noted that in a specific embodiment, ω 1 can be set to 0.6, and ω 2 can be set to 0.4. In aquaculture, water quality is a crucial factor. Good water quality is the basis for the survival, growth and health of aquatic animals. Although breeding cost is also an important consideration factor in aquaculture, its connection with the improvement effect of aquaculture is relatively indirect. Breeding costs include feed costs, water conditioning agent costs, hydropower costs, etc. Higher breeding costs do not necessarily bring better breeding improvement effects. Therefore, the weight corresponding to the water quality evaluation coefficient is higher.

[0070] The management database is used to store the standard feeding amounts corresponding to each growth stage of Siniperca chuatsi.

[0071] Please refer to Figure 3 as shown. In addition, the present invention provides an intelligent feeding management method based on aquaculture on demand, and the specific steps of the management method are as follows: S1. Feeding signal detection: Monitor the feeding state parameters of Siniperca chuatsi in each density aquaculture environment at each time period, and the feeding state parameters include the feeding sound frequency value, the feeding sound intensity, and the feeding sound duration.

[0072] S2. Feeding state analysis: Analyze the feeding activity of Siniperca chuatsi at each time period in each density aquaculture environment according to the feeding state parameters of Siniperca chuatsi at each time period in each density aquaculture environment.

[0073] S3. Feeding time period analysis: Construct a time period - feeding activity curve for each density to obtain the optimal feeding time period of Siniperca chuatsi in each density aquaculture environment.

[0074] S4. Feeding amount analysis: Analyze the positive influence degree of each density aquaculture environment on the feeding activity of Siniperca chuatsi and the growth correlation value of the feeding behavior of Siniperca chuatsi to determine the optimal feeding amount of Siniperca chuatsi in each density aquaculture environment.

[0075] S5. Water quality parameter analysis: Obtain the water quality evaluation coefficients of each density experimental aquaculture pond and each density control aquaculture pond by detecting the water quality parameters of each density experimental aquaculture pond and each density control aquaculture pond at each time point, and the water quality parameters include the pH value, the dissolved oxygen content, and the ammonia nitrogen content.

[0076] S6. Aquaculture cost analysis: Obtain the aquaculture costs of each density experimental aquaculture pond and each density control aquaculture pond during the aquaculture cycle.

[0077] S7. Improvement effect analysis: Obtain the improvement effect evaluation index of aquaculture by comparing the water quality evaluation coefficients and aquaculture costs of each density experimental aquaculture pond and each density control aquaculture pond, and give feedback on it.

[0078] This system analyzes the feeding activity of Siniperca chuatsi at each time period in each density aquaculture environment according to the feeding state parameters of Siniperca chuatsi at each time period in each density aquaculture environment, and then constructs a time period - feeding activity curve for each density to obtain the optimal feeding time period of Siniperca chuatsi in each density aquaculture environment. By analyzing the positive influence degree of each density aquaculture environment on the feeding activity of Siniperca chuatsi and the growth correlation value of the feeding behavior of Siniperca chuatsi, the optimal feeding amount of Siniperca chuatsi in each density aquaculture environment is determined. The improvement effect evaluation index of aquaculture is obtained according to the water quality evaluation coefficients and aquaculture costs of each density experimental aquaculture pond and each density control aquaculture pond, and feedback is given on it, which helps to avoid blind feeding, improve the accuracy of feeding, and optimize the aquaculture environment.

[0079] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention, and still be covered by the protection scope of the present invention.

Claims

1. An on-demand intelligent feeding management system based on aquaculture, characterized in that: The system specifically includes the following modules: A feeding signal detection module is used to monitor the feeding state parameters of Siniperca chuatsi in various time periods under various density breeding environments. The feeding state parameters include the feeding sound frequency value, feeding sound intensity, and feeding sound duration; A feeding state analysis module is used to analyze the feeding activity of Siniperca chuatsi in each time period under each density breeding environment according to the feeding state parameters of Siniperca chuatsi in each time period under each density breeding environment; The feeding time period analysis module is used to construct the time period-feeding activity curve under each density, so as to obtain the optimal feeding time period for Siniperca chuatsi under each density breeding environment; The feeding amount analysis module is used to analyze the positive impact of different density breeding environments on the feeding activity of Siniperca chuatsi, the feeding behavior growth correlation value of Siniperca chuatsi, and determine the optimal feeding amount for Siniperca chuatsi under different density breeding environments; The water quality parameter analysis module is used to obtain the water quality evaluation coefficients of each density experimental breeding pool and each density control breeding pool by detecting the water quality parameters of each density experimental breeding pool and each density control breeding pool at each time point. The water quality parameters include pH, dissolved oxygen content, and ammonia nitrogen content; The breeding cost analysis module is used to obtain the breeding costs of experimental breeding ponds of various densities and control breeding ponds of various densities during the breeding cycle; The improvement effect analysis module is used to obtain the improvement effect evaluation index of aquaculture by comparing the water quality evaluation coefficients and breeding costs of experimental breeding ponds of various densities and control breeding ponds of various densities, and to provide feedback thereon; The management database is used to store the standard feeding amount corresponding to each growth stage of Siniperca chuatsi.

2. According to claim 1, the on-demand intelligent feeding management system based on aquaculture is characterized by: The specific analysis method of the feeding signal detection module is: A1. A culture environment of different densities is obtained by stocking different numbers of Siniperca chuatsi fry in each culture pond of the same volume, recorded as each density culture environment, and feeding the corresponding feeding amount of each density culture pond according to the preset unit feeding amount of fry; A2. Selecting each time period according to a fixed duration, using a hydrophone to continuously monitor the feeding sound signals of Siniperca chuatsi in each time period under each culture density environment, recording the time period in which the frequency of the feeding sound signal continuously appears within the set frequency range as a feeding event, thereby screening and determining each feeding event in each time period under each culture density environment; A3. By performing spectrum analysis on the sound signals of each feeding event in each time period, the frequency points of each peak of each feeding sound signal are obtained, which are recorded as the main frequency values ​​of the sound in each feeding event, and the feeding sound frequency values ​​of each time period under each density breeding environment are obtained by mean calculation; A4. Calculate the average sound intensity of each feeding event in each time period under each density breeding environment, and obtain the feeding sound intensity in each time period under each density breeding environment by mean calculation; A5. The duration of each feeding event is obtained by determining the time from the start to the end, and the mean value is calculated to obtain the duration of the feeding sound in each time period under each density breeding environment.

3. According to claim 2, the on-demand intelligent feeding management system based on aquaculture is characterized in that: The specific analysis method of the feeding status analysis module is: Read the frequency value of feeding sound, feeding sound intensity, and feeding sound duration in each time period under each density breeding environment, and record them as F im , K im , T im , i represents the number of the i-th density breeding environment, i=1,2,...,n, m represents the number of the m-th time period, m=1,2,...,q, through the formula The feeding activity H of Siniperca chuatsi in each period of time under each density culture environment was obtained. im , F0, K0, and T0 respectively represent the reference values ​​of the set feeding sound frequency value, feeding sound intensity, and feeding sound duration, and φ1, φ2, and φ3 respectively represent the weight factors of the preset feeding sound frequency value, feeding sound intensity, and feeding sound duration.

4. The on-demand intelligent feeding management system based on aquaculture according to claim 3 is characterized in that: The specific analysis method of the feeding time period analysis module is: A two-dimensional coordinate system was constructed with the time period as the horizontal axis and the feeding activity of the mandarin fish as the vertical axis. Several points were marked in the constructed two-dimensional coordinate system according to the feeding activity of the mandarin fish in each time period to form the time period-feeding activity curve under each density. The time period corresponding to the peak feeding activity was obtained from the time period-feeding activity curve under each density, and recorded as the optimal feeding time period for the mandarin fish under each density breeding environment.

5. The on-demand intelligent feeding management system based on aquaculture according to claim 4 is characterized in that: The specific analysis method of the positive impact of each density breeding environment on the feeding activity of Siniperca chuatsi is as follows: Read the feeding activity H of Siniperca chuatsi in different time periods under different culture density environments im , through the formula The positive influence degree of each density breeding environment on the feeding activity of Siniperca chuatsi was obtained i , n represents the number of density culture environments, and q represents the number of time periods.

6. The on-demand intelligent feeding management system based on aquaculture according to claim 5, characterized in that: The specific analysis method of the feeding amount analysis module is: B1. Through the formula The feeding behavior growth correlation value ε of Siniperca chuatsi was obtained. The weight factors representing the preset feeding sound frequency value, feeding sound intensity, and feeding sound duration are respectively used to determine the growth stage of Siniperca chuatsi according to the preset feeding behavior growth correlation value range corresponding to each growth stage of Siniperca chuatsi. The standard feeding amount corresponding to each growth stage of Siniperca chuatsi is extracted from the management database, and the standard feeding amount corresponding to the growth stage of Siniperca chuatsi is obtained from it, which is recorded as σ; B2. The positive impact of each density culture environment on the feeding activity of Siniperca chuatsi θ i Substituting into the formula σ' i =θ i *κ+σ to get the optimal feeding amount of Siniperca chuatsi under different culture density environments σ' i , κ represents the proportionality constant between the positive effect of the preset feeding activity and the feeding amount.

7. The on-demand intelligent feeding management system based on aquaculture according to claim 1, characterized in that: The specific analysis method of the water quality parameter analysis module is: C1. Set the breeding cycle according to the preset breeding time, and select a number of time points with equal time intervals within the breeding cycle, which are recorded as each time point; C2. Select several breeding ponds from each density breeding environment, and adjust the feeding parameters of the corresponding feeding equipment according to the optimal feeding time period and optimal feeding amount of Siniperca chuatsi under each density breeding environment during the breeding cycle. The breeding ponds with adjusted feeding parameters are recorded as experimental breeding ponds of each density, and the feeding parameters of the other breeding ponds remain unchanged and are recorded as control breeding ponds of each density; C3. The pH, dissolved oxygen content, and ammonia nitrogen content of each density experimental breeding pool and each density control breeding pool at each time point were tested by a water quality detector, and then the water quality evaluation coefficient of each density experimental breeding pool and each density control breeding pool was obtained by analysis, which is recorded as 8. The on-demand intelligent feeding management system based on aquaculture according to claim 7, characterized in that: The specific analysis method of the breeding cost analysis module is: Obtain the feed cost, water adjustment preparation cost, and water and electricity cost of each density experimental breeding pond and each density control breeding pond during the breeding cycle, and assign corresponding weights to the feed cost, water adjustment preparation cost, and water and electricity cost according to the settings. By multiplying the feed cost, water adjustment preparation cost, and water and electricity cost with their corresponding weights and adding them up, the breeding costs of each density experimental breeding pond and each density control breeding pond are obtained, which is recorded as C i , C' i .

9. The on-demand intelligent feeding management system based on aquaculture according to claim 8, characterized in that: The specific analysis method of the improvement effect analysis module is: The water quality evaluation coefficients and breeding costs of experimental breeding ponds of each density and control breeding ponds of each density were compared to calculate the evaluation index of the improvement effect of aquaculture ω1 and ω2 represent the preset water quality evaluation coefficient and the weight factor of the breeding cost respectively, n represents the number of density breeding environments, and the improvement effect evaluation index of aquaculture is compared with the preset improvement effect evaluation index threshold. If the improvement effect evaluation index of aquaculture is greater than or equal to the preset improvement effect evaluation index threshold, it means that the improvement effect of aquaculture is qualified, otherwise it means that the improvement effect of aquaculture is unqualified, and the improvement effect of aquaculture is fed back to the system.

10. An on-demand intelligent feeding management method based on aquaculture, characterized in that: The specific steps of the control method are as follows: S1. Feeding signal detection: monitoring the feeding state parameters of Siniperca chuatsi in different breeding density environments at different time periods. The feeding state parameters include the feeding sound frequency value, feeding sound intensity, and feeding sound duration; S2. Feeding status analysis: According to the feeding status parameters of Siniperca chuatsi in different culture density environments at different time periods, the feeding activity of Siniperca chuatsi in different culture density environments at different time periods was analyzed; S3. Feeding time period analysis: constructing the time period-feeding activity curve under each density, so as to obtain the optimal feeding time period for Siniperca chuatsi under each density breeding environment; S4. Feeding amount analysis: Analyze the positive impact of each density culture environment on the feeding activity of Siniperca chuatsi, the feeding behavior growth correlation value of Siniperca chuatsi, and determine the optimal feeding amount of Siniperca chuatsi under each density culture environment; S5. Analysis of water quality parameters: water quality evaluation coefficients of each density experimental breeding pool and each density control breeding pool are obtained by testing the water quality parameters of each density experimental breeding pool and each density control breeding pool at each time point. The water quality parameters include pH, dissolved oxygen content, and ammonia nitrogen content. S6. Breeding cost analysis: obtain the breeding costs of experimental breeding ponds of each density and control breeding ponds of each density during the breeding cycle; S7. Improvement effect analysis: By comparing the water quality evaluation coefficients and breeding costs of experimental breeding ponds of various densities and control breeding ponds of various densities, the improvement effect evaluation index of aquaculture is obtained and feedback is provided.

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