Breeding platform and system based on big data

Abstract

The present invention belongs to the technical field of fishery information management, and discloses a breeding platform and system based on big data, including a breeding area division module, a collection sequence planning module, a fish school information collection module, a fish school information analysis module, a bait delivery module, a growth abnormality identification module, a breeding abnormality identification module, a fish quantity abnormality identification module and a data storage library. The present invention collects the number of fish bodies and the average length of fish bodies corresponding to each breeding area of ​​a breeding lake to obtain the amount of bait delivered to each breeding area, thereby improving the accuracy of bait delivery, avoiding the probability of problems such as affecting fish growth and environmental pollution caused by improper bait delivery, and at the same time analyzing the growth abnormality and breeding abnormality to identify the abnormal directional tendency of the breeding lake, so as to timely discover specific problems and potential problems in each breeding area, improve the scientific nature of fish breeding, and reduce breeding costs and increase output.

Description

Technical Field

[0001] The present invention belongs to the technical field of fishery information management and relates to a breeding platform and system based on big data. Background Art

[0002] To meet people's growing demand for more food, large-scale fish farming has become an indispensable part of today's society. Ensuring the healthy and efficient growth of fish is an important part of fish farming. Intelligent fish farming based on big data technology can significantly improve the production efficiency and management level of fish farming, reduce production costs and improve economic benefits.

[0003] Existing fishery farming mainly collects information on aquaculture water bodies through various sensors or monitoring systems and combines the experience of aquaculture personnel to manually release bait or use fixed machinery to release bait. This farming method has the following shortcomings: 1. Existing bait release is mainly achieved by manually driving boats to various points on the aquaculture lake or using fixed machinery to release bait. This bait release method requires a lot of manpower and time, and it is difficult to meet the needs of fast and efficient release. In addition, the release range is limited, which can easily cause water pollution and increase aquaculture costs.

[0004] 2. In the existing fish farming process, the amount of bait released is determined by fuzzy calculation based on the management personnel's farming experience and the growth stage of the fish. This calculation method is not based on the actual number, distribution and growth stage of the fish in the farming lake. It may cause the amount of bait released to deviate from the actual amount of bait required by the fish. If the bait is insufficient, the fish may grow slowly due to malnutrition. If the bait is excessive, it may cause water pollution and cause fish to become sick, which reduces the scientific nature and breeding efficiency of fish farming and may also cause economic losses to farmers.

[0005] 3. Existing fish farming technology focuses on growth abnormalities in farming indicators. This analysis method is rather one-sided and may lead to misjudgment of the fish growth environment, thereby reducing the efficiency of dealing with potential problems. At the same time, testing the entire lake area cannot accurately locate the source of the problem. Finally, ignoring the differences in indicators may lead to problems such as improper feed placement and unreasonable water quality regulation, thereby increasing farming costs and reducing production. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a breeding platform and system based on big data is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: A breeding platform based on big data, including: a breeding area division module, which is used to divide the breeding lake into breeding areas to obtain a number of breeding areas.

[0008] The collection sequence planning module is used to plan the collection sequence of the breeding area based on the position of the UAV flight platform and the center point position of each breeding area.

[0009] The fish school information collection module is used to set a high-definition camera on the drone, and the drone flies above the breeding lake and then uses the high-definition camera to collect fish school information in each breeding area, including the number and length of fish.

[0010] The fish school information analysis module is used to analyze the fish school indicators of each breeding area based on the collected fish school information corresponding to each breeding area, wherein the fish school indicators specifically include the fish school distribution density, the average length of the fish body and the difference in fish school growth.

[0011] The bait delivery module is used to analyze the fish distribution information of each breeding area based on the collected image information, analyze the bait delivery amount of each breeding area based on the fish school indicators, and locate the delivery position based on the fish distribution information, and then use the drone to deliver bait at the delivery position according to the analyzed bait delivery amount.

[0012] The growth abnormality identification module is used to analyze the corresponding growth abnormality of each breeding area based on the average length of fish bodies and the growth difference of fish schools in each breeding area and determine whether growth abnormality occurs in each breeding area.

[0013] The aquaculture anomaly identification module is used to analyze the aquaculture anomaly degree of each aquaculture area based on the fish distribution density and fish growth difference in each aquaculture area, and then identify the tendency of the aquaculture lake to be abnormal.

[0014] The fish quantity abnormality identification module is used to accumulate the corresponding number of fish in each breeding area to obtain the fish quantity in the breeding lake, and compare the fish quantity in the breeding lake with the fish quantity counted when the bait was released last time, so as to determine whether there is any abnormal fish quantity in the breeding lake.

[0015] The data repository is used to store growth stage and fish body growth length comparison tables, aquaculture lake plan outlines and unit standard food intake corresponding to each growth stage.

[0016] In a preferred embodiment of the present invention, the aquaculture lake surface is divided into aquaculture areas as follows: a plan outline map of the aquaculture lake is extracted from a data repository, and the center point of the aquaculture lake is located, and then a number of rays are radiated from the center point of the aquaculture lake to the surrounding area, and the angles of corresponding angles formed by each adjacent ray are the same.

[0017] The lake surface area formed by each adjacent ray and the corresponding intercepted edge line of the aquaculture lake is recorded as the aquaculture area. Each aquaculture area corresponds to a different lake surface area, and then each aquaculture area is numbered.

[0018] In a preferred embodiment of the present invention, the steps for planning the collection sequence of the breeding areas are as follows: the position of the drone flight platform is recorded as the initial position for information collection, and the center point of each breeding area is used as the information collection point.

[0019] The breeding area corresponding to the information collection point closest to the initial information collection position is recorded as the next information collection area, and then the breeding areas are sorted in a clockwise direction starting from the information collection area.

[0020] The order of breeding areas is recorded as the order of information collection of breeding areas.

[0021] In a preferred embodiment of the present invention, the steps of collecting fish school information in each breeding area are as follows: pre-setting the collection height according to the resolution of the high-definition camera.

[0022] The high-definition camera set on the drone is used to fly to the pre-set collection height above the lake surface and collect images of each breeding area according to the collection sequence of the breeding area to obtain a number of collected images. The fish schools in each collected image are counted using image recognition technology to obtain the number of fish bodies in each breeding area X. i and fish body length Y ij , where i represents the number of the breeding area, i=1, 2...m, and j represents the number of the fish body, j=1, 2...n.

[0023] In a preferred embodiment of the present invention, the fish index of each breeding area is analyzed as follows: the contour map of each breeding area is imported into the CAD software to obtain the area S of each breeding area i , and then substitute into the formula Get the fish distribution density λ in the i-th breeding area i .

[0024] Based on the number of fish in each breeding area and the length of each fish, the average length of fish in the breeding area is obtained

[0025] Compare the lengths of the fish in each breeding area to get the maximum length Y of the fish in each breeding area i (max) and minimum length Y i (min), and then substitute it into the formula Get the fish growth difference Φ corresponding to each breeding area i .

[0026] In a preferred embodiment of the present invention, the analysis of the bait delivery amount in each breeding area and the positioning of the delivery position are specifically as follows: the average length of the fish in each breeding area is matched with the growth stage and fish growth length comparison table stored in the data storage library to obtain the actual growth stage P of the fish in each breeding area. i, where P represents the growth stage number, P = 1, 2...o.

[0027] Then, the actual growth stage of the fish in each breeding area is matched with the unit standard food intake corresponding to each growth stage stored in the data repository to obtain the unit standard food intake corresponding to the fish in each breeding area.

[0028] The number of fish corresponding to each breeding area is X i Substitute into formula M i =X i *M i (0)*(1+Φ i ) to obtain the bait input amount M corresponding to the i-th breeding area i , where M i (0) represents the standard food intake of fish in the ith breeding area.

[0029] Analyze the images of each breeding area collected by drones, locate the fish distribution area in each image, focus on each fish distribution area and calculate the number of fish corresponding to each area, and select the fish distribution position corresponding to the maximum number of fish as the corresponding bait placement position for each breeding area.

[0030] In a preferred embodiment of the present invention, the steps of analyzing the growth index corresponding to each breeding area and judging whether abnormal growth occurs in each breeding area are as follows: based on the difference between the date of fish school information collection corresponding to each breeding area and the date of fry release, the standard growth stage P of the fish body is obtained. i (0).

[0031] The standard growth stage P of fish in each breeding area obtained by analysis i (0), the actual growth stage of the fish body P i and fish population growth difference Φ i Substitute into the analysis formula Get the growth abnormality η of the i-th breeding area i , where ξ and ζ represent the weights of growth stage and fish population growth difference, respectively.

[0032] The fish growth abnormality degree η in each breeding area i Substitute into the growth abnormality judgment model The evaluation results of the abnormal growth conditions corresponding to each detection area are obtained, wherein η′ is the preset allowable abnormal growth degree of the fish school, Q=V1 indicates that there is no abnormal growth in the breeding area, and Q=V2 indicates that there is abnormal growth in the breeding area.

[0033] In a preferred embodiment of the present invention, the steps of analyzing the aquaculture abnormality corresponding to each aquaculture area and then identifying the abnormal tendency of the aquaculture lake are as follows: the fish distribution density λ corresponding to each aquaculture area iand fish population growth difference Φ i Substitute into the formula Get the breeding abnormality degree Ψ of the i-th breeding area i , where e represents a natural constant, α and β represent the weights corresponding to the fish distribution density and fish growth difference, respectively.

[0034] The abnormality degree and growth abnormality degree corresponding to each breeding area are compared to obtain the abnormality direction corresponding to each breeding area.

[0035] The abnormal directions corresponding to each breeding area are compared, and the breeding areas corresponding to the same abnormal directions are classified to obtain the number of breeding areas corresponding to each abnormal direction. By comparing the number of breeding areas corresponding to the breeding abnormal directions with the number of breeding areas corresponding to the growth abnormal directions, the abnormal directions with larger numbers are selected as the breeding lake tendency abnormal directions.

[0036] In a preferred embodiment of the present invention, the determination of whether the fish quantity in the aquaculture lake is abnormal is specifically as follows: the number of fish corresponding to each aquaculture area is accumulated and calculated to obtain the number of fish in the aquaculture lake X, and then substituted into the formula The abnormality degree θ of the fish quantity in the breeding lake is obtained, where X0 represents the fish quantity counted when the bait was last put in.

[0037] Substitute the abnormality degree θ of the fish quantity in the aquaculture lake into the fish quantity abnormality judgment model The judgment results of the abnormal fish quantity in each detection area are obtained, where θ′ is the preset allowable abnormal fish quantity, L=Z1 indicates that there is no abnormal fish quantity in the breeding lake, and L=Z2 indicates that there is an abnormal fish quantity in the breeding lake.

[0038] A second aspect of the present invention provides a breeding system based on big data, which includes the above-mentioned breeding platform based on big data.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention utilizes drones to collect images and release bait to various breeding areas on the breeding lake surface. This bait release method can quickly cover a large area of ​​water, save labor and time costs in the bait release operation process, and improve breeding efficiency. On the other hand, drones can achieve accurate and timely bait release through advanced flight control systems and sensors.

[0040] (2) The present invention collects the number and length of fish in each breeding area, and then calculates the growth stage of fish and the amount of bait released in real time. This breeding method fully considers the actual number and growth status of fish, avoids the deviation between the amount of bait released and the actual amount of bait required by fish, improves the scientific nature of fishery breeding, reduces the additional cost caused by improper bait release, and increases breeding benefits. At the same time, the distribution position of fish is analyzed and targeted bait release is carried out, which can realize the utilization rate of bait and promote the growth of fish.

[0041] (3) The present invention conducts a comprehensive analysis of the status of fish in the breeding area from three aspects: abnormal growth, abnormal breeding and abnormal fish quantity. This analysis method comprehensively understands the health status and breeding status of fish from multiple angles, avoids the limitation of single indicator analysis, and more comprehensively reflects the overall situation of fish; on the other hand, by comprehensively analyzing multiple abnormal indicators, it is possible to more accurately judge the specific problems of fish and improve the accuracy of analysis; on the other hand, by classifying and analyzing abnormal situations, problems such as improper bait placement and unreasonable water quality regulation are avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0043] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

[0044] Figure 2 This is a schematic diagram of the information collection sequence planning of the breeding area of ​​the present invention.

[0045] Reference numerals:

[0046] 1——UAV flight platform location, 2——edge outline of aquaculture lake, 3——center point of aquaculture lake, 4——center point of aquaculture area, 5——aquaculture area, 6——separation line of aquaculture area, 7——information collection route of UAV aquaculture area. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figure 1 As shown, the present invention provides a breeding platform and system based on big data, including a breeding area division module, a collection sequence planning module, a fish school information collection module, a fish school information analysis module, a bait delivery module, a growth abnormality identification module, a breeding abnormality identification module, a fish quantity abnormality identification module and a data storage library, wherein the breeding area division module is connected with the collection sequence planning module, the collection sequence planning module is connected with the fish school information collection module, the fish school information collection module is connected with the fish school information analysis module, the fish school information analysis module is respectively connected with the bait delivery module, the breeding abnormality identification module, the growth abnormality identification module and the fish quantity abnormality identification module, the data storage library is respectively connected with the breeding area division module and the growth abnormality identification module, and the breeding abnormality identification module is connected with the growth abnormality identification module.

[0049] The aquaculture area division module is used to divide the aquaculture lake into aquaculture areas to obtain a number of aquaculture areas.

[0050] Preferably, see Figure 2 As shown, the plan outline of the aquaculture lake is extracted from the data repository, and the center point of the aquaculture lake is located, and then a number of rays are radiated from the center point of the aquaculture lake to the surrounding area, and the angles of the corresponding angles formed by each adjacent ray are the same.

[0051] The lake surface area formed by each adjacent ray and the corresponding intercepted edge line of the aquaculture lake is recorded as the aquaculture area. Each aquaculture area corresponds to a different lake surface area, and then each aquaculture area is numbered.

[0052] It should be noted that the benefits of dividing the breeding areas are as follows: First, regional division helps to optimize the allocation of breeding resources. By reasonably dividing the breeding areas, it can ensure the effective utilization of breeding resources and improve breeding benefits based on the environmental conditions, water quality characteristics, climate and other factors of different regions; in addition, regional division can also help improve breeding management efficiency. By dividing different breeding areas, more refined management measures can be implemented, which can not only reduce breeding costs, but also promptly discover and solve problems that arise in the breeding process, and improve the success rate of breeding; finally, through zoning breeding, the breeding density and feed feeding amount can be better controlled, and the quality and market competitiveness of fish can be improved.

[0053] It should be explained that the present invention adopts the above-mentioned area division method because aquaculture lakes are generally irregular in shape. The area division method described in the present invention can be applied to all aquaculture lakes with irregular shapes with the greatest probability.

[0054] The collection sequence planning module is used to plan the collection sequence of the breeding area based on the position of the UAV flight platform and the center point position of each breeding area.

[0055] Preferably, the position of the drone flight platform is recorded as the initial position for information collection, and the center point of each breeding area is used as the information collection point.

[0056] It should be noted that the center point of each breeding area is used as the information collection point because: first, the center point position can represent the overall situation of the breeding area. Whether it is water quality, feed distribution, fish activity or other related factors, they can be relatively comprehensively reflected here. By collecting information from the center point, the basic conditions of the entire breeding area can be understood more accurately; secondly, the center point is usually located in a relatively open and unobstructed place in the breeding area. This open environment is also conducive to signal transmission, ensuring that the collected data can be transmitted to the analysis and processing center in a timely and accurate manner; in addition, selecting the center point as the information collection point also helps to improve the efficiency and accuracy of information collection. Since the center point is fixed and easy to identify, the drone can quickly locate and collect information, avoiding the situation of blindly searching for collection points in a vast breeding area. The data at the center point is also more representative, which can reduce data deviations caused by improper collection locations.

[0057] The breeding area corresponding to the information collection point closest to the initial information collection position is recorded as the next information collection area, and then the breeding areas are sorted in a clockwise direction starting from the information collection area.

[0058] It should be added that the reason for recording the breeding area corresponding to the information collection point closest to the initial information collection position as the next information collection area is: first, it helps to improve the efficiency of information collection. The drone starts from the initial position and selects the nearest collection point to reduce the flight distance and time, quickly reach the target area for information collection, and improve the overall collection efficiency; secondly, selecting the nearest information collection point is conducive to maintaining the battery life and stability of the drone. The shorter flight distance also helps to reduce the unstable factors that the drone may encounter during flight, such as wind, airflow, etc., and improve the stability of information collection; in addition, from the perspective of practical operation, selecting the collection point closest to the initial position also facilitates the positioning and navigation of the drone. The drone can quickly find the nearest collection point according to the preset route or navigation system, reduce human intervention and positioning errors, and improve the accuracy and reliability of information collection.

[0059] The order of breeding areas is recorded as the order of information collection of breeding areas.

[0060] It should be noted that when the UAV operates in accordance with the information collection sequence corresponding to the planned breeding area, when the UAV flies to the corresponding information collection point of a breeding area, it uses a high-definition camera to collect image information of the breeding area and analyzes it, and then obtains the bait delivery point. When the bait delivery point coincides with the information collection point, the UAV remains suspended to deliver the bait; when the bait delivery point does not coincide with the information collection point, the UAV flies to the bait delivery point obtained by analysis to deliver the bait; after the bait delivery is completed, the UAV continues to collect information and deliver bait to the next breeding area in accordance with the information collection sequence.

[0061] The fish school information collection module is used to set a high-definition camera on the drone, and the drone flies above the breeding lake and then uses the high-definition camera to collect fish school information in each breeding area, specifically including the number and length of fish.

[0062] Preferably, the acquisition height is pre-set according to the resolution of the high-definition camera.

[0063] It should be explained that the acquisition height is pre-set according to the resolution of the high-definition camera because: first, the resolution of the high-definition camera determines the degree of detail of the image it captures. Cameras with different resolutions capture images with different clarity at the same height. Pre-setting the acquisition height according to the camera resolution can ensure that the images captured by the drone at a specific height meet the required clarity requirements, thereby accurately capturing information about fish in the breeding area; secondly, an appropriate acquisition height helps avoid image distortion or blur. If the drone flies too high, the camera may not be able to capture enough details, and if it flies too low, the image may be too blurry or distorted; in addition, pre-setting the acquisition height can also improve the efficiency of information collection. When the drone collects information at a fixed height, the number of height adjustments during flight can be reduced, thereby shortening the acquisition time. A fixed acquisition height also helps maintain the stability of the drone's flight and reduce image jitter or deformation caused by changes in flight altitude.

[0064] The high-definition camera set on the drone is used to fly to the pre-set collection height above the lake surface and collect images of each breeding area according to the collection sequence of the breeding area to obtain a number of collected images. The fish schools in each collected image are counted using image recognition technology to obtain the number of fish bodies in each breeding area X. i and fish body length Y ij , where i represents the number of the breeding area, i=1, 2...m, and j represents the number of the fish body, j=1, 2...n.

[0065] It should be noted that the present invention utilizes drones to collect images and release bait in various breeding areas of the breeding lake. This bait release method can quickly cover a large area of ​​water, save labor and time costs in the bait release operation process, and improve breeding efficiency. On the other hand, drones can achieve accurate and timely bait release through advanced flight control systems and sensors.

[0066] It should be noted that the reasons for choosing the number of fish and the length of fish as the fish information of each breeding area are: first, the number of fish is an important indicator reflecting the scale of fish in the breeding area. By counting the number of fish in each breeding area, we can intuitively understand the breeding density and the scale of fish in each area, and provide a direct basis for breeding output for breeders. At the same time, changes in the number of fish can also reflect the impact of factors such as the breeding environment and feed input on the growth of fish, helping breeders to find problems and make adjustments in time; secondly, the length of fish is a key parameter for evaluating the growth status of fish. The growth rate, health status and breeding effect of fish can be reflected by changes in the length of fish. By measuring the length of fish in each breeding area, we can compare the growth conditions in different areas or different time periods, and provide breeders with information on fish growth trends and growth rates. This information helps breeders understand the growth status of fish and adjust breeding strategies to achieve the best breeding effect.

[0067] It should be added that the steps for calculating the length of the fish are as follows: use edge detection technology to separate the fish body from the background, extract features related to the length of the fish body, specifically the head and tail points of the fish body, and then determine the length of the fish body by measuring the distance between the head and tail points.

[0068] The fish school information analysis module is used to analyze the fish school indicators of each breeding area based on the collected fish school information corresponding to each breeding area, wherein the fish school indicators specifically include fish school distribution density, average fish body length and fish school growth difference.

[0069] Preferably, the outline map of each breeding area is imported into the CAD software to obtain the area S of each breeding area. i , and then substitute into the formula Get the fish distribution density λ in the i-th breeding area i .

[0070] The number of fish in each breeding area is X i and fish body length Y ij Substitute into the formula Get the average length of fish in the i-th breeding area

[0071] Compare the lengths of the fish in each breeding area to get the maximum length Y of the fish in each breeding area i (max) and minimum length Yi (min), and then substitute it into the formula Get the fish growth difference Φ corresponding to each breeding area i .

[0072] The bait delivery module is used to analyze the fish distribution information of each breeding area based on the collected image information, analyze the bait delivery amount of each breeding area based on the fish school index and locate the delivery position based on the fish distribution information, and then use the drone to deliver bait at the delivery position according to the analyzed bait delivery amount.

[0073] Preferably, the average length of fish in each breeding area is matched with the growth stage and fish growth length comparison table stored in the data storage library to obtain the actual growth stage P of fish in each breeding area. i , where P represents the growth stage number, P = 1, 2...o.

[0074] Exemplarily, when the growth stage is 0-2 weeks, P=1; when the growth stage is 3-4 weeks, P=2; when the growth stage is 5-8 weeks, P=3; when the growth stage is 9-12 weeks, P=4; when the growth stage is 13-16 weeks, P=5.

[0075] Then, the actual growth stage of the fish in each breeding area is matched with the unit standard food intake corresponding to each growth stage stored in the data repository to obtain the unit standard food intake corresponding to the fish in each breeding area.

[0076] It needs to be explained that the unit standard food intake refers to the standard food intake required by a single fish at a certain growth stage to meet its own energy consumption and growth needs.

[0077] The number of fish corresponding to each breeding area is X i Substitute into formula M i =X i *M i (0)*(1+Φ i ) to obtain the bait input amount M corresponding to the i-th breeding area i , where M i (0) represents the standard food intake of fish in the ith breeding area.

[0078] It should be noted that the present invention collects the number and length of fish in each breeding area, and then calculates the growth stage of fish and the amount of bait released in real time. This breeding method fully considers the actual number and growth status of fish, avoids the deviation between the amount of bait released and the actual amount of bait required by fish, improves the scientific nature of fishery breeding, reduces the additional cost caused by improper bait release, and increases breeding efficiency; at the same time, the distribution position of fish is analyzed and targeted bait release is carried out, which can realize the utilization rate of bait and promote the growth of fish.

[0079] It should be added that the reasons for using growth differences to correct the amount of bait released are: first, growth differences reflect the differences in growth conditions of fish populations in farming areas. Through this difference, we can more accurately understand the growth conditions of each farming area, and thus more accurately adjust the amount of bait released; secondly, the amount of bait released directly affects the growth rate and health of fish. If the amount released is too much, it will cause waste and may also cause water quality deterioration and affect fish growth. If the amount released is insufficient, it will not meet the growth needs of fish, resulting in slow growth or poor health. Therefore, correcting the amount of bait released according to growth differences can ensure that fish in each farming area can obtain an appropriate amount of feed and achieve balanced growth; by accurately adjusting the amount of bait released in each area, the effectiveness of the feed can be maximized, waste can be reduced, and farming costs can be reduced.

[0080] It should be noted that the growth difference directly reflects the suitability of the amount of bait put in. When the growth difference is large, it means that the amount of bait put in is small, and some fish in the fish farming households cannot consume enough bait, and thus cannot meet their own growth needs.

[0081] Further preferably, the images of each breeding area collected by the drone are analyzed, the fish distribution area in each image is located, the fish distribution area is focused on and the number of fish corresponding to each area is calculated, and the fish distribution position corresponding to the maximum number of fish is selected as the bait placement position corresponding to each breeding area.

[0082] It should be noted that the reason for selecting the fish distribution position corresponding to the largest number of fish as the corresponding bait placement position in each breeding area is: first, it indicates that the area is the place where fish activities are most concentrated. Placing bait in such a place can ensure that more fish have the opportunity to ingest food, thereby meeting their feeding needs, which can not only improve the utilization rate of bait and reduce waste, but also promote the growth and reproduction of fish. Secondly, due to the large number of fish in this location, the competition between fish is relatively large. In such an environment, placing bait here can more effectively stimulate the feeding behavior of fish, increase their activity and appetite, and further promote the growth of fish; in addition, selecting the fish distribution position corresponding to the largest number of fish as the bait placement point is also helpful to optimize breeding management. By accurately placing bait, the problem of uneven feeding of fish caused by uneven distribution of bait can be reduced, and the breeding risk can be reduced. At the same time, this can also reduce the waste of bait and pollution to the environment, and maintain the ecological balance of the breeding area; finally, from the perspective of economic benefits, selecting the fish distribution position corresponding to the largest number of fish as the bait placement point can maximize the effectiveness of bait and improve breeding output and quality. This will help increase farmers' income and improve the overall efficiency of the breeding industry.

[0083] The growth abnormality identification module is used to analyze the corresponding growth abnormality of each breeding area based on the average length of fish bodies and the growth difference of fish schools in each breeding area, and to determine whether growth abnormality occurs in each breeding area.

[0084] Preferably, the standard growth stage P of the fish body is obtained by performing difference calculation based on the date of fish school information collection in each breeding area and the date of fry release. i (0).

[0085] The standard growth stage P of fish in each breeding area obtained by analysis i (0), the actual growth stage of the fish body P i and fish population growth difference Φ i Substitute into the analysis formula Get the growth abnormality η of the i-th breeding area i , where ξ and ζ represent the weights of growth stage and fish population growth difference, respectively.

[0086] As a preferred example of the above scheme, ξ=0.6 and ζ=0.4.

[0087] The fish growth abnormality degree η in each breeding area i Substitute into the growth abnormality judgment model The evaluation results of the abnormal growth conditions corresponding to each detection area are obtained, wherein η′ is the preset allowable abnormal growth degree of the fish school, Q=V1 indicates that there is no abnormal growth in the breeding area, and Q=V2 indicates that there is abnormal growth in the breeding area.

[0088] Exemplarily, η′=0.12.

[0089] The aquaculture anomaly identification module is used to analyze the aquaculture anomaly degree corresponding to each aquaculture area based on the fish distribution density and fish growth difference in each aquaculture area, and then identify the tendency abnormality direction of the aquaculture lake.

[0090] Preferably, the fish distribution density λ corresponding to each breeding area i and fish population growth difference Φ i Substitute into the formula Get the breeding abnormality degree Ψ of the i-th breeding area i , where e represents a natural constant, α and β represent the weights corresponding to the fish distribution density and fish growth difference, respectively.

[0091] As a preferred example of the above solution, α=0.65 and β=0.35.

[0092] The abnormality degree and growth abnormality degree corresponding to each breeding area are compared to obtain the abnormality direction corresponding to each breeding area.

[0093] It should be added that the value ranges of the breeding abnormality and the growth abnormality in the present invention are both (0, 1). When the breeding abnormality of a certain breeding area is greater than the growth abnormality, the abnormality corresponding to the breeding area is breeding abnormality; when the breeding abnormality of a certain breeding area is less than the growth abnormality, the abnormality corresponding to the breeding area is growth abnormality.

[0094] The abnormal directions corresponding to each breeding area are compared, and the breeding areas corresponding to the same abnormal directions are classified to obtain the number of breeding areas corresponding to each abnormal direction. By comparing the number of breeding areas corresponding to the breeding abnormal directions with the number of breeding areas corresponding to the growth abnormal directions, the abnormal directions with larger numbers are selected as the breeding lake tendency abnormal directions.

[0095] The fish quantity abnormality identification module is used to accumulate the number of fish bodies corresponding to each breeding area to obtain the fish quantity in the breeding lake, and compare the fish quantity in the breeding lake with the fish quantity counted when the bait was last released, so as to determine whether there is any abnormal fish quantity in the breeding lake.

[0096] Preferably, the number of fish corresponding to each breeding area is accumulated and calculated to obtain the number of fish in the breeding lake X, which is then substituted into the formula The abnormality degree θ of the fish quantity in the breeding lake is obtained, where X0 represents the fish quantity counted when the bait was last put in.

[0097] Substitute the abnormality degree θ of the fish quantity in the aquaculture lake into the fish quantity abnormality judgment model The judgment results of the abnormal fish quantity in each detection area are obtained, where θ′ is the preset allowable abnormal fish quantity, L=Z1 indicates that there is no abnormal fish quantity in the breeding lake, and L=Z2 indicates that there is an abnormal fish quantity in the breeding lake.

[0098] It should be added that the control amounts in all the judgment models in the present invention, such as Z1, Z2 and V1, V2, can take values ​​of 0 and 1.

[0099] Exemplarily, θ′=0.15.

[0100] It should be noted that the present invention conducts a comprehensive analysis of the status of fish in the breeding area from three aspects: abnormal growth, abnormal breeding and abnormal fish quantity. This analysis method comprehensively understands the health status and breeding status of fish from multiple angles, avoids the limitation of single indicator analysis, and more comprehensively reflects the overall situation of fish; on the other hand, by comprehensively analyzing multiple abnormal indicators, it is possible to more accurately judge the specific problems of fish and improve the accuracy of analysis; on another hand, by classifying and analyzing abnormal situations, problems such as improper bait placement and unreasonable water quality regulation are avoided.

[0101] The data storage library is used to store a growth stage and fish body growth length comparison table, a breeding lake plane outline map and a unit standard food amount corresponding to each growth stage.

[0102] Table 1 Comparison of growth stages and fish body length

[0103] Growth stage length 1 1-2cm 2 2-5cm 3 5-12cm 4 12-20cm 5 20cm+

[0104] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A breeding platform based on big data, characterized by: The platform includes: The aquaculture area division module is used to divide the aquaculture lake into a number of aquaculture areas; The collection sequence planning module is used to plan the collection sequence of the breeding area based on the location of the UAV flight platform and the center point of each breeding area; The fish school information collection module is used to set a high-definition camera on the drone, and the drone flies over the aquaculture lake to collect fish school information in each aquaculture area using the high-definition camera, where the fish school information specifically includes the number and length of fish; A fish school information analysis module is used to analyze the fish school indicators of each breeding area based on the collected fish school information corresponding to each breeding area, wherein the fish school indicators specifically include fish school distribution density, average fish body length and fish school growth difference; The bait delivery module is used to analyze the fish distribution information of each breeding area based on the collected image information, analyze the bait delivery amount of each breeding area based on the fish school index, and locate the delivery position based on the fish distribution information, and then use the drone to deliver the bait at the delivery position according to the analyzed bait delivery amount; The growth abnormality identification module is used to analyze the corresponding growth abnormality of each breeding area based on the average length of fish bodies and the growth difference of fish schools in each breeding area and determine whether there is growth abnormality in each breeding area; The aquaculture anomaly identification module is used to analyze the aquaculture anomaly of each aquaculture area based on the fish distribution density and fish growth difference in each aquaculture area, and then identify the tendency of the aquaculture lake to be abnormal; The fish quantity abnormality identification module is used to accumulate the number of fish bodies corresponding to each breeding area to obtain the fish quantity in the breeding lake, and compare the fish quantity in the breeding lake with the fish quantity counted when the bait was last released, so as to determine whether there is any abnormality in the fish quantity in the breeding lake; A data repository for storing growth stage and fish body length comparison tables, aquaculture lake plan outlines, and unit standard food intake corresponding to each growth stage; The steps of analyzing the growth index corresponding to each breeding area and determining whether abnormal growth occurs in each breeding area are as follows: The standard growth stage of fish is obtained by calculating the difference between the date of fish collection in each breeding area and the date of fry release. ; The standard growth stages of fish in each breeding area obtained by analysis , the actual growth stage of the fish and fish growth differences Substitute into the analysis formula Get the first Growth abnormality in the breeding area ,in Respectively represent the weights of growth stage and fish population growth difference; The abnormal growth of fish stocks in each breeding area Substitute into the growth abnormality judgment model The evaluation results of the abnormal growth conditions corresponding to each detection area are obtained, among which The pre-set allowable abnormal growth degree of fish school, It means that there is no abnormal growth in the breeding area. Indicates that there are abnormal growth conditions in the breeding area; The steps of analyzing the aquaculture abnormality of each aquaculture area and then identifying the abnormal tendency of the aquaculture lake are as follows: The fish distribution density corresponding to each breeding area and fish growth differences Substitute into the formula Get the first Abnormality of breeding in breeding areas ,in represents a natural constant, They represent the weights corresponding to the fish distribution density and fish growth difference respectively; Compare the breeding abnormality and growth abnormality of each breeding area to obtain the abnormal direction of each breeding area; The abnormal directions corresponding to each breeding area are compared, and the breeding areas corresponding to the same abnormal directions are classified to obtain the number of breeding areas corresponding to each abnormal direction. By comparing the number of breeding areas corresponding to the breeding abnormal direction with the number of breeding areas corresponding to the growth abnormal direction, the abnormal direction with the larger number is selected as the abnormal direction of the breeding lake tendency; The specific details of judging whether the fish quantity in the aquaculture lake is abnormal are as follows: The number of fish corresponding to each breeding area is accumulated and calculated to obtain the current number of fish in the breeding lake. , and then substitute into the formula Get the abnormality of the amount of fish in the breeding lake ,in Indicates the number of fish counted when bait was last placed; The abnormality of the amount of fish in the cultivated lake Substitute into the fish quantity abnormality judgment model The evaluation results of abnormal fish quantity in each detection area are obtained, among which The pre-set allowable fish quantity abnormality. It means that there is no abnormal fish population in the breeding lake. It means that there is an abnormal amount of fish in the breeding lake.

2. The breeding platform based on big data according to claim 1, characterized in that: The specific division of aquaculture areas on the aquaculture lake surface is as follows: Extract the plan outline of the aquaculture lake from the data repository, locate the center point of the aquaculture lake, and then radiate a number of rays from the center point of the aquaculture lake to the surroundings, and the angles of the corresponding angles formed by each adjacent ray are the same; The lake surface area formed by each adjacent ray and the corresponding intercepted edge line of the aquaculture lake is recorded as the aquaculture area. Each aquaculture area corresponds to a different lake surface area, and then each aquaculture area is numbered.

3. The breeding platform based on big data according to claim 1, characterized in that: The steps for planning the collection sequence of the breeding area are as follows: The position of the UAV flight platform was recorded as the initial position for information collection, and the center point of each breeding area was used as the information collection point; The breeding area corresponding to the information collection point closest to the initial information collection position is recorded as the next information collection area, and then the breeding areas are sorted in a clockwise direction starting from the information collection area; The order of breeding areas is recorded as the order of information collection of breeding areas.

4. The breeding platform based on big data according to claim 1, characterized in that: The steps for collecting fish school information in each breeding area are as follows: Pre-set the acquisition height according to the resolution of the HD camera; The high-definition camera installed on the drone is used to fly to the pre-set collection height above the lake surface and collect images of each breeding area according to the collection sequence of the breeding area to obtain a number of collected images. The fish schools in each collected image are counted using image recognition technology to obtain the number of fish in each breeding area. and fish body length ,in Indicates the number of the breeding area, , Indicates the number of the fish body, .

5. The breeding platform based on big data as claimed in claim 4, characterized in that: The specific fish indicators analyzed in each breeding area are as follows; Import the outline map of each breeding area into the CAD software to obtain the area of ​​each breeding area , and then substitute into the formula Get the first Fish distribution density in the breeding area ; Based on the number of fish in each breeding area and the length of each fish, the average length of fish in the breeding area is obtained ; Compare the lengths of the fish in each breeding area to get the maximum length of the fish in each breeding area and minimum length , and then substitute into the formula Get the fish growth difference corresponding to each breeding area .

6. The big data-based breeding platform according to claim 5, characterized in that: The analysis of the amount of bait released in each breeding area and the location of the release are as follows: The average length of fish in each breeding area is matched with the growth stage and fish growth length comparison table stored in the data repository to obtain the actual growth stage of fish in each breeding area. ,in Indicates the growth stage number, ; Then, the actual growth stage of the fish in each breeding area is matched with the unit standard food intake corresponding to each growth stage stored in the data storage library to obtain the unit standard food intake corresponding to the fish in each breeding area; The number of fish corresponding to each breeding area Substitute into the formula Get the first The amount of feed released per breeding area ,in Indicates The standard food intake of fish in each breeding area; Analyze the images of each breeding area collected by drones, locate the fish distribution area in each image, focus on each fish distribution area and calculate the number of fish corresponding to each area, and select the fish distribution position corresponding to the maximum number of fish as the corresponding bait placement position for each breeding area.

7. The breeding system based on big data is characterized by: The system comprises the big data-based breeding platform described in claims 1-6.

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