CN117192985A - Silkworm breeding control method based on big data - Google Patents
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Abstract
The application provides a silkworm breeding control method based on big data, which relates to the technical field of silkworm breeding process supervision and comprises the steps of acquiring and recording breeding tracking data fed back by a plurality of co-breeding rooms in each breeding area, wherein the breeding tracking data comprises duty data, silkworm growth data and environmental data; the method comprises the steps of counting the cocoon yields of a plurality of co-cultivation chambers in a plurality of different cultivation areas, statistically analyzing the coupling relation between the cocoon quality and cultivation tracking data of the co-cultivation chambers in each different area through a big data algorithm, and constructing a cultivation regulation model.
Description
Technical Field
The application relates to the technical field of monitoring of silkworm breeding processes, in particular to a silkworm breeding control method based on big data.
Background
The conventional silkworm breeding mainly depends on spontaneous breeding of farmers, and silkworm commercial period silkworm cocoon purchasing mode, and the silkworm breeding of the farmers lacks scientific management by virtue of own experience, so that the silkworm cocoon yield is low, however, along with popularization of big data and artificial intelligence algorithms, how to improve the yield of the farmers in a single breeding period and further improve the income of the farmers by configuring corresponding intelligent co-breeding rooms for the farmers and combining with intelligent management and control methods such as big data and artificial intelligence becomes a urgent technical problem.
Disclosure of Invention
The application aims to provide a silkworm breeding control method, device and equipment based on big data and a readable storage medium, so as to solve the problems. In order to achieve the above purpose, the technical scheme adopted by the application is as follows:
in a first aspect, the present application provides a silkworm rearing control method based on big data, including: acquiring and recording breeding tracking data fed back by a plurality of co-breeding rooms in each breeding area, wherein the breeding tracking data comprises duty data, silkworm growth data and environment data; after a cultivation period is finished, the silkworm cocoon yields corresponding to the plurality of co-cultivation chambers in the plurality of different cultivation areas are counted, the coupling relation between the silkworm cocoon quality of the co-cultivation chambers in each different area and cultivation tracking data is counted and analyzed through a big data algorithm, and a cultivation regulation model is built, wherein the cultivation regulation model is a BP neural network model and is used for regulating corresponding environment data and duty data of the co-cultivation chambers according to real-time silkworm growth data.
Optionally, the statistics of the cocoon yields corresponding to the plurality of co-cultivation chambers in the plurality of different cultivation areas, the statistics analysis of the coupling relation between the cocoon quality of the co-cultivation chamber and the cultivation tracking data in each different area through a big data algorithm, and the construction of the cultivation regulation and control model comprise:
the method comprises the steps of regularly receiving a growth detection pattern fed back by each co-cultivation room, wherein the growth detection pattern is a silkworm activity video which is shot by a first camera arranged on a unit cultivation platform in the co-cultivation room and is positioned in a first observation area on the unit cultivation platform;
identifying the length, the width and the liveness of each silkworm in each growth detection pattern based on an image processing algorithm, and simultaneously recording the temperature and the humidity of the corresponding co-cultivation room so as to obtain historical growth data and historical environment data corresponding to each co-cultivation room, wherein the historical growth data comprises body type data and liveness of silkworms in different periods, and the historical environment data comprises the historical temperature and the humidity of the co-cultivation room;
continuously receiving real-time monitoring videos fed back by each co-cultivation room, identifying attendance time of farmers based on a visual algorithm, and simultaneously recording Sang Sheken appetite of the corresponding co-cultivation room, wherein Sang Sheken appetite is obtained by calculating the weight of the replaced mulberry leaves after the farmers finish the replacement operation of the mulberry leaves on a plurality of unit cultivation platforms in the co-cultivation room, and uploading the obtained mulberry leaves through a mobile monitoring terminal, so that historical duty data corresponding to each co-cultivation room is obtained, and the historical duty data comprises historical replacement time and gnawing appetite of each batch of mulberry leaves;
collecting the historical growth data, the historical environment data, the historical duty data and the silkworm cocoon yield corresponding to each co-cultivation room, and further obtaining corresponding historical training data;
based on the altitude of the culture area corresponding to each historical training data, the training weight of each historical training data is distributed, the historical training data with the distributed weight is input into a pre-constructed BP neural network model for training, and the trained BP neural network model is recorded as a culture regulation model.
Optionally, the adjusting the corresponding co-rearing room environment data and duty data according to the real-time silkworm growth data comprises:
the method comprises the steps of regularly receiving a first growth detection pattern fed back by a first co-cultivation room and a co-cultivation room number, and finding a first altitude of a cultivation area where the co-cultivation room is located based on the co-cultivation room number;
identifying a pattern of each silkworm in the first growth detection pattern based on an image feature extraction algorithm, calculating a moving distance of each silkworm in a unit time period and first body type data of each silkworm, and removing silkworms with the moving distance of zero in the unit time period and silkworms leaving a first observation area in the unit time period, wherein the first body type data are body length and body middle width when bodies of silkworms are straight lines;
calculating an average moving distance of silkworms in the first growth detection pattern, and evaluating first liveness of silkworms in the first coordination room based on the average moving distance and the first body type data;
inputting the first activity, the first altitude and the first body data into a breeding regulation model so that the breeding regulation model judges the growth period of silkworms in the first common breeding room at present, and timely adjusts the floor heating power and the mulberry leaf replacement period in the first common breeding room based on the optimal environment data and Sang Sheken appetite corresponding to the growth period and the altitude, thereby adjusting the environment data and the duty data of the first common breeding room.
The beneficial effects of the application are as follows:
according to the method, an intelligent silkworm breeding co-breeding room is introduced into a silkworm breeding area, intelligent monitoring of the growth environment of silkworms such as temperature, humidity and the like in the co-breeding room is realized through big data and an artificial intelligent algorithm, and the historical silkworm cocoon yield and the historical silkworm cocoon breeding parameters are analyzed and compared based on the big data and a neural network algorithm, so that the breeding parameters of the current breeding period are continuously optimized, and the higher silkworm cocoon yield is obtained.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a silkworm rearing control method based on big data according to an embodiment of the application;
fig. 2 is a schematic structural diagram of a silkworm rearing control device based on big data according to an embodiment of the present application.
The marks in the figure: 800. a silkworm rearing control device based on big data; 801. a processor; 802. a memory; 803. a multimedia component; 804. an I/O interface; 805. a communication component.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1:
the embodiment provides a silkworm breeding control method based on big data.
Referring to fig. 1, the method is shown to include steps S1 and S2.
S1, acquiring and recording breeding tracking data fed back by a plurality of co-breeding chambers in each breeding area, wherein the breeding tracking data comprise duty data, silkworm growth data and environment data;
s2, after a cultivation period is finished, counting the cocoon yields corresponding to a plurality of co-cultivation chambers in a plurality of different cultivation areas, carrying out statistics analysis on coupling relations between the cocoon quality of the co-cultivation chambers in each different area and cultivation tracking data through a big data algorithm, and constructing a cultivation regulation model, wherein the cultivation regulation model is a BP neural network model and is used for regulating corresponding co-cultivation chamber environment data and duty data according to real-time silkworm growth data.
In the embodiment, an intelligent silkworm breeding co-breeding room is introduced into a silkworm breeding area, intelligent monitoring of the growth environment of silkworms such as temperature, humidity and the like in the co-breeding room is realized through big data and an artificial intelligent algorithm, and analysis and comparison are performed on the historical silkworm cocoon yield and the historical silkworm cocoon breeding parameters based on the big data and a neural network algorithm, so that the breeding parameters of the current breeding period are continuously optimized, and further higher silkworm cocoon yield is obtained.
The specific implementation method for counting the silkworm cocoon yields corresponding to the plurality of co-cultivation chambers in the plurality of different cultivation areas in the step S2, statistically analyzing the coupling relation between the silkworm cocoon quality of the co-cultivation chamber in each different area and cultivation tracking data through a big data algorithm, and constructing a cultivation regulation model comprises the following steps:
s21, regularly receiving a growth detection pattern fed back by each co-cultivation room, wherein the growth detection pattern is a silkworm activity video which is shot by a first camera arranged on a unit cultivation platform in the co-cultivation room and is positioned in a first observation area on the unit cultivation platform;
s22, identifying the length, the width and the liveness of each silkworm in each growth situation detection pattern based on an image processing algorithm, and simultaneously recording the temperature and the humidity of a corresponding co-cultivation room so as to obtain historical growth data and historical environment data corresponding to each co-cultivation room, wherein the historical growth data comprise body type data and liveness of silkworms in different periods, and the historical environment data comprise historical temperature and humidity of the co-cultivation room;
s23, continuously receiving real-time monitoring videos fed back by each co-cultivation room, identifying attendance time of farmers based on a visual algorithm, and simultaneously recording Sang Sheken appetite of the corresponding co-cultivation room, wherein Sang Sheken appetite is obtained by calculating the weight of the replaced mulberry leaves after the farmers finish the replacement operation of the mulberry leaves on a plurality of unit cultivation platforms in the co-cultivation room, and uploading the obtained mulberry leaves through a mobile monitoring terminal, so that historical duty data corresponding to each co-cultivation room is obtained, and the historical duty data comprises historical replacement time and gnawing amount of each batch of mulberry leaves;
s24, collecting the historical growth data, the historical environment data, the historical duty data and the silkworm cocoon yield corresponding to each co-cultivation room, and further obtaining corresponding historical training data;
s25, based on the altitude of the culture area corresponding to each historical training data, distributing the training weight of each historical training data, inputting the weighted historical training data into a pre-constructed BP neural network model for training, and marking the trained BP neural network model as a culture regulation model.
Next, in the step S2, the adjusting the corresponding co-rearing room environment data and duty data according to the real-time silkworm growth data includes:
s26, periodically receiving a first growth detection pattern and a co-cultivation room number fed back by the first co-cultivation room, finding a first altitude of a cultivation area where the co-cultivation room is located based on the co-cultivation room number, wherein certain differences exist in silkworm egg cultivation flows corresponding to cultivation areas at different altitudes;
s27, identifying a pattern of each silkworm in a first growth detection pattern based on an image feature extraction algorithm, calculating a moving distance of each silkworm in a unit time period and first body type data of each silkworm, and removing silkworms with the moving distance of zero in the unit time period and silkworms leaving a first observation area in the unit time period, wherein the first body type data are body length and body middle width when bodies of silkworms are straight lines;
s28, calculating the average moving distance of silkworms in the first growth detection pattern, and evaluating the first activity of silkworms in the first coordination room based on the average moving distance and the first body data;
s29, inputting the first activity, the first altitude and the first body data into a breeding regulation model, so that the breeding regulation model judges the growth period of silkworms in a first common room at present, and timely adjusts the floor heating power and the mulberry leaf replacement period in the first common room based on the optimal environment data and Sang Sheken appetite corresponding to the growth period and the altitude, and further adjusts the environment data and duty data of the first common room.
Secondly, there are four molts in the silkworm breeding process, the silkworm is motionless during each molt, the head is lifted upwards to cause larger overlook measuring and calculating error of the camera, if the regularly intercepted growth detection pattern is just the pattern during the period, the mode of detecting the body size and the activity through overlook detection is not applicable any more, the growth detection pattern is an invalid image, and how to effectively reject the invalid image becomes a technical problem to be solved urgently.
Identifying the pattern of each silkworm in the first growth detection pattern based on an image feature extraction algorithm, calculating the moving distance of each silkworm in unit time length and first body type data of each silkworm, and eliminating silkworms with the moving distance of zero in unit time length and silkworms leaving a first observation area in unit time length, wherein the method further comprises the following steps:
counting the number of identified silkworms, estimating the least effective observed value corresponding to the current cultivation time period through a Kalman filtering algorithm based on the fluctuation condition of the first historical number corresponding to the early-stage multiple effective growth vigor detection patterns, and identifying the first growth vigor detection patterns as invalid patterns if the number of identified silkworms is smaller than the least effective observed value;
if the number of the identified silkworms is larger than the lowest effective observed value, counting a second number corresponding to silkworms with the moving distance of zero in the unit time, and if the second number/the number of the identified silkworms is smaller than or equal to a second effective threshold value, judging that the first growth detection pattern is identified as an ineffective pattern.
Example 2:
corresponding to the above method embodiment, there is also provided a silkworm rearing control device based on big data, and a silkworm rearing control device based on big data described below and a silkworm rearing control method based on big data described above may be referred to correspondingly with each other.
Fig. 2 is a block diagram illustrating a silkworm rearing control device 800 based on big data according to an exemplary embodiment. As shown in fig. 2, the big data based silkworm rearing control device 800 may include: a processor 801, a memory 802. The big data based silkworm rearing control device 800 may further comprise one or more of a multimedia component 803, an i/O interface 804, and a communication component 805.
Wherein the processor 801 is for controlling the overall operation of the big data based silkworm rearing control device 800 to complete all or part of the steps of the big data based silkworm rearing control method described above. The memory 802 is used to store various types of data to support the operation of the big data based silkworm rearing control device 800, which may include, for example, instructions for any application or method operating on the big data based silkworm rearing control device 800, as well as application related data, such as contact data, transceived messages, pictures, audio, video, etc. The Memory 802 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication unit 805 is used for wired or wireless communication between the big data based silkworm rearing control device 800 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near FieldCommunication, NFC for short), 2G, 3G or 4G, or a combination of one or more thereof, the respective communication component 805 may thus comprise: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the big data based silkworm rearing control device 800 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processor (DigitalSignal Processor, abbreviated as DSP), digital signal processing device (Digital Signal Processing Device, abbreviated as DSPD), programmable logic device (Programmable Logic Device, abbreviated as PLD), field programmable gate array (Field Programmable Gate Array, abbreviated as FPGA), controller, microcontroller, microprocessor, or other electronic component for performing the big data based silkworm rearing control method described above.
In another exemplary embodiment, there is also provided a computer-readable storage medium including program instructions which, when executed by a processor, implement the steps of the above-described big data based silkworm rearing control method. For example, the computer-readable storage medium may be the above-described memory 802 including the program instructions executable by the processor 801 of the big data based silkworm rearing control device 800 to accomplish the above-described big data based silkworm rearing control method.
Example 4:
corresponding to the above method embodiment, there is also provided a readable storage medium in this embodiment, and a readable storage medium described below and a silkworm raising control method based on big data described above may be referred to correspondingly with each other.
A readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the big data based silkworm rearing control method of the above method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, and the like.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (3)
1. A silkworm breeding control method based on big data is characterized by comprising the following steps:
acquiring and recording breeding tracking data fed back by a plurality of co-breeding rooms in each breeding area, wherein the breeding tracking data comprises duty data, silkworm growth data and environment data;
after a cultivation period is finished, the silkworm cocoon yields corresponding to the plurality of co-cultivation chambers in the plurality of different cultivation areas are counted, the coupling relation between the silkworm cocoon quality of the co-cultivation chambers in each different area and cultivation tracking data is counted and analyzed through a big data algorithm, and a cultivation regulation model is built, wherein the cultivation regulation model is a BP neural network model and is used for regulating corresponding environment data and duty data of the co-cultivation chambers according to real-time silkworm growth data.
2. The big data-based silkworm rearing control method according to claim 1, wherein the statistics of the cocoon yields corresponding to the plurality of co-cultivation chambers in the plurality of different cultivation areas, the statistics analysis of the coupling relation between the cocoon quality of the co-cultivation chamber and the cultivation tracking data in each different area through a big data algorithm, and the construction of the cultivation regulation model comprise:
the method comprises the steps of regularly receiving a growth detection pattern fed back by each co-cultivation room, wherein the growth detection pattern is a silkworm activity video which is shot by a first camera arranged on a unit cultivation platform in the co-cultivation room and is positioned in a first observation area on the unit cultivation platform;
identifying the length, the width and the liveness of each silkworm in each growth detection pattern based on an image processing algorithm, and simultaneously recording the temperature and the humidity of the corresponding co-cultivation room so as to obtain historical growth data and historical environment data corresponding to each co-cultivation room, wherein the historical growth data comprises body type data and liveness of silkworms in different periods, and the historical environment data comprises the historical temperature and the humidity of the co-cultivation room;
continuously receiving real-time monitoring videos fed back by each co-cultivation room, identifying attendance time of farmers based on a visual algorithm, and simultaneously recording Sang Sheken appetite of the corresponding co-cultivation room, wherein Sang Sheken appetite is obtained by calculating the weight of the replaced mulberry leaves after the farmers finish the replacement operation of the mulberry leaves on a plurality of unit cultivation platforms in the co-cultivation room, and uploading the obtained mulberry leaves through a mobile monitoring terminal, so that historical duty data corresponding to each co-cultivation room is obtained, and the historical duty data comprises historical replacement time and gnawing appetite of each batch of mulberry leaves;
collecting the historical growth data, the historical environment data, the historical duty data and the silkworm cocoon yield corresponding to each co-cultivation room, and further obtaining corresponding historical training data;
based on the altitude of the culture area corresponding to each historical training data, the training weight of each historical training data is distributed, the historical training data with the distributed weight is input into a pre-constructed BP neural network model for training, and the trained BP neural network model is recorded as a culture regulation model.
3. The big data based silkworm rearing control method according to claim 1, wherein the adjusting the corresponding co-rearing room environment data and duty data according to the real-time silkworm growth data comprises:
the method comprises the steps of regularly receiving a first growth detection pattern fed back by a first co-cultivation room and a co-cultivation room number, and finding a first altitude of a cultivation area where the co-cultivation room is located based on the co-cultivation room number;
identifying a pattern of each silkworm in the first growth detection pattern based on an image feature extraction algorithm, calculating a moving distance of each silkworm in a unit time period and first body type data of each silkworm, and removing silkworms with the moving distance of zero in the unit time period and silkworms leaving a first observation area in the unit time period, wherein the first body type data are body length and body middle width when bodies of silkworms are straight lines;
calculating an average moving distance of silkworms in the first growth detection pattern, and evaluating first liveness of silkworms in the first coordination room based on the average moving distance and the first body type data;
inputting the first activity, the first altitude and the first body data into a breeding regulation model so that the breeding regulation model judges the growth period of silkworms in the first common breeding room at present, and timely adjusts the floor heating power and the mulberry leaf replacement period in the first common breeding room based on the optimal environment data and Sang Sheken appetite corresponding to the growth period and the altitude, thereby adjusting the environment data and the duty data of the first common breeding room.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117422483A (en) * | 2023-12-19 | 2024-01-19 | 四川主干信息技术有限公司 | Cocoon industrial chain tracing platform and method |
| CN118279080A (en) * | 2024-05-08 | 2024-07-02 | 汇链通产业供应链数字科技(厦门)有限公司 | Artificial intelligence-based large-model layer breeding system optimization method |
| CN118605185A (en) * | 2024-08-07 | 2024-09-06 | 广东省农业科学院植物保护研究所 | Intelligent regulation-based natural enemy insect hatching and culturing method and system |
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- 2023-09-11 CN CN202311163618.5A patent/CN117192985A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117422483A (en) * | 2023-12-19 | 2024-01-19 | 四川主干信息技术有限公司 | Cocoon industrial chain tracing platform and method |
| CN117422483B (en) * | 2023-12-19 | 2024-03-19 | 四川主干信息技术有限公司 | Silkworm cocoon industrial chain tracing method |
| CN118279080A (en) * | 2024-05-08 | 2024-07-02 | 汇链通产业供应链数字科技(厦门)有限公司 | Artificial intelligence-based large-model layer breeding system optimization method |
| CN118605185A (en) * | 2024-08-07 | 2024-09-06 | 广东省农业科学院植物保护研究所 | Intelligent regulation-based natural enemy insect hatching and culturing method and system |
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