CN111011247A - Sustainable and intelligent pig raising method - Google Patents
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Abstract
The invention provides a sustainable and intelligent pig raising method, which is used for adaptively adjusting and processing the environment state of a breeding site, the pig breeding feeding state and the external discharge state of the breeding site by acquiring detection information about the external environment of a pig, the growth state of a live pig and the external discharge of the breeding site, so as to realize intelligent operation of the pig breeding process and reduce the waste output of the pig breeding process, thereby improving the environmental sustainability and the breeding intelligence of the pig breeding.
Description
Technical Field
The invention relates to the technical field of pig breeding, in particular to a sustainable and intelligent pig raising method.
Background
Live pig breeding belongs to the highly integrated breeding industry, and in order to improve the output of live pigs, the intensification level of live pig breeding needs to be increased, but a certain part of domestic live pig breeding at present belongs to a family breeding mode, the breeding scale of the family breeding mode is small, the breeding technology is backward, the live pig breeding is still carried out in a rough form, the live pig breeding in the rough form has the advantages of high breeding input cost, low risk resistance and high pollution in the breeding process, and great pressure is generated on the environment. The live pig breeding method in the prior art focuses on improving the growth speed of live pigs and reducing the breeding cost of the live pigs, and how to improve the environmental sustainability and the breeding intelligence degree of live pig breeding is not considered.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a sustainable and intelligent pig raising method, which comprises the following steps: step S1, acquiring first detection information about the external environment of the pig raising and second detection information about the growth state of the live pig, and generating breeding feedback information according to the first detection information and/or the second detection information; step S2, according to the breeding feedback information, adjusting the environmental conditions and/or feeding conditions of the current breeding site; step S3, acquiring third detection information about the external emission of the culture site, and generating emission feedback information according to the third detection information; step S4, adjusting the outward discharge mode of the current breeding site according to the discharge feedback information; therefore, the sustainable and intelligent pig raising method can be used for adaptively adjusting the environment state of the pig raising site, the pig raising feeding state and the external discharge state of the pig raising site by acquiring the detection information of the external pig raising environment, the growth state of the pigs and the external discharge of the pig raising site, so that the intelligent operation of the pig raising process is realized, the waste output of the pig raising process is reduced, and the environment sustainability and the pig raising intelligence degree of the pig raising are improved.
The invention provides a sustainable and intelligent pig raising method which is characterized by comprising the following steps:
step S1, acquiring first detection information about the external environment of the pig raising and second detection information about the growth state of the live pig, and generating breeding feedback information according to the first detection information and/or the second detection information;
step S2, according to the breeding feedback information, adjusting the environmental conditions and/or feeding conditions of the current breeding site;
step S3, acquiring third detection information about the external emission of the culture site, and generating emission feedback information according to the third detection information;
step S4, adjusting the outward discharge mode of the current breeding site according to the discharge feedback information;
further, in step S1, the obtaining first detection information about the external environment of the pig and second detection information about the growth status of the live pig, and the generating of the breeding feedback information according to the first detection information and/or the second detection information specifically includes,
step S101, obtaining a plurality of current different binocular images about a culture site, and generating terrain and/or hydrologic state information about the culture site according to the different binocular images;
step S102, arranging a plurality of environment detection sensors of different types on the culture site according to the terrain and/or hydrological state information to acquire first detection information;
step S103, acquiring a dynamic image and/or a static image related to the living state of the live pig, and generating part of second detection information according to the dynamic image and/or the static image;
step S104, acquiring a plurality of physiological state parameters of the live pig, and analyzing and processing the physiological state parameters to calculate part of the second detection information;
step S105, learning, analyzing and processing the first detection information and/or the second detection information through a preset breeding site quality evaluation neural network model to generate breeding feedback information;
further, in the step S101, acquiring a plurality of current different binocular images about a breeding site, and generating topographic and/or hydrological state information about the breeding site according to the plurality of different binocular images specifically includes,
step S1011, acquiring a plurality of different binocular images related to the culture site and different angle positions and/or height positions, and performing overlapping construction processing on the plurality of different binocular images according to the corresponding angle positions and/or height positions to obtain a three-dimensional geographic environment model related to the culture site;
step S1012, carrying out land and water feature identification and extraction processing on the three-dimensional geographic environment model to obtain terrain and/or hydrological state information about the culture site;
further, in the step S102, according to the topographic and/or hydrological state information, arranging a plurality of different types of environment detection sensors on the cultivation site to obtain the first detection information specifically includes,
step S1021, determining a land boundary and a hydrologic boundary of the three-dimensional geographic environment model corresponding to the culture site according to the topographic/or hydrographic state information so as to obtain an altitude topographic distribution state and a water system distribution state of the culture site;
step S1022, according to the altitude terrain distribution state and the water system distribution state, at least one of an illuminance sensor, a temperature sensor, a humidity sensor, a wind speed/direction sensor, and a diffusion particle sensor is disposed in different location areas of the culture site;
step S1023, according to at least one of the illuminance sensor, the temperature sensor, the humidity sensor, the wind speed/direction sensor and the diffusion particle sensor, acquiring at least one of illumination data, temperature data, humidity data, wind speed/direction data and diffusion particle concentration data of a corresponding area of the culture site as the first detection information;
further, in step S103, acquiring a dynamic image and/or a static image related to the living condition of the live pig, and generating a part of the second detection information according to the dynamic image and/or the static image specifically includes,
step S1031, performing visible light shooting and thermal infrared light shooting on the living state of the live pig to obtain a visible light dynamic image and/or a visible light static image about the live pig and an infrared light dynamic image and/or an infrared light static image about the live pig;
step S1032, performing first image processing on the visible light dynamic image and/or the visible light static image according to the shooting timing of the visible light shooting to obtain first image change information about the live pig;
step S1033 of obtaining body type change information on the live pig as part of the second examination information based on the first image change information;
step S1034, according to the shooting time sequence of the infrared light shooting, carrying out second image processing on the infrared light dynamic image and/or the infrared light static image to obtain second image change information about the live pig;
step S1035 of obtaining weight change information about the live pig as part of the second detection information, based on the second image change information;
further, in step S104, acquiring a plurality of physiological parameters related to the live pig, and performing analysis processing on the plurality of physiological parameters to calculate part of the second detection information specifically includes,
step S1041, obtaining a body temperature state parameter, a body fat ratio state parameter and an organ function state parameter of the live pig as the plurality of physiological state parameters;
step S1042, analyzing and processing the body temperature state parameter, the body fat ratio state parameter and the organ function state parameter according to a preset pig growth physiological neural network model to obtain a comprehensive physiological state evaluation index related to pig growth;
step S1043, performing multidimensional evaluation conversion processing on the comprehensive physiological state evaluation index to obtain part of the second detection information;
further, in step S2, the adjusting process of the environmental conditions and/or feeding conditions of the current cultivation site according to the cultivation feedback information specifically includes,
step S201, according to the breeding feedback information, determining a first condition difference between the environmental condition of the current breeding site and a preset standard environmental condition and/or a second condition difference between the feeding condition of the current breeding site and a preset standard feeding condition;
step S202, determining a first adjustment tolerance of at least one of an illumination condition, a temperature condition, a ventilation condition and a cultivation density condition of the current cultivation site according to the first condition difference;
step S203, determining a second adjustment tolerance of at least one of the feeding amount, the feeding frequency, the cleaning frequency and the live pig activity amount of the current breeding site according to the second condition difference;
step S204, adjusting the environmental conditions and/or feeding conditions of the current breeding site according to the first adjustment tolerance and/or the second adjustment tolerance;
further, in the step S3, the obtaining of third detection information about the external emission of the cultivation site, and the generating of the emission feedback information according to the third detection information specifically includes,
step S301, acquiring external discharge state data of at least one of wastewater, waste gas and solid waste in a preset time period of the culture site;
step S302, according to the external emission state data, carrying out correlation evaluation processing on the external emission and the environmental pollution on the culture site;
step S303, generating the third detection information about the external emission intensity of the culture site according to the result of the relevance evaluation processing;
step S304, determining the influence parameters of the current external discharge state of the culture site on the external environment according to the third detection information, so as to generate the discharge feedback information;
further, in the step S4, the adjusting the external discharge mode of the current cultivation site according to the discharge feedback information specifically includes,
step S401, constructing an external emission prediction model about the current breeding site according to the emission feedback information;
step S402, according to the external emission prediction model, predicting the external emission situation of at least one of the waste water, the waste gas and the solid waste in the culture site in a preset time period;
step S403, adjusting an external emission mode of the current culture site according to the prediction result of the external emission condition;
further, in the step S403, adjusting the external emission mode of the current cultivation site according to the result of predicting the external emission condition specifically includes,
according to the prediction result of the external emission situation, adjusting the recycling treatment intensity of the current aquaculture site relative to at least one of the waste water, the waste gas and the solid waste and/or controlling the output of at least one of the waste water, the waste gas and the solid waste.
Compared with the prior art, the sustainable and intelligent pig raising method comprises the following steps: step S1, acquiring first detection information about the external environment of the pig raising and second detection information about the growth state of the live pig, and generating breeding feedback information according to the first detection information and/or the second detection information; step S2, according to the breeding feedback information, adjusting the environmental conditions and/or feeding conditions of the current breeding site; step S3, acquiring third detection information about the external emission of the culture site, and generating emission feedback information according to the third detection information; step S4, adjusting the outward discharge mode of the current breeding site according to the discharge feedback information; therefore, the sustainable and intelligent pig raising method can be used for adaptively adjusting the environment state of the pig raising site, the pig raising feeding state and the external discharge state of the pig raising site by acquiring the detection information of the external pig raising environment, the growth state of the pigs and the external discharge of the pig raising site, so that the intelligent operation of the pig raising process is realized, the waste output of the pig raising process is reduced, and the environment sustainability and the pig raising intelligence degree of the pig raising are improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the embodiments or technical descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a sustainable and intelligent pig-raising method provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a schematic flow chart of a sustainable and intelligent pig-raising method provided by the invention is shown. The sustainable and intelligent pig raising method comprises the following steps:
step S1, acquiring first detection information about the external environment of the pig raising and second detection information about the growth state of the live pig, and generating breeding feedback information according to the first detection information and/or the second detection information;
step S2, according to the breeding feedback information, adjusting the environmental conditions and/or feeding conditions of the current breeding site;
step S3, acquiring third detection information about the external emission of the culture site, and generating emission feedback information according to the third detection information;
and step S4, adjusting the outward discharge mode of the current breeding site according to the discharge feedback information.
Preferably, in the step S1, the obtaining of the first detection information about the external environment of the pig and the second detection information about the growth state of the live pig, and the generating of the breeding feedback information according to the first detection information and/or the second detection information specifically includes,
step S101, acquiring a plurality of current different binocular images of a culture site, and generating terrain and/or hydrologic state information of the culture site according to the different binocular images;
step S102, according to the terrain and/or hydrologic state information, arranging a plurality of environment detection sensors of different types on the culture site to acquire first detection information;
step S103, acquiring a dynamic image and/or a static image related to the living state of the live pig, and generating part of second detection information according to the dynamic image and/or the static image;
step S104, acquiring a plurality of physiological state parameters of the live pig, and analyzing and processing the physiological state parameters to calculate part of the second detection information;
step S105, learning, analyzing and processing the first detection information and/or the second detection information through a preset breeding site quality evaluation neural network model to generate the breeding feedback information.
Preferably, in the step S101, acquiring a plurality of current different binocular images about the breeding site, and generating topographic and/or hydrological state information about the breeding site according to the plurality of different binocular images specifically includes,
step S1011, acquiring a plurality of different binocular images related to the culture site and different angle positions and/or height positions, and performing overlapping construction processing on the plurality of different binocular images according to the corresponding angle positions and/or height positions to obtain a three-dimensional geographic environment model related to the culture site;
and step S1012, performing land and water feature identification and extraction processing on the three-dimensional geographic environment model to obtain topographic and/or hydrological state information about the culture site.
Preferably, in step S102, according to the topographic and/or hydrological state information, arranging a plurality of different types of environment detection sensors for the cultivation site to obtain the first detection information specifically includes,
step S1021, according to the topographic/or hydrographic state information, determining and processing a land boundary and a hydrographic boundary of the three-dimensional geographic environment model corresponding to the culture site to obtain an altitude topographic distribution state and a water system distribution state of the culture site;
step S1022, according to the altitude terrain distribution state and the water system distribution state, at least one of an illuminance sensor, a temperature sensor, a humidity sensor, a wind speed/direction sensor, and a diffusion particle sensor is installed in different location areas of the cultivation site;
step S1023, according to at least one of the illuminance sensor, the temperature sensor, the humidity sensor, the wind speed/direction sensor and the diffusion particle sensor, acquiring at least one of light data, temperature data, humidity data, wind speed/direction data and diffusion particle concentration data of the corresponding area of the farm as the first detection information.
Preferably, in step S103, a dynamic image and/or a static image about the living condition of the live pig is acquired, and the generating of part of the second detection information specifically includes,
step S1031, performing visible light shooting and thermal infrared light shooting on the living state of the live pig to obtain a visible light dynamic image and/or a visible light static image about the live pig and an infrared light dynamic image and/or an infrared light static image about the live pig;
step S1032, performing first image processing on the visible light dynamic image and/or the visible light static image according to the shooting timing of the visible light shooting to obtain first image change information about the live pig;
step S1033 of obtaining body type change information on the live pig as part of the second examination information based on the first image change information;
step S1034, according to the shooting time sequence of the infrared light shooting, performing second image processing on the infrared light dynamic image and/or the infrared light static image to obtain second image change information about the live pig;
in step S1035, weight change information about the live pig is obtained as part of the second detection information based on the second image change information.
Preferably, in step S104, acquiring a plurality of physiological parameters related to the live pig, and performing analysis processing on the plurality of physiological parameters to calculate part of the second detection information specifically includes,
step S1041, obtaining a body temperature state parameter, a body fat ratio state parameter and an organ function state parameter of the live pig as a plurality of physiological state parameters;
step S1042, according to a preset pig growth physiological neural network model, analyzing and processing the body temperature state parameter, the body fat ratio state parameter and the organ function state parameter to obtain a comprehensive physiological state evaluation index related to pig growth;
step S1043, performing multidimensional evaluation conversion processing on the comprehensive physiological state evaluation index to obtain part of the second detection information.
Preferably, in step S2, the adjusting process of the environmental conditions and/or feeding conditions of the current cultivation site according to the cultivation feedback information specifically includes,
step S201, according to the breeding feedback information, determining a first condition difference between the environmental condition of the current breeding site and a preset standard environmental condition and/or a second condition difference between the feeding condition of the current breeding site and a preset standard feeding condition;
step S202, determining a first adjustment tolerance of at least one of the illumination condition, the temperature condition, the ventilation condition and the culture density condition of the current culture site according to the first condition difference;
step S203, determining a second adjustment tolerance of at least one of the feeding amount, the feeding frequency, the cleaning frequency and the live pig activity amount of the current breeding site according to the second condition difference;
and step S204, adjusting the environmental conditions and/or feeding conditions of the current culture site according to the first adjustment tolerance and/or the second adjustment tolerance.
Preferably, in the step S3, the obtaining of the third detection information about the external emission of the cultivation site, and the generating of the emission feedback information according to the third detection information specifically includes,
step S301, acquiring external discharge state data of at least one of wastewater, waste gas and solid waste in a preset time period of the culture site;
step S302, according to the external emission state data, carrying out correlation evaluation processing on the external emission and the environmental pollution of the culture site;
step S303, generating the third detection information about the external emission intensity of the culture site according to the result of the relevance evaluation processing;
step S304, determining the influence parameters of the current external discharge state of the culture site on the external environment according to the third detection information, so as to generate the discharge feedback information.
Preferably, in step S4, the adjusting the outward discharge mode of the current cultivation site specifically includes,
step S401, according to the emission feedback information, an external emission prediction model related to the current breeding site is constructed;
step S402, according to the external emission prediction model, predicting the external emission situation of at least one of the waste water, the waste gas and the solid waste in the culture site in a preset time period;
and S403, adjusting the external emission mode of the current culture site according to the prediction result of the external emission situation.
Preferably, in step S403, adjusting the external emission mode of the current farm according to the result of predicting the external emission condition specifically includes,
according to the prediction result of the external emission situation, the recycling treatment intensity of at least one of the waste water, the waste gas and the solid waste on the current aquaculture site is adjusted and/or the output quantity of at least one of the waste water, the waste gas and the solid waste is controlled.
According to the content of the embodiment, the sustainable and intelligent pig raising method is used for adaptively adjusting the environment state of the pig raising site, the pig raising feeding state and the external discharge state of the pig raising site by acquiring the detection information of the external pig raising environment, the pig growth state and the external discharge of the pig raising site, so that the intelligent operation of the pig raising process is realized, the waste output of the pig raising process is reduced, and the environment sustainability and the pig raising intelligence degree of the pig raising process are improved.
Claims (10)
1. A sustainable and intelligent pig raising method is characterized by comprising the following steps:
step S1, acquiring first detection information about the external environment of the pig raising and second detection information about the growth state of the live pig, and generating breeding feedback information according to the first detection information and/or the second detection information;
step S2, according to the breeding feedback information, adjusting the environmental conditions and/or feeding conditions of the current breeding site;
step S3, acquiring third detection information about the external emission of the culture site, and generating emission feedback information according to the third detection information;
and step S4, adjusting the outward discharge mode of the current breeding site according to the discharge feedback information.
2. The sustainable and intelligent swine farming method of claim 1, wherein:
in step S1, the obtaining of the first detection information about the external environment of the pig and the second detection information about the growth status of the live pig, and the generating of the feeding feedback information according to the first detection information and/or the second detection information specifically includes,
step S101, obtaining a plurality of current different binocular images about a culture site, and generating terrain and/or hydrologic state information about the culture site according to the different binocular images;
step S102, arranging a plurality of environment detection sensors of different types on the culture site according to the terrain and/or hydrological state information to acquire first detection information;
step S103, acquiring a dynamic image and/or a static image related to the living state of the live pig, and generating part of second detection information according to the dynamic image and/or the static image;
step S104, acquiring a plurality of physiological state parameters of the live pig, and analyzing and processing the physiological state parameters to calculate part of the second detection information;
step S105, learning, analyzing and processing the first detection information and/or the second detection information through a preset breeding site quality evaluation neural network model to generate the breeding feedback information.
3. The sustainable and intelligent swine farming method of claim 2, wherein:
in the step S101, acquiring a plurality of current binocular images about a breeding site, and generating topographic and/or hydrological state information about the breeding site according to the plurality of different binocular images specifically includes,
step S1011, acquiring a plurality of different binocular images related to the culture site and different angle positions and/or height positions, and performing overlapping construction processing on the plurality of different binocular images according to the corresponding angle positions and/or height positions to obtain a three-dimensional geographic environment model related to the culture site;
step S1012, performing land and water feature recognition and extraction processing on the stereoscopic geographic environment model to obtain topographic and/or hydrological state information about the culture site.
4. The sustainable and intelligent swine farming method of claim 2, wherein:
in step S102, arranging a plurality of different types of environment detection sensors on the cultivation site according to the topographic and/or hydrological state information to obtain the first detection information specifically includes,
step S1021, determining a land boundary and a hydrologic boundary of the three-dimensional geographic environment model corresponding to the culture site according to the topographic/or hydrographic state information so as to obtain an altitude topographic distribution state and a water system distribution state of the culture site;
step S1022, according to the altitude terrain distribution state and the water system distribution state, at least one of an illuminance sensor, a temperature sensor, a humidity sensor, a wind speed/direction sensor, and a diffusion particle sensor is disposed in different location areas of the culture site;
step S1023, according to at least one of the illuminance sensor, the temperature sensor, the humidity sensor, the wind speed/direction sensor, and the diffusion particle sensor, acquiring at least one of light data, temperature data, humidity data, wind speed/direction data, and diffusion particle concentration data of an area corresponding to the farm as the first detection information.
5. The sustainable and intelligent swine farming method of claim 2, wherein:
in step S103, acquiring a dynamic image and/or a static image related to the living condition of the live pig, and generating a part of the second detection information according to the dynamic image and/or the static image specifically includes,
step S1031, performing visible light shooting and thermal infrared light shooting on the living state of the live pig to obtain a visible light dynamic image and/or a visible light static image about the live pig and an infrared light dynamic image and/or an infrared light static image about the live pig;
step S1032, performing first image processing on the visible light dynamic image and/or the visible light static image according to the shooting timing of the visible light shooting to obtain first image change information about the live pig;
step S1033 of obtaining body type change information on the live pig as part of the second examination information based on the first image change information;
step S1034, according to the shooting time sequence of the infrared light shooting, carrying out second image processing on the infrared light dynamic image and/or the infrared light static image to obtain second image change information about the live pig;
step S1035, obtaining weight change information about the live pig as part of the second detection information, according to the second image change information.
6. The sustainable and intelligent swine farming method of claim 2, wherein:
in step S104, acquiring a plurality of physiological parameters related to the live pig, and performing analysis processing on the plurality of physiological parameters to calculate part of the second detection information specifically includes,
step S1041, obtaining a body temperature state parameter, a body fat ratio state parameter and an organ function state parameter of the live pig as the plurality of physiological state parameters;
step S1042, analyzing and processing the body temperature state parameter, the body fat ratio state parameter and the organ function state parameter according to a preset pig growth physiological neural network model to obtain a comprehensive physiological state evaluation index related to pig growth;
step S1043, performing multidimensional evaluation conversion processing on the comprehensive physiological state evaluation index to obtain part of the second detection information.
7. The sustainable and intelligent swine farming method of claim 1, wherein:
in step S2, the adjusting process of the environmental conditions and/or feeding conditions of the current cultivation site according to the cultivation feedback information specifically includes,
step S201, according to the breeding feedback information, determining a first condition difference between the environmental condition of the current breeding site and a preset standard environmental condition and/or a second condition difference between the feeding condition of the current breeding site and a preset standard feeding condition;
step S202, determining a first adjustment tolerance of at least one of an illumination condition, a temperature condition, a ventilation condition and a cultivation density condition of the current cultivation site according to the first condition difference;
step S203, determining a second adjustment tolerance of at least one of the feeding amount, the feeding frequency, the cleaning frequency and the live pig activity amount of the current breeding site according to the second condition difference;
and S204, adjusting the environmental conditions and/or feeding conditions of the current culture site according to the first adjustment tolerance and/or the second adjustment tolerance.
8. The sustainable and intelligent swine farming method of claim 1, wherein:
in step S3, the obtaining of third detection information about the external emission of the cultivation site, and the generating of the emission feedback information according to the third detection information specifically includes,
step S301, acquiring external discharge state data of at least one of wastewater, waste gas and solid waste in a preset time period of the culture site;
step S302, according to the external emission state data, carrying out correlation evaluation processing on the external emission and the environmental pollution on the culture site;
step S303, generating the third detection information about the external emission intensity of the culture site according to the result of the relevance evaluation processing;
step S304, determining the influence parameters of the current external discharge state of the culture site on the external environment according to the third detection information, so as to generate the discharge feedback information.
9. The sustainable and intelligent swine farming method of claim 1, wherein:
in step S4, the adjusting of the external discharge mode of the current cultivation site according to the discharge feedback information specifically includes,
step S401, constructing an external emission prediction model about the current breeding site according to the emission feedback information;
step S402, according to the external emission prediction model, predicting the external emission situation of at least one of the waste water, the waste gas and the solid waste in the culture site in a preset time period;
and S403, adjusting the external emission mode of the current culture site according to the prediction result of the external emission condition.
10. The sustainable and intelligent swine farming method of claim 9, wherein:
in step S403, adjusting the external emission mode of the current farm according to the result of predicting the external emission condition specifically includes,
according to the prediction result of the external emission situation, adjusting the recycling treatment intensity of the current aquaculture site relative to at least one of the waste water, the waste gas and the solid waste and/or controlling the output of at least one of the waste water, the waste gas and the solid waste.
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