CN113709260A - Data analysis ODM system based on feed production thing networking - Google Patents
Data analysis ODM system based on feed production thing networking Download PDFInfo
- Publication number
- CN113709260A CN113709260A CN202111267513.5A CN202111267513A CN113709260A CN 113709260 A CN113709260 A CN 113709260A CN 202111267513 A CN202111267513 A CN 202111267513A CN 113709260 A CN113709260 A CN 113709260A
- Authority
- CN
- China
- Prior art keywords
- animal
- feed
- content
- individual
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 24
- 238000007405 data analysis Methods 0.000 title claims abstract description 20
- 230000006855 networking Effects 0.000 title description 2
- 241001465754 Metazoa Species 0.000 claims abstract description 250
- 235000015097 nutrients Nutrition 0.000 claims abstract description 83
- 239000000126 substance Substances 0.000 claims abstract description 66
- 235000021049 nutrient content Nutrition 0.000 claims abstract description 37
- 238000004458 analytical method Methods 0.000 claims abstract description 34
- 238000001514 detection method Methods 0.000 claims abstract description 19
- 230000036541 health Effects 0.000 claims abstract description 19
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 42
- 235000016709 nutrition Nutrition 0.000 claims description 25
- 201000010099 disease Diseases 0.000 claims description 21
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 21
- 235000021316 daily nutritional intake Nutrition 0.000 claims description 17
- 230000007613 environmental effect Effects 0.000 claims description 15
- 235000021050 feed intake Nutrition 0.000 claims description 13
- 235000012631 food intake Nutrition 0.000 claims description 11
- 230000003862 health status Effects 0.000 claims description 11
- 230000000366 juvenile effect Effects 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 8
- 230000037396 body weight Effects 0.000 claims description 6
- 238000000034 method Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 5
- 230000035764 nutrition Effects 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 2
- 239000011575 calcium Substances 0.000 description 11
- 239000004615 ingredient Substances 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000000050 nutritive effect Effects 0.000 description 4
- 229940079593 drug Drugs 0.000 description 3
- 239000003814 drug Substances 0.000 description 3
- 235000012041 food component Nutrition 0.000 description 2
- 230000036039 immunity Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 1
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 150000001413 amino acids Chemical class 0.000 description 1
- 235000019728 animal nutrition Nutrition 0.000 description 1
- 229910052791 calcium Inorganic materials 0.000 description 1
- 238000010924 continuous production Methods 0.000 description 1
- 235000021051 daily weight gain Nutrition 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 235000021048 nutrient requirements Nutrition 0.000 description 1
- 239000011574 phosphorus Substances 0.000 description 1
- 229910052698 phosphorus Inorganic materials 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 235000013619 trace mineral Nutrition 0.000 description 1
- 239000011573 trace mineral Substances 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y10/00—Economic sectors
- G16Y10/05—Agriculture
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Life Sciences & Earth Sciences (AREA)
- Tourism & Hospitality (AREA)
- Agronomy & Crop Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Computer Networks & Wireless Communication (AREA)
- Primary Health Care (AREA)
- Medical Informatics (AREA)
- Signal Processing (AREA)
- Mining & Mineral Resources (AREA)
- Accounting & Taxation (AREA)
- Marine Sciences & Fisheries (AREA)
- Educational Administration (AREA)
- Animal Husbandry (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a data analysis ODM system based on the feed production Internet of things, in particular to the technical field of data processing of the Internet of things, which comprises an acquisition module, a storage module and an analysis module, wherein the acquisition module is used for acquiring animal growth information in real time and is connected with the analysis module; the analysis module is used for analyzing the animal growth information and is connected with the adjustment module; when the analysis module analyzes the animal growth information, the comparison unit judges the health state of the animals at each stage according to the acquired animal weight A; the adjusting module is used for adjusting the components of the produced feed according to the analysis result and is connected with the detecting module; the adjusting module adjusts the content C of the nutrient substances in the feed according to the health state of the animals; and the detection module is used for detecting the adjusted feed feeding result. The invention effectively improves the feeding efficiency of animals by accurately controlling the nutrient content of the feed of the animals at each stage.
Description
Technical Field
The invention relates to the technical field of data processing of the Internet of things, in particular to a data analysis ODM system based on the Internet of things for feed production.
Background
ODM is a production method in which a buyer requests a manufacturer to provide all services from development, design, production, and post-maintenance in the manufacturing industry. With the progress of big data analysis technology, the ODM Internet of things technology develops rapidly. The feed ODM system is established by continuous production and actual accumulation through closely combining a big data analysis technology and an Internet of things technology on the basis of theories and technologies of animal husbandry science such as animal nutrition, animal production, feed science and the like.
The ODM is established on the Internet plus and big data, the potential nutritional value of the feed is fully mined by analyzing the whole data of the feed raw materials, and the feed technology with the optimal nutrient components and quantity ratio is provided in daily ration, so that the feed is absorbed and utilized by animals to the maximum, thereby reducing the nutrient loss, saving the feeding cost and reducing the problem of the pollution to the culture environment.
In the prior art, when animals are raised, fixed feed is usually adopted for raising, feed components cannot be adjusted in time according to the health state of the animals, so that the efficiency of animal raising is low, and the raised animals are not controlled.
Disclosure of Invention
Therefore, the invention provides a data analysis ODM system based on the Internet of things for feed production, which is used for solving the problem of low feeding efficiency caused by the fact that feed ingredients cannot be accurately adjusted according to the health state of animals in the prior art.
In order to achieve the above object, the present invention provides a data analysis ODM system based on the internet of things for feed production, comprising,
the acquisition module is used for acquiring animal growth information in real time and is connected with the analysis module;
the analysis module is used for analyzing the animal growth information and is connected with the adjustment module; when the analysis module analyzes the animal growth information, the comparison unit judges the health state of the animals at each stage according to the acquired animal weight A;
the adjusting module is used for adjusting the components of the produced feed according to the analysis result and is connected with the detecting module; the adjusting module firstly obtains the content C of nutrient substances in the feed, adjusts the content C of the nutrient substances in the feed according to the health state of animals, compensates the adjusted content of the nutrient substances according to the disease state of the animals after the adjustment is finished, and corrects the content of the nutrient substances in the feed according to the daily feed intake D of individual animals after the compensation is finished; the adjusting module is also used for adjusting the water content Q of the feed according to the environment humidity H of the animal feeding environment;
the detection module is used for detecting the adjusted feed feeding result; the detection module judges the growth speed of the animal after the feed is adjusted according to the daily gain M of the individual animal; and the adjusting module adjusts the content of the nutrient again according to the judgment result made by the detecting module.
Further, when the analysis module analyzes the animal growth information, the comparison unit compares the acquired animal weight A with each preset weight, and the judgment unit judges the health state of the animal according to the comparison result, wherein,
if the animal is in the juvenile stage, when A is less than A11, the judging unit judges that the animal individual is thin and weak; when A11 is not less than A < A12, the judging unit judges that the weight of the animal individual is normal; when A12 is less than or equal to A, the judging unit judges that the animal individual is obese;
if the animal is in the growth period, when A is less than A21, the judging unit judges that the animal individual is thin and weak; when A21 is not less than A < A22, the judging unit judges that the weight of the animal individual is normal; when A22 is less than or equal to A, the judging unit judges that the animal individual is obese;
if the animal is in the adult stage, when A is less than A31, the judging unit judges that the animal individual is thin and weak; when A31 is not less than A < A32, the judging unit judges that the weight of the animal individual is normal; when A32 is less than or equal to A, the judging unit judges that the animal individual is obese;
wherein A11 is the first predetermined juvenile phase weight, A12 is the second predetermined juvenile phase weight, A11 is less than A12; a21 is the weight of the first preset growing period, A22 is the weight of the second preset growing period, A21 is more than A22; a31 is the first predetermined adult body weight, A32 is the second predetermined adult body weight, A31 < A32.
Further, after the weight analysis of the individual animals is completed, the adjusting module obtains the content C of the nutrient substances in the feed, and the adjusting module adjusts the content C of the nutrient substances in the feed according to the health state of the animals, wherein,
when the animal individual is judged to be weak, the adjusting module adjusts the content of nutrient substances in the feed to be C1, sets C1= C × a1, sets a1 as a nutrition increasing coefficient, and sets the value of 1 & lt a1 & lt 1.2;
when the animal individual is judged to be obese, the adjusting module adjusts the content of the nutrient in the feed to be C2, sets C2= C × a2 and a2 as a nutrient reduction coefficient, wherein 0.8 < a1 < 1.
Further, after the adjustment module finishes adjusting the content of the nutrient substances in the feed, the adjustment module acquires the disease state of the individual animal, compensates the adjusted content Ci of the nutrient substances according to the disease state, and sets i =1,2, wherein,
when the animal individual does not have the disease, compensation is not carried out;
when the animal individuals have diseases, the adjusting module compensates the content of the nutrient substances in the feed to Ci ', and Ci' = Ci x b is set, wherein b is a nutrient compensation coefficient, and 1 < b < 1.1.
Further, after the adjustment module completes the compensation of the content of the nutrient substances in the feed, the adjustment module obtains the daily food consumption D of the individual animal, compares the daily food consumption D of the individual animal with each preset daily food consumption, and corrects the content of the nutrient substances in the feed according to the comparison result, wherein,
when D < D1, the adjustment module determines that the individual animal has insufficient daily food intake and corrects the nutrient content in the feed to Ca, setting Ca = Ci '+ Ci' × (D1-D)/D1;
when D1 is not less than D2, the adjusting module judges that the daily food intake of the animal individual is normal and does not carry out correction;
when D2 is less than D, the adjusting module judges that the daily food intake of the animal is excessive, corrects the nutrient content in the feed to Cb, and sets Cb = Ci '-Ci' × (D-D2)/D;
wherein D1 is the first preset daily food intake, D2 is the second preset daily food intake, and D1 is less than D2.
Further, after the adjustment module finishes the correction of the content of the nutrient substances in the feed, the adjustment module acquires the water content Q of the feed and adjusts the water content Q according to the environmental humidity H of the animal feeding environment, the adjustment module compares the environmental humidity H of the feeding environment with each preset environmental humidity and adjusts the water content Q according to the comparison result, wherein,
when H is less than H1, the adjusting module judges that the feeding environment is dry, the water content of the feed is adjusted to be Q1, Q1= Q + Q x (H1-H)/H1 is set, when Q1 is more than or equal to Qmax, the adjusting module takes Qmax as the water content of the feed, and Qmax is the maximum preset water content of the feed;
when H1 is not less than H < H2, the adjusting module judges that the humidity of the feeding environment is normal and does not adjust;
when H2 is less than or equal to H, the adjusting module judges that the feeding environment is moist and adjusts the water content of the feed to be Q2, and sets Q2= Q-Qx (H-H2)/H, when Q2 is less than or equal to Qmin, the adjusting module takes Qmin as the water content of the feed, and the Qmin is the preset minimum water content of the feed;
wherein H1 is the first preset environmental humidity, H2 is the second preset environmental humidity, H1 is less than H2.
Further, after the adjustment of the components and the water content of the feed by the adjustment module is completed, the obtaining unit of the detection module obtains the daily gain M of the animal individual after the adjusted feed is fed for T time, T is a preset value, the determination unit of the detection module compares the daily gain M with the preset daily gain M0 and determines the adjustment of the feed according to the comparison result, wherein,
if the content of the nutrient substances is adjusted, the animal individual is judged to be weak, and when M is less than M0, the judgment unit judges that the growth speed of the animal individual is slow; when M is larger than or equal to M0, the judging unit judges that the growth speed of the animal individual is normal;
if the nutrient content is adjusted, judging that the animal individual is obese, and when M is less than or equal to M0, judging that the growth speed of the animal individual is normal by the judging unit; when M > M0, the judging unit judges that the individual animal grows fast.
Further, when the determination unit determines that the growth speed of the individual animal is slow, the adjustment module adjusts the corrected nutrient content again, adjusts the nutrient content to Cm, sets Cm = Ca + Ca × (M0-M)/M0 or Cb + Cb × (M0-M)/M0, and feeds the animal with the adjusted feed after the adjustment of the nutrient content is completed.
Further, when the determination unit determines that the growth rate of the individual animal is high, the adjustment module adjusts the corrected nutrient content again, adjusts the nutrient content to Cn, sets Cn = Ca-Ca x (M-M0)/M or Cb-Cb x (M-M0)/M, and feeds the animal with the adjusted feed after the adjustment of the nutrient content is completed.
Further, when the judgment unit judges that the growth speed of the animal individual is normal, the adjustment module does not adjust the content of the nutrient substances any more, and repeats the adjustment process of the content of the nutrient substances according to the animal growth information acquired in real time.
Compared with the prior art, the invention has the advantages that the analysis module judges the health state of the animal according to the growth information of the animal so as to determine the health state of the individual animal, thereby adjusting the feed components to ensure that the adjusted feed meets the nutritional requirements of individual animals, comparing the acquired animal weight A with each preset value by the analysis module, and judges the animal individual according to the comparison result, by comparing the obtained animal weight with the preset value, can accurately reflect the health status of animal individuals, and the analysis module also adopts different parameters to carry out comparison judgment according to different periods of the animals when carrying out judgment so as to improve the judgment accuracy, thereby accurately acquiring the health state of individual animals, and further facilitating the adjustment of feed ingredients so as to meet the nutritional requirements of the animals; after the analysis module analyzes the health state of the animal individual, the adjusting module adjusts the feed according to the health state of the animal individual, the content of nutrient substances in the feed is increased when the animal individual is thin and weak, and the content of the nutrient substances in the feed is reduced when the animal individual is fat and weak, so that the weight of the animal is maintained in a normal range by adjusting the content of the nutrient substances, the adjusted feed further meets the nutritional requirements of the animal, and the feeding efficiency of the animal is effectively improved by accurately controlling the nutrient contents of the feed of the animal at each stage.
In particular, the adjusting module compensates the nutrient content according to the disease state of the individual animal, when the individual animal has diseases, the required nutrient content is more, so as to enhance the immunity, and therefore, the compensated nutrient content can meet the nutritional requirement of the animal better through compensation.
Particularly, after the adjustment module completes compensation of the content of the nutrient substances in the feed, the adjustment module corrects the content of the nutrient substances by obtaining the daily feed intake D of the individual animal, compares the daily feed intake D with a preset standard value, proves that the daily feed intake of the individual animal is small when the daily feed intake D is less than a preset value and the content of the nutrient substances in the feed needs to be increased, proves that the daily feed intake of the individual animal is large when the daily feed intake D is greater than the preset value and the content of the nutrient substances in the feed needs to be reduced so that the corrected content of the nutrient substances meets the nutritional requirements of the animal, and sets the adjustment module according to the difference between the daily feed intake D and the preset value when the corrected content of the nutrient substances is set so as to increase the correction accuracy and further ensure that the corrected content of the nutrient substances meets the nutritional requirements of the animal.
Especially, the adjustment module still adjusts the water content of fodder according to the ambient humidity H of raising the environment to guarantee that the nutritive substance in the fodder is fully absorbed simultaneously, the adjustment module compares ambient humidity H and each default, when ambient humidity H is less than the default, then increases the water content, when ambient humidity H is more than the default, then reduces the water content, through the water content of accurate control fodder, with the loss that reduces nutritive substance in the fodder, thereby makes the fodder satisfy the nutritional requirement of animal.
Particularly, after the adjustment of the components and the water content of the feed is completed, whether the adjusted feed meets the nutritional requirements of the animals is verified through the detection module, the detection module obtains the daily gain M of the individual animals and compares the daily gain M with a preset value so as to judge the growth condition of the individual animals, meanwhile, the detection module also sets different judgment standards according to the health state of the animals so that the judgment result is more accurate, and through judging the growth state of the individual animals, whether the nutritional components of the feed meet the requirements can be effectively reflected so as to improve the accuracy of the control of the nutritional components of the feed.
Particularly, after the detection module judges the growth speed of an individual animal, the adjustment module adjusts the content of nutrient substances in the feed in time according to a judgment result so as to further increase the accuracy of controlling the nutrient components of the feed, so that the growth process of the animal is effectively combined with feed production, and the nutrient requirement of the animal on the feed is further met.
Drawings
Fig. 1 is a schematic structural diagram of a data analysis ODM system based on the internet of things for feed production according to the embodiment;
FIG. 2 is a schematic structural diagram of an acquisition module in this embodiment;
FIG. 3 is a schematic structural diagram of an analysis module in this embodiment;
fig. 4 is a schematic structural diagram of the detection module in this embodiment.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, a schematic structural diagram of a data analysis ODM system based on the internet of things for feed production according to the present embodiment is shown, the system includes,
the acquisition module is used for acquiring animal growth information in real time and is connected with the analysis module, and the animal growth information comprises information such as growth stage, weight, disease state, environment humidity, daily feed intake, daily weight gain, feed ingredients and the like of animals;
the analysis module is used for analyzing the animal growth information and is connected with the adjustment module;
the adjusting module is used for adjusting the components of the produced feed according to the analysis result and is connected with the detecting module;
and the detection module is used for detecting the adjusted feed feeding result.
Referring to fig. 2, the collecting module includes an information collecting unit for collecting the animal growth information, the information collecting unit is connected to an information transmitting unit, and the information transmitting unit is used for transmitting the animal growth information.
Referring to fig. 3, the analysis module includes a comparison unit for comparing values, the comparison unit is connected to the determination unit, and the determination unit is used for determining according to the comparison result.
Referring to fig. 4, the detecting module includes an obtaining unit for obtaining the daily gain of the individual animal, and is connected to a determining unit for determining the growth rate of the individual animal.
Specifically, the ODM described in this embodiment is a deep customization of animal feed according to different growth stages, different animal groups, different market conditions and different marketing times, so that the nutritional ingredients of the customized feed meet the nutritional requirements of animals.
Specifically, when the analysis module analyzes the animal growth information, the comparison unit compares the acquired animal weight A with each preset weight, and the judgment unit judges the health state of the animal according to the comparison result, wherein,
if the animal is in the juvenile stage, when A is less than A11, the judging unit judges that the animal individual is thin and weak; when A11 is not less than A < A12, the judging unit judges that the weight of the animal individual is normal; when A12 is less than or equal to A, the judging unit judges that the animal individual is obese;
if the animal is in the growth period, when A is less than A21, the judging unit judges that the animal individual is thin and weak; when A21 is not less than A < A22, the judging unit judges that the weight of the animal individual is normal; when A22 is less than or equal to A, the judging unit judges that the animal individual is obese;
if the animal is in the adult stage, when A is less than A31, the judging unit judges that the animal individual is thin and weak; when A31 is not less than A < A32, the judging unit judges that the weight of the animal individual is normal; when A32 is less than or equal to A, the judging unit judges that the animal individual is obese;
wherein A11 is the first predetermined juvenile phase weight, A12 is the second predetermined juvenile phase weight, A11 is less than A12; a21 is the weight of the first preset growing period, A22 is the weight of the second preset growing period, A21 is more than A22; a31 is the first predetermined adult body weight, A32 is the second predetermined adult body weight, A31 < A32.
Specifically, in this embodiment, the analysis module determines the health status of the animal according to the animal growth information to determine the health status of the individual animal, so as to adjust the feed composition, so that the adjusted feed meets the nutritional requirement of the individual animal, the analysis module compares the collected animal weight a with each preset value, determines the individual animal according to the comparison result, and can accurately reflect the health status of the individual animal by comparing the obtained animal weight with the preset value, and when the analysis module determines, the analysis module further takes different parameters for comparison and determination according to different periods of time of the animal, so as to improve the determination accuracy, thereby accurately obtaining the health status of the individual animal, and further facilitating the adjustment of the feed composition to meet the nutritional requirement of the animal. It can be understood that, in the embodiment, when the analysis module determines the health status of an animal, different sets of preset values are adopted for determination in different growth stages to ensure the accuracy of the determination, and a person skilled in the art can further set a set of preset values for determination, but the preset values need to be comprehensively set in combination with the weights in different growth stages to make the determination result more accurate.
Specifically, after the weight analysis of the individual animals is completed, the adjusting module obtains the content C of nutrient substances in the feed, wherein the nutrient substances comprise amino acid, calcium, phosphorus, trace elements, protein and the like, the content of the nutrient substances is the proportion of the nutrient substances in the total ingredients of the feed, the adjusting module adjusts the content C of the nutrient substances in the feed according to the health state of the animals, wherein,
when the animal individual is judged to be weak, the adjusting module adjusts the content of nutrient substances in the feed to be C1, sets C1= C × a1, sets a1 as a nutrition increasing coefficient, and sets the value of 1 & lt a1 & lt 1.2;
when the animal individual is judged to be obese, the adjusting module adjusts the content of the nutrient in the feed to be C2, sets C2= C × a2 and a2 as a nutrient reduction coefficient, wherein 0.8 < a1 < 1.
Specifically, in this embodiment, after the analysis module analyzes the health status of the individual animal, the adjustment module adjusts the feed according to the health status of the individual animal, and when the individual animal is lean, the content of the nutrient in the feed is increased, and when the individual animal is fat, the content of the nutrient in the feed is decreased, so that the weight of the animal is maintained within a normal range by adjusting the content of the nutrient, thereby further enabling the adjusted feed to meet the nutritional requirement of the animal. It can be understood that, in the embodiment, when the content C of the nutrient substances in the feed is adjusted, the adjustment is performed according to the health status of the individual animal, and the adjustment is performed according to the growth status of the animal, and a person skilled in the art can set three groups of preset values according to the growth status of the animal, so as to perform more fine adjustment.
Specifically, after the adjustment module finishes adjusting the content of the nutrient substances in the feed, the adjustment module acquires the disease state of an individual animal and compensates the adjusted content Ci of the nutrient substances according to the disease state, and sets i =1,2, wherein,
when the animal individual does not have the disease, compensation is not carried out;
when the animal individuals have diseases, the adjusting module compensates the content of the nutrient substances in the feed to Ci ', and Ci' = Ci x b is set, wherein b is a nutrient compensation coefficient, and 1 < b < 1.1.
Specifically, the adjustment module in this embodiment also compensates the nutrient content according to the disease state of the individual animal, and when the individual animal has a disease, the required nutrient content is more, so as to enhance the immunity, and therefore, the compensated nutrient content can more meet the nutritional requirement of the animal by compensation. It can be understood that, because the animal has diseases, the animal can be treated by adding medicines into the feed according to the types of the diseases, and the animal can be fed with the medicines independently only by meeting the requirements of the animal on the medicines.
Specifically, after the adjustment module completes the compensation of the content of the nutrient substances in the feed, the adjustment module obtains the daily food consumption D of the individual animal, compares the daily food consumption D of the individual animal with each preset daily food consumption, and corrects the content of the nutrient substances in the feed according to the comparison result, wherein,
when D < D1, the adjustment module determines that the individual animal has insufficient daily food intake and corrects the nutrient content in the feed to Ca, setting Ca = Ci '+ Ci' × (D1-D)/D1;
when D1 is not less than D2, the adjusting module judges that the daily food intake of the animal individual is normal and does not carry out correction;
when D2 is less than D, the adjusting module judges that the daily food intake of the animal is excessive, corrects the nutrient content in the feed to Cb, and sets Cb = Ci '-Ci' × (D-D2)/D;
wherein D1 is the first preset daily food intake, D2 is the second preset daily food intake, and D1 is less than D2.
Specifically, in this embodiment, after the adjustment module completes the compensation of the nutrient content in the feed, the adjustment module also corrects the nutrient content by obtaining the daily food consumption D of the individual animal, the adjusting module compares the daily food intake D with a preset standard value, when the daily food intake D is less than the preset value, the animal individual has small daily feed intake and needs to increase the content of nutrient substances in the feed, when the daily feed intake D is more than a preset value, the daily food consumption of individual animals is proved to be large, the content of nutrient substances in the feed needs to be reduced so that the corrected content of the nutrient substances meets the nutritional requirement of the animals, when the corrected content of the nutrient substances is set by the adjusting module, and setting according to the difference value between the daily feed intake D and the preset value to increase the correction accuracy, thereby further ensuring that the corrected nutrient content meets the nutritional requirements of the animals.
Specifically, after the adjustment module finishes the correction of the content of the nutrient substances in the feed, the adjustment module obtains the water content Q of the feed and adjusts the water content Q according to the environmental humidity H of the animal feeding environment, the adjustment module compares the environmental humidity H of the feeding environment with each preset environmental humidity and adjusts the water content Q according to the comparison result, wherein,
when H is less than H1, the adjusting module judges that the feeding environment is dry, the water content of the feed is adjusted to be Q1, Q1= Q + Q x (H1-H)/H1 is set, when Q1 is more than or equal to Qmax, the adjusting module takes Qmax as the water content of the feed, and Qmax is the maximum preset water content of the feed;
when H1 is not less than H < H2, the adjusting module judges that the humidity of the feeding environment is normal and does not adjust;
when H2 is less than or equal to H, the adjusting module judges that the feeding environment is moist and adjusts the water content of the feed to be Q2, and sets Q2= Q-Qx (H-H2)/H, when Q2 is less than or equal to Qmin, the adjusting module takes Qmin as the water content of the feed, and the Qmin is the preset minimum water content of the feed;
wherein H1 is the first preset environmental humidity, H2 is the second preset environmental humidity, H1 is less than H2.
Particularly, the adjustment module still adjusts the water content of fodder according to the ambient humidity H of raising the environment in this embodiment to guarantee that the nutritive substance in the fodder is fully absorbed simultaneously, the adjustment module compares ambient humidity H and each default, when ambient humidity H is less than the default, then increases the water content, when ambient humidity H is more than the default, then reduces the water content, through the water content of accurate control fodder, with the loss that reduces nutritive substance in the fodder, thereby makes the fodder satisfy the nutritional requirement of animal.
Specifically, after the adjustment of the components and the water content of the feed is completed by the adjustment module, the obtaining unit of the detection module obtains the daily gain M of the individual animal after the adjusted feed is fed for T time, T is a preset value, the determination unit of the detection module compares the daily gain M with the preset daily gain M0 and determines the adjustment of the feed according to the comparison result, wherein,
if the content of the nutrient substances is adjusted, the animal individual is judged to be weak, and when M is less than M0, the judgment unit judges that the growth speed of the animal individual is slow; when M is larger than or equal to M0, the judging unit judges that the growth speed of the animal individual is normal;
if the nutrient content is adjusted, judging that the animal individual is obese, and when M is less than or equal to M0, judging that the growth speed of the animal individual is normal by the judging unit; when M > M0, the judging unit judges that the individual animal grows fast.
Specifically, in this embodiment, after the adjustment of the ingredients and the water content of the feed is completed, whether the adjusted feed meets the nutritional requirements of the animals is verified through the detection module, the detection module obtains the daily gain M of the individual animals and compares the daily gain M with a preset value to determine the growth condition of the individual animals, and meanwhile, the detection module further sets different determination standards according to the health state of the animals, so that the determination result is more accurate, and by determining the growth state of the individual animals, whether the nutritional ingredients of the feed meet the requirements can be effectively reflected, so that the accuracy of controlling the nutritional ingredients of the feed is improved.
Specifically, when the determination unit determines that the growth rate of the individual animal is slow, the adjustment module readjusts the corrected nutrient content and adjusts the nutrient content to Cm, sets Cm = Ca + Ca × (M0-M)/M0 or Cb + Cb × (M0-M)/M0, and after the adjustment of the nutrient content is completed, the animal is fed with the adjusted feed.
Specifically, when the determination unit determines that the growth rate of the individual animal is high, the adjustment module adjusts the corrected nutrient content again to Cn, sets Cn = Ca-Ca × (M-M0)/M or Cb-Cb × (M-M0)/M, and raises the animal with the adjusted feed after the adjustment of the nutrient content is completed.
Specifically, when the determination unit determines that the growth speed of the individual animal is normal, the adjustment module does not adjust the content of the nutrient substances any more, and repeats the adjustment process of the content of the nutrient substances according to the animal growth information acquired in real time.
Specifically, in this embodiment, after the detection module determines the growth rate of an individual animal, the adjustment module adjusts the content of nutrients in the feed in time according to the determination result to further increase the accuracy of controlling the nutrients in the feed, so that the growth process of the animal is effectively combined with the production of the feed, and the nutritional requirement of the animal on the feed is further met.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (10)
1. A data analysis ODM system based on the feed production Internet of things is characterized by comprising,
the acquisition module is used for acquiring animal growth information in real time and is connected with the analysis module;
the analysis module is used for analyzing the animal growth information and is connected with the adjustment module; when the analysis module analyzes the animal growth information, the comparison unit judges the health state of the animals at each stage according to the acquired animal weight A;
the adjusting module is used for adjusting the components of the produced feed according to the analysis result and is connected with the detecting module; the adjusting module firstly obtains the content C of nutrient substances in the feed, adjusts the content C of the nutrient substances in the feed according to the health state of animals, compensates the adjusted content of the nutrient substances according to the disease state of the animals after the adjustment is finished, and corrects the content of the nutrient substances in the feed according to the daily feed intake D of individual animals after the compensation is finished; the adjusting module is also used for adjusting the water content Q of the feed according to the environment humidity H of the animal feeding environment;
the detection module is used for detecting the adjusted feed feeding result; the detection module judges the growth speed of the animal after the feed is adjusted according to the daily gain M of the individual animal; and the adjusting module adjusts the content of the nutrient again according to the judgment result made by the detecting module.
2. The ODM system for data analysis based on the Internet of things for feed production according to claim 1, wherein when the analysis module analyzes the animal growth information, the comparison unit compares the collected animal weight A with each preset weight, and the judgment unit judges the health status of the animal according to the comparison result, wherein,
if the animal is in the juvenile stage, when A is less than A11, the judging unit judges that the animal individual is thin and weak; when A11 is not less than A < A12, the judging unit judges that the weight of the animal individual is normal; when A12 is less than or equal to A, the judging unit judges that the animal individual is obese;
if the animal is in the growth period, when A is less than A21, the judging unit judges that the animal individual is thin and weak; when A21 is not less than A < A22, the judging unit judges that the weight of the animal individual is normal; when A22 is less than or equal to A, the judging unit judges that the animal individual is obese;
if the animal is in the adult stage, when A is less than A31, the judging unit judges that the animal individual is thin and weak; when A31 is not less than A < A32, the judging unit judges that the weight of the animal individual is normal; when A32 is less than or equal to A, the judging unit judges that the animal individual is obese;
wherein A11 is the first predetermined juvenile phase weight, A12 is the second predetermined juvenile phase weight, A11 is less than A12; a21 is the weight of the first preset growing period, A22 is the weight of the second preset growing period, A21 is more than A22; a31 is the first predetermined adult body weight, A32 is the second predetermined adult body weight, A31 < A32.
3. The ODM system for data analysis based on the Internet of things for feed production according to claim 2, wherein the adjusting module obtains the content C of the nutrient substances in the feed after the weight analysis of the individual animals is completed, and adjusts the content C of the nutrient substances in the feed according to the health status of the animals, wherein,
when the animal individual is judged to be weak, the adjusting module adjusts the content of nutrient substances in the feed to be C1, sets C1= C × a1, sets a1 as a nutrition increasing coefficient, and sets the value of 1 & lt a1 & lt 1.2;
when the animal individual is judged to be obese, the adjusting module adjusts the content of the nutrient in the feed to be C2, sets C2= C × a2 and a2 as a nutrient reduction coefficient, wherein 0.8 < a1 < 1.
4. The ODM system for data analysis based on the Internet of things for feed production as claimed in claim 3, wherein the adjusting module obtains disease state of individual animal after adjusting the content of nutrient substances in the feed, and compensates the adjusted content Ci of nutrient substances according to the disease state, and sets i =1,2,
when the animal individual does not have the disease, compensation is not carried out;
when the animal individuals have diseases, the adjusting module compensates the content of the nutrient substances in the feed to Ci ', and Ci' = Ci x b is set, wherein b is a nutrient compensation coefficient, and 1 < b < 1.1.
5. The ODM system for data analysis based on the Internet of things for feed production according to claim 4, wherein after the adjustment module completes the compensation of the content of the nutrients in the feed, the adjustment module obtains the daily food consumption D of the individual animal, compares the daily food consumption D of the individual animal with each preset daily food consumption, and corrects the content of the nutrients in the feed according to the comparison result,
when D < D1, the adjustment module determines that the individual animal has insufficient daily food intake and corrects the nutrient content in the feed to Ca, setting Ca = Ci '+ Ci' × (D1-D)/D1;
when D1 is not less than D2, the adjusting module judges that the daily food intake of the animal individual is normal and does not carry out correction;
when D2 is less than D, the adjusting module judges that the daily food intake of the animal is excessive, corrects the nutrient content in the feed to Cb, and sets Cb = Ci '-Ci' × (D-D2)/D;
wherein D1 is the first preset daily food intake, D2 is the second preset daily food intake, and D1 is less than D2.
6. The ODM system for data analysis based on the Internet of things for feed production according to claim 5, wherein after the adjustment module finishes the correction of the content of the nutrient substances in the feed, the adjustment module obtains the water content Q of the feed and adjusts the water content Q according to the environmental humidity H of the animal feeding environment, the adjustment module compares the environmental humidity H of the feeding environment with each preset environmental humidity and adjusts the water content Q according to the comparison result, wherein,
when H is less than H1, the adjusting module judges that the feeding environment is dry, the water content of the feed is adjusted to be Q1, Q1= Q + Q x (H1-H)/H1 is set, when Q1 is more than or equal to Qmax, the adjusting module takes Qmax as the water content of the feed, and Qmax is the maximum preset water content of the feed;
when H1 is not less than H < H2, the adjusting module judges that the humidity of the feeding environment is normal and does not adjust;
when H2 is less than or equal to H, the adjusting module judges that the feeding environment is moist and adjusts the water content of the feed to be Q2, and sets Q2= Q-Qx (H-H2)/H, when Q2 is less than or equal to Qmin, the adjusting module takes Qmin as the water content of the feed, and the Qmin is the preset minimum water content of the feed;
wherein H1 is the first preset environmental humidity, H2 is the second preset environmental humidity, H1 is less than H2.
7. The ODM system for data analysis based on the Internet of things for feed production according to claim 6, wherein after the adjustment of the components and the water content of the feed is completed, the obtaining unit of the adjusting module obtains the daily gain M of the animal individual after the feed is fed for the adjusted time T, T is a preset value, the determining unit of the detecting module compares the daily gain M with the preset daily gain M0 and determines the adjustment of the feed according to the comparison result, wherein,
if the content of the nutrient substances is adjusted, the animal individual is judged to be weak, and when M is less than M0, the judgment unit judges that the growth speed of the animal individual is slow; when M is larger than or equal to M0, the judging unit judges that the growth speed of the animal individual is normal;
if the nutrient content is adjusted, judging that the animal individual is obese, and when M is less than or equal to M0, judging that the growth speed of the animal individual is normal by the judging unit; when M > M0, the judging unit judges that the individual animal grows fast.
8. The data analysis ODM system based on the Internet of things for feed production of claim 7, wherein when the judging unit judges that the growth speed of the animal individual is slow, the adjusting module adjusts the corrected nutrient content again and adjusts the nutrient content to Cm, and the Cm = Ca + Ca x (M0-M)/M0 or Cb + Cb x (M0-M)/M0 is set, and the animal is fed with the adjusted feed after the adjustment of the nutrient content is completed.
9. The ODM system for data analysis based on the Internet of things for feed production as claimed in claim 7, wherein when the determination unit determines that the growth rate of individual animals is high, the adjustment module readjusts the corrected nutrient content and adjusts the nutrient content to Cn, wherein Cn = Ca-Ca x (M-M0)/M or Cb-Cb x (M-M0)/M is set, and after the adjustment of the nutrient content is completed, the animals are fed with the adjusted feed.
10. The ODM system for data analysis based on the Internet of things for feed production according to claim 7, wherein when the judging unit judges that the growth speed of the individual animal is normal, the adjusting module does not adjust the content of the nutrient substances any more, and the adjusting process of the content of the nutrient substances is repeated according to animal growth information collected in real time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111267513.5A CN113709260B (en) | 2021-10-29 | 2021-10-29 | Data analysis ODM system based on feed production thing networking |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111267513.5A CN113709260B (en) | 2021-10-29 | 2021-10-29 | Data analysis ODM system based on feed production thing networking |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113709260A true CN113709260A (en) | 2021-11-26 |
| CN113709260B CN113709260B (en) | 2022-01-07 |
Family
ID=78647442
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111267513.5A Active CN113709260B (en) | 2021-10-29 | 2021-10-29 | Data analysis ODM system based on feed production thing networking |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113709260B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114442555A (en) * | 2022-01-04 | 2022-05-06 | 广东海思智能装备有限公司 | Electrical control system of numerical control machine tool |
| CN114557460A (en) * | 2022-04-27 | 2022-05-31 | 北京喜禽药业有限公司 | Big data-based animal feed production method, system and storage medium |
| CN116029499A (en) * | 2022-12-12 | 2023-04-28 | 广东讯源营养科技有限公司 | Big data-based feed intelligent quantitative proportioning management system |
| CN116779130A (en) * | 2022-03-15 | 2023-09-19 | 舒志伟 | Remote health monitoring management system |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5827517A (en) * | 1994-08-05 | 1998-10-27 | Wisconsin Alumni Research Foundation | CCK antibodies used to improve feed efficiency |
| UA8475U (en) * | 2004-12-08 | 2005-08-15 | Biochem Ltd Ltd Liability Comp | Method for raising productivity of pigs |
| CN101720718A (en) * | 2009-11-16 | 2010-06-09 | 刘瀚昌 | Method for raising high-yield egg chicken |
| CN102524605A (en) * | 2012-03-06 | 2012-07-04 | 天津联合顺兴奶牛养殖专业合作社 | Lactating dairy cow feed formula and use method thereof |
| CN103238562A (en) * | 2013-05-06 | 2013-08-14 | 李云杨 | Healthy efficient pig raising method |
| CN104663484A (en) * | 2015-03-11 | 2015-06-03 | 海摩尔（北京）科技有限公司 | Method and system for searching growth rate inflection points of group-breeding domestic animals and fowls in real time to adjust feedstuffs |
| MD1044Y (en) * | 2015-09-28 | 2016-06-30 | Larisa CAISÎN | Process for feeding pigs |
| CN106797892A (en) * | 2015-11-26 | 2017-06-06 | 范石军 | A kind of daily ration concentration Contrast tuned imaging and feedback feeding system |
| CN107173327A (en) * | 2017-06-30 | 2017-09-19 | 重庆问天农业科技有限公司 | A kind of high intensity steer ration method |
| CN109685678A (en) * | 2018-12-28 | 2019-04-26 | 广州影子科技有限公司 | Domestic animal intelligence management system for breeding and method based on big data technology |
| CN112602667A (en) * | 2020-12-14 | 2021-04-06 | 华南农业大学 | Pig dynamic feedback feeding method and system |
| CN112825795A (en) * | 2021-01-13 | 2021-05-25 | 余姚市谐丰模塑有限公司 | Feeding detection system |
| TWI736415B (en) * | 2020-09-10 | 2021-08-11 | 國立臺灣海洋大學 | A smart shrimp and/or crab feeding management system and the method thereof |
-
2021
- 2021-10-29 CN CN202111267513.5A patent/CN113709260B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5827517A (en) * | 1994-08-05 | 1998-10-27 | Wisconsin Alumni Research Foundation | CCK antibodies used to improve feed efficiency |
| UA8475U (en) * | 2004-12-08 | 2005-08-15 | Biochem Ltd Ltd Liability Comp | Method for raising productivity of pigs |
| CN101720718A (en) * | 2009-11-16 | 2010-06-09 | 刘瀚昌 | Method for raising high-yield egg chicken |
| CN102524605A (en) * | 2012-03-06 | 2012-07-04 | 天津联合顺兴奶牛养殖专业合作社 | Lactating dairy cow feed formula and use method thereof |
| CN103238562A (en) * | 2013-05-06 | 2013-08-14 | 李云杨 | Healthy efficient pig raising method |
| CN104663484A (en) * | 2015-03-11 | 2015-06-03 | 海摩尔（北京）科技有限公司 | Method and system for searching growth rate inflection points of group-breeding domestic animals and fowls in real time to adjust feedstuffs |
| MD1044Y (en) * | 2015-09-28 | 2016-06-30 | Larisa CAISÎN | Process for feeding pigs |
| CN106797892A (en) * | 2015-11-26 | 2017-06-06 | 范石军 | A kind of daily ration concentration Contrast tuned imaging and feedback feeding system |
| CN107173327A (en) * | 2017-06-30 | 2017-09-19 | 重庆问天农业科技有限公司 | A kind of high intensity steer ration method |
| CN109685678A (en) * | 2018-12-28 | 2019-04-26 | 广州影子科技有限公司 | Domestic animal intelligence management system for breeding and method based on big data technology |
| TWI736415B (en) * | 2020-09-10 | 2021-08-11 | 國立臺灣海洋大學 | A smart shrimp and/or crab feeding management system and the method thereof |
| CN112602667A (en) * | 2020-12-14 | 2021-04-06 | 华南农业大学 | Pig dynamic feedback feeding method and system |
| CN112825795A (en) * | 2021-01-13 | 2021-05-25 | 余姚市谐丰模塑有限公司 | Feeding detection system |
Non-Patent Citations (1)
| Title |
|---|
| 李忠超: "饲料ODM 物联网技术研发实践与应用", 《湖南饲料》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114442555A (en) * | 2022-01-04 | 2022-05-06 | 广东海思智能装备有限公司 | Electrical control system of numerical control machine tool |
| CN116779130A (en) * | 2022-03-15 | 2023-09-19 | 舒志伟 | Remote health monitoring management system |
| CN114557460A (en) * | 2022-04-27 | 2022-05-31 | 北京喜禽药业有限公司 | Big data-based animal feed production method, system and storage medium |
| CN114557460B (en) * | 2022-04-27 | 2022-07-19 | 北京喜禽药业有限公司 | Big data-based animal feed production method, system and storage medium |
| CN116029499A (en) * | 2022-12-12 | 2023-04-28 | 广东讯源营养科技有限公司 | Big data-based feed intelligent quantitative proportioning management system |
| CN116029499B (en) * | 2022-12-12 | 2023-08-04 | 广东讯源营养科技有限公司 | Big data-based feed intelligent quantitative proportioning management system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113709260B (en) | 2022-01-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113709260B (en) | Data analysis ODM system based on feed production thing networking | |
| AU2017258820B2 (en) | Systems and methods for estimating feed efficiency and carbon footprint for milk producing animal | |
| CN103596448A (en) | Methods and systems for adjusting ruminally digestible starch and fiber in animal diets | |
| CN103300221A (en) | Favorite forage grass of Qinling Mountain forest musk deer and collecting time of favorite forage grass | |
| Oba et al. | In vitro digestibility of forages. | |
| CN102326681B (en) | Low-protein amino acid balanced nursing sow feed | |
| Katsande et al. | Apparent digestibility and microbial protein yield of Desmodium uncinatum, Mucuna pruriens and Vigna unguiculata forage legumes in goats | |
| Edwards et al. | In vitro ruminal fermentation parameters of tanner grass (Brachiaria arrecta) supplemented with leaves from three forage trees | |
| CN109393144A (en) | The method for preparing biological fermentation feed using the by-product of wet production cornstarch | |
| Katić et al. | Variation of protein, cellulose and mineral contents of lucerne as influenced by cultivar and cut | |
| Jiang et al. | Interactions of dietary protein and threonine on growth performance in Pekin ducklings from 1 to 14 days of age | |
| CN103431211A (en) | Green and economical nursing sow feed | |
| CN108244400A (en) | A kind of compound feed for snakeheads and preparation method thereof | |
| Hartono et al. | E-aquaponics: aquaculture and hydroponic integration using electronical control and monitoring | |
| WO2005034644A1 (en) | Fermentation byproduct feed formulation and processing | |
| CN110999831B (en) | Pelteobagrus fulvidraco fry high in vegetable protein tolerance and breeding method thereof | |
| CN101283707B (en) | Application of glucose or starch in replace of whey powder(lactose) in calf milk substitute | |
| Brand et al. | Use of varying combinations of energy and protein sources as supplementary feed for lambing ewes grazing cereal stubble | |
| CN117909695B (en) | Intelligent feeding management system for aquaculture | |
| CN110973366A (en) | Ruminant fermented total mixed ration and preparation method thereof | |
| CN109601774A (en) | A kind of Ba Shayu adult fish fermentation expanded pellet diet and preparation method thereof | |
| CN118318924A (en) | Preparation method of high-efficiency dried biological feed | |
| KR102489383B1 (en) | Method for estimating phosphorus requirement of swine | |
| Cabrera et al. | Rumen fermentative activity in the goat and sheep | |
| Adler et al. | Farmers’ Approaches Towards Increased Self-Sufficiency With Feed On Organic Dairy Farms |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |