CN113449009A - Intelligent management method, equipment and medium for animal husbandry production management - Google Patents
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Abstract
The invention relates to the technical field of animal husbandry management, in particular to an intelligent management method, equipment and a medium for animal husbandry production management, wherein the method comprises the following steps: receiving raw data of livestock input by a client; wherein the raw data comprises a plurality of sets; outputting a presumed data value range corresponding to each group of the original data through a correspondingly trained preset model; receiving the update data of the livestock input by the client; the update data comprises a plurality of groups, and the plurality of groups of update data represent update parameters different from each group of the raw livestock data or are matched data matched with each group of the raw livestock data; and judging whether the plurality of groups of updating data are in the corresponding range of the presumed data value range. The technical scheme provided by the invention can form an ordered feeding management system and an excellent management method.
Description
Technical Field
The invention relates to the technical field of animal husbandry management, in particular to an intelligent management method, equipment and medium for animal husbandry production management.
Background
With the development of animal husbandry, animal husbandry management methods need to be further improved. Therefore, there is a need for an intelligent management method, apparatus and medium for animal husbandry production management with data falsification that can form an ordered feeding management system and an excellent management method.
Disclosure of Invention
The main object of the present invention is to provide an intelligent management method, device and medium for animal husbandry production management, which can form an ordered feeding management system and excellent management method for data falsification.
In order to achieve the above object, a first aspect of the present invention provides an intelligent management method for livestock production management, the method comprising:
receiving raw data of livestock input by a client; wherein the raw data comprises a plurality of sets;
outputting a presumed data value range corresponding to each group of the original data from each group of the original data through a correspondingly trained preset model;
receiving the update data of the livestock input by the client; the update data comprises a plurality of groups, and the plurality of groups of update data represent update parameters different from each group of the raw livestock data or are matched data matched with each group of the raw livestock data;
judging whether the multiple groups of updating data are in the corresponding range of the presumed data value range;
when a group of the updating data is not in the range of the corresponding value range of the speculated data, updating and displaying the updating data and storing an updating record.
In some other embodiments, the method further comprises:
and when one group of the updated data is in the corresponding range of the presumed data value range, discarding the group of the updated data which is not in the corresponding range of the presumed data value range, and popping up a warning window to remind the client to input the updated data of the livestock again.
In some other embodiments, the raw data comprises production data comprising an initial weight, an initial body length, and an initial age; the training of the preset model comprises the following steps:
receiving initial age, initial weight and initial body length input by a client;
generating a predicted weight value range and a preset body length value range according to the initial age;
when the initial weight and the initial body length are in a predicted weight value range and a preset body length value range, judging that the training is successful;
and when the initial weight and the initial body length exceed the predicted weight value field and the preset body length value field, recording the initial weight and the initial body length into the predicted weight value field and the preset body length value field.
In some other embodiments, the raw data comprises breeding data comprising individual varieties in need of breeding; the training of the preset model comprises the following steps:
receiving breeding data input by a client; the breeding parameters comprise variety parameters;
generating a predicted mating variety according to the variety parameters;
when the variety parameters are matched with the predicted mating varieties, judging that the training is successful;
when the breed parameter and the predicted matched breed do not match, deleting the predicted matched breed and regenerating another predicted matched breed.
In some other embodiments, the method further comprises:
preprocessing the original data;
judging whether the preprocessed original data meet a first standard or not;
receiving the raw data when the preprocessed raw data meets a first criterion.
In some other embodiments, the method further comprises:
and when the preprocessed original data do not meet the first standard, discarding the original data, generating an input box and returning the input box to the client.
In some other embodiments, the first criterion includes whether a data format of the raw data conforms to a preset format; the preset format comprises one of a text type, a numerical type, a floating point type and a date type.
In some other embodiments, the first criterion further comprises whether the raw data meets an actual condition; wherein the actual condition is whether the original data appears in the database.
The second aspect of the invention discloses an intelligent management device for production management of animal husbandry, which comprises:
the first data receiving module: the livestock data processing system is used for receiving raw data of livestock input by a client; wherein the original data comprises a plurality of groups;
a data speculation module: the device is used for outputting a speculative data value range corresponding to each group of the original data through a correspondingly trained preset model;
the second data receiving module: the livestock management system is used for receiving the updated data of the livestock input by the client; the update data comprises a plurality of groups, and the plurality of groups of update data represent update parameters different from each group of the raw livestock data or are matched data matched with each group of the raw livestock data;
a judging module: the device is used for judging whether the plurality of groups of updating data are in the corresponding range of the presumed data value range or not;
and a data updating module: and the device is used for updating and displaying the update data and storing an update record when a group of the update data is not in the corresponding range of the presumed data value range.
In a third aspect, the invention discloses a storage medium, which stores an executable program, and when the executable program is executed, the intelligent management method for animal husbandry production management is realized.
The technical scheme provided by the invention has the following advantages:
receiving raw data of livestock input by a client; wherein the raw data comprises a plurality of sets; outputting a presumed data value range corresponding to each group of the original data from each group of the original data through a correspondingly trained preset model; receiving the update data of the livestock input by the client; the update data comprises a plurality of groups, and the plurality of groups of update data represent update parameters different from each group of the raw livestock data or are matched data matched with each group of the raw livestock data; judging whether the multiple groups of updating data are in the corresponding range of the presumed data value range; when a group of the updating data is not in the range of the corresponding value range of the speculated data, updating and displaying the updating data and storing an updating record. Can form an ordered feeding management system and an excellent management method.
Drawings
Fig. 1 is a schematic flow chart of an intelligent management method for livestock husbandry production management according to an embodiment of the present invention.
Fig. 2 is a scene schematic diagram of an intelligent management method for animal husbandry production management according to another embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an intelligent management device for stock farming production management according to an embodiment of the present invention.
Fig. 4 is a block diagram of a server according to another embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. 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 and 2, an aspect of the present invention provides an intelligent management method for animal husbandry production management, applied to a system consisting of a client, a server and a database, the method comprising:
step S10: receiving raw data of livestock input by a client; wherein the raw data comprises a plurality of sets.
In some possible embodiments, the server receives raw data of livestock input by the client; wherein the raw data comprises a plurality of sets. For example, the raw data includes production data including an initial weight, an initial body length, and an initial age.
Step S20: and outputting a presumed data value range corresponding to each group of the original data through a correspondingly trained preset model.
The training of the preset model comprises the following steps:
receiving initial age, initial weight and initial body length input by a client;
generating a predicted weight value range and a preset body length value range according to the initial age;
when the initial weight and the initial body length are in a predicted weight value range and a preset body length value range, judging that the training is successful;
and when the initial weight and the initial body length exceed the predicted weight value field and the preset body length value field, recording the initial weight and the initial body length into the predicted weight value field and the preset body length value field.
The training of the preset model may also include:
receiving breeding data input by a client; the breeding parameters comprise variety parameters;
generating a predicted mating variety according to the variety parameters;
when the variety parameters are matched with the predicted mating varieties, judging that the training is successful;
when the breed parameter and the predicted matched breed do not match, deleting the predicted matched breed and regenerating another predicted matched breed. The matching rule can be determined according to the inbred coefficient X, and when the variety parameter and the predicted mating variety are within the inbred coefficient X, the matching is successful.
Step S30: receiving the update data of the livestock input by the client; the update data comprises a plurality of sets of update data being update parameters representing different from each set of the raw animal data or matching data matching each set of the raw animal data.
In some possible embodiments, the type of the update data may be various types of data such as a weight value, a day age, or a race type of livestock. Can follow the all-round data of making statistics of like this, understand the information of a plurality of angles of livestock, more conveniently carry out the record to the growth condition of livestock, be convenient for master the growth law of livestock.
Step S40: and judging whether the plurality of groups of updating data are in the corresponding range of the presumed data value range.
In some possible embodiments, the server determines whether the plurality of sets of update data are within the range of the corresponding range of the speculative data value range. By comparing the update data with the speculative data value range, a result can be obtained as to whether the update data is valid.
Step S50: and when a group of the updating data is not in the range of the corresponding presumed data value range, updating and displaying the updating data and storing an updating record. For example, the species of livestock is live pigs, the initial age is 15 days, the predicted weight is 3.3KG-5.3KG, the initial weight is 5.5KG, and since 10KG is not between 3.3KG-5.3KG, the data for absorbing 5.5KG will predict a weight of 3.3KG-5.5 KG; when the initial weight is 4.6KG, the weight is stored in the database.
Step S60: when one group of the updated data is in the corresponding range of the presumed data value range, abandoning the group of the updated data which is not in the corresponding range of the presumed data value range and popping up an alarm window to remind the client to input the updated data of the livestock again.
In some possible embodiments, errors are not made because the speculative data value field is trained, including all of the training samples. When a group of the updated data is in the corresponding range of the value domain of the presumed data, discarding the group of the updated data which is not in the corresponding range of the value domain of the presumed data and popping up an alarm window to remind the client to re-input the updated data of the livestock, wherein the updated data is invalid data and should be discarded.
In some possible embodiments, the method further comprises:
preprocessing the original data;
judging whether the preprocessed original data meet a first standard or not;
receiving the raw data when the preprocessed raw data meets a first criterion.
In some possible embodiments, the method further comprises:
and when the preprocessed original data do not meet the first standard, discarding the original data, generating an input box and returning the input box to the client.
In some possible embodiments, the first criterion includes whether a data format of the raw data conforms to a preset format; the preset format comprises one of a text type, a numerical type, a floating point type and a date type.
In some possible embodiments, the first criterion further comprises whether the raw data meets an actual condition; wherein the actual condition is whether the original data appears in the database.
Referring to fig. 3, the present application also provides an intelligent management device for animal husbandry production management, the device comprising:
the first reception data module 10: the livestock data processing system is used for receiving raw data of livestock input by a client; wherein the raw data comprises a plurality of sets;
the data speculation module 20: the device is used for outputting a speculative data value range corresponding to each group of the original data through a corresponding trained preset model;
the second reception data module 30: the livestock management system is used for receiving the updated data of the livestock input by the client; the update data comprises a plurality of groups, and the plurality of groups of update data represent update parameters different from each group of the raw livestock data or are matched data matched with each group of the raw livestock data;
the judging module 40: for determining whether the plurality of sets of update data are within the corresponding range of the speculative data value domain;
the update data module 50: and the device is used for updating and displaying the update data and storing an update record when a group of the update data is not in the corresponding range of the presumed data value field.
Referring to fig. 4, the present application further provides a server 30, where the server 30 includes a memory 301 and a processor 302, where the memory 301 and the processor 302 are electrically connected through a bus 303.
The memory 301 includes at least one type of readable storage medium, which includes flash memory, hard disk, multi-media card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, and the like. The memory 301 may in some embodiments be an internal storage unit of the server 30, such as a hard disk of the server 30. The memory 301 may also be an external storage device of the server 30 in other embodiments, such as a plug-in hard disk provided on the server 30, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), and the like. The memory 301 may be used to store not only application software installed in the vehicle-mounted device and various data, such as codes of computer-readable programs, but also temporarily store data that has been output or will be output, that is, the first memory may be used as a storage medium storing an intelligent management program for animal husbandry production management executable by a computer.
The processor 302 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor or other data Processing chip in some embodiments, and the processor 302 may call an intelligent management program for animal husbandry production management stored in the memory 301 to implement the following steps:
step S10: receiving raw data of livestock input by a client; wherein the raw data comprises a plurality of sets.
In some possible embodiments, the server receives raw data of livestock input by the client; wherein the raw data comprises a plurality of sets. For example, the raw data includes production data including an initial weight, an initial body length, and an initial age.
Step S20: and outputting a presumed data value range corresponding to each group of the original data through a correspondingly trained preset model.
The training of the preset model comprises the following steps:
receiving initial age, initial weight and initial body length input by a client;
generating a predicted weight value range and a preset body length value range according to the initial age;
when the initial weight and the initial body length are in a predicted weight value range and a preset body length value range, judging that the training is successful;
and when the initial weight and the initial body length exceed the predicted weight value field and the preset body length value field, recording the initial weight and the initial body length into the predicted weight value field and the preset body length value field.
The training of the preset model may also include:
receiving breeding data input by a client; the breeding parameters comprise variety parameters;
generating a predicted mating variety according to the variety parameters;
when the variety parameters are matched with the predicted mating varieties, judging that the training is successful;
when the breed parameter and the predicted matched breed do not match, deleting the predicted matched breed and regenerating another predicted matched breed. The matching rule can be determined according to the inbred coefficient X, and when the variety parameter and the predicted mating variety are within the inbred coefficient X, the matching is successful.
Step S30: receiving the update data of the livestock input by the client; the update data comprises a plurality of sets of update data being update parameters representing different from each set of the raw animal data or matching data matching each set of the raw animal data.
In some possible embodiments, the type of the update data may be various types of data such as a weight value, a day age, or a race type of livestock. Can follow the all-round data of making statistics of like this, understand the information of a plurality of angles of livestock, more conveniently carry out the record to the growth condition of livestock, be convenient for master the growth law of livestock.
Step S40: and judging whether the plurality of groups of updating data are in the corresponding range of the presumed data value range.
In some possible embodiments, the server determines whether the plurality of sets of update data are within the range of the corresponding range of the speculative data value range. By comparing the update data with the speculative data value range, a result can be obtained as to whether the update data is valid.
Step S50: and when a group of the updating data is not in the range of the corresponding presumed data value range, updating and displaying the updating data and storing an updating record. For example, the species of livestock is live pigs, the initial age is 15 days, the predicted weight is 3.3KG-5.3KG, the initial weight is 5.5KG, and since 10KG is not between 3.3KG-5.3KG, the data for absorbing 5.5KG will predict a weight of 3.3KG-5.5 KG; when the initial weight is 4.6KG, the weight is stored in the database.
Step S60: when one group of the updated data is in the corresponding range of the presumed data value range, abandoning the group of the updated data which is not in the corresponding range of the presumed data value range and popping up an alarm window to remind the client to input the updated data of the livestock again.
In some possible embodiments, errors are not made because the speculative data value field is trained, including all of the training samples. When a group of the updated data is in the corresponding range of the value domain of the presumed data, discarding the group of the updated data which is not in the corresponding range of the value domain of the presumed data and popping up an alarm window to remind the client to re-input the updated data of the livestock, wherein the updated data is invalid data and should be discarded.
In some possible embodiments, the method further comprises:
preprocessing the original data;
judging whether the preprocessed original data meet a first standard or not;
receiving the raw data when the preprocessed raw data meets a first criterion.
In some possible embodiments, the method further comprises:
and when the preprocessed original data do not meet the first standard, discarding the original data, generating an input box and returning the input box to the client.
In some possible embodiments, the first criterion includes whether a data format of the raw data conforms to a preset format; the preset format comprises one of a text type, a numerical type, a floating point type and a date type.
In some possible embodiments, the first criterion further comprises whether the raw data meets an actual condition; wherein the actual condition is whether the original data appears in the database.
The technical scheme provided by the invention has the following advantages:
receiving raw data of livestock input by a client; wherein the raw data comprises a plurality of sets; outputting a presumed data value range corresponding to each group of the original data from each group of the original data through a correspondingly trained preset model; receiving the update data of the livestock input by the client; the update data comprises a plurality of groups, and the plurality of groups of update data represent update parameters different from each group of the raw livestock data or are matched data matched with each group of the raw livestock data; judging whether the multiple groups of updating data are in the corresponding range of the presumed data value range; when a group of the updating data is not in the range of the corresponding value range of the speculated data, updating and displaying the updating data and storing an updating record. Can form an ordered feeding management system and an excellent management method.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An intelligent management method for animal husbandry production management, the method comprising:
receiving raw data of livestock input by a client; wherein the raw data comprises a plurality of sets;
outputting a presumed data value range corresponding to each group of the original data through a correspondingly trained preset model;
receiving the update data of the livestock input by the client; the update data comprises a plurality of groups, and the plurality of groups of update data represent update parameters different from each group of the raw livestock data or are matched data matched with each group of the raw livestock data;
judging whether the multiple groups of updating data are in the corresponding range of the presumed data value range;
and when a group of the updating data is not in the range of the corresponding presumed data value range, updating and displaying the updating data and storing an updating record.
2. The intelligent management method for animal husbandry production management as claimed in claim 1, wherein said method further comprises:
and when one group of the updated data is in the corresponding range of the presumed data value range, discarding the group of the updated data which is not in the corresponding range of the presumed data value range, and popping up an alarm window to remind the client of re-inputting the updated data of the livestock.
3. The intelligent management method for animal husbandry production management as claimed in claim 1, wherein said raw data comprises production data, said production data comprising initial weight, initial length and initial age; the training of the preset model comprises the following steps:
receiving initial age, initial weight and initial body length input by a client;
generating a predicted weight value range and a preset body length value range according to the initial age;
when the initial weight and the initial body length are in a predicted weight value range and a preset body length value range, judging that the training is successful;
and when the initial weight and the initial body length exceed the predicted weight value field and the preset body length value field, recording the initial weight and the initial body length into the predicted weight value field and the preset body length value field.
4. The intelligent management method for animal husbandry production management as claimed in claim 1, wherein the raw data comprises breeding data, the breeding data comprising individual varieties to be bred; the training of the preset model comprises the following steps:
receiving breeding data input by a client; the breeding parameters comprise variety parameters;
generating a predicted mating variety according to the variety parameters;
when the variety parameters are matched with the predicted mating varieties, judging that the training is successful;
when the breed parameter and the predicted matched breed do not match, deleting the predicted matched breed and regenerating another predicted matched breed.
5. The intelligent management method for animal husbandry production management as claimed in claim 1, wherein said method further comprises:
preprocessing the original data;
judging whether the preprocessed original data meet a first standard or not;
receiving the raw data when the preprocessed raw data meets a first criterion.
6. The intelligent management method for animal husbandry production management as claimed in claim 5, wherein said method further comprises:
and when the preprocessed original data do not meet the first standard, discarding the original data, generating an input box and returning the input box to the client.
7. The intelligent management method for animal husbandry production management as claimed in claim 5, wherein the first standard includes whether a data format of the raw data conforms to a preset format; the preset format comprises one of a text type, a numerical type, a floating point type and a date type.
8. The intelligent management method for animal husbandry production management as claimed in claim 7, wherein the first criterion further comprises whether the raw data meets actual conditions; wherein the actual condition is whether the original data appears in the database.
9. An intelligent management device for animal husbandry production management, the device comprising:
the first data receiving module: the livestock data processing system is used for receiving raw data of livestock input by a client; wherein the raw data comprises a plurality of sets;
a data speculation module: the device is used for outputting a speculative data value range corresponding to each group of the original data through a correspondingly trained preset model;
the second data receiving module: the livestock management system is used for receiving the updated data of the livestock input by the client; the update data comprises a plurality of groups, and the plurality of groups of update data represent update parameters different from each group of the raw livestock data or are matched data matched with each group of the raw livestock data;
a judging module: the device is used for judging whether the plurality of groups of updating data are in the corresponding range of the presumed data value range or not;
and a data updating module: and the device is used for updating and displaying the update data and storing an update record when a group of the update data is not in the corresponding range of the presumed data value range.
10. A medium, characterized in that it stores an executable program which, when executed, implements an intelligent management method for animal husbandry production management according to any one of claims 1-8.
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