Animal husbandry feeding and discharging precise control system based on Internet of Things

Abstract

The invention discloses an accurate feeding and discharging control system for animal husbandry based on the Internet of Things, and belongs to the technical field of animal breeding. In order to solve the problem that residual food is accumulated and easy to go bad due to different eating habits of different livestock and inaccurate feed feeding, the data processing unit adjusts and specifies a targeted feed feeding scheme of each livestock, accurate feed feeding is performed through the system control unit, and the different eating habits of the different livestock are analyzed; the daily feeding times, frequency, food intake and corresponding weight increase and decrease data of different livestock can be mastered, a targeted feed feeding scheme is specified, and accurate feeding can be performed on different livestock according to the targeted feed feeding scheme; it is guaranteed that the single feeding amount and the daily feeding frequency are matched with daily feeding habits and food requirements of livestock, the situation of excessive feeding or too little feeding is avoided, and the problem that the quality is affected due to the fact that remaining food in the feeding trough is accumulated and exposed for a long time is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of animal breeding, and in particular to an accurate feed feeding control system for animal husbandry based on the Internet of Things. Background Art

[0002] With the development of various network systems and cloud computing technologies, the Internet of Things has realized the connection, management and control of things, people and all things and networks through various wireless and wired communication networks. It provides personalized real-time online detection, location tracing, remote control, remote maintenance, online upgrade and other management and service functions, realizing the integration of "management, control and operation" of all things.

[0003] Existing animal husbandry is all artificially produced and fed, so there is a need for a animal husbandry control system for the Internet of Things to comprehensively control the animal husbandry environment and monitor the site. There are related patents. For example, publication number CN104536496A discloses a animal husbandry control system for the Internet of Things, which consists of a data acquisition component, a control component, a display template and a transmission module. The data acquisition component is used to collect animal husbandry light illumination, temperature, humidity and wind speed signals, which are transmitted to the display template through the transmission module. The operator adjusts the control component according to actual needs. The system is easy to operate, reduces labor costs, and is timely adjusted to improve production efficiency. At the same time, the system is simple, and the Internet of Things is used for animal husbandry, which has many expandable functions, thereby realizing intelligence.

[0004] The above patent actually has the following problems in actual operation: 1. When raising livestock, different livestock have different eating habits and needs. It is difficult to accurately adapt to the growth needs of different livestock individuals if they are fed according to a unified feeding standard. 2. When raising livestock, if too little feed is fed, the livestock will not eat enough. If too much feed is fed, it will cause surplus food to accumulate inside the trough. Long-term accumulation will cause deterioration or reduction in quality, affecting the feeding effect and livestock growth. Summary of the invention

[0005] The purpose of the present invention is to provide a precise control system for feeding livestock based on the Internet of Things to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above purpose, the present invention provides the following technical solution: a precise control system for feeding livestock based on the Internet of Things, comprising: Data acquisition unit for: Interact with the peripheral component unit to collect livestock breeding related data, including single feeding weight, residual food weight in the trough, livestock weight before feeding, livestock ear tag identity information, breeding environment temperature and humidity, and generate livestock breeding data set; Data processing unit for: Retrieving livestock breeding data sets, calculating the livestock breeding fluctuation data corresponding to the ear tag identity information of each livestock, and adjusting and specifying the targeted feed feeding plan for each livestock based on the calculation results; System control unit for: Generate a human-machine interactive interface for the operator to control the system in real time, identify the feeding plan based on the targeted feed plan, and perform precise feeding; Feeding monitoring unit for: Monitor the temperature and humidity data in the livestock pen and generate an environmental data change table, conduct correlation analysis on the breeding fluctuation data at each time point and the environmental temperature and humidity data at the same time point, and issue abnormal alarms for abnormal conditions in the livestock breeding process; Peripheral component unit for: The specific functions of feed feeding, trough weighing, livestock weighing, livestock identification, and environmental monitoring in livestock breeding are realized by external equipment arranged in the breeding environment. The external equipment includes an automatic feeder arranged above the livestock trough, a weighing sensor arranged inside the automatic feeder, a weighing device arranged at the bottom of the trough, an electronic floor scale for livestock weighing arranged in front of the trough, a livestock ear tag reader arranged in the trough area, a temperature and humidity monitor arranged in the breeding environment, and room temperature cooling and heating equipment. The external equipment is connected to the data acquisition unit through the Internet of Things; Control platform for: Used to store, run and implement the data acquisition unit, data processing unit, system control unit and feeding monitoring unit, including at least one login terminal and at least one simulation control terminal. When the simulation control terminal is working, the data acquisition unit, data processing unit, system control unit and feeding monitoring unit are operated and the precise control system of animal husbandry feeding and feeding based on the Internet of Things is realized. The login terminal is connected to the peripheral component unit, the simulation control terminal and the server through the local Internet of Things; Servers for: Based on the control instructions issued by the system control unit, the control instructions are sent to the front-end controller through the interface machine, and the front-end controller is then connected to the peripheral component unit to control the peripheral component unit according to the control instructions.

[0007] Furthermore, the data acquisition unit includes: Data acquisition module, used to: Interact with peripheral component units to collect livestock breeding related data and timestamp the data according to the collection time of each data; Data association module for: The breeding-related data collected by external devices located in the same livestock pen are correlated and matched, and the single feeding weight in the livestock pen, the residual food weight in the trough, and the weight of the livestock before eating corresponding to the ear tag identity information of each livestock are matched to generate a livestock breeding data set. Each livestock breeding data set corresponds to each livestock ear tag identity information one by one, and the livestock breeding data set is marked according to the timestamp.

[0008] Furthermore, the data acquisition unit also includes: A data information sending module, used to send livestock breeding data sets with timestamps to the cloud platform; A time extraction module, used for the cloud platform to extract the timestamp of the livestock breeding data set with the timestamp when receiving the livestock breeding data set with the timestamp, and obtain the data collection time corresponding to the timestamp; A data quality evaluation module is used for the cloud platform to obtain the data collection quality evaluation score of the livestock breeding data set at the data collection time; wherein the data collection quality evaluation score is obtained by the following formula: , in, F represents the data collection quality assessment score; F 0 represents the baseline score; T max and T min They represent the maximum and minimum data collection time intervals of the livestock breeding data set respectively; Ti Indicates i The time when the data is collected; n Represents the number of data in the livestock breeding dataset; m Indicates the number of times the data collection time interval in the livestock breeding dataset changes; k Indicates the number of data collections experienced between two adjacent time intervals with changed time lengths when the data collection time interval changes; The data quality judgment module is used for the cloud platform to judge whether the quality of the current livestock breeding data set meets the data collection requirements according to the data collection quality assessment score.

[0009] Furthermore, the data quality judgment module includes: A historical data extraction module, used to extract data collection quality assessment scores of data sets that meet data collection requirements in historical storage records and data collection quality assessment scores of data sets that do not meet data collection requirements in historical storage records; The score threshold setting module is used to set the current score threshold according to the data collection quality assessment score of the data set that meets the data collection requirements in the historical storage records and the data collection quality assessment score of the data set that does not meet the data collection requirements in the historical storage records, wherein the score threshold is obtained by the following formula: , in, F y represents the score threshold; F 01 Indicates the highest threshold value that has appeared in the score threshold setting history; F 02 Indicates the lowest threshold value that appears in the score threshold setting history; F c Indicates the preset initial threshold; a and b Respectively represent the number of data sets that meet the data collection requirements in the historical storage records and the number of data sets that do not meet the data collection requirements in the historical storage records; F ai Indicates i The data collection quality assessment score corresponding to the data set that meets the data collection requirements; F yai Indicates i The score threshold corresponding to the data set that meets the data collection requirements; F bi Indicates i The data collection quality assessment scores of the data sets that do not meet the data collection requirements; F ybi Indicates i The score threshold corresponding to the data set that does not meet the data collection requirements; The quality judgment module is used to generate an alarm message on the cloud platform and send the alarm message to the control platform when the data collection quality assessment score of the current livestock breeding data set is lower than a preset score threshold.

[0010] Furthermore, the data processing unit includes: Data retrieval module, used for: Retrieve the livestock breeding data set created by the data collection unit; Data calculation module, used for: Calculate the livestock breeding fluctuation data corresponding to the livestock ear tag identity information based on the livestock breeding data set, wherein the livestock breeding fluctuation data includes the livestock weight gain value, the livestock single feed intake, the livestock single feeding time, the feed remaining amount in the trough and the livestock single daily feed intake; Solution adjustment module, used to: Based on the calculation results of the data calculation module, the targeted feed feeding plan for the livestock corresponding to the ear tag identity information of each livestock is adjusted and specified.

[0011] Furthermore, the scheme adjustment module further includes: Livestock information submodule for: Establishing a livestock information table, in which each livestock is distinguished by ear tag identity information, and storing the feeding data of each livestock in groups, with the ear tag identity information being used as the grouping basis when grouping the data; Trend simulation submodule, used for: Based on the livestock information table, a curve image is created according to the feeding data of each livestock, and a feeding status curve is simulated for the daily food intake and weight gain trend of each livestock; Feeding adjustment submodule, used to: Based on the simulation results of the feeding status curve, the daily feeding time, frequency and feeding amount of each livestock are adjusted, and a targeted feeding plan is generated for each livestock.

[0012] Furthermore, the system control unit includes: Interaction modules for: Generate a human-computer interaction interface, display the real-time control information and real-time status of the operation instructions of the data acquisition unit, the data processing unit, the peripheral component unit and the feeding monitoring unit through the interaction module, and the operator can control the data acquisition unit, the data processing unit, the peripheral component unit and the feeding monitoring unit in real time through the interaction module; Control execution module, used to: Execute the control instructions generated by the interactive module, and realize precise control of feeding and feeding by interacting with the data acquisition unit, data processing unit, peripheral component unit and feeding monitoring unit; Smart feeding module for: The targeted feeding plan is generated based on the data processing unit, and the livestock is fed with precise feed based on the plan identification result.

[0013] Furthermore, the intelligent feeding module further includes: The scheme receiving submodule is used to: Receiving the targeted feeding plan generated by the data processing unit, identifying the feeding plans of different livestock individuals corresponding to different ear tags in the feeding plan, and generating a current feeding amount plan according to the feeding plan of each livestock individual; Feed delivery submodule, used for: Based on the current feed feeding amount plan, feed delivery instructions are sent to the automatic feeder corresponding to each individual livestock, and the specified weight of feed is accurately delivered to the specified trough according to the current feed feeding amount plan.

[0014] Further, the feeding monitoring unit comprises: Environmental monitoring module for: Acquire and monitor the overall temperature and humidity data of the breeding environment and livestock pen, and generate an environmental data change table; Association analysis module, used to: Based on the breeding fluctuation data and environmental data change table, the breeding fluctuation data at each time point and the environmental temperature and humidity data at the same time point are correlated and analyzed to obtain the correlation between livestock feed intake and weight gain under different temperatures and humidities, and to coordinately control the temperature and humidity suitable for breeding and feeding; The exception identification module is used to: Based on the monitoring results of the environmental monitoring module and the analysis results of the correlation analysis module, abnormal identification and alarm will be issued when there are changes in livestock food intake, weight gain or loss, or abnormal temperature and humidity control during animal husbandry.

[0015] Furthermore, the control platform includes a mobile phone, a tablet and a PC terminal.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. Under the prior art, when livestock breeding is carried out, different livestock have different eating habits and needs. It is difficult to accurately adapt to the growth needs of different livestock individuals if feeding is carried out according to a unified feeding standard. The data processing unit of the present invention retrieves a livestock breeding data set, calculates the breeding fluctuation data of the livestock corresponding to the ear tag identity information of each livestock, and adjusts and specifies the targeted feed feeding plan of each livestock based on the calculation result. Through the data acquisition unit to collect various relevant data, the data processing unit can process the breeding related data, thereby analyzing the different eating habits of different livestock, and can master the daily eating times, frequencies, food intakes and corresponding weight gain and loss data of different livestock, and based on the above data, the targeted feed feeding plan is specified for different livestock, so that the feed can be accurately fed to different livestock, and the feeding amount and feeding frequency of the feed are adapted to the livestock, so as to achieve targeted feeding of livestock, ensure the feeding quality, and improve the quality of livestock; 2. Under the prior art, when livestock farming is carried out, if too little feed is fed, the livestock will not eat enough, and if too much feed is fed, the surplus grain will accumulate inside the trough. Long-term accumulation will cause deterioration or quality reduction, affecting the feeding effect and livestock growth. The system control unit of the present invention identifies the plan based on the targeted feed feeding plan and performs precise feed feeding. Through the intelligent feeding module, different livestock can be accurately fed according to the targeted feed feeding plan, ensuring that the single feeding amount and the daily feeding frequency are adapted to the daily eating habits and food needs of the livestock. At the same time, the feed feeding can be better controlled to avoid overfeeding or underfeeding, reduce the possibility of feed retention in the trough, and thus avoid the problem of surplus grain accumulation in the trough or long-term exposure affecting the quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a module schematic diagram of the precise material feeding control system of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 , the present invention provides the following technical solutions: An accurate control system for feeding livestock based on the Internet of Things, including: Data acquisition unit for: Interact with the peripheral component unit to collect livestock breeding related data, including single feeding weight, residual food weight in the trough, livestock weight before feeding, livestock ear tag identity information, breeding environment temperature and humidity, and generate livestock breeding data set; Data processing unit for: Retrieving livestock breeding data sets, calculating the livestock breeding fluctuation data corresponding to the ear tag identity information of each livestock, and adjusting and specifying the targeted feed feeding plan for each livestock based on the calculation results; System control unit for: Generate a human-machine interactive interface for the operator to control the system in real time, identify the feeding plan based on the targeted feed plan, and perform precise feeding; Feeding monitoring unit for: Monitor the temperature and humidity data in the livestock pen and generate an environmental data change table, conduct correlation analysis on the breeding fluctuation data at each time point and the environmental temperature and humidity data at the same time point, and issue abnormal alarms for abnormal conditions in the livestock breeding process; Peripheral component unit for: The specific functions of feed feeding, trough weighing, livestock weighing, livestock identification, and environmental monitoring in livestock breeding are realized by external equipment arranged in the breeding environment. The external equipment includes an automatic feeder arranged above the livestock trough, a weighing sensor arranged inside the automatic feeder, a weighing device arranged at the bottom of the trough, an electronic floor scale for livestock weighing arranged in front of the trough, a livestock ear tag reader arranged in the trough area, a temperature and humidity monitor arranged in the breeding environment, and room temperature cooling and heating equipment. The external equipment is connected to the data acquisition unit through the Internet of Things; Control platform for: It is used to store, run and implement the data acquisition unit, data processing unit, system control unit and feeding monitoring unit, including at least one login terminal and at least one simulation control terminal. When the simulation control terminal is working, the data acquisition unit, data processing unit, system control unit and feeding monitoring unit are operated and the precise control system of animal husbandry feeding and feeding based on the Internet of Things is realized. The login terminal is connected to the peripheral component unit, the simulation control terminal and the server through the local Internet of Things. The control platform includes mobile phones, tablets and PC terminals; Servers for: Based on the control instructions issued by the system control unit, the control instructions are sent to the front-end controller through the interface machine, and the front-end controller is then connected to the peripheral component unit to control the peripheral component unit according to the control instructions.

[0020] Specifically, when the system is working, the data acquisition unit interacts with the peripheral component unit to collect data on the ear tag identity information of each livestock and the feeding conditions of the livestock corresponding to the ear tag identity information, and then summarizes and processes these data through the data processing unit, and performs statistical calculations based on the various data collected corresponding to the ear tag identity information of each livestock, to obtain the eating habits and growth status of each livestock, and formulate a targeted feeding plan for each livestock. Thereafter, the targeted feeding plan for each livestock is implemented through the system control unit, so as to achieve targeted and accurate feeding of each livestock. At the same time, the targeted feeding plan can be updated in real time through subsequent collected data to ensure accurate control of feeding. At this time, the feeding monitoring unit can monitor and adjust the feeding situation and the overall livestock environment in the livestock shed, and respond to abnormalities in a timely manner.

[0021] Specifically, the data acquisition unit also includes: A data information sending module, used to send livestock breeding data sets with timestamps to the cloud platform; A time extraction module, used for the cloud platform to extract the timestamp of the livestock breeding data set with the timestamp when receiving the livestock breeding data set with the timestamp, and obtain the data collection time corresponding to the timestamp; A data quality evaluation module is used for the cloud platform to obtain the data collection quality evaluation score of the livestock breeding data set at the data collection time; wherein the data collection quality evaluation score is obtained by the following formula: , in, F represents the data collection quality assessment score; F 0 represents the baseline score; T max and T min They represent the maximum and minimum data collection time intervals of the livestock breeding data set respectively; Ti Indicates i The time when the data is collected; n Represents the number of data in the livestock breeding dataset; m Indicates the number of times the data collection time interval in the livestock breeding dataset changes; k Indicates the number of data collections experienced between two adjacent time intervals with changed time lengths when the data collection time interval changes; The data quality judgment module is used for the cloud platform to judge whether the quality of the current livestock breeding data set meets the data collection requirements according to the data collection quality assessment score.

[0022] The technical effect of the above technical solution is that the data information sending module is used to send the livestock breeding data set with a timestamp to the cloud platform. In this way, the breeding data can be transmitted from the livestock farm or other collection equipment to the cloud platform for subsequent processing and analysis.

[0023] When the cloud platform receives a livestock breeding data set with a timestamp, it extracts the timestamp through the time extraction module to obtain the data collection time. In this way, the collection time of each data can be known for subsequent analysis and sorting.

[0024] The data quality evaluation module controls the cloud platform to evaluate the data collection quality of the livestock breeding data set using the data quality evaluation module at the time of data collection and generates a corresponding evaluation score. The evaluation score can be used to understand the quality of the data in terms of accuracy, completeness and consistency.

[0025] The data quality judgment module controls the cloud platform to judge whether the quality of the current livestock breeding data set meets the data collection requirements based on the data collection quality assessment score. In this way, timely measures can be taken to correct or exclude low-quality data based on the data quality judgment results to ensure the reliability and validity of the data.

[0026] Therefore, the above technical solution monitors and evaluates the time and quality of data collection by sending the breeding data set with timestamps to the cloud platform and using the time extraction module, data quality evaluation module and data quality judgment module. This helps to ensure the accuracy and reliability of the livestock breeding data set and improve the accuracy and credibility of data analysis and decision-making. At the same time, the evaluation parameters obtained by the above formula can effectively improve the accuracy of data quality detection of the data set.

[0027] Specifically, the data quality judgment module includes: A historical data extraction module, used to extract data collection quality assessment scores of data sets that meet data collection requirements in historical storage records and data collection quality assessment scores of data sets that do not meet data collection requirements in historical storage records; The score threshold setting module is used to set the current score threshold according to the data collection quality assessment score of the data set that meets the data collection requirements in the historical storage records and the data collection quality assessment score of the data set that does not meet the data collection requirements in the historical storage records, wherein the score threshold is obtained by the following formula: , in, F y represents the score threshold; F 01 Indicates the highest threshold value that has appeared in the score threshold setting history; F 02 Indicates the lowest threshold value that appears in the score threshold setting history; F c Indicates the preset initial threshold; a and b Respectively represent the number of data sets that meet the data collection requirements in the historical storage records and the number of data sets that do not meet the data collection requirements in the historical storage records; F ai Indicates i The data collection quality assessment score corresponding to the data set that meets the data collection requirements; F yai Indicates i The score threshold corresponding to the data set that meets the data collection requirements; F bi Indicates iThe data collection quality assessment scores of the data sets that do not meet the data collection requirements; F ybi Indicates i The score threshold corresponding to the data set that does not meet the data collection requirements; The quality judgment module is used to generate an alarm message on the cloud platform and send the alarm message to the control platform when the data collection quality assessment score of the current livestock breeding data set is lower than a preset score threshold.

[0028] The technical effect of the above technical solution is that the historical data extraction module is used to extract the data collection quality assessment scores of the data sets that meet the data collection requirements and the data sets that do not meet the data collection requirements from the historical storage records. In this way, the historical data can be screened and evaluated to understand the quality of the data.

[0029] The score threshold setting module is used to set the current score threshold according to the data collection quality assessment scores of the data sets that meet the data collection requirements and the data sets that do not meet the data collection requirements in the historical storage records. By setting the score threshold, the quality standard of the current data can be determined according to the quality of the historical data.

[0030] When the data collection quality assessment score of the current livestock breeding data set is lower than the preset score threshold, the quality judgment module will generate an alarm message and send the alarm message to the control platform. This can achieve real-time monitoring of data quality and abnormal alarms, and take corresponding measures to deal with low-quality data in a timely manner.

[0031] Therefore, the above technical solution realizes the quality assessment and judgment of historical data and current data through the historical data extraction module, the score threshold setting module and the quality judgment module. This helps to timely discover low-quality data and generate alarm information for further processing. Through data quality monitoring and alarm, the reliability and effectiveness of data can be improved, and timely decision-making and control measures can be supported.

[0032] The data acquisition unit includes: Data acquisition module, used to: Interact with peripheral component units to collect livestock breeding related data and timestamp the data according to the collection time of each data; Data association module for: The breeding-related data collected by external devices located in the same livestock pen are correlated and matched, and the single feeding weight in the livestock pen, the residual food weight in the trough, and the weight of the livestock before eating corresponding to the ear tag identity information of each livestock are matched to generate a livestock breeding data set. Each livestock breeding data set corresponds to each livestock ear tag identity information one by one, and the livestock breeding data set is marked according to the timestamp.

[0033] Specifically, the data collection unit can be used to comprehensively collect various relevant data on animal husbandry, so that a comprehensive analysis can be made on the feeding and feeding of each livestock based on the collected data, and it is also convenient to make targeted and accurate adjustments to the feeding plan at a later stage.

[0034] The data processing unit includes: Data retrieval module, used for: Retrieve the livestock breeding data set created by the data collection unit; Data calculation module, used for: Calculate the livestock breeding fluctuation data corresponding to the livestock ear tag identity information based on the livestock breeding data set, wherein the livestock breeding fluctuation data includes the livestock weight gain value, the livestock single feed intake, the livestock single feeding time, the feed remaining amount in the trough and the livestock single daily feed intake; Solution adjustment module, used to: Based on the calculation results of the data calculation module, the targeted feed feeding plan for the livestock corresponding to the ear tag identity information of each livestock is adjusted and specified.

[0035] The program adjustment module also includes: Livestock information submodule for: Establishing a livestock information table, in which each livestock is distinguished by ear tag identity information, and storing the feeding data of each livestock in groups, with the ear tag identity information being used as the grouping basis when grouping the data; Trend simulation submodule, used for: Based on the livestock information table, a curve image is created according to the feeding data of each livestock, and a feeding status curve is simulated for the daily food intake and weight gain trend of each livestock; Feeding adjustment submodule, used to: Based on the simulation results of the feeding status curve, the daily feeding time, frequency and feeding amount of each livestock are adjusted, and a targeted feeding plan is generated for each livestock.

[0036] Specifically, through the collection of various relevant data by the data collection unit, the data processing unit can process the feeding related data, thereby analyzing the different eating habits of different livestock, and can grasp the daily eating times, frequencies, food intake and corresponding weight gain and loss data of different livestock, and based on the above data, specific feed feeding plans are specified for different livestock, so that different livestock can be fed accurately, ensuring that the feeding amount and feeding frequency of the feed are compatible with the livestock, so as to achieve targeted feeding of livestock, ensure feeding quality, and improve livestock quality.

[0037] The system control unit includes: Interaction modules for: Generate a human-computer interaction interface, display the real-time control information and real-time status of the operation instructions of the data acquisition unit, the data processing unit, the peripheral component unit and the feeding monitoring unit through the interaction module, and the operator can control the data acquisition unit, the data processing unit, the peripheral component unit and the feeding monitoring unit in real time through the interaction module; Control execution module, used to: Execute the control instructions generated by the interactive module, and realize precise control of feeding and feeding by interacting with the data acquisition unit, data processing unit, peripheral component unit and feeding monitoring unit; Smart feeding module for: The targeted feeding plan is generated based on the data processing unit, and the livestock is fed with precise feed based on the plan identification result.

[0038] The intelligent feeding module also includes: The scheme receiving submodule is used to: Receiving the targeted feeding plan generated by the data processing unit, identifying the feeding plans of different livestock individuals corresponding to different ear tags in the feeding plan, and generating a current feeding amount plan according to the feeding plan of each livestock individual; Feed delivery submodule, used for: Based on the current feed feeding amount plan, feed delivery instructions are sent to the automatic feeder corresponding to each individual livestock, and the specified weight of feed is accurately delivered to the specified trough according to the current feed feeding amount plan.

[0039] Specifically, the intelligent feeding module can accurately feed different livestock according to targeted feeding plans, ensuring that the single feeding amount and daily feeding frequency are adapted to the livestock's daily eating habits and food needs. At the same time, it can better control the feeding of feed to avoid overfeeding or underfeeding, reduce the possibility of feed retention in the trough, and thus avoid the accumulation of surplus grain in the trough or long-term exposure affecting the quality.

[0040] The feeding monitoring unit includes: Environmental monitoring module for: Acquire and monitor the overall temperature and humidity data of the breeding environment and livestock pen, and generate an environmental data change table; Association analysis module, used to: Based on the breeding fluctuation data and environmental data change table, the breeding fluctuation data at each time point and the environmental temperature and humidity data at the same time point are correlated and analyzed to obtain the correlation between livestock feed intake and weight gain under different temperatures and humidities, and to coordinately control the temperature and humidity suitable for breeding and feeding; The exception identification module is used to: Based on the monitoring results of the environmental monitoring module and the analysis results of the correlation analysis module, abnormal identification and alarm will be issued when there are changes in livestock food intake, weight gain or loss, or abnormal temperature and humidity control during animal husbandry.

[0041] Specifically, the temperature and humidity conditions of the breeding environment can be monitored through the feeding monitoring unit, thereby associating it with the relevant data on the livestock's food intake and weight gain, so that the correlation between the two can be discovered and adaptive adjustments can be made to ensure that the livestock's eating and feeding are not affected by the environment. At the same time, when the livestock's appetite decreases and the amount of food intake decreases, it can be discovered in time and the problem can be solved, thereby improving the safety of livestock breeding.

[0042] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A precise control system for feeding livestock based on the Internet of Things, characterized in that: include: Data acquisition unit for: Interact with the peripheral component unit to collect livestock breeding related data, including single feeding weight, residual food weight in the trough, livestock weight before feeding, livestock ear tag identity information, breeding environment temperature and humidity, and generate livestock breeding data set; Data processing unit for: Retrieving livestock breeding data sets, calculating the livestock breeding fluctuation data corresponding to the ear tag identity information of each livestock, and adjusting and specifying the targeted feed feeding plan for each livestock based on the calculation results; System control unit for: Generate a human-machine interactive interface for the operator to control the system in real time, identify the feeding plan based on the targeted feed plan, and perform precise feeding; Feeding monitoring unit for: Monitor the temperature and humidity data in the livestock pen and generate an environmental data change table, conduct correlation analysis on the breeding fluctuation data at each time point and the environmental temperature and humidity data at the same time point, and issue abnormal alarms for abnormal conditions in the livestock breeding process; Peripheral component unit for: The specific functions of feed feeding, trough weighing, livestock weighing, livestock identification, and environmental monitoring in livestock breeding are realized by external equipment arranged in the breeding environment. The external equipment includes an automatic feeder arranged above the livestock trough, a weighing sensor arranged inside the automatic feeder, a weighing device arranged at the bottom of the trough, an electronic floor scale for livestock weighing arranged in front of the trough, a livestock ear tag reader arranged in the trough area, a temperature and humidity monitor arranged in the breeding environment, and room temperature cooling and heating equipment. The external equipment is connected to the data acquisition unit through the Internet of Things; Control platform for: Used to store, run and implement the data acquisition unit, data processing unit, system control unit and feeding monitoring unit, including at least one login terminal and at least one simulation control terminal. When the simulation control terminal is working, the data acquisition unit, data processing unit, system control unit and feeding monitoring unit are operated and the precise control system of animal husbandry feeding and feeding based on the Internet of Things is realized. The login terminal is connected to the peripheral component unit, the simulation control terminal and the server through the local Internet of Things; Servers for: Based on the control instructions issued by the system control unit, the control instructions are sent to the front-end controller through the interface machine, and the front-end controller is then connected to the peripheral component unit to control the peripheral component unit according to the control instructions.

2. The precise control system for feeding livestock based on the Internet of Things as claimed in claim 1, characterized in that: The data acquisition unit comprises: Data acquisition module, used to: Interact with peripheral component units to collect livestock breeding related data and timestamp the data according to the collection time of each data; Data association module for: The breeding-related data collected by external devices located in the same livestock pen are correlated and matched, and the single feeding weight in the livestock pen, the residual food weight in the trough, and the weight of the livestock before eating corresponding to the ear tag identity information of each livestock are matched to generate a livestock breeding data set. Each livestock breeding data set corresponds to each livestock ear tag identity information one by one, and the livestock breeding data set is marked according to the timestamp.

3. The precise control system for feeding livestock based on the Internet of Things as claimed in claim 2, characterized in that: The data acquisition unit also includes: A data information sending module, used to send livestock breeding data sets with timestamps to the cloud platform; A time extraction module, used for the cloud platform to extract the timestamp of the livestock breeding data set with the timestamp when receiving the livestock breeding data set with the timestamp, and obtain the data collection time corresponding to the timestamp; A data quality evaluation module is used for the cloud platform to obtain the data collection quality evaluation score of the livestock breeding data set at the data collection time; wherein the data collection quality evaluation score is obtained by the following formula: ， in, F represents the data collection quality assessment score; F 0 represents the baseline score; T max and T min They represent the maximum and minimum data collection time intervals of the livestock breeding data set respectively; Ti Indicates i The time when the data is collected; n Represents the number of data in the livestock breeding dataset; m Indicates the number of times the data collection time interval in the livestock breeding dataset changes; k Indicates the number of data collections experienced between two adjacent time intervals with changed time lengths when the data collection time interval changes; The data quality judgment module is used for the cloud platform to judge whether the quality of the current livestock breeding data set meets the data collection requirements according to the data collection quality assessment score.

4. The precise control system for feeding livestock based on the Internet of Things as claimed in claim 3, characterized in that: The data quality judgment module includes: A historical data extraction module, used to extract data collection quality assessment scores of data sets that meet data collection requirements in historical storage records and data collection quality assessment scores of data sets that do not meet data collection requirements in historical storage records; The score threshold setting module is used to set the current score threshold according to the data collection quality assessment score of the data set that meets the data collection requirements in the historical storage records and the data collection quality assessment score of the data set that does not meet the data collection requirements in the historical storage records, wherein the score threshold is obtained by the following formula: ， in, F y represents the score threshold; F 01 Indicates the highest threshold value that has appeared in the score threshold setting history; F 02 Indicates the lowest threshold value that appears in the score threshold setting history; F c Indicates the preset initial threshold; a and b Respectively represent the number of data sets that meet the data collection requirements in the historical storage records and the number of data sets that do not meet the data collection requirements in the historical storage records; F ai Indicates i The data collection quality assessment score corresponding to the data set that meets the data collection requirements; F yai Indicates i The score threshold corresponding to the data set that meets the data collection requirements; F bi Indicates i The data collection quality assessment scores of the data sets that do not meet the data collection requirements; F ybi Indicates i The score threshold corresponding to the data set that does not meet the data collection requirements; The quality judgment module is used to generate an alarm message on the cloud platform and send the alarm message to the control platform when the data collection quality assessment score of the current livestock breeding data set is lower than a preset score threshold.

5. The precise control system for feeding livestock based on the Internet of Things as claimed in claim 1, characterized in that: The data processing unit comprises: Data retrieval module, used for: Retrieve the livestock breeding data set created by the data collection unit; Data calculation module, used for: Calculate the livestock breeding fluctuation data corresponding to the livestock ear tag identity information based on the livestock breeding data set, wherein the livestock breeding fluctuation data includes the livestock weight gain value, the livestock single feed intake, the livestock single feeding time, the feed remaining amount in the trough and the livestock single daily feed intake; Solution adjustment module, used to: Based on the calculation results of the data calculation module, the targeted feed feeding plan for the livestock corresponding to the ear tag identity information of each livestock is adjusted and specified.

6. The precise control system for feeding livestock based on the Internet of Things as claimed in claim 5, characterized in that: The scheme adjustment module further includes: Livestock information submodule for: Establishing a livestock information table, in which each livestock is distinguished by ear tag identity information, and storing the feeding data of each livestock in groups, with the ear tag identity information being used as the grouping basis when grouping the data; Trend simulation submodule, used for: Based on the livestock information table, a curve image is created according to the feeding data of each livestock, and a feeding status curve is simulated for the daily food intake and weight gain trend of each livestock; Feeding adjustment submodule, used to: Based on the simulation results of the feeding status curve, the daily feeding time, frequency and feeding amount of each livestock are adjusted, and a targeted feeding plan is generated for each livestock.

7. The precise control system for feeding livestock based on the Internet of Things as claimed in claim 1, characterized in that: The system control unit comprises: Interaction modules for: Generate a human-computer interaction interface, display the real-time control information and real-time status of the operation instructions of the data acquisition unit, the data processing unit, the peripheral component unit and the feeding monitoring unit through the interaction module, and the operator can control the data acquisition unit, the data processing unit, the peripheral component unit and the feeding monitoring unit in real time through the interaction module; Control execution module, used to: Execute the control instructions generated by the interactive module, and realize precise control of feeding and feeding by interacting with the data acquisition unit, data processing unit, peripheral component unit and feeding monitoring unit; Smart feeding module for: The targeted feeding plan is generated based on the data processing unit, and the livestock is fed with precise feed based on the plan identification result.

8. The precise control system for feeding livestock based on the Internet of Things as claimed in claim 7, characterized in that: The intelligent feeding module further includes: The scheme receiving submodule is used to: Receiving the targeted feeding plan generated by the data processing unit, identifying the feeding plans of different livestock individuals corresponding to different ear tags in the feeding plan, and generating a current feeding amount plan according to the feeding plan of each livestock individual; Feed delivery submodule, used for: Based on the current feed feeding amount plan, feed delivery instructions are sent to the automatic feeder corresponding to each individual livestock, and the specified weight of feed is accurately delivered to the specified trough according to the current feed feeding amount plan.

9. The precise control system for feeding livestock based on the Internet of Things as claimed in claim 1, characterized in that: The feeding monitoring unit comprises: Environmental monitoring module for: Acquire and monitor the overall temperature and humidity data of the breeding environment and livestock pen, and generate an environmental data change table; Association analysis module, used to: Based on the breeding fluctuation data and environmental data change table, the breeding fluctuation data at each time point and the environmental temperature and humidity data at the same time point are correlated and analyzed to obtain the correlation between livestock feed intake and weight gain under different temperatures and humidities, and to coordinately control the temperature and humidity suitable for breeding and feeding; The exception identification module is used to: Based on the monitoring results of the environmental monitoring module and the analysis results of the correlation analysis module, abnormal identification and alarm will be issued when there are changes in livestock food intake, weight gain or loss, or abnormal temperature and humidity control during animal husbandry.

10. The precise control system for feeding livestock based on the Internet of Things as claimed in claim 1, characterized in that: The control platform includes mobile phones, tablets and PC terminals.

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