Intelligent feeding and eating data tracing system at edge of pig farm and control method thereof

Abstract

This invention provides an intelligent feeding and feed intake data traceability system and its control method at the edge of a pig farm. An individual identification module is used to verify the identity of pigs entering the feeding station. This module includes an ear tag RFID identification unit and a visual identification unit. The ear tag RFID identification unit reads the pig's ear tag identification information, and the visual identification unit acquires the corresponding image feature information of the pig. By setting up an individual identification module consisting of an ear tag RFID identification unit and a visual identification unit, this invention performs dual identification and consistency verification on the pig's identity when it enters the feeding station, and only starts the intelligent feeding process after identity verification. This reduces the risk of individual mismatch caused by ear tag damage, obstruction, or misreading when using a single identification method. It ensures that data such as single feed intake, feeding speed, and feeding interval can accurately correspond to specific pig individuals, improving the authenticity and reliability of feed intake data collection.

Description

Technical Field

[0001] This invention relates to the field of smart farming technology, and in particular to a smart feeding and feed intake data traceability system and its control method at the edge of a pig farm. Background Technology

[0002] With the rapid development of large-scale, intensive pig farms, pig farming management is gradually shifting from traditional extensive management to digital, refined, and intelligent management. In the pig feeding process, feeding behavior is crucial data reflecting the pig's nutritional intake, growth performance, and health changes. Accurate identification, continuous recording, and timely analysis of the feeding process of individual pigs can help improve feeding precision, optimize feed utilization efficiency, and provide a basis for abnormal feeding warnings and health management.

[0003] However, in the current pig farm feeding management methods, feeding is generally done uniformly on a pen or group basis, which lacks the ability to identify individuals and adjust accordingly. It is difficult to accurately grasp key data such as the actual feed intake, feeding speed and feeding interval of a single pig. Even if some systems are equipped with ear tag recognition or automatic feeding equipment, they are mostly limited to single identification or single-point control. There are problems such as insufficient reliability of individual identification, inaccurate correlation of feeding data and lag in feeding adjustments, making it difficult to form a stable and continuous individual feeding management mechanism. In addition, in existing technologies, feed intake data, weight data, health inspection data, and immunization medication data are often stored in different devices or management systems, and there is a lack of effective correlation between the data. As a result, when pigs show abnormal feed intake, slow growth, or health fluctuations, it is difficult for managers to quickly trace their historical feed intake changes and feeding intervention processes, which affects the efficiency of abnormal judgment and the level of precision management.

[0004] To address this, a smart feeding and feed intake data traceability system and its control method for the edge of pig farms are proposed. Summary of the Invention

[0005] In view of this, the present invention provides an intelligent feeding and feed intake data traceability system and control method at the edge of a pig farm to solve or alleviate the technical problems existing in the prior art, and at least provides a beneficial option.

[0006] The technical solution of the present invention is implemented as follows: a smart feeding and feed intake data traceability system at the edge of a pig farm is provided, including an individual identification module, a smart feeding module, a feed intake data acquisition module, an edge computing analysis module, a data traceability management module, and a linkage control module; The individual identification module is used to confirm the identity of pigs entering the feeding station. The individual identification module includes an ear tag RFID identification unit and a visual identification unit. The ear tag RFID identification unit is used to read the identity information of the pig's ear tag, and the visual identification unit is used to obtain the image feature information of the corresponding pig and verify the consistency between the image recognition result and the ear tag recognition result to generate an individual identity confirmation result. The intelligent feeding module is connected to the individual identification module and is used to execute the corresponding feeding task after the individual identity confirmation result is generated. The intelligent feeding module includes a quantitative feeding unit, a feeding execution unit and a feed trough status detection unit. The quantitative feeding unit is used to output the target feed amount according to the preset feeding strategy. The feeding execution unit is used to complete the feeding according to the control command. The feed trough status detection unit is used to detect the remaining feed status in the feed trough. The feeding data acquisition module is connected to the intelligent feeding module and is used to collect the feeding data of the corresponding pigs. The feeding data includes at least the amount of feed consumed per feeding, the feeding speed, and the feeding interval. The edge computing analysis module is connected to the individual identification module, the intelligent feeding module, and the feed data acquisition module respectively. It is used to receive the individual identity confirmation results, the status of the feed trough, and the feed data. Based on a set time window, it analyzes the trend of feed intake change, feed speed fluctuation, and feed interval distribution characteristics of a single pig to output feeding control instructions. The data traceability management module is connected to the edge computing analysis module to establish a data traceability file indexed by the individual pig's identity, and to associate and store feeding data with pig growth data, health data and historical feeding data; The linkage control module is connected to the intelligent feeding module, the edge computing analysis module, and the data traceability management module respectively. It is used to control the intelligent feeding module to perform dynamic feeding according to the feeding control command, and write the identification data, feeding data, analysis results and control results of this time into the data traceability management module.

[0007] More preferably, the visual recognition unit can acquire at least one of the following: pig head image, face image, and body contour image, and extract the corresponding visual features to match and verify the identity information read by the ear tag RFID recognition unit; By using a dual-path cross-confirmation approach combining visual recognition and ear tag recognition, the accuracy of individual identification can be improved, and the risk of misidentification caused by a single identification method can be reduced.

[0008] In a further preferred embodiment, the individual identification module can also output an identity confirmation signal when the ear tag identification result matches the visual identification result, and output a verification identification signal when the two are inconsistent or either identification fails, and restrict the intelligent feeding module from entering the automatic feeding state, thereby avoiding accidental triggering of automatic feeding under conditions of unknown identity or identification conflict.

[0009] In a further preferred embodiment, the feed intake data acquisition module is also used to collect the duration and frequency of feed intake, and the edge computing analysis module is used to compare and analyze the historical feed intake baseline constructed based on the current feed intake data and the corresponding historical feed intake data of pigs, so as to identify the normal feed intake state, the low feed intake state, the sudden increase in feed intake state or the abnormal fluctuation in feed intake state, and form corresponding feeding control instructions accordingly.

[0010] More preferably, the feeding control instructions include at least one or more of the following: feed amount adjustment instructions, feed replenishment rhythm adjustment instructions, and diet formula adjustment instructions, so that the system can not only implement dynamic feed replenishment based on the pigs' current feeding behavior, but also adaptively optimize the subsequent diet supply strategy based on the continuous changing trend.

[0011] In a further optimized version, the data traceability management module constructs a multi-dimensional index structure based on individual pigs, time nodes, and data categories. The data categories include at least identification data, feeding behavior data, weight growth data, immunization and medication data, and health abnormality event data, enabling feeding data and other breeding data to be correlated, retrieved, and analyzed in a unified data chain.

[0012] In a further preferred embodiment, the linkage control module can also output abnormal feeding prompts when the edge computing analysis module detects a continuous decrease in feed intake, a feeding speed lower than a preset threshold, or a feeding interval longer than a preset threshold. The abnormal feeding prompts are then associated with and written into the data traceability file of the corresponding individual pig, so that managers can conduct health inspections and feeding interventions.

[0013] This invention also provides a control method for an intelligent feeding and feed intake data traceability system at the edge of a pig farm, comprising the following steps: S1. Read the ear tag identity information of the pig to be fed through the ear tag RFID identification unit, and obtain the image feature information of the corresponding pig through the visual recognition unit. Match and verify the two types of identification results to generate an individual identity confirmation result. S2. After individual identity is confirmed, retrieve the historical feeding data, growth data and health data corresponding to the pig, determine the current feeding strategy parameters, and control the intelligent feeding module to execute this feeding. S3. During the feeding process of pigs, the amount of feed consumed at one time, the feeding speed and the feeding interval are collected in real time, and the feeding time sequence record corresponding to the individual pig is formed by combining the state of the remaining feed in the feed trough. S4. Based on the set time window, compare and analyze the current feeding time sequence record with the historical feeding baseline constructed from the corresponding historical feeding data of pigs, and output feeding control instructions. The trend of feed intake, fluctuation of feed intake rate and distribution characteristics of feed intake interval of a single pig are analyzed together, and at least one of the following commands is generated: feed amount adjustment command, feed replenishment rhythm adjustment command and diet formula adjustment command. When a continuous decrease in feed intake, a feeding speed lower than a preset threshold, or a feeding interval longer than a preset threshold is detected, an abnormal feeding prompt message is output, and the abnormal feeding prompt message is associated with the data traceability file of the corresponding pig individual as a basis for health inspection and feeding intervention.

[0014] S5. According to the feeding control command, control the intelligent feeding module to dynamically adjust the subsequent feed amount, feeding rhythm or diet formula, and simultaneously write the identification data, feeding data, analysis results and control results into the data traceability management module. S6. The feeding data is linked and updated with the pig growth data and health data to form a continuous traceability data chain for the corresponding individual pig.

[0015] I. This invention constructs an individual identification module by setting up an ear tag RFID identification unit and a visual identification unit. When a pig enters the feeding station, its identity is dually identified and its consistency is verified. The intelligent feeding process is only started after the identity is confirmed. This reduces the risk of individual mismatch caused by ear tag damage, obstruction or misreading when a single identification method is used. It enables data such as single feed intake, feeding speed and feeding interval to be accurately matched to specific pig individuals, thereby improving the authenticity and reliability of feeding data collection. Second, by setting up a feed intake data acquisition module, an edge computing analysis module, a linkage control module, and a data traceability management module, this invention can perform real-time analysis of the feed intake change trend, feed speed fluctuation, and feed interval distribution characteristics of individual pigs at the edge of the pig farm, and output instructions to adjust feed amount, feeding rhythm, or diet formula accordingly, thereby achieving dynamic feeding control. At the same time, the feed intake data is associated and stored with pig growth data, health data, and historical feeding data to form a continuous traceability data chain indexed by individual identity, which facilitates retrospective analysis and management intervention for abnormal feed intake, growth fluctuations, and health changes.

[0016] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a system architecture diagram of the present invention. Detailed Implementation

[0019] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0020] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0021] like Figure 1 As shown, the system comprises a data acquisition module, an edge computing analysis module, a data traceability management module, and a linkage control module. The individual identification module is located at the entrance to the feeding station or in the corresponding identification area. The intelligent feeding module is located at the feeding station. The feeding data acquisition module records data during the feeding process. The edge computing analysis module is located at the local edge terminal in the pigsty. The data traceability management module establishes individual profiles. The linkage control module connects the identification, analysis, control, and traceability processes.

[0022] The individual identification module includes an ear tag RFID identification unit and a visual identification unit. The ear tag RFID identification unit reads the pig's ear tag identification information, while the visual identification unit acquires images of the pig's head, face, or body contour and extracts corresponding visual feature information. The system verifies the consistency between the ear tag identification result and the visual identification result. When they match, an individual identification confirmation result is generated; when they do not match or either identification fails, a verification signal is output, and the intelligent feeding module is restricted from entering automatic feeding mode. This dual identification method reduces the risk of misidentification or data corruption caused by a single identification method.

[0023] The intelligent feeding module includes a quantitative feeding unit, a feeding execution unit, and a feed trough status detection unit. The quantitative feeding unit outputs the target feed amount according to a preset feeding strategy. The feeding execution unit completes the feeding based on control commands sent by the linkage control module. The feed trough status detection unit detects the remaining feed in the feed trough. After individual identification is completed, the system retrieves the corresponding historical feeding data, growth data, and health data for that pig, determines the current feeding strategy parameters, and controls the intelligent feeding module to execute the feeding.

[0024] The feed intake data acquisition module is used to collect feed intake data for the corresponding pigs. This data includes at least the amount of feed consumed per feeding, feeding speed, and feeding interval. It can also further collect feeding duration and frequency. After individual identification, the module creates a record of the feeding event and continuously records relevant data during the feeding process. This data, combined with the remaining feed in the trough, forms a feeding timeline record corresponding to the pig's identity. The system can also establish a historical feeding baseline for each pig based on data from several consecutive days or feeding cycles for subsequent analysis and judgment.

[0025] The edge computing analysis module is connected to the individual identification module, the intelligent feeding module, and the feed intake data acquisition module. It receives individual identification results, feed trough status, and feed intake data, and analyzes the feed intake trend, feed rate fluctuations, and feed interval distribution characteristics of individual pigs based on a set time window. The edge computing analysis module compares the current feed intake data with a historical feed intake baseline constructed from the corresponding pig's historical feed intake data to identify normal feed intake, low feed intake, sudden increase in feed intake, or abnormal fluctuations in feed intake, and outputs corresponding feeding control commands. These feeding control commands include at least one or more of the following: feed quantity adjustment commands, feed replenishment rhythm adjustment commands, and diet formula adjustment commands.

[0026] When the edge computing analysis module detects a continuous decrease in feed intake, a feeding speed below a preset threshold, or a feeding interval longer than a preset threshold, it outputs an abnormal feeding alert. This alert may include the individual's identity, the time of the abnormality, and the type of abnormality, and is sent to the linkage control module and the data traceability management module for subsequent health inspections and management interventions.

[0027] The data traceability management module is used to establish a data traceability file indexed by the individual pig's identity, and to associate and store feed intake data with pig growth data, health data, and historical feeding data. Preferably, the data traceability management module constructs a multi-dimensional index structure according to individual pigs, time nodes, and data categories. The data categories include at least identification data, feed intake behavior data, weight growth data, immunization and medication data, and health abnormality event data. Through this traceability file, managers can query and trace the historical changes in feed intake, feeding adjustment processes, and health status of a particular pig.

[0028] The linkage control module connects to the intelligent feeding module, edge computing analysis module, and data traceability management module. It controls the intelligent feeding module to perform dynamic feeding based on feeding control commands output by the edge computing analysis module, and writes the identification data, feed intake data, analysis results, and control results to the data traceability management module. When abnormal feed intake is detected, the linkage control module can also associate abnormal feed intake alerts with the corresponding pig's data traceability file.

[0029] Based on the above system, the present invention also provides a control method for an intelligent feeding and feed intake data traceability system at the edge of a pig farm, the specific steps of which are as follows.

[0030] Step S1 involves reading the ear tag identification information of the pigs to be fed using an RFID ear tag identification unit and obtaining the corresponding image feature information of the pigs using a visual recognition unit. The two identification results are then matched and verified to generate an individual identification confirmation result. This step is fundamental for subsequent precise feeding and data traceability; only when the individual identification is reliable can the subsequent feeding data have traceability value. If the two identification results are inconsistent, a review identification process is initiated, and automatic feeding is restricted.

[0031] Step S2: After the individual identity is confirmed, the system retrieves the historical feeding data, growth data and health data corresponding to the pig, determines the current feeding strategy parameters, and controls the intelligent feeding module to execute this feeding. The feeding strategy parameters can be determined based on the pig's current growth stage, historical feeding performance, recent weight changes and health status, including the basic feed amount, supplementary feed trigger conditions, allowable supplementary feed range and suitable daily ration formula, etc.

[0032] Step S3: During the pig's feeding process, the system collects data in real time on the amount of feed consumed per feeding, the feeding speed, and the feeding interval, and combines this data with the status of remaining feed in the trough to form a feeding time sequence record corresponding to the individual pig's identity. If necessary, the system can also simultaneously collect the duration and frequency of feeding to more completely reflect the characteristics of feeding behavior. This step generates continuous process data, rather than isolated feeding results.

[0033] Step S4: Based on a set time window, the system compares and analyzes the current feeding time sequence record with the historical feeding baseline constructed from the corresponding historical feeding data of the pigs. The analysis includes at least the trend of feed intake change, fluctuation of feeding speed, and distribution characteristics of feeding intervals. By jointly analyzing the above indicators, the system generates at least one of the following: feed amount adjustment instruction, feeding rhythm adjustment instruction, and diet formula adjustment instruction. When a continuous decrease in feed intake, a feeding speed lower than a preset threshold, or a feeding interval longer than a preset threshold is detected, the system outputs an abnormal feeding prompt and marks the pig as an abnormal concern.

[0034] Step S5: According to the feeding control instructions, the system controls the intelligent feeding module to dynamically adjust the subsequent feed amount, feeding rhythm or diet formula, and simultaneously writes the identification data, feeding data, analysis results and control results into the data traceability management module. Through this step, the system not only completes the analysis and judgment, but also completes the control execution and control traceability.

[0035] Step S6 involves linking and updating the current feeding data with the pig's growth and health data to form a continuous traceability data chain for the corresponding individual pig. If new weight data, immunization records, health inspection results, or treatment medication records are subsequently entered, they can also be linked and written into the pig's traceability file to ensure the continued integrity of the file.

[0036] In one embodiment, when a pig enters the feeding station, the system confirms its individual identity through an ear tag RFID identification unit and a visual recognition unit. After confirmation, the intelligent feeding module feeds the pig according to its current feeding strategy. During feeding, the feeding data acquisition module records the pig's feed intake, feeding speed, and feeding interval in real time. The edge computing analysis module compares the current feeding data with its historical feeding baseline. When it detects a decrease in feed intake and a slowdown in feeding speed, it generates a control command to reduce the frequency of subsequent feeding and outputs an abnormal feeding prompt. The linkage control module adjusts subsequent feeding actions accordingly and writes the current feeding data, analysis results, and abnormal prompt information into the pig's traceability file for subsequent inspection and management.

[0037] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A pig farm edge end intelligent feeding and eating data tracing system, characterized in that, include: An individual identification module is used to identify the pigs entering the feeding station. The individual identification module includes an ear tag RFID identification unit and a visual identification unit. The ear tag RFID identification unit is used to read the identity information of the pig's ear tag, and the visual identification unit is used to obtain the image feature information of the corresponding pig and verify the consistency between the image recognition result and the ear tag recognition result to generate an individual identification result. The intelligent feeding module is connected to the individual identification module and is used to execute the corresponding feeding task after the individual identity confirmation result is generated. The intelligent feeding module includes a quantitative feeding unit, a feeding execution unit and a feed trough status detection unit. The quantitative feeding unit is used to output the target feed amount according to the preset feeding strategy. The feeding execution unit is used to complete the feeding according to the control command. The feed trough status detection unit is used to detect the remaining feed status in the feed trough. A feeding data acquisition module, connected to the intelligent feeding module, is used to collect feeding data of the corresponding pigs. The feeding data includes at least the amount of feed consumed per feeding, the feeding speed, and the feeding interval. The edge computing analysis module is connected to the individual identification module, the intelligent feeding module, and the feed data acquisition module, respectively. It is used to receive the individual identity confirmation result, the status of the feed trough and the feed data, and analyze the feed intake change trend, feed speed fluctuation and feed interval distribution characteristics of a single pig based on a set time window, so as to output feeding control instructions. The data traceability management module is connected to the edge computing analysis module and is used to establish a data traceability file indexed by the individual pig's identity, and to associate and store feeding data with pig growth data, health data and historical feeding data. The linkage control module is connected to the intelligent feeding module, the edge computing analysis module, and the data traceability management module respectively. It is used to control the intelligent feeding module to perform dynamic feeding according to the feeding control command, and write the current identification data, feeding data, analysis results, and control results into the data traceability management module.

2. The pig farm edge end intelligent feeding and eating data tracing system according to claim 1, characterized in that: The visual recognition unit is used to acquire at least one of the following: pig head image, face image, and body contour image, and extract the corresponding visual features to match and verify the identity information read by the ear tag RFID recognition unit.

3. The pig farm edge end intelligent feeding and eating data tracing system according to claim 1, characterized in that: The individual identification module is also used to output an identity confirmation signal when the ear tag identification result is consistent with the visual identification result, output a verification identification signal when the two are inconsistent or either identification fails, and restrict the intelligent feeding module from entering the automatic feeding state.

4. The intelligent feeding and feed intake data traceability system at the edge of a pig farm according to claim 1, characterized in that: The feeding data acquisition module is also used to collect the duration and frequency of feeding. The edge computing analysis module is used to compare and analyze the historical feeding baseline constructed based on the current feeding data and the corresponding historical feeding data of pigs, so as to identify normal feeding status, low feeding status, sudden increase in feeding status or abnormal fluctuation in feeding status.

5. The intelligent feeding and feed intake data traceability system at the edge of a pig farm according to claim 1, characterized in that: The feeding control instructions include at least one or more of the following: feed amount adjustment instructions, feed replenishment rhythm adjustment instructions, and diet formula adjustment instructions.

6. The intelligent feeding and feed intake data traceability system at the edge of a pig farm according to claim 1, characterized in that: The data traceability management module constructs a multi-dimensional index structure according to individual pigs, time nodes, and data categories. The data categories include at least identification data, feeding behavior data, weight growth data, immunization medication data, and health abnormality event data.

7. The intelligent feeding and feed intake data traceability system at the edge of a pig farm according to claim 1, characterized in that: The linkage control module is also used to output abnormal feeding prompt information when the edge computing analysis module detects a continuous decrease in feed intake, a feeding speed lower than a preset threshold, or a feeding interval longer than a preset threshold, and to associate the abnormal feeding prompt information into the data traceability file of the corresponding pig individual.

8. The control method for the intelligent feeding and feed intake data traceability system at the edge of a pig farm according to claims 1-7, characterized in that, Includes the following steps: S1. Read the ear tag identity information of the pig to be fed through the ear tag RFID identification unit, and obtain the image feature information of the corresponding pig through the visual recognition unit. Match and verify the two types of identification results to generate an individual identity confirmation result. S2. After individual identity is confirmed, retrieve the historical feeding data, growth data and health data corresponding to the pig, determine the current feeding strategy parameters, and control the intelligent feeding module to execute this feeding. S3. During the feeding process of pigs, the amount of feed consumed at one time, the feeding speed and the feeding interval are collected in real time, and the feeding time sequence record corresponding to the individual pig is formed by combining the state of the remaining feed in the feed trough. S4. Based on the set time window, compare and analyze the current feeding time sequence record with the historical feeding baseline constructed from the corresponding historical feeding data of pigs, and output feeding control instructions. S5. According to the feeding control command, control the intelligent feeding module to dynamically adjust the subsequent feed amount, feeding rhythm or diet formula, and simultaneously write the identification data, feeding data, analysis results and control results into the data traceability management module. S6. The feeding data is linked and updated with the pig growth data and health data to form a continuous traceability data chain for the corresponding individual pig.

9. The control method according to claim 8, characterized in that: In step S4, the trend of feed intake change, fluctuation of feed intake rate and distribution characteristics of feed intake interval of a single pig are jointly analyzed, and at least one of the following is generated: feed amount adjustment instruction, feed replenishment rhythm adjustment instruction and diet formula adjustment instruction.

10. The control method according to claim 8, characterized in that: In step S4, when a continuous decrease in feed intake, a feeding speed lower than a preset threshold, or a feeding interval longer than a preset threshold is detected, an abnormal feeding prompt is output, and the abnormal feeding prompt is associated with the data traceability file of the corresponding pig individual as a basis for health inspection and feeding intervention.

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