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CN-122000061-A - Quail health state early warning and management method based on individual behavior monitoring

CN122000061ACN 122000061 ACN122000061 ACN 122000061ACN-122000061-A

Abstract

The invention provides a quail health state early warning and management method based on individual behavior monitoring, which belongs to the field of animal health monitoring and comprises the steps of constructing a multi-mode monitoring scheme to synchronously collect quail data and establish identity mapping, preprocessing, extracting and screening characteristics, early warning by using an edge end and cloud model, dynamically calibrating a threshold value, tracing and grading pathology, and finally implementing individual and group level intervention according to the result to form a closed loop. By adopting the quail health state early warning and management method based on individual behavior monitoring, the early identification, etiology tracing and accurate management of the quail health state are realized, and the cultivation efficiency is improved.

Inventors

  • LIU XIN
  • LI SISI
  • LU LIZHI
  • XU YIBIN

Assignees

  • 中国计量大学

Dates

Publication Date
20260508
Application Date
20260126

Claims (8)

  1. 1. A quail health state early warning and management method based on individual behavior monitoring is characterized by comprising the following steps: S1, constructing a multi-mode monitoring scheme of 'micro wearable sensor-machine vision-RFID auxiliary positioning', synchronously acquiring multi-dimensional behavior data, environment parameters and individual basic information of a quail individual, and establishing a unique mapping relation of the identity of the quail individual; S2, carrying out noise rejection, data complementation, standardization and sample balancing treatment on the acquired data to obtain high-quality preprocessed data; s3, extracting time sequence characteristics of the preprocessed data, constructing combined characteristics and screening mutual information characteristics to obtain core characteristics; S4, performing abnormal initial judgment through an edge-end modified random forest model, and combining a cloud Bi-GRU model to realize accurate early warning of the health state; s5, performing dynamic threshold self-adaptive calibration by adopting K-means clustering, realizing pathology tracing by means of a fusion framework of a multi-label classification model, an ontology knowledge base and a rule inference engine, and outputting a risk classification result; and S6, based on a risk classification result, performing differential intervention from an individual level, and performing risk prediction, preventive intervention and data traceability optimization from a group level to form complete management closed-loop intervention.
  2. 2. The method for early warning and managing the health status of quails based on individual behavior monitoring according to claim 1, wherein the multi-modal monitoring scheme comprises: The method comprises the steps of wearing a foot ring type acceleration sensor with the weight less than 1g for each quail, collecting time sequence data, installing a high-definition infrared camera at the top of each cage position, collecting visual data, configuring an edge computing module to perform data preliminary processing and local caching, and configuring a cloud server to be responsible for global data integration and complex model operation.
  3. 3. The quail health status early warning and management method based on individual behavior monitoring, which is characterized in that the establishment of the unique mapping relation of individual identities comprises the steps of implanting a miniature RFID chip within 48-72 hours after the quail is taken out of a shell, storing unique identity codes, acquiring a quail foot texture image by using a camera, combining an RFID chip position signal, and establishing the unique mapping relation of RFID code-foot texture feature-individual identity by a K nearest neighbor algorithm.
  4. 4. The method for early warning and managing the health state of quails based on individual behavior monitoring according to claim 1, wherein the specific step of preprocessing the data in S2 comprises the following steps: removing noise data by adopting a dynamic sliding window filtering and isolated forest noise reduction method; The short-time missing data is complemented by using a linear interpolation method, the long-time missing data is marked, and the self-checking of the equipment is triggered; The sample balancing treatment of the staged Z-score standardization and the SMOTE-ENN algorithm is carried out.
  5. 5. The quail health state early warning and management method based on individual behavior monitoring, which is characterized by comprising the steps of extracting basic time sequence statistical characteristics and wavelet transformation characteristics, designing and calculating combined characteristics, and screening the core characteristics by adopting a mutual information characteristic selection method.
  6. 6. The quail health status early warning and management method based on individual behavior monitoring of claim 1, wherein the accurate health status early warning comprises: the edge deployment modified random forest model performs real-time abnormal initial judgment to output individual abnormal probability, the cloud uses a bidirectional GRU model to perform accurate early warning, and long-term dependency of time sequence behaviors is captured.
  7. 7. The method for early warning and managing the health status of quails based on individual behavior monitoring of claim 1, wherein managing the closed-loop intervention comprises: performing a powerful intervention means of isolation, administration and local disinfection for high risk individuals; supplementing nutrition or special medicine for medium risk individuals, adjusting illumination time length, and monitoring behavior data every day; Adjusting environmental parameters aiming at low-risk individuals, adding nutrients, and re-judging the health state; And constructing a group risk propagation model, and carrying out risk prediction, preventive intervention and data traceability optimization.
  8. 8. The method for early warning and managing the health state of quails based on individual behavior monitoring according to claim 1, wherein managing the closed-loop intervention further comprises a data feedback and optimization mechanism, and specifically comprises: recording the specific content and effect of each intervention operation in detail, and feeding back the data to a cloud knowledge base; continuously optimizing pathological association rules, intervention suggestions and model parameters through analysis and summarization of feedback data; Incremental training of the model based on the newly added pathology-behavior data per month ensures that the model's fitness and accuracy remain high over time.

Description

Quail health state early warning and management method based on individual behavior monitoring Technical Field The invention relates to the field of animal health monitoring, in particular to a quail health state early warning and management method based on individual behavior monitoring. Background In the quail large-scale cultivation industry, the individual quails are small in size and high in cultivation density, and early identification and accurate management of the health state of the quails are all the pain points of the industry. In the prior art, part of cultivation scenes try to introduce a single sensor or a simple visual monitoring scheme, but the schemes have a plurality of defects to be solved urgently. Due to the functional limitation, the single sensor can only collect limited behavior data, and cannot reflect the health condition of quails in multiple dimensions such as digestion, respiration, movement, stress and the like in an omnibearing and multi-angle manner. The visual monitoring scheme lacks an effective individual identity recognition mechanism, so that a unique corresponding relation is difficult to establish between behavior data and a specific individual, the acquired data cannot be fully utilized, and resource waste is caused. More importantly, the existing monitoring system has not constructed a complete closed-loop system of data acquisition, processing, early warning and intervention. Even if abnormal conditions can be found, the precise etiology traceability and personalized intervention schemes are lacking. In actual operation, only rough management means such as group medication, comprehensive disinfection and the like can be adopted. The management mode not only greatly increases the culture cost, but also can cause a series of problems such as drug residue, bacterial flora imbalance and the like, and has adverse effects on the health of quails and the culture environment. In addition, quails with different ages, sexes and egg laying states have great differences in behavior standard. The prior art lacks a dynamically adaptive threshold calibration mechanism, which easily causes false alarm or missing alarm in the monitoring process, further reduces the cultivation efficiency and influences the product quality. Disclosure of Invention The invention aims to provide a quail health state early warning and management method based on individual behavior monitoring, which overcomes the defects of the prior art, realizes early identification, etiology tracing and accurate management of the quail health state, and improves the cultivation efficiency. In order to achieve the above purpose, the invention provides a quail health state early warning and management method based on individual behavior monitoring, which comprises the following steps: S1, constructing a multi-mode monitoring scheme of 'micro wearable sensor-machine vision-RFID auxiliary positioning', synchronously acquiring multi-dimensional behavior data, environment parameters and individual basic information of a quail individual, and establishing a unique mapping relation of the identity of the quail individual; S2, carrying out noise rejection, data complementation, standardization and sample balancing treatment on the acquired data to obtain high-quality preprocessed data; s3, extracting time sequence characteristics of the preprocessed data, constructing combined characteristics and screening mutual information characteristics to obtain core characteristics; S4, performing abnormal initial judgment through an edge-end modified random forest model, and combining a cloud Bi-GRU model to realize accurate early warning of the health state; s5, performing dynamic threshold self-adaptive calibration by adopting K-means clustering, realizing pathology tracing by means of a fusion framework of a multi-label classification model, an ontology knowledge base and a rule inference engine, and outputting a risk classification result; and S6, based on a risk classification result, performing differential intervention from an individual level, and performing risk prediction, preventive intervention and data traceability optimization from a group level to form complete management closed-loop intervention. Preferably, the multimodal monitoring scheme includes: The method comprises the steps of wearing a foot ring type acceleration sensor with the weight less than 1g for each quail, collecting time sequence data, installing a high-definition infrared camera at the top of each cage position, collecting visual data, configuring an edge computing module to perform data preliminary processing and local caching, and configuring a cloud server to be responsible for global data integration and complex model operation. Preferably, the establishment of the unique mapping relation of the individual identity comprises the steps of implanting a miniature RFID chip within 48-72 hours after the quail is taken out of the shell, storing unique ide