Chicken health state evaluation system and evaluation method in automatic chicken raising equipment based on machine vision

Abstract

The invention discloses a chicken health state assessment method and system in automatic chicken raising equipment based on machine vision, and relates to the technical field of intelligent assessment. Through cooperative work of a fixed camera and a movable camera, image information of healthy chickens and to-be-assessed chickens is collected, and first image data and second image data are generated; extracting a first feature parameter of the healthy chicken and a second feature parameter of the to-be-evaluated chicken by using a preset image analysis algorithm and a deep learning technology, constructing a reference health parameter of the healthy chicken and an initial health parameter of the to-be-evaluated chicken based on a feature weighting algorithm, and calculating a health evaluation interval, the health state of the chicken is determined by judging whether the initial health parameters are within the health assessment interval, in addition, the system performs secondary assessment on the chicken with poor health conditions, the assessment accuracy is further improved, the real-time and accurate monitoring of the health state of the chicken is realized, the labor cost is reduced, the breeding efficiency is improved, and the economic benefit is increased. And technical support is provided for breeding.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent evaluation, and more specifically, to a chicken health status evaluation system and evaluation method in an automated chicken farming device based on machine vision. Background Art

[0002] With growing global attention to food safety and animal welfare, the poultry industry faces unprecedented challenges. As the primary source of meat and eggs, the health of chickens directly impacts production efficiency and product quality. Traditional methods for monitoring chicken health rely heavily on manual observation, which is not only inefficient but also susceptible to subjective factors, leading to misjudgments and missed detections. In recent years, the rapid development of machine vision technology and deep learning algorithms has provided new solutions for chicken health monitoring. Deep learning-based object detection algorithms have become a research hotspot due to their high efficiency and real-time performance. In the context of intelligent farming, the scale of farms continues to expand, and traditional monitoring methods can no longer meet the needs of modern farming. The combination of intelligent inspection robots and machine vision technology makes it possible to monitor chicken health in real time. By working in tandem with fixed and mobile cameras, comprehensive image information of chickens can be collected, and deep learning algorithms can be used to extract feature parameters, enabling accurate assessment of chicken health. Furthermore, the widespread adoption of intelligent farming equipment has also driven the development of related technologies. For example, intelligent inspection robots, equipped with high-definition cameras and multiple sensors, can replace manual labor to complete complex inspection tasks. The application of these technologies not only improves farming efficiency but also reduces labor costs, providing technical support for the sustainable development of the farming industry.

[0003] The existing technology has the disadvantages of low efficiency of manual observation and easy misjudgment and missed judgment, and the existing equipment has limited monitoring capabilities and lacks intelligence. Summary of the Invention

[0004] In order to overcome the problems of low efficiency and easy misjudgment and missed judgment in manual observation in the existing technology, and the limited monitoring capability and insufficient intelligence of existing equipment, the present invention discloses a chicken health status assessment system and assessment method in automated chicken farming equipment based on machine vision, which can effectively solve the above technical problems.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] A method for assessing the health status of chickens in automated chicken farming equipment based on machine vision, the method comprising:

[0007] As the intelligent inspection robot moves through the chicken house, it uses fixed cameras pre-deployed in a standard area of ​​the chicken house to collect image information of healthy chickens to generate first image data. At the same time, the intelligent inspection robot uses a mobile camera on board to obtain image information of the chickens to be evaluated to generate second image data. The standard area is a specific area in the chicken house where the health of the chickens is known to be stable.

[0008] Based on a preset image analysis algorithm, the first image data and the second image data are processed respectively to extract a first characteristic parameter corresponding to a healthy chicken and a second characteristic parameter corresponding to a chicken to be evaluated;

[0009] Determining reference health parameters of healthy chickens and initial health parameters of the chickens to be evaluated based on the first characteristic parameter and the second characteristic parameter; and calculating a health assessment interval based on the reference health parameters and the initial health parameters corresponding to the healthy chickens in the initial health parameters;

[0010] Based on the health assessment interval and the initial health parameters, a health assessment result of the chicken to be assessed is determined.

[0011] Preferably, the method of collecting image information of healthy chickens using a fixed camera pre-deployed in a standard area of ​​the chicken house to generate the first image data includes:

[0012] When the intelligent inspection robot passes through the standard area, it collects images of the chickens’ daily activities through fixed cameras;

[0013] After the intelligent inspection robot leaves the standard area, it collects images of the chickens while they are resting;

[0014] First image data is generated based on the acquired image.

[0015] Preferably, the step of acquiring image information of the chicken to be evaluated by a mobile camera carried by the intelligent inspection robot to generate the second image data includes:

[0016] As the intelligent inspection robot moves through the chicken house, it uses a mobile camera to collect images of the chickens to be evaluated. The images contain key information about the health status of the chickens.

[0017] Second image data is generated based on the acquired image.

[0018] Preferably, before processing the first image data and the second image data respectively based on a preset image analysis algorithm, the method further includes:

[0019] Processing the second image data based on a preset convolutional autoencoder neural network to obtain a feature mapping signal related to the health characteristics of the chicken;

[0020] The second image data is processed based on a preset image analysis algorithm, including: processing the feature mapping signal according to the preset image analysis algorithm to extract a second feature parameter corresponding to the chicken to be evaluated.

[0021] Preferably, determining the reference health parameters of healthy chickens and the initial health parameters of the chickens to be evaluated based on the first characteristic parameter and the second characteristic parameter includes:

[0022] Based on a feature weighting algorithm, the morphological information and behavioral information in the first feature parameter and the second feature parameter are respectively used to construct reference health parameters corresponding to healthy chickens and initial health parameters corresponding to the chickens to be evaluated.

[0023] Preferably, determining the health assessment result of the chicken to be assessed based on the health assessment interval and the initial health parameters includes:

[0024] Determining whether the initial health parameters corresponding to the chicken to be evaluated are within the health evaluation range;

[0025] If it is within the range, the chicken health assessment result is determined to be healthy;

[0026] If it is not within the range, the chicken health assessment result is determined to be unhealthy.

[0027] Preferably, after determining the health assessment result of the chicken to be assessed based on the health assessment interval and the initial health parameters, the method further comprises:

[0028] Selecting a number of target chickens that are judged to be in poor health from the health assessment results of the chickens to be assessed;

[0029] Temporary cameras are added around the chicken cages where the target chickens are located, and when the intelligent inspection robot passes by the target chickens, the temporary cameras collect image information of the target chickens to generate third image data;

[0030] Processing the third image data using a preset image analysis algorithm to extract corresponding third characteristic parameters;

[0031] Based on the third characteristic parameter, the health assessment interval, and the characteristic change rate and characteristic matching coefficient of the target chicken relative to the healthy chicken obtained by the first characteristic parameter and the third characteristic parameter, the target chicken is re-evaluated to obtain a new health assessment result corresponding to the target chicken; the characteristic matching coefficient is a numerical value corresponding to the preset health judgment criterion.

[0032] The chicken health status assessment system in automated chicken farming equipment based on machine vision includes:

[0033] The image acquisition unit is used to collect image information of healthy chickens using fixed cameras pre-deployed in the standard area of ​​the chicken house while the intelligent inspection robot shuttles through the chicken house to generate first image data; at the same time, the mobile camera carried by the intelligent inspection robot obtains image information of the chicken to be evaluated to generate second image data;

[0034] An image analysis unit is configured to process the first image data and the second image data based on a preset image analysis algorithm, and extract a first characteristic parameter corresponding to a healthy chicken and a second characteristic parameter corresponding to a chicken to be evaluated;

[0035] a health parameter calculation unit, configured to determine a reference health parameter of a healthy chicken and an initial health parameter of a chicken to be evaluated based on the first characteristic parameter and the second characteristic parameter;

[0036] An evaluation interval calculation unit is configured to calculate a health evaluation interval based on the reference health parameter and the initial health parameter corresponding to the healthy chicken in the initial health parameter; specifically, to calculate the deviation between the initial health parameter corresponding to the healthy chicken and the reference health parameter; and to construct a health evaluation interval based on the reference health parameter and the deviation;

[0037] The chicken assessment unit is used to determine the health assessment result of the chicken to be assessed based on the health assessment interval and the initial health parameters.

[0038] An electronic device comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned evaluation method when executed by the processor.

[0039] A computer-readable storage medium is characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the evaluation method described above are implemented.

[0040] Compared with the existing technology, the beneficial effects of the present invention are as follows: the present invention solves the problem of low efficiency and easy misjudgment and missed judgment of manual observation through the coordinated work of fixed cameras and mobile cameras. The fixed camera collects images of the chickens' daily activities when the intelligent inspection robot passes through the standard area, and collects images of the chickens at rest after leaving to generate first image data; the mobile camera collects images of the chickens to be evaluated while the robot shuttles through the chicken house to generate second image data. This multi-angle and all-round image acquisition method ensures the comprehensiveness and accuracy of the data and avoids the limitations of manual observation; the application of preset image analysis algorithms and convolutional autoencoder neural networks solves the problems of limited monitoring capabilities and intelligent monitoring of existing equipment. In order to solve the problem of insufficient health, the first characteristic parameters of healthy chickens and the second characteristic parameters of chickens to be evaluated are extracted through deep learning technology, which improves the accuracy and intelligence level of feature extraction. Based on the feature weighting algorithm, the reference health parameters of healthy chickens and the initial health parameters of chickens to be evaluated are constructed, and the health assessment interval is calculated, which further improves the scientificity and reliability of the assessment. In addition, the system conducts a secondary assessment on chickens in poor health, collects image information by adding a temporary camera, generates third image data, and conducts a re-evaluation based on the feature change rate and feature matching coefficient to ensure the accuracy of the assessment results. This secondary assessment mechanism effectively avoids misjudgment and missed judgment, and improves the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the implementation methods or the description of the prior art. Obviously, the drawings described below are merely exemplary. For ordinary technicians in this field, other implementation drawings can be derived based on the provided drawings without any creative work.

[0042] Figure 1 It is a step diagram of the method of the present invention;

[0043] Figure 2 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0044] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0045] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0046] It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0047] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0048] Example 1

[0049] A method for evaluating the health status of chickens in automated chicken farming equipment based on machine vision, such as Figure 1 As shown, the method includes:

[0050] As the intelligent inspection robot moves through the chicken house, it uses fixed cameras pre-deployed in a standard area of ​​the chicken house to collect image information of healthy chickens to generate first image data. At the same time, the intelligent inspection robot uses a mobile camera on board to obtain image information of the chickens to be evaluated to generate second image data. The standard area is a specific area in the chicken house where the health of the chickens is known to be stable.

[0051] Based on a preset image analysis algorithm, the first image data and the second image data are processed respectively to extract a first characteristic parameter corresponding to a healthy chicken and a second characteristic parameter corresponding to a chicken to be evaluated;

[0052] Determining reference health parameters of healthy chickens and initial health parameters of the chickens to be evaluated based on the first characteristic parameter and the second characteristic parameter; and calculating a health assessment interval based on the reference health parameters and the initial health parameters corresponding to the healthy chickens in the initial health parameters;

[0053] Based on the health assessment interval and the initial health parameters, a health assessment result of the chicken to be assessed is determined.

[0054] The method of collecting image information of healthy chickens using a fixed camera pre-deployed in a standard area of ​​a chicken house to generate first image data includes:

[0055] When the intelligent inspection robot passes through the standard area, it collects images of the chickens’ daily activities through fixed cameras;

[0056] After the intelligent inspection robot leaves the standard area, it collects images of the chickens while they are resting;

[0057] First image data is generated based on the acquired image.

[0058] The method of obtaining image information of the chicken to be evaluated by using a mobile camera carried by the intelligent inspection robot and generating second image data includes:

[0059] As the intelligent inspection robot moves through the chicken house, it uses a mobile camera to collect images of the chickens to be evaluated. The images contain key information about the health status of the chickens.

[0060] Second image data is generated based on the acquired image.

[0061] Before processing the first image data and the second image data respectively based on a preset image analysis algorithm, the method further includes:

[0062] Processing the second image data based on a preset convolutional autoencoder neural network to obtain a feature mapping signal related to the health characteristics of the chicken;

[0063] The second image data is processed based on a preset image analysis algorithm, including: processing the feature mapping signal according to the preset image analysis algorithm to extract a second feature parameter corresponding to the chicken to be evaluated.

[0064] Determining the reference health parameters of healthy chickens and the initial health parameters of the chickens to be evaluated based on the first characteristic parameter and the second characteristic parameter includes:

[0065] Based on a feature weighting algorithm, the morphological information and behavioral information in the first feature parameter and the second feature parameter are respectively used to construct reference health parameters corresponding to healthy chickens and initial health parameters corresponding to the chickens to be evaluated.

[0066] Determining the health assessment results of the chickens to be assessed based on the health assessment interval and the initial health parameters includes:

[0067] Determining whether the initial health parameters corresponding to the chicken to be evaluated are within the health evaluation range;

[0068] If it is within the range, the chicken health assessment result is determined to be healthy;

[0069] If it is not within the range, the chicken health assessment result is determined to be unhealthy.

[0070] After determining the health assessment result of the chicken to be assessed based on the health assessment interval and the initial health parameters, the method further includes:

[0071] Selecting a number of target chickens that are judged to be in poor health from the health assessment results of the chickens to be assessed;

[0072] Temporary cameras are added around the chicken cages where the target chickens are located, and when the intelligent inspection robot passes by the target chickens, the temporary cameras collect image information of the target chickens to generate third image data;

[0073] Processing the third image data using a preset image analysis algorithm to extract corresponding third characteristic parameters;

[0074] Based on the third characteristic parameter, the health assessment interval, and the characteristic change rate and characteristic matching coefficient of the target chicken relative to the healthy chicken obtained by the first characteristic parameter and the third characteristic parameter, the target chicken is re-evaluated to obtain a new health assessment result corresponding to the target chicken; the characteristic matching coefficient is a numerical value corresponding to the preset health judgment criterion.

[0075] An electronic device comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned evaluation method when executed by the processor.

[0076] A computer-readable storage medium is characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the evaluation method described above are implemented.

[0077] In the specific implementation, the intelligent inspection robot shuttles through the chicken house along the established route. When it passes through the standard area, the fixed camera is activated to capture images of the chickens' daily activities and record the chickens' walking, pecking, drinking and other behaviors. After the intelligent inspection robot leaves the standard area, the fixed camera again collects images of the chickens when they are resting, including the chickens' lying posture, feather status, etc. These collected images are integrated to generate the first image data, which represents the image information of healthy chickens in different states.

[0078] As the intelligent inspection robot shuttles through the chicken coop, its mobile camera continues to work, taking pictures of the chickens to be evaluated in the chicken coop. The pictures cover key information about the chickens' health status, such as their overall shape, feather color, eye condition, feeding behavior, and activity ability. After the collection is completed, the second image data is generated.

[0079] The generated second image data is processed using a preset convolutional autoencoder neural network, which can extract feature mapping signals related to health characteristics in chicken images, highlighting key health features of chickens, such as feather texture and comb color changes, and then input these feature mapping signals into a preset image analysis algorithm.

[0080] The first and second image data are processed separately using a preset image analysis algorithm. Morphological information of healthy chickens, such as body size and feather fullness, and behavioral information, such as activity frequency and feeding patterns, are extracted from the first image data to form first characteristic parameters. Similarly, corresponding morphological and behavioral information of the chicken to be evaluated is extracted from the second image data to obtain second characteristic parameters.

[0081] A feature-weighted algorithm is used to construct reference health parameters and initial health parameters based on the morphological and behavioral information of healthy chickens and chickens to be evaluated. For example, a higher weight is assigned to the feature of feather fullness because the feather status can intuitively reflect the nutritional and health status of the chicken. For activity frequency, appropriate weights are assigned according to the growth stage of the chicken, and the reference health parameters of healthy chickens and the initial health parameters of the chickens to be evaluated are determined respectively.

[0082] Compare the reference health parameters and the parts of the initial health parameters corresponding to healthy chickens, and calculate the deviation between the two. For example, if the average feeding frequency of healthy chickens is 5 times per hour, and the feeding frequency of a chicken to be evaluated in the initial health parameters is 4 times per hour, calculate the frequency deviation. Based on the reference health parameters and the calculated deviation, construct a health assessment interval. Assume that the health assessment interval of feeding frequency is [4, 6] times / hour.

[0083] Determine whether the initial health parameters of the chicken to be evaluated are within the health assessment range. If the feeding frequency of a chicken to be evaluated is within the above-mentioned [4, 6] times / hour range, and other key initial health parameters, such as feather fullness, comb color and other corresponding parameters are also within their respective health assessment ranges, then the health assessment result of the chicken is determined to be healthy; if any key initial health parameter is not within the corresponding range, such as the feather color of a chicken is dull and the corresponding parameter is not within the health assessment range, then the health assessment result of the chicken is determined to be unhealthy.

[0084] Target chickens that are judged to be in poor health are screened out from the health assessment results, and temporary cameras are added around the chicken cages where the target chickens are located. When the intelligent inspection robot passes by, the temporary cameras collect image information of the target chickens, generate third image data, and process the third image data through a preset image analysis algorithm to extract third feature parameters.

[0085] Calculate the characteristic change rate of the target chicken relative to that of healthy chickens, such as the degree of change in the feather color of the target chicken compared to that of healthy chickens; at the same time, determine the characteristic matching coefficient based on the preset health judgment criteria, and re-evaluate the target chicken in combination with the third characteristic parameter, the health assessment interval, the characteristic change rate and the characteristic matching coefficient to obtain a new health assessment result. If the target chicken's feather color change rate is large and the characteristic matching coefficient is low, even if some parameters are at the edge of the health assessment interval, it may be judged that its health status is still poor.

[0086] Example 2

[0087] Chicken health status assessment system in automated chicken farming equipment based on machine vision, such as Figure 2 Shown, including:

[0088] The image acquisition unit is used to collect image information of healthy chickens using fixed cameras pre-deployed in the standard area of ​​the chicken house while the intelligent inspection robot shuttles through the chicken house to generate first image data; at the same time, the mobile camera carried by the intelligent inspection robot obtains image information of the chicken to be evaluated to generate second image data;

[0089] An image analysis unit is configured to process the first image data and the second image data based on a preset image analysis algorithm, and extract a first characteristic parameter corresponding to a healthy chicken and a second characteristic parameter corresponding to a chicken to be evaluated;

[0090] a health parameter calculation unit, configured to determine a reference health parameter of a healthy chicken and an initial health parameter of a chicken to be evaluated based on the first characteristic parameter and the second characteristic parameter;

[0091] An evaluation interval calculation unit is configured to calculate a health evaluation interval based on the reference health parameter and the initial health parameter corresponding to the healthy chicken in the initial health parameter; specifically, to calculate the deviation between the initial health parameter corresponding to the healthy chicken and the reference health parameter; and to construct a health evaluation interval based on the reference health parameter and the deviation;

[0092] The chicken assessment unit is used to determine the health assessment result of the chicken to be assessed based on the health assessment interval and the initial health parameters.

[0093] The evaluation system adopts a layered architecture, including data acquisition layer, data processing layer and result output layer.

[0094] The data collection layer consists of fixed cameras in standard areas of the chicken house, mobile cameras onboard intelligent inspection robots, and temporary cameras added when necessary. These cameras capture images of the chickens and transmit the data to the data processing layer.

[0095] Data processing layer: The image acquisition unit receives image data collected by fixed cameras, mobile cameras and temporary cameras, generates first image data, second image data and third image data respectively, and stores the data in the system database.

[0096] The image analysis unit reads the first image data and the second image data from the database, processes them using a preset image analysis algorithm, and extracts the first characteristic parameters of healthy chickens and the second characteristic parameters of chickens to be evaluated; when a second evaluation is required, the third image data is read and the third characteristic parameters are extracted, and the processed characteristic parameters are stored back in the database.

[0097] The health parameter calculation unit uses a feature weighting algorithm based on the first feature parameter and the second feature parameter to determine the reference health parameters of the healthy chicken and the initial health parameters of the chicken to be evaluated, and stores these parameters in a database.

[0098] The evaluation interval calculation unit calculates the deviation and constructs the health evaluation interval based on the reference health parameters and the part of the initial health parameters corresponding to the healthy chickens, and stores the health evaluation interval information in the database.

[0099] The chicken assessment unit reads the health assessment interval and initial health parameters, determines the health status of the chicken to be assessed, and obtains the health assessment results; for chickens with poor health, a secondary assessment is performed based on the third characteristic parameter, characteristic change rate, and characteristic matching coefficient, the health assessment results are updated, and all assessment results are stored in the database.

[0100] The result output layer is connected to the terminal devices of chicken farm managers, such as computers and mobile phones, and presents the chicken health assessment results to managers in a visual way. For example, the health status distribution of different chickens is displayed through charts, and chickens with poor health are marked and reminded, so that managers can take timely measures.

[0101] The camera in the data acquisition layer collects image data according to the set rules and transmits it to the image acquisition unit in the data processing layer; the image acquisition unit generates and stores the corresponding image data, the image analysis unit extracts the feature parameters, the health parameter calculation unit determines the health parameters, the evaluation interval calculation unit constructs the evaluation interval, and the chicken evaluation unit obtains the health assessment results. Each unit is executed in sequence and the intermediate and final results are stored in the database; the result output layer obtains the evaluation results from the database and displays them to the chicken farm managers in an intuitive manner, thereby realizing effective monitoring and management of the health status of the chickens.

[0102] The same or similar reference numerals correspond to the same or similar components;

[0103] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0104] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the field, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the health status of chickens in automated chicken farming equipment based on machine vision, characterized in that: The method includes: As the intelligent inspection robot moves through the chicken house, it uses fixed cameras pre-deployed in a standard area of ​​the chicken house to collect image information of healthy chickens to generate first image data. At the same time, the intelligent inspection robot uses a mobile camera on board to obtain image information of the chickens to be evaluated to generate second image data. The standard area is a specific area in the chicken house where the health of the chickens is known to be stable. Based on a preset image analysis algorithm, the first image data and the second image data are processed respectively to extract a first characteristic parameter corresponding to a healthy chicken and a second characteristic parameter corresponding to a chicken to be evaluated; Determining reference health parameters of healthy chickens and initial health parameters of the chickens to be evaluated based on the first characteristic parameter and the second characteristic parameter; and calculating a health assessment interval based on the reference health parameters and the initial health parameters corresponding to the healthy chickens in the initial health parameters; Based on the health assessment interval and the initial health parameters, a health assessment result of the chicken to be assessed is determined.

2. The evaluation method according to claim 1, wherein: The method of collecting image information of healthy chickens using a fixed camera pre-deployed in a standard area of ​​a chicken house to generate first image data includes: When the intelligent inspection robot passes through the standard area, it collects images of the chickens’ daily activities through fixed cameras; After the intelligent inspection robot leaves the standard area, it collects images of the chickens while they are resting; First image data is generated based on the acquired image.

3. The evaluation method according to claim 1, wherein: The method of obtaining image information of the chicken to be evaluated by using a mobile camera carried by the intelligent inspection robot and generating second image data includes: As the intelligent inspection robot moves through the chicken house, it uses a mobile camera to collect images of the chickens to be evaluated. The images contain key information about the health status of the chickens. Second image data is generated based on the acquired image.

4. The evaluation method according to claim 1, wherein: Before processing the first image data and the second image data respectively based on a preset image analysis algorithm, the method further includes: Processing the second image data based on a preset convolutional autoencoder neural network to obtain a feature mapping signal related to the health characteristics of the chicken; The second image data is processed based on a preset image analysis algorithm, including: processing the feature mapping signal according to the preset image analysis algorithm to extract a second feature parameter corresponding to the chicken to be evaluated.

5. The evaluation method according to claim 1, wherein: Determining the reference health parameters of healthy chickens and the initial health parameters of the chickens to be evaluated based on the first characteristic parameter and the second characteristic parameter includes: Based on a feature weighting algorithm, the morphological information and behavioral information in the first feature parameter and the second feature parameter are respectively used to construct reference health parameters corresponding to healthy chickens and initial health parameters corresponding to the chickens to be evaluated.

6. The evaluation method according to any one of claims 1 to 5, characterized in that: Determining the health assessment results of the chickens to be assessed based on the health assessment interval and the initial health parameters includes: Determining whether the initial health parameters corresponding to the chicken to be evaluated are within the health evaluation range; If it is within the range, the chicken health assessment result is determined to be healthy; If it is not within the range, the chicken health assessment result is determined to be unhealthy.

7. The evaluation method according to claim 6, characterized in that After determining the health assessment result of the chicken to be assessed based on the health assessment interval and the initial health parameters, the method further includes: Selecting a number of target chickens that are judged to be in poor health from the health assessment results of the chickens to be assessed; Temporary cameras are added around the chicken cages where the target chickens are located, and when the intelligent inspection robot passes by the target chickens, the temporary cameras collect image information of the target chickens to generate third image data; Processing the third image data using a preset image analysis algorithm to extract corresponding third characteristic parameters; Based on the third characteristic parameter, the health assessment interval, and the characteristic change rate and characteristic matching coefficient of the target chicken relative to the healthy chicken obtained by the first characteristic parameter and the third characteristic parameter, the target chicken is re-evaluated to obtain a new health assessment result corresponding to the target chicken; the characteristic matching coefficient is a numerical value corresponding to the preset health judgment criterion.

8. A system for evaluating the health status of chickens in automated chicken farming equipment based on machine vision, implemented by the evaluation method according to any one of claims 1 to 7, characterized in that: include: The image acquisition unit is used to collect image information of healthy chickens using fixed cameras pre-deployed in the standard area of ​​the chicken house while the intelligent inspection robot shuttles through the chicken house to generate first image data; at the same time, the mobile camera carried by the intelligent inspection robot obtains image information of the chicken to be evaluated to generate second image data; An image analysis unit is configured to process the first image data and the second image data based on a preset image analysis algorithm, and extract a first characteristic parameter corresponding to a healthy chicken and a second characteristic parameter corresponding to a chicken to be evaluated; a health parameter calculation unit, configured to determine a reference health parameter of a healthy chicken and an initial health parameter of a chicken to be evaluated based on the first characteristic parameter and the second characteristic parameter; An evaluation interval calculation unit, configured to calculate a health evaluation interval based on the reference health parameters and the initial health parameters corresponding to healthy chickens in the initial health parameters; Specifically, the deviation between the initial health parameters corresponding to healthy chickens and the reference health parameters is calculated; constructing a health assessment interval based on the reference health parameter and the deviation; The chicken assessment unit is used to determine the health assessment result of the chicken to be assessed based on the health assessment interval and the initial health parameters.

9. An electronic device, characterized in that: The device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the evaluation method according to claims 1 to 7 when executed by the processor.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the evaluation method according to claims 1 to 7 are implemented.

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