Intelligent henhouse door control system and movement detection method thereof

Abstract

The invention relates to the technical field of automatic control, in particular to an intelligent chicken house door control system and a movement detection method thereof, and the system comprises a density monitoring module, an automatic door control module, a region isolation and anomaly recognition module and a door control adjustment module. According to the invention, by introducing machine vision and infrared sensing technologies, the control precision and efficiency of the henhouse door are significantly improved, the density of chicken flocks around the henhouse door is analyzed by using video data and compared with a set threshold value, the opening and closing adjustment of the door is directly influenced, the chicken flocks are prevented from being excessively crowded, the animal welfare and safety are improved, and the henhouse door is more intelligent. The continuous monitoring of the behavior trend helps to identify behavior abnormity in real time, plays a key role in preventing disease spreading in advance and reducing loss caused by emergency situations, and ensures the production efficiency by adjusting the opening and closing of the door in real time, optimizing the flow of chicken flocks, avoiding the crowding phenomenon and reducing stress response and injury through automatic adjustment, so that the production cost is reduced, and the production efficiency is improved. And the labor cost and the management difficulty are greatly reduced, so that the chicken house management is more intelligent.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to an intelligent chicken house door control system and a movement detection method thereof. Background Art

[0002] Automatic control technology is a technology widely used in many fields such as industry, transportation, and home. It involves the use of various control systems to operate equipment in order to achieve the purpose of automatically completing specific tasks without human intervention. It mainly studies how to enable machines and equipment to automatically monitor and adjust operating conditions to ensure the optimal performance of the system. Automatic control systems can respond in real time based on sensor inputs, adjust outputs through feedback mechanisms, and control the behavior of machinery or systems. This is widely used in automated production lines in the manufacturing industry, intelligent transportation systems, automated warehousing, and various home automation devices.

[0003] Among them, the intelligent chicken house door control system refers to the use of automatic control technology to manage the opening and closing of the chicken house entrance and exit, and to control the entry and exit of the chickens in an automated manner to improve breeding efficiency and safety. The system automatically controls the opening and closing of the door by sensing environmental factors (such as light, temperature) or the activities of the chickens, which not only reduces the demand for human resources, but also effectively prevents the escape of the chickens and the entry of intruders, ensuring the safety and health of the chickens. The application of this system makes chicken house management more intelligent, helping farmers improve management efficiency and breeding quality.

[0004] Existing technologies mainly rely on basic timing control or simple sensor feedback for automatic door control. They lack in-depth analysis of the environment and chicken behavior and are unable to respond to complex changes. This technical limitation makes it impossible to effectively deal with crowding caused by unexpected gatherings of chickens or emergencies caused by sudden changes in the environment in actual operations. For example, when there is a sudden change in light or a sharp drop in temperature, traditional control systems cannot adjust the opening and closing of the door in time, increasing the risk of chickens being impacted. Traditional systems usually have a delayed response to abnormal behavior, causing diseases and aggressive behaviors to spread among chickens. The shortcomings of existing technologies in dynamic monitoring and real-time response will lead to inefficient management and easily cause animal welfare issues. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent chicken house door control system and a movement detection method thereof.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, an intelligent chicken house door control system includes:

[0007] The density monitoring module collects video data of the area around the chicken house door, uses machine vision technology to perform image analysis, analyzes the density of chickens around the chicken house door, obtains density data, compares it with the set density threshold, determines whether it is crowded, and obtains the crowding status assessment result;

[0008] The automatic gating module uses infrared sensors to analyze the behavior of the chickens based on the crowding state assessment results, distinguishes between normal and abnormal behaviors of the chickens, generates behavior analysis results, identifies changes in the overall behavior state of the chickens around the chicken house door, and captures the activity intensity of the chickens on the movement trajectory of the chicken house door. If a chicken gathering is detected, automatic gating is performed to drive the chickens away and establish a gating behavior preset;

[0009] The regional isolation and abnormality identification module sets multiple chicken house doors for pre-isolation according to the gated behavior preset. If the infrared sensor senses that the number of chickens in the isolated area exceeds the preset value, the associated chicken house door is automatically opened to guide the chickens to move to the isolated area with fewer chickens, analyze the chicken behavior trend, identify the critical point of abnormal behavior, obtain behavior trend data, analyze potential behavioral anomalies, and obtain abnormal behavior warning indicators;

[0010] The door control adjustment module adjusts the opening and closing status of the chicken house door through the abnormal behavior warning indicator, executes the door opening and closing action, adjusts the degree of door opening, controls the flow of chickens and avoids crowding, obtains the door state control plan, adjusts the door opening and closing status in real time, and obtains the chicken house door control effect evaluation result.

[0011] As a further solution of the present invention, the step of acquiring the density data is specifically as follows:

[0012] Collect video data of the area around the chicken house door, and use machine vision technology to capture the dynamics of the chickens and obtain a video data set;

[0013] According to the video data set, edge detection and dynamic tracking of the movement of the chickens are performed to identify the position and movement path of the chickens in each frame, and obtain the dynamic distribution data of the chickens;

[0014] According to the chicken image distribution data, the formula is applied:

[0015]

[0016] Calculate the density of the flock and obtain density data;

[0017] Where D is the density of the chicken group, N count is the total number of chickens detected in the image, W adj is the density adjustment weight, A is the total area of ​​the monitoring area, C norm is the normalization coefficient.

[0018] As a further solution of the present invention, the step of obtaining the congestion status evaluation result is specifically:

[0019] Through the density data, using a time series analysis method, the density values ​​at key time points are extracted to obtain density information at key time points;

[0020] Based on the density information of the key time points, the formula is used:

[0021]

[0022] Evaluate the congestion state and obtain the congestion state indicator SA;

[0023] Among them, D current represents the real-time density value, T is the threshold, K is the adjustment coefficient, F temp is the temperature factor adjustment;

[0024] According to the crowding state index, it is determined whether the real-time density of the chickens around the chicken house door exceeds a preset threshold. If it exceeds, it is determined to be crowded, and if it does not exceed, it is non-crowded, and a crowding state evaluation result is obtained.

[0025] As a further solution of the present invention, the steps of obtaining the behavior analysis results are specifically as follows:

[0026] Using the crowding state assessment results, the dynamic behavior of the chickens is continuously monitored using infrared sensors, and the parameters of the behavior, including movement speed and residence time, are recorded to obtain behavior data records;

[0027] According to the behavioral data records, the regularity and abnormality of the chicken group's behavior are identified, the frequency of the differentiated behavioral patterns is calculated, and the behavioral pattern classification results are obtained;

[0028] The behavior pattern classification results are used to evaluate the probability of the behavior pattern of the flock, using the formula:

[0029]

[0030] Calculate information entropy I, evaluate the diversity and abnormality of chicken flock behavior, and obtain behavior analysis results;

[0031] Among them, p i represents the probability of occurrence of the i-th behavior pattern, and N represents the total number of behavior samples.

[0032] As a further solution of the present invention, the steps for obtaining the preset gating behavior are specifically:

[0033] According to the behavior analysis results, statistically analyzing the abnormal and normal behaviors of the chickens around the chicken house door, identifying the occurrence frequency and duration of multiple types of behaviors, and obtaining behavior pattern data;

[0034] The behavior state parameters are calculated through the behavior pattern data using the formula:

[0035]

[0036] The weighted sum of abnormal behavior and normal behavior is obtained to obtain the weighted behavior index S;

[0037] Among them, w 1 and w 2 are the weight coefficients of abnormal and normal behaviors, N abnormal and N normal Represents the number of abnormal behaviors and normal behaviors, N total represents the total number of behavioral events;

[0038] Based on the weighted behavior index, the impact of the differentiated behavior patterns of the chickens on the movement trajectory of the chicken house door on the overall behavior state of the chickens is analyzed, the changing trend of the chicken behavior around the chicken house door is identified, and the gate behavior preset is established.

[0039] As a further solution of the present invention, the step of obtaining the abnormal behavior warning indicator is specifically as follows:

[0040] Using the gated behavior preset, multiple chicken house doors are set to isolate chickens, and the average and standard deviation of the differentiated behavior patterns are calculated to obtain a baseline behavior pattern;

[0041] The real-time gating behavior preset is compared with the baseline behavior pattern using the formula:

[0042]

[0043] Calculate the deviation Z of the differentiated behavior and obtain the deviation analysis result;

[0044] Where X represents the currently observed gating behavior preset, μ is the mean value of the differentiated behavior pattern, and σ is the standard deviation;

[0045] The threshold of abnormal behavior is set based on the deviation analysis result. If the preset Z score of the gated behavior exceeds the threshold, it is marked as abnormal behavior, and an abnormal behavior warning indicator is obtained.

[0046] As a further solution of the present invention, the steps of obtaining the door state control solution are specifically as follows:

[0047] Analyze the abnormal behavior warning indicators, compare the abnormal behavior patterns with preset thresholds, analyze the risks of abnormal behavior of the chickens, identify the risk level, and obtain risk assessment results;

[0048] Based on the risk assessment results, calculate the degree of door opening of the chicken house using the formula:

[0049]

[0050] Calculate the gate adjustment requirements and obtain the gate adjustment instructions;

[0051] Where R represents the gate adjustment index, b d is a real-time behavioral deviation indicator. is the historical average behavior deviation, w d is the behavioral deviation weight, N is the number of behavioral indicators;

[0052] The door control adjustment instruction is executed to automatically adjust the opening and closing state of the door, match the flow of chickens and avoid crowding, verify the stability of the chicken house environment, and obtain the door state control solution.

[0053] As a further solution of the present invention, the steps for obtaining the evaluation result of the chicken house door control effect are specifically as follows:

[0054] Execute the door state control scheme to automatically adjust the opening and closing state of the chicken house door, and monitor the flow and distribution of the chickens to obtain a real-time monitoring data set;

[0055] Based on the real-time monitoring data set, the flow efficiency and crowding inside the chicken house were evaluated using the formula:

[0056]

[0057] Analyze the degree of deviation between the real-time situation and the ideal state, and obtain the deviation analysis results;

[0058] Among them, E represents the efficiency of the control effect, d opt is the ideal chicken population density, d act It is the chicken population distribution density monitored in real time;

[0059] By using the deviation analysis results, referring to the chicken house environmental stability and chicken flock health identification, the implementation effect of the door status control scheme is evaluated to obtain the chicken house door control effect evaluation result.

[0060] A smart chicken house door movement detection method, the smart chicken house door movement detection method is performed based on the above-mentioned smart chicken house door control system, comprising the following steps:

[0061] S1: Collect video data of the area around the chicken house door, use machine vision technology to perform image analysis, analyze the density of chickens around the chicken house door, and compare it with the set density threshold to determine the crowding situation and obtain the density status index;

[0062] S2: using the density state index and infrared sensors to identify the behavior of the chickens, classify the behavior of the chickens, distinguish normal and abnormal behaviors of the chickens, summarize the data, and obtain a behavior classification index;

[0063] S3: Analyze behavior trends using the behavior classification indicators, identify critical points of abnormal behavior, and establish abnormal behavior warning indicators;

[0064] S4: According to the abnormal behavior warning indicator, adjust the opening and closing state of the chicken house door, execute the door opening and closing action, control the flow of chickens and avoid crowding, perform dynamic balance of the environment in the chicken house, and obtain the door state movement plan;

[0065] S5: Based on the door status movement scheme, detect the real-time opening and closing status of the chicken house door, collect real-time flow data, identify the impact of the chicken house door movement, and obtain the chicken house door movement detection log.

[0066] Compared with the prior art, the advantages and positive effects of the present invention are:

[0067] In the present invention, by introducing machine vision and infrared sensing technology, the control accuracy and efficiency of the chicken house door are significantly improved. The density of chickens around the chicken house door is analyzed by video data and compared with the set threshold, which directly affects the opening and closing adjustment of the door, avoids overcrowding of chickens, and improves animal welfare and safety. Continuous monitoring of behavioral trends helps to identify behavioral abnormalities in real time, which plays a key role in preventing the spread of diseases in advance and reducing losses caused by emergencies. By adjusting the opening and closing of the door in real time, the flow of chickens is optimized, crowding is avoided, and stress reactions and injuries are reduced. Automatic adjustment not only ensures production efficiency, but also greatly reduces labor costs and management difficulties, making chicken house management more intelligent and. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a system flow chart of the present invention;

[0069] Figure 2 is a flow chart of density data in the present invention;

[0070] Figure 3 A flow chart of the congestion status evaluation result in the present invention;

[0071] Figure 4 A flow chart of the behavior analysis results in the present invention;

[0072] Figure 5 A flow chart preset for the gating behavior in the present invention;

[0073] Figure 6 This is a flow chart of abnormal behavior warning indicators in the present invention;

[0074] Figure 7It is a flow chart of the door state control scheme in the present invention;

[0075] Figure 8 It is a flow chart of the evaluation results of the chicken house door control effect in the present invention. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0077] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0078] See also Figure 1 The present invention provides a technical solution, an intelligent chicken house door control system comprising:

[0079] The density monitoring module collects video data of the area around the chicken house door, uses machine vision technology to perform image analysis, analyzes the density of chickens around the chicken house door, obtains density data, compares it with the set density threshold, determines whether it is crowded, and obtains the crowding status assessment result;

[0080] The automatic gating module uses infrared sensors to analyze the behavior of chickens based on the crowding status assessment results, distinguishes between normal and abnormal behaviors of chickens, generates behavior analysis results, identifies changes in the overall behavior of chickens around the chicken house door, and captures the activity intensity of chickens on the movement trajectory of the chicken house door. If chickens are detected gathering, automatic gating is performed to drive the chickens away and establish a gating behavior preset.

[0081] The regional isolation and abnormal identification module sets multiple chicken house doors for pre-isolation according to the gated behavior preset. If the infrared sensor senses that the number of chickens in the isolated area exceeds the preset value, it will automatically open the associated chicken house door to guide the chickens to move to the isolated area with fewer chickens, analyze the chicken behavior trend, identify the critical point of abnormal behavior, analyze potential behavioral anomalies, and obtain abnormal behavior warning indicators;

[0082] The door control module adjusts the opening and closing status of the chicken house door through abnormal behavior warning indicators, executes the door opening and closing actions, adjusts the degree of door opening, controls the flow of chickens and avoids crowding, obtains the door status control plan, adjusts the door opening and closing status in real time, and obtains the chicken house door control effect evaluation results.

[0083] Density data includes image analysis results of video data, chicken density value, and comparison results with set thresholds. Crowding status assessment results include judgment of whether it is crowded, comparative analysis of chicken density, and rating of crowding status. Behavior analysis results include distinction between normal and abnormal behaviors of chickens, analysis of behavior patterns, and assessment of behavior stability. Gating behavior presets include change indicators of the overall behavior state of the chickens, duration of behavior patterns, and statistical data of behavior frequency. Abnormal behavior warning indicators include warning signals of potential behavioral abnormalities, degree of deviation of behavior trends, and urgency of warning response. Door state control scheme includes opening and closing actions of chicken house doors, adjustment scheme of opening degree, and timing adjustment of chicken house door control. Chicken house door control effect evaluation results include real-time adjustment effect of opening and closing state of chicken house doors, and assessment of impact on chicken flow.

[0084] See also Figure 2 , the specific steps for obtaining density data are:

[0085] Collect video data of the area around the chicken house door, and use machine vision technology to capture the dynamics of the chickens and obtain a video data set;

[0086] Using advanced camera technology, every dynamic scene can be recorded in detail. The data set consists of raw video files, marked with timestamps and corresponding chicken house numbers to ensure accurate correspondence for subsequent analysis. During this process, cameras are configured in multiple corners and tops of the chicken house to cover a wide viewing angle and capture the activities of chickens from different angles. High-quality video data sets are connected to a central server for real-time data transmission and storage. The server uses powerful image processing software to analyze the video stream in real time and monitor the activity intensity and behavior patterns of the chickens. The focus is on capturing dynamic details, providing sufficient raw materials for the next step of image processing, and providing managers with instant data support to help make quick decisions and obtain video data sets.

[0087] Based on the video data set, edge detection and dynamic tracking of the movement of the chickens are performed to identify the position and movement path of the chickens in each frame and obtain the dynamic distribution data of the chickens;

[0088] Advanced image analysis software uses algorithms to identify the outline of each chicken, edge detection technology helps identify the outline of the chicken, and dynamic tracking technology records the movement path of the chicken. The data is used to build a dynamic distribution model. The position and movement speed of each chicken are calculated and recorded, providing a distribution map of the flock. This process not only provides location information, but also reflects the activity patterns and changes in spatial distribution of the flock. Through continuous monitoring, the dynamic changes of the flock within a certain period of time are depicted, which is crucial for analyzing the behavioral habits of the flock and its response to environmental changes, ensuring the safety and practicality of density data, and further helping managers optimize feeding strategies and improve the design of chicken houses to obtain dynamic distribution data of the flock.

[0089] Based on the chicken image distribution data, the formula is applied:

[0090]

[0091] Calculate the density of the flock and obtain density data;

[0092] Where D is the density of the chicken group, N count is the total number of chickens detected in the image, W adj is the density adjustment weight, A is the total area of ​​the monitoring area, C norm is the normalization coefficient;

[0093] N count To represent the total number of chickens detected from the image, counted from each frame using advanced image recognition technology, it is set that the image analysis algorithm identifies 500 chickens in a certain monitoring;

[0094] W adj To represent the density adjustment weight, we set it to 1.05 to adjust the actual density estimation, considering the visual distortion caused by different camera shooting angles and distances;

[0095] A is the total area of ​​the monitoring area, which is provided by the site drawing and is set to 200 square meters;

[0096] C norm is the normalization coefficient, which standardizes the density calculations under different area units and is set to 1.2 to reflect obstacles or uneven distribution in the area;

[0097] Calculation process:

[0098]

[0099] The results show that there are approximately 2.19 chickens per square meter. This density indicator helps farm managers assess whether there is crowding and further decide whether action is needed to adjust the distribution of chickens or improve the chicken house environment, providing a scientific basis for management strategies.

[0100] See also Figure 3 , the specific steps for obtaining the congestion status evaluation results are:

[0101] Through density data, using time series analysis methods, we extract density values ​​at key time points and obtain density information at key time points;

[0102] Based on the density data obtained by advanced time series analysis methods, this technology can accurately capture and analyze the dynamic changes of chickens in the chicken house. During the implementation process, the continuous video data collected from machine vision is frame extracted, and the edge detection algorithm is used to identify the boundary between the chickens and the background. The object tracking algorithm is used to track the movement trajectory of each chicken. By calculating the number of chickens in each frame and combining the actual area of ​​the chicken house, the density of the chickens at each time point is calculated. The data is integrated through data fusion technology and stored in a central database for subsequent analysis. It not only improves the efficiency of data collection, but also ensures the information quality and real-time nature, allowing chicken house managers to respond faster based on real-time data, such as adjusting the ventilation system or feed supply to maintain the health and production efficiency of the chickens and obtain density information at key time points.

[0103] Based on the density information of key time points, use the formula:

[0104]

[0105] Evaluate the congestion state and obtain the congestion state indicator SA;

[0106] Among them, D current represents the real-time density value, T is the threshold, K is the adjustment coefficient, F temp is the temperature factor adjustment;

[0107] Parameter explanation and calculation process:

[0108] D current For real-time density, set the detection of 300 chickens in an area of ​​100 square meters, D current = 3 chickens / m2;

[0109] T is the crowding threshold, which is set at 2.5 chickens / m2;

[0110] K is the adjustment coefficient, which was determined to be 1.2 based on multiple previous experiments and is used to adjust the evaluation sensitivity;

[0111] F temp Adjust the temperature factor. Considering the temperature affects the activity of chickens, when the temperature is 35℃, set F temp =1.1;

[0112] The calculation process is as follows:

[0113]

[0114] The result shows that the current density is 52.8% higher than the threshold, indicating that the chicken house is relatively crowded. This value helps farm managers determine whether measures need to be taken to adjust the distribution of chickens or improve environmental conditions.

[0115] According to the crowding status index, it is judged whether the real-time density of the chickens around the chicken house door exceeds the preset threshold. If it exceeds, it is considered crowded, and if it does not exceed, it is not crowded, and the crowding status evaluation result is obtained;

[0116] After obtaining the density information at key time points, this information is used for comparative analysis with the preset crowding threshold. The comparative analysis process involves digital processing and logical comparison of density information. All collected density data are standardized through mathematical models to ensure the comparability of data under different conditions. Using logical judgment statements, for example, if the current density data exceeds the set percentage of the threshold, the system automatically marks it as crowded. This judgment process is not only based on a single threshold comparison, but also takes into account the influence of environmental factors such as temperature and humidity. Therefore, an adjustment coefficient is introduced to fine-tune the sensitivity of crowding perception. It can directly affect the management decisions of the chicken house, such as whether to start emergency ventilation or adjust the feed supply strategy, so as to ensure that the welfare and production efficiency of the chickens are not negatively affected by the crowded state, and obtain the crowding state evaluation results.

[0117] See also Figure 4 ,The specific steps for obtaining the behavior analysis results are:

[0118] Using the crowding status assessment results, the dynamic behavior of the flock is continuously monitored using infrared sensors, and the parameters of the behavior, including movement speed and residence time, are recorded to obtain behavioral data records;

[0119] The key to the installation strategy of infrared sensors lies in the location selection. Infrared sensors are placed at multiple angles and positions in the chicken house to fully capture the behavioral dynamics of the chickens. The sensors continuously monitor and record the behavioral data of the chickens, especially the movement speed and residence time. The data is transmitted to the central data processing unit in real time. The preliminary data analysis uses a specific algorithm to classify different behavioral patterns. The accuracy of the monitoring system is particularly important. Therefore, the system ensures high accuracy of the data through high-frequency sampling, making the behavioral data records not only accurate but also representative. Through such monitoring, not only can the daily behavior patterns of the chickens be analyzed, but any abnormal behavior can also be discovered in time, laying a solid foundation for further behavioral analysis and obtaining behavioral data records.

[0120] According to the behavioral data records, the regularity and abnormality of the chicken group's behavior are identified, the frequency of the differentiated behavioral patterns is calculated, and the behavioral pattern classification results are obtained;

[0121] Advanced data mining techniques are used to process and analyze the collected behavioral data, and algorithmic analysis is used to identify the regularity and abnormality of behavior. Each type of behavioral pattern is classified according to the frequency of occurrence. This process involves not only statistical analysis of the frequency, but also calculation of the duration and interval of the behavior. The identification of abnormal behavior is based on the set threshold, which is pre-defined based on the statistical data of the normal behavior of the flock. A detailed description of each behavioral pattern not only helps to understand the behavioral habits of the flock, but also provides a scientific basis for managers to formulate behavioral intervention strategies and obtain the classification results of behavioral patterns.

[0122] The behavior pattern classification results are used to evaluate the probability of the behavior pattern of the flock, using the formula:

[0123]

[0124] Calculate information entropy I, evaluate the diversity and abnormality of chicken flock behavior, and obtain behavior analysis results;

[0125] Among them, p i represents the probability of occurrence of the i-th behavior pattern, and N represents the total number of behavior samples;

[0126] By considering the probability of occurrence of all behavioral patterns, the diversity and abnormality of behaviors can be comprehensively evaluated, which helps to identify behavioral problems from batch behavioral data and provide important guidance for breeding management.

[0127] There are three behavior modes, with the probability of occurrence being p 1 =0.5, p 2 =0.3, p 3 =0.2;

[0128] The total number of behavioral samples N = 100;

[0129] Calculate the information contribution of the first behavior mode 0.3465;

[0131] The second

[0132] The third

[0133] Total information entropy

[0134] This indicates that the volatility of behavioral states is low, the behavioral patterns of the flocks are more regular, and there are fewer abnormal behaviors, which indicates that the breeding environment is stable and the management measures are appropriate, which is conducive to the development of effective behavioral management and intervention measures.

[0135] See also Figure 5 , the specific steps for obtaining the preset gated behavior are:

[0136] Based on the results of behavioral analysis, the abnormal and normal behaviors of the chickens around the chicken house door are statistically analyzed to identify the frequency and duration of multiple types of behaviors and obtain behavioral pattern data;

[0137] In the process of monitoring the behavior of chickens, the role of infrared sensors is not limited to the collection of dynamic behavior data, but also includes highly detailed classification and analysis of data. This data processing includes the duration and frequency of behavior and the interaction between behavior patterns. Through the data, the regularity and abnormality of behavior can be identified, which is achieved by applying advanced data mining technology, which can effectively extract meaningful behavior patterns from batch data. The identification of abnormal behavior is particularly critical because it directly affects the accuracy and reliability of behavior analysis. It not only provides basic data for subsequent behavior prediction and health management, but also provides important ideas about the behavioral health of chickens, which can better understand the relationship between behavior patterns and health conditions and obtain behavior pattern data.

[0138] Through the behavior pattern data, the behavior state parameters are calculated using the formula:

[0139]

[0140] The weighted sum of abnormal behavior and normal behavior is obtained to obtain the weighted behavior index S;

[0141] Among them, w 1 and w 2 are the weight coefficients of abnormal and normal behaviors, N abnormal and N normal Represents the number of abnormal behaviors and normal behaviors, N total represents the total number of behavioral events;

[0142] By weighting the abnormal and normal behaviors, the weight parameter w can be adjusted according to the actual situation. 1 and w 2 ,flexibly reflects the impact of different types of behaviors on the overall gating behavior preset;

[0143] Set the number of abnormal behaviors N recorded by the infrared sensor during a specific monitoring period abnormal =150;

[0144] Normal behavior number N normal =850; total number of behavioral events N total =1000;

[0145] According to the influence of the behavior, set w 1 =0.6 and w 2 =0.4;

[0146] The calculation process of the gate behavior preset is as follows:

[0147]

[0148] The results show that after taking the weight factor into account, the overall behavioral status weighted index is 43%, reflecting that abnormal behavior has little impact on the overall behavioral status and the behavioral status of this group is relatively stable. This numerical result not only provides managers with a quantitative health monitoring tool, but also illustrates the effectiveness of the monitoring system, helping decision makers to take more targeted behavioral management measures.

[0149] Based on the weighted behavior index, the impact of the differentiated behavior patterns of the chickens on the movement trajectory of the chicken house door on the overall behavior state of the chickens is analyzed, the changing trend of the chicken behavior around the chicken house door is identified, and the gate behavior preset is established;

[0150] Further analyze the impact of different behavioral patterns on the overall behavioral state to ensure that the analysis results can specifically reflect how different behavioral patterns affect the overall health and welfare of the flock. In this process, it is particularly important to refine the classification and comparison of behavioral data. It can not only provide a detailed description of the behavioral pattern, but also conduct a more quantitative analysis of the behavioral pattern by comparing the frequency and duration of different behavioral patterns. It can further optimize and adjust the behavioral monitoring model and improve the prediction accuracy and practicality of the model. It is not only the quantification of a single behavioral pattern, but also an important parameter that comprehensively reflects the overall behavioral trend and health warning, providing a scientific basis for the health management and welfare of the flock and establishing a gated behavior preset.

[0151] See also Figure 6 ,The specific steps for obtaining abnormal behavior warning indicators are:

[0152] Using gated behavior presets, multiple chicken house doors are set to isolate chickens, and the mean and standard deviation of differentiated behavior patterns are calculated to obtain baseline behavior patterns;

[0153] By collecting the daily activity data of the chickens, such as walking, eating, resting and other behaviors, and obtaining the raw data from the monitoring sensor system, further data cleaning is performed, such as removing obviously erroneous data points, filling in missing data, and ensuring the integrity and availability of the data. Data processing provides a stable input basis for model analysis and ensures the accuracy of behavior pattern analysis. By applying advanced data screening techniques, such as outlier detection and data interpolation technology, more accurate and representative behavioral features can be extracted from the raw behavioral data collected in batches. The data not only reflects the activity patterns of the chickens, but can also be used to establish a baseline for daily behavior, laying the foundation for subsequent behavioral anomaly detection. After data cleaning, it involves the use of statistical methods to evaluate the consistency and trend of behavioral data and obtain a baseline behavior pattern.

[0154] The real-time gating behavior preset is compared with the baseline behavior pattern using the formula:

[0155]

[0156] Calculate the deviation Z of the differentiated behavior and obtain the deviation analysis result;

[0157] Where X represents the currently observed gating behavior preset, μ is the mean value of the differentiated behavior pattern, and σ is the standard deviation;

[0158] By calculating the standardized deviation, the degree of deviation of each gating behavior preset from the baseline mode can be quantified, making the detection of abnormal behavior more sensitive.

[0159] Set the mean value μ of the baseline gating behavior preset to 50, the standard deviation σ to 10, and the currently observed gating behavior preset X to 75; substitute the parameters into the formula:

[0160]

[0161] The result shows that the currently observed gating behavior preset deviates from the baseline mode by 2.5 standard deviations, exceeding the set abnormal threshold of 2. Therefore, the behavior is marked as abnormal. It can objectively evaluate the degree of deviation of each behavior and provide early warning for behavioral abnormalities. It not only improves the sensitivity of anomaly detection, but also adjusts the threshold according to statistical data in actual operation to optimize the sensitivity and specificity of anomaly detection.

[0162] The threshold of abnormal behavior is set based on the deviation analysis results. If the preset Z score of the gated behavior exceeds the threshold, it is marked as abnormal behavior, and the abnormal behavior warning indicator is obtained;

[0163] By calculating the frequency and duration of each behavior, a baseline behavior pattern is established. The pattern can reflect the normal range of behavior. Further data analysis includes using statistical methods to determine the mean and standard deviation of the baseline behavior pattern, which provides a basis for subsequent anomaly detection. The gated behavior preset provides key data support for anomaly detection in subsequent steps. This process involves complex data modeling techniques, including time series analysis and identification of group behavior patterns. Through advanced technology, the typical pattern of chicken behavior can be depicted in detail, and compared with actual observation data, behaviors that deviate from the norm can be identified in a timely manner. Behaviors that deviate from the norm are early indicators of health problems or environmental stress, which not only improves the scientific nature of chicken behavior management, but also helps to optimize the breeding environment, improve the overall breeding efficiency, and obtain abnormal behavior warning indicators.

[0164] See also Figure 7 , the specific steps for obtaining the door state control scheme are:

[0165] Analyze abnormal behavior warning indicators, compare abnormal behavior patterns with preset thresholds, analyze the risks of abnormal behavior in the flock, identify risk levels, and obtain risk assessment results;

[0166] The behavior inside the chicken house is recorded in real time, and image recognition technology is used to analyze the chicken's behavior pattern. Behaviors that differ greatly from the preset model, such as excessive aggregation or abnormal dispersion, are identified. The behavior is marked and the deviation value is calculated. The system compares the behavior deviation data with the historical behavior data collected in the database for a long time, and uses the difference analysis algorithm to determine the degree of deviation between the current behavior and the historical behavior. This process involves complex data comparison and abnormal point identification to ensure that each behavior deviation indicator is calculated. The deviation data will be summarized and used as an input parameter for subsequent formula calculations. For the detected behavior deviations, the specific areas and behavior types that need intervention are pointed out, which helps to adjust the response strategy of the gating system in real time, optimize the frequency and amplitude of door opening and closing, reduce energy consumption and avoid unnecessary interference, improve the overall operating efficiency of the chicken house, and obtain risk assessment results.

[0167] Based on the risk assessment results, calculate the degree of opening of the chicken house door using the formula:

[0168]

[0169] Calculate the gate adjustment requirements and obtain the gate adjustment instructions;

[0170] Where R represents the gate adjustment index, b d is a real-time behavioral deviation indicator. is the historical average behavior deviation, w d is the behavioral bias weight, N is the number of behavioral indicators;

[0171] By taking into account the weight of each behavioral deviation, a weighted approach is provided to calculate the gate adjustment index, allowing for a more detailed and adaptive gate response that more closely matches the actual needs within the chicken house and avoids unnecessary gate opening and closing actions due to standardized responses.

[0172] Set to monitor 3 behavioral deviation data points, value is b 1 =5, b 2 =7, b 3 =4;

[0173] Historical average behavior deviation

[0174] The weights are w 1 =0.5, w 2 =0.3, w 3 =0.2;

[0175] The calculation steps are as follows:

[0176] Molecular computing:

[0177] (5-6)×0.5+(7-6)×0.3+(4-6)×0.2=-0.5×0.5+1×0.3-2×0.2=-0.25+0.3-0.4=-0.35;

[0178] Denominator calculation: 0.5+0.3+0.2=1;

[0179] Calculate the gate adjustment index:

[0180]

[0181] The results show that the current behavioral deviation is slightly lower than the historical data, and the chicken house door needs to be fine-tuned to open less to reduce non-critical chicken house door opening and closing, optimize energy use and improve the stability of the internal environment of the chicken house.

[0182] Execute the door control adjustment command, automatically adjust the door opening and closing status, match the flow of chickens and avoid crowding, verify the stability of the chicken house environment, and obtain the door status control plan;

[0183] According to the output of the gate control adjustment instruction, the automatic control system takes over the operation of the door. The system controls the opening and closing of the door through the received gate control adjustment instruction. The control system calculates the optimal angle and duration of the door opening. The calculation is based on the current internal flow data of the chicken house and the historical efficiency data of the door opening and closing. The dynamic adjustment algorithm is used to optimize the movement path of the door to ensure that the opening and closing action can meet the needs of preventing crowding while maintaining the lowest energy consumption. The physical operation of the door is performed, including motor starting, transmission mechanical adjustment and feedback system monitoring. The operation ensures that the actual movement of the door strictly corresponds to the system instructions. By continuously monitoring the state of the door and changes in the external environment, the system can make fine adjustments when necessary to respond to emergencies or environmental changes. It not only records the details of each opening and closing of the door, but also includes the effect evaluation of each operation, such as energy consumption data and specific indicators of anti-crowding effect, to provide a basis for future operation optimization and generate a door state control plan.

[0184] See also Figure 8 , the specific steps for obtaining the evaluation results of the chicken house door control effect are as follows:

[0185] Execute the door status control scheme to automatically adjust the opening and closing status of the chicken house door, while monitoring the flow and distribution of the chicken flock to obtain a real-time monitoring data set;

[0186] The high-resolution cameras and sensors of the monitoring system collect warning indicators of abnormal behavior, such as excessive aggregation and frequent movement. The behavior is automatically detected by a special behavior recognition algorithm. The algorithm converts video frames into grayscale images, detects dynamic changes in the flock through image processing technology, and classifies various behavior patterns through machine learning models, matching real-time data with the preset behavior model database. Whenever abnormal behavior is detected, the system automatically records the timestamp and duration of the event, and calculates a behavior index based on the type and intensity of the behavior. The index is obtained by combining multiple behavior parameters such as movement speed and density of the aggregation area. The behavior index is compared with the preset threshold. The system calculates the frequency of occurrence of each behavior through an algorithm, lists all detected behavior types and corresponding behavior indexes, which provides data support for further analysis. According to the data, it can be judged that some behaviors are normal and some need attention, effectively preventing abnormal behavior of the flock and obtaining a real-time monitoring data set.

[0187] Based on the real-time monitoring data set, the flow efficiency and crowding inside the chicken house are evaluated using the formula:

[0188]

[0189] Analyze the degree of deviation between the real-time situation and the ideal state, and obtain the deviation analysis results;

[0190] Among them, E represents the efficiency of the control effect, d opt is the ideal chicken population density, d act It is the chicken population distribution density monitored in real time;

[0191] By comparing the ideal flock distribution density with the actual monitored distribution density, the effect of gating regulation can be accurately evaluated and better adjustment decisions can be made;

[0192] Set the ideal chicken population distribution density d opt 10 chickens per square meter;

[0193] The actual monitored density d act 12 chickens per square meter;

[0194] The calculation process is:

[0195]

[0196] This means that the actual density is 20% higher than the ideal density, indicating that the door opening degree needs to be increased to reduce crowding in the chicken house and improve animal welfare and production efficiency.

[0197] Using the deviation analysis results, referring to the stability of the chicken house environment and the identification of chicken health, the implementation effect of the door status control scheme was evaluated, and the evaluation results of the chicken house door control effect were obtained;

[0198] After executing the door state control scheme, the system automatically adjusts the opening and closing state of the door through sensors installed at the entrance and exit of the chicken house. The sensors can detect the flow speed and number of chickens. According to the flow data, the system uses advanced data analysis technology to calculate the flow efficiency of the chickens in real time. By comparing with the preset flow efficiency model, the system can determine whether the current door opening degree is appropriate. If congestion is detected, the system will immediately adjust the door opening width and increase or decrease the door opening time to ensure that the chickens can enter and exit smoothly. Not only that, the system will also monitor the impact of environmental factors such as temperature and humidity on the behavior of the chickens. The data is comprehensively taken into account to ensure the scientificity and effectiveness of the gate control adjustment measures. By continuously monitoring the health data and production data of the chickens, such as egg production and chicken survival rate, the system evaluates the long-term effect of the gate control strategy, and adjusts the gate control strategy with scientific data support. Through comprehensive adjustment measures, the continuous optimization of the gate control effect is achieved to ensure the welfare and production efficiency of the chickens are maximized, and the measures are effectively implemented to obtain the evaluation results of the door state control effect.

[0199] A smart chicken house door movement detection method, which is executed based on the above-mentioned smart chicken house door control system, comprises the following steps:

[0200] S1: Collect video data of the area around the chicken house door, use machine vision technology to perform image analysis, analyze the density of chickens around the chicken house door, and compare it with the set density threshold to determine the crowding situation and obtain the density status index;

[0201] S2: Using density state index and infrared sensors to identify and classify chicken behaviors, distinguish normal and abnormal behaviors of chickens, summarize data, and obtain behavior classification indicators;

[0202] S3: Use behavioral classification indicators to analyze behavioral trends, identify critical points of behavioral anomalies, and establish abnormal behavior warning indicators;

[0203] S4: According to the abnormal behavior warning indicators, adjust the opening and closing status of the chicken house door, execute the door opening and closing action, control the flow of chickens and avoid crowding, perform dynamic balance of the environment in the chicken house, and obtain the door state movement plan;

[0204] S5: Based on the door status movement scheme, detect the real-time opening and closing status of the chicken house door, collect real-time flow data, identify the impact of chicken house door movement, and obtain the chicken house door movement detection log.

[0205] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent chicken house door control system, characterized in that: The system comprises: The density monitoring module collects video data of the area around the chicken house door, uses machine vision technology to perform image analysis, analyzes the density of chickens around the chicken house door, obtains density data, compares it with the set density threshold, determines whether it is crowded, and obtains the crowding status assessment result; The automatic gating module uses infrared sensors to analyze the behavior of the chickens based on the crowding state assessment results, distinguishes between normal and abnormal behaviors of the chickens, generates behavior analysis results, identifies changes in the overall behavior state of the chickens around the chicken house door, and captures the activity intensity of the chickens on the movement trajectory of the chicken house door. If a chicken gathering is detected, automatic gating is performed to drive the chickens away and establish a gating behavior preset; The regional isolation and abnormality identification module sets multiple chicken house doors for pre-isolation according to the gated behavior preset. If the infrared sensor senses that the number of chickens in the isolated area exceeds the preset value, the associated chicken house door is automatically opened to guide the chickens to move to the isolated area with fewer chickens, analyze the chicken behavior trend, identify the critical point of abnormal behavior, analyze potential behavioral anomalies, and obtain abnormal behavior warning indicators; The door control adjustment module adjusts the opening and closing status of the chicken house door through the abnormal behavior warning indicator, executes the door opening and closing action, adjusts the degree of door opening, controls the flow of chickens and avoids crowding, obtains the door state control plan, adjusts the door opening and closing status in real time, and obtains the chicken house door control effect evaluation result.

2. The intelligent chicken house door control system according to claim 1 is characterized in that: The steps for obtaining the density data are specifically as follows: Collect video data of the area around the chicken house door, and use machine vision technology to capture the dynamics of the chickens and obtain a video data set; According to the video data set, edge detection and dynamic tracking of the movement of the chickens are performed to identify the position and movement path of the chickens in each frame, and obtain the dynamic distribution data of the chickens; According to the chicken image distribution data, the formula is applied: Calculate the density of the flock and obtain density data; Where D is the density of the chicken group, N count is the total number of chickens detected in the image, W adj is the density adjustment weight, A is the total area of ​​the monitoring area, C norm is the normalization coefficient.

3. The intelligent chicken house door control system according to claim 2 is characterized in that: The steps for obtaining the congestion status evaluation result are specifically as follows: Through the density data, using a time series analysis method, the density values ​​at key time points are extracted to obtain density information at key time points; Based on the density information of the key time points, the formula is used: Evaluate the congestion state and obtain the congestion state indicator SA; Among them, D current represents the real-time density value, T is the threshold, K is the adjustment coefficient, F temp is the temperature factor adjustment; According to the crowding state index, it is determined whether the real-time density of the chickens around the chicken house door exceeds a preset threshold. If it exceeds, it is determined to be crowded, and if it does not exceed, it is non-crowded, and a crowding state evaluation result is obtained.

4. The intelligent chicken house door control system according to claim 3 is characterized in that: The steps for obtaining the behavior analysis results are specifically as follows: Using the crowding state assessment results, the dynamic behavior of the chickens is continuously monitored using infrared sensors, and the parameters of the behavior, including movement speed and residence time, are recorded to obtain behavior data records; According to the behavioral data records, the regularity and abnormality of the chicken group's behavior are identified, the frequency of the differentiated behavioral patterns is calculated, and the behavioral pattern classification results are obtained; The behavior pattern classification results are used to evaluate the probability of the behavior pattern of the flock, using the formula: Calculate information entropy I, evaluate the diversity and abnormality of chicken flock behavior, and obtain behavior analysis results; Among them, p i represents the probability of occurrence of the i-th behavior pattern, and N represents the total number of behavior samples.

5. The intelligent chicken house door control system according to claim 4, characterized in that: The steps for obtaining the preset gate behavior are specifically as follows: According to the behavior analysis results, statistically analyzing the abnormal and normal behaviors of the chickens around the chicken house door, identifying the occurrence frequency and duration of multiple types of behaviors, and obtaining behavior pattern data; The behavior state parameters are calculated through the behavior pattern data using the formula: The weighted sum of abnormal behavior and normal behavior is obtained to obtain the weighted behavior index S; Among them, w1 and w2 are the weight coefficients of abnormal and normal behaviors respectively, N abnormal and N normal Represents the number of abnormal behaviors and normal behaviors, N total represents the total number of behavioral events; Based on the weighted behavior index, the impact of the differentiated behavior patterns of the chickens on the movement trajectory of the chicken house door on the overall behavior state of the chickens is analyzed, the changing trend of the chicken behavior around the chicken house door is identified, and the gate behavior preset is established.

6. The intelligent chicken house door control system according to claim 5, characterized in that: The steps for obtaining the abnormal behavior warning indicator are specifically as follows: Using the gated behavior preset, multiple chicken house doors are set to isolate chickens, and the average and standard deviation of the differentiated behavior patterns are calculated to obtain a baseline behavior pattern; The real-time gating behavior preset is compared with the baseline behavior pattern using the formula: Calculate the deviation Z of the differentiated behavior and obtain the deviation analysis result; Where X represents the currently observed gating behavior preset, μ is the mean value of the differentiated behavior pattern, and σ is the standard deviation; The threshold of abnormal behavior is set based on the deviation analysis result. If the preset Z score of the gated behavior exceeds the threshold, it is marked as abnormal behavior, and an abnormal behavior warning indicator is obtained.

7. The intelligent chicken house door control system according to claim 6, characterized in that: The steps for obtaining the door state control scheme are specifically as follows: Analyze the abnormal behavior warning indicators, compare the abnormal behavior patterns with preset thresholds, analyze the risks of abnormal behavior of the chickens, identify the risk level, and obtain risk assessment results; Based on the risk assessment results, calculate the degree of door opening of the chicken house using the formula: Calculate the gate adjustment requirements and obtain the gate adjustment instructions; Where R represents the gate adjustment index, b d is a real-time behavioral deviation indicator, is the historical average behavior deviation, w d is the behavioral bias weight, N is the number of behavioral indicators; The door control adjustment instruction is executed to automatically adjust the opening and closing state of the door, match the flow of chickens and avoid crowding, verify the stability of the chicken house environment, and obtain the door state control solution.

8. The intelligent chicken house door control system according to claim 7, characterized in that: The steps for obtaining the chicken house door control effect evaluation result are specifically as follows: Execute the door state control scheme to automatically adjust the opening and closing state of the chicken house door, and monitor the flow and distribution of the chickens to obtain a real-time monitoring data set; Based on the real-time monitoring data set, the flow efficiency and crowding inside the chicken house were evaluated using the formula: Analyze the degree of deviation between the real-time situation and the ideal state, and obtain the deviation analysis results; Among them, E represents the efficiency of the control effect, d opt is the ideal chicken population density, d act It is the chicken population distribution density monitored in real time; By using the deviation analysis results, referring to the chicken house environmental stability and chicken flock health identification, the implementation effect of the door status control scheme is evaluated to obtain the chicken house door control effect evaluation result.

9. A smart chicken house door movement detection method, characterized in that: The intelligent chicken house door control system according to any one of claims 1 to 8 comprises the following steps: Collect video data of the area around the chicken house door, use machine vision technology to perform image analysis, analyze the chicken density around the chicken house door, and compare it with the set density threshold to determine the crowding situation and obtain the density status index; The density state index is adopted, and the infrared sensor is used to identify the chicken flock behavior, and the chicken flock behavior is classified, and the normal and abnormal behaviors of the chicken flock are distinguished, and the data is summarized to obtain the behavior classification index; Utilize the behavior classification indicators to analyze behavior trends, identify critical points of abnormal behavior, and establish abnormal behavior warning indicators; According to the abnormal behavior warning indicators, the opening and closing status of the chicken house door is adjusted, the door opening and closing action is executed, the flow of chickens is controlled and crowding is avoided, the dynamic balance of the environment in the chicken house is performed, and the door state movement plan is obtained; Based on the door status movement scheme, the real-time opening and closing status of the chicken house door is detected, real-time flow data is collected, the impact of the chicken house door movement is identified, and the chicken house door movement detection log is obtained.

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