242 results about "Flock" patented technology

The invention relates to the technical field of poultry breeding monitoring, and discloses a chicken flock state inspection monitoring system and method. The method comprises the following steps: firstly, collecting chicken flock visual images, sound signals, environment temperature and humidity and individual activity track data, and constructing a multi-modal data set through labeling and preprocessing; different modal data features are extracted and fused; training a self-supervised contrast learning model to generate a discrimination model, and optimizing hyper-parameters in combination with a genetic algorithm; collecting data in real time, calculating a health state probability, generating an abnormal score, and dynamically updating an early warning threshold value; and if the abnormal score exceeds a threshold value, grading early warning and abnormal positioning are carried out. The system comprises a data acquisition and preprocessing module, a feature extraction module, a feature fusion module and the like. According to the method, the health of the chicken flocks is accurately monitored by using multi-modal data, dynamic early warning and model adaptive optimization are realized, the breeding benefits are improved, and the disease risk is reduced.

The invention discloses a bird repelling method and related equipment for a power distribution iron tower, and the method employs a multi-dimensional sensor for cooperative operation, and collects infrared, sound, images and radar reflection signals in real time. Through multi-dimensional data fusion, the appearance, sound wave and behavior characteristics of the bird flock are deeply mined, the bird flock type is accurately identified, and the problem of insufficient detection of a single sensor is solved. Based on the sensitivity difference of different bird flocks to vision, hearing and ultrasonic waves, bird repelling modes such as strong light stroboflash, sound or ultrasonic waves are dynamically matched from the preset bird repelling strategy library, the adaptability of birds to a single bird repelling means is avoided, and the bird repelling success rate is remarkably increased. And a closed-loop optimization mechanism is formed by iteratively optimizing a strategy library matching rule by using bird repelling effect feedback data, so that the system can adapt to the environment and bird behavior changes, and the long-term bird repelling effect is enhanced. Compared with the prior art, line fault risks caused by bird activities are effectively reduced, and safe and reliable operation of a power system is powerfully guaranteed.

The invention discloses a shortest-time-based bird flock repelling effect evaluation method for an airport intelligent bird repelling system, and the method comprises the steps: S1, collecting the multi-dimensional monitoring data of an airport bird flock repelling process through a biological feature sensing system, and enabling the multi-dimensional monitoring data to comprise a repelling duration parameter, a region clearing index, and bird flock adaptability response data; s2, based on the multi-dimensional monitoring data, analyzing the spatio-temporal behavior characteristics of the bird flock through an intelligent analysis terminal, and constructing a multi-dimensional bird flock expelling effect characteristic matrix; s3, based on the feature matrix, performing dynamic weight optimization of bird flock expelling effect evaluation indexes with the shortest expelling time as a target, and generating a self-adaptive weight coefficient group; and S4, based on the self-adaptive weight coefficient group, performing panoramic evaluation on the bird flock repelling effect by adopting a time sequence perception memory network, and outputting a repelling efficiency analysis result taking the repelling timeliness as a core. While the flight safety is guaranteed, efficient utilization of bird repelling resources is realized.

The invention relates to the technical field of airport safety guarantee, and particularly discloses an airport bird situation monitoring and early warning bird repelling equipment linkage control method comprising the following steps: obtaining bird situation data vectors collected by sensors, calculating fused bird situation data vectors, and obtaining bird flock information; constructing a bird situation risk assessment model based on machine learning, combining the fused bird situation data with an aircraft take-off and landing plan, a runway use state and airport surrounding environment information to form an input feature vector, calculating a risk value through a risk assessment function, and dividing risk levels according to the risk value; according to a risk grade result of the risk assessment module, corresponding bird repelling equipment is controlled to work according to a hierarchical linkage bird repelling strategy; and in the bird repelling process, the risk level is re-evaluated according to the new bird situation data, the parameters of the bird repelling equipment are adjusted, and the bird repelling strategy is optimized by using a reinforcement learning algorithm. According to the invention, the defects of monitoring range and precision of a single sensor are effectively overcome, and the reliability of bird monitoring is greatly improved.

The invention relates to the technical field of poultry breeding environment monitoring, and discloses a multi-layer intelligent coop and temperature and humidity control system based on environment feedback, an environment prediction module predicts the temperature and humidity conditions of each layer in the future by using historical environment data of the multi-layer coop and chicken flock behavior data; the control decision module determines an adjustment strategy of the temperature and humidity of each layer according to the predicted value and outputs a preliminary control instruction; and the instruction optimization module optimizes the preliminary control instruction by comparing the predicted temperature and humidity with the actually monitored temperature and humidity, and outputs a final control instruction. The system control center combines actual data, generates a preliminary instruction through environment prediction and control decision to adjust the environment, continuously monitors actual temperature and humidity, compares the actual temperature and humidity with a prediction result, and triggers a deviation correction model to recalculate a final instruction when a significant deviation is found. According to the invention, accurate control of the temperature and humidity of each layer in the multi-layer coop is realized, and the comfort and growth efficiency of chicken flocks are improved.

The invention relates to a task allocation and route autonomous planning method for multi-unmanned aerial vehicle cooperative bird repelling. The method comprises the following steps: step 1, digital modeling; 2, collecting information; step 3, task list establishment: adding a bird flock needing to be driven into a task list according to a constraint condition; 4, calculating parameters; step 5, task allocation: using a genetic algorithm to call an objective function to allocate bird repelling tasks to each unmanned aerial vehicle platform, and formulating a bird repelling sequence; 6, flight path planning: unmanned aerial vehicle group flight path autonomous planning based on an artificial field potential method is adopted, an ambush method bird repelling strategy is called to estimate the expected position and speed of each unmanned aerial vehicle, and finally flight path planning is realized; 7, effect evaluation: returning to the step 2 to collect information, evaluating the bird repelling effect, judging whether to continue the step 3 or not, and re-planning the task; the method has the advantages of multi-UAV collaborative bird repelling, bird repelling task allocation optimization, autonomous route planning based on artificial field potential and introduction of path optimization.

The invention discloses a bird condition information statistical method fusing bird detection radar data and a transect method, and solves the problems that the bird population density cannot be counted and the radar data and the artificial investigation data are not fully fused when the bird detection radar data and the artificial investigation data are fused in the existing method. Fusing the enhanced data set with the bird population density, extracting the features of the data fusion set by the target recognition model based on the micro-Doppler features, and outputting a statistical result of the bird situation information in the bird flock; according to the invention, the target identification model achieves the precise evaluation of the statistical result of the bird condition information through the fusion of the bird detection radar data and the transect method, and can effectively reduce the uncertainty in the statistical process through the fusion of the enhanced data set and the bird population density with the bird population density as the prior probability. Therefore, the accuracy of bird condition information statistics is improved, and the stability and reliability of the target recognition model can be improved by using the enhanced data set.

The invention provides a laying hen feeding scheme adjustment method and system based on moulting image recognition, and relates to the technical field of intelligent breeding, and the method comprises the steps: obtaining a multi-angle body surface image sequence synchronously collected by a plurality of laying hen individuals in a target chicken flock under a natural light condition; performing semantic segmentation according to the multi-angle body surface image sequence to obtain a feather contour segmentation map set; performing group feature joint extraction according to the feather contour segmentation map set to obtain a multi-dimensional feature tensor representing individual features and group distribution information at the same time; performing group state collaborative inference according to the multi-dimensional feature tensor to obtain a three-dimensional probability distribution vector; performing group nutrition demand mapping according to the three-dimensional probability distribution vector to obtain an optimal nutrition parameter combination suitable for the whole chicken flock; and generating a feeding scheme according to the optimal nutrition parameter combination to obtain a feeding instruction stream. The accuracy and adaptability of nutrition regulation and control in the moulting period are effectively improved.

The invention relates to the technical field of intelligent agriculture and computer vision, in particular to a small-sample high-density chicken counting framework based on a deep learning Mamba structure, which comprises a feature extraction network, a support-query enhancement module and a decoder. According to the method, a multi-scale feature extraction network based on a residual block (ResNet Block) is introduced, so that local detail information is effectively reserved; and then, a support-query enhancement module is constructed by introducing a Mamba structure, and context interaction between support features and query features is effectively enhanced by utilizing the long sequence modeling capability of linear complexity of the support-query enhancement module, so that the problems of individual overlapping and boundary fuzziness in a high-density chicken flock scene are solved. Experimental results on a PoultryCount real breeding data set show that the mean absolute error (MAE) and the root-mean-square error (RMSE) of the method are reduced compared with those of an existing method, and the chicken counting precision and generalization ability under the condition of a small number of labeled samples are remarkably improved.

The invention discloses a small target bird flock motion monitoring system, and belongs to the technical field of bird identification, and the system specifically comprises the steps: obtaining a millimeter wave radar echo signal, a laser radar point cloud data set and a visible light image of a target region; performing spatial clustering segmentation on the laser radar point cloud to generate target point cloud clusters, acquiring a three-dimensional convex hull vertex set for any target point cloud cluster, and mapping a centroid to a millimeter wave radar coordinate system; performing spectrum analysis on the radar echo signals based on the mapping coordinates to obtain Doppler features, and calculating the three-dimensional position of the spatial extremal region in combination with the three-dimensional convex hull vertex set; and mapping the three-dimensional position to a visible light image to generate a spatial constraint mask, extracting an image feature vector and a spatial feature vector, and finally inputting the two types of feature vectors into a classifier model to obtain a bird recognition result, thereby realizing the type recognition of the small target bird flock in a complex scene.

The invention discloses an AI intelligent bird repelling system and method based on multi-modal perception and dynamic deterrence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining multi-source historical data of birds in an area, extracting bird activity attribute preferences, and generating an initial thermodynamic diagram of long-term activities of the birds; monitoring a bird activity track, a population category and a behavior mode in real time, and dynamically correcting the bird initial thermodynamic diagram to obtain a dynamically updated bird accurate activity distribution diagram; constructing a virtual simulation bird repelling model, obtaining an optimal bird repelling strategy, and generating a dynamic adaptive multi-mode cooperative deterrent instruction set; according to the dynamic self-adaptive multi-mode cooperative deterrent instruction set, a deterrent unit is issued, the deterrent reaction degree is monitored in real time, if it is displayed that the bird flock is effectively repelled, execution is performed according to the current strategy, if it is displayed that the current strategy is invalid or poor in effect, feedback is performed to the virtual simulation bird repelling model, and a new-generation upgrading strategy instruction is generated. The bird repelling device has the advantages that safe repelling of bird flocks is guaranteed, and the influence on the ecological environment is reduced.

The invention belongs to the technical field of radar detection, and discloses a bird positioning method of a quantum radar bird detection system, which is characterized in that quantum characteristics such as polarization state and phase of echo photons are accurately analyzed by means of a quantum state demodulation technology, and high-precision measurement of the flight time of the photons is combined, so that the resolution limitation of the traditional radar is broken through from the quantum level; the technical combination not only can capture fine actions such as wing flapping frequency and steering angle of birds, but also can exert remarkable advantages in a dense bird flock scene: by utilizing quantum state differences of echo photons of different birds, overlapping signals are decomposed into independent individual signals through a quantum state separation algorithm; the individual distinguishing problem caused by traditional radar signal aliasing is solved; in high-precision demand scenes such as airports and the like, small bird targets can be accurately locked, flight potential safety hazards caused by misjudgment are greatly reduced, and the system can physically resist copying and deception interference of the outside to quantum signals based on the quantum unclonable principle.

The invention relates to the technical field of early warning, prevention and control of airport bird flocks, and particularly discloses an airport bird flock timely early warning, prevention and control system and method based on radar monitoring, and the method comprises the following steps: obtaining a minimum single spatial distance value in single spatial distance values in a bird flock, and comparing the minimum single spatial distance value with a warning airspace radius value of a warning airspace to obtain a warning signal; obtaining a single space point location of the bird flock at each detection time node; calculating to obtain a bird flock flight path prediction limit value at a detection time node m moment; comparing the pre-warning airspace radius value with the pre-warning airspace radius value to obtain a pre-warning signal or a prediction signal; the influence of laser and sound waves on the vision and hearing of the bird flock is reduced, the unpredictable influence caused by the stress flight condition after the bird flock is stimulated is avoided, the use of the laser and the sound waves is reduced, the interference on other organisms in the surrounding environment of an airport or other airplanes in the flight process is avoided, and the potential safety hazard is reduced.

The invention discloses a chicken health monitoring method and a chicken health monitoring system, and relates to the technical field of poultry breeding. The method comprises the following steps: S100, randomly selecting chicken samples and obtaining the number of samples; S200, collecting the appearance time and frequency of the target colors of the sample conveyor belt; S300, collecting real-time images of the chicken samples; S400, sending the information collected in steps S200 and S300 to the background for analysis and processing; S500, the background calculates the total frequency of the target color and the ratio of the sample number to obtain the sample broken egg rate, and judges whether the chickens are healthy by comparing the sample broken egg rate with the healthy broken egg rate, and locates the abnormal chickens and the number of the target colors, and sends the photos and the number of the abnormal chickens to the terminal. By selecting a specific color scale, a relatively accurate marker such as broken eggs is introduced, and the abnormal chicken manure can be located on a specific abnormal chicken, and finally, it is verified by manual observation, which has high accuracy and is very meaningful for chicken health guidance.

The invention relates to the technical field of bird strike prevention and control, in particular to an airport bird flock timely early warning prevention and control method and system based on radar monitoring, which can scan a monitoring area in real time by using a radar to obtain real-time dynamic data of bird flock activities, integrate the real-time dynamic data into a monitoring data set and send the monitoring data set to a cloud server. Generating spatial mapping data capable of reflecting spatial density distribution of bird flocks in the monitoring area and gathering hot spots, and defining a risk area; if it is judged that the bird flock enters the risk area according to the real-time dynamic data, dynamic adjustment parameters are generated, and a scanning strategy of the radar monitoring system is optimized and adjusted; and generating a real-time updated monitoring report according to the real-time dynamic data, further generating a prevention and control instruction data set, and generating a final response scheme. According to the technical scheme, the target area is monitored based on the radar, the data is accurate, the coverage range is large, the radar scanning strategy is adjusted after the bird flock enters the risk area, the data monitoring precision is improved, and the generated monitoring report is high in precision and timeliness.

The invention discloses a bird flock-imitating unmanned aerial vehicle cluster density and area control method and an unmanned aerial vehicle, and relates to the unmanned aerial vehicle technology, and the method comprises the steps: initializing the position and speed of the unmanned aerial vehicle in an unmanned aerial vehicle cluster; calculating an interaction force applied to the unmanned aerial vehicles in the unmanned aerial vehicle cluster, wherein the interaction force comprises an alignment force and an attraction-repulsive force based on the Lanna-Jones potential; updating the speed and the position of the unmanned aerial vehicle according to the resultant force borne by the unmanned aerial vehicle; and calculating the area of the unmanned aerial vehicle cluster after stabilization, and if the expectation is not met, repeatedly adjusting the rejection distance between the unmanned aerial vehicles until the unmanned aerial vehicle cluster is controlled to reach the expected density and area. According to the method, the effect of coordinated flight of the unmanned aerial vehicle cluster can be achieved by adjusting related parameters in the control model, the density and area of the cluster are flexibly regulated and controlled, and then the purpose of simulating the bird flock form in the flight process of the unmanned aerial vehicle cluster is achieved.

The invention provides a heat stress activation state threshold regulation and control method and system based on poultry group behavior feedback, and relates to the technical field of intelligent management and control of livestock and poultry breeding environment, and the method comprises the steps: recognizing the number of poultry heads in a heat wheezing state and the total number of the poultry heads based on poultry head images, and obtaining the real-time heat wheezing proportion of poultry groups; based on a distributed environment real-time acquisition network, obtaining an in-cage environment temperature and humidity index; aiming at each region, integrating data pairs synchronized by a time window, fitting a piecewise linear regression relationship of the real-time heat wheezing proportion of the poultry group along with the change of the temperature and humidity index of the environment in the cage, dynamically generating a heat stress grading threshold value of the region based on a characteristic turning point, generating a house-level regulation and control instruction and a region-level regulation and control instruction, and carrying out region-level regulation and control on the region-level regulation and control instruction. The local environment of the corresponding area is adjusted and alarmed, and a heat stress activation state threshold regulation and control result is obtained. The problems that poultry group heat stress is difficult to accurately and dynamically assess and poultry house environment is difficult to finely and hierarchically control in different production scenes are solved.

To provide an information processing device capable quickly and easily discriminating an addition amount of flocculant added to form a good quality flock.SOLUTION: An information processing device 10 includes a CPU 11 and a SSD 14, flocks grow in a flocculation basin in which many flocks are formed by injecting flocculant into water to be treated and coagulating suspended matters contained in the water to be treated, and is used for tap water treatment in which grown flocks sedimentate in a sedimentation tank to be removed from the water to be treated, and the CPU 11 trims an image of each flock from image data showing a state of the flocculation basin to generate a trimming image of each flock, calculates a size of each flock and a fractal dimension of the trimming image of each flock on the basis of the trimming image of each flock, and also calculates a sedimentation velocity of each block on the basis of the size of each block and each fractal dimension.SELECTED DRAWING: Figure 1

The utility model relates to a device for improving cage rearing of chicks, which comprises a device body, a movable frame and a swing frame are arranged in the device body, the movable frame comprises a long perching frame, a platform is arranged on the long perching frame, a window shade is arranged on the side face of the platform, the swing frame comprises a short perching frame, and the short perching frame is arranged on the platform. Pecking blocks are arranged at the two ends of the short roost, one end of a pecking rope is connected to each pecking block, a hook is connected to the other end of each pecking rope, and the hooks are connected to the top of the device body. By increasing the richness of a feeding environment, increasing chick flock exercise, activity and inhabitation opportunities, reducing stress caused by changes of various conditions such as transportation, immunization, flock transferring, temperature and the like, reducing pecking, promoting growth development and skeletal development, improving the survival rate of chicks, improving the number and quality of eggs in an egg producing period, and improving the survival rate of the chicks. Therefore, the production performance of chicken flocks in the whole life cycle is improved.

A method for generating a standard growth curve for chicken flock breeding comprises the following specific steps that a, the same kind of chicken flock is divided into N groups to be bred, and it is guaranteed that all chickens in each group of chicken flock are born on the same day; b, recording the daily average weight of each group of chicken flocks; c, taking the daily average weight of all groups of chicken flocks as a training set, learning in a learning model, averaging, and finally generating a daily age average weight standard growth curve of the same type of chicken flocks by means of a Gompertz fitting model; and d, changing any condition of variety, gender, region, henhouse control mode, climate and breeding mode to form a new type of chicken flocks, and repeating the steps b and c to obtain daily age average weight standard growth curves of different types of chicken flocks. The daily age average weight standard growth curve obtained by the method is more consistent with the actual weight of the chicken flocks, the average weight prediction of the chicken flocks is more scientific and accurate, and meanwhile, the problem of physical health of the chickens caused by a mode of catching the chickens by sampling is avoided.

The invention belongs to the technical field of computer vision and ecological monitoring, and particularly relates to a large-scale bird flock counting system and method based on pure synthetic data training. In a model training stage, a large-scale bird flock composite image and a pixel-level label thereof are used as a unique training set, and end-to-end training is performed on a point prediction deep learning counting network using VGG19-BN as a backbone network to obtain a trained bird flock counting model; in the model training stage, real world bird flock images to be counted are input into the trained bird flock counting model, the model directly outputs predicted bird individual position points, a final counting result of the bird flock is obtained by counting the number of prediction points with high confidence, and a bird flock density distribution diagram can be generated for visualization. The invention provides a method and a system for realizing real scene high-precision counting only through synthetic data training based on a point prediction network aiming at the technical problems of strong dependence of real labeled data and insufficient cross-domain generalization ability of a model in a bird flock counting task.

The embodiment of the utility model provides a bird repelling cannon. The bird repelling cannon comprises a cannon carriage device, an ignition device, a holder control device and a transport vehicle. The gun carriage device is used for launching bird repelling bullets and can rotate around a horizontal shaft so as to adjust the pitching angle of launching; the ignition device is connected with the gun carriage device and used for igniting fuses of the bird repelling bullets; the holder control device is connected with the ignition device and is used for detecting bird flock and controlling the ignition action of the ignition device; the gun carriage device, the ignition device and the holder control device are all installed on the transport vehicle. When the bird repelling cannon is used, whether a bird flock appears or not, the appearing direction and position information are detected through the holder control device, and after the bird flock and the direction and position of the bird flock are detected and the bird flock enters the range range of the bird repelling bomb, the ignition device is controlled to ignite the fuse of the bird repelling bomb and launch the bird repelling bomb at a proper time; compared with an existing mode that bird flocks are found by personnel and then bird repelling bullets are manually launched, the bird repelling efficiency and the bird repelling accuracy are improved.

The invention relates to the technical field of artificial intelligence, and discloses a bird flock detection method and system based on a holographic staring radar, so as to improve the overall performance. The method comprises the following steps of: obtaining coordinate information and initial confidence of a detection frame of a potential bird flock target in each frame of radar distance-Doppler image based on a folov8 network model inserted with an X-CBAM attention mechanism; then identifying a single bird target for each detection frame by using a radar traditional CFAR detection method, fitting a two-dimensional elliptical broadening according to the distribution condition of each identified single bird, and obtaining a confidence correction value of each detection frame based on a ratio value of a short axis to a long axis of a fitted ellipse; and then correcting the initial confidence of each detection frame in each frame of image, which is obtained by the yov8 network model, as a potential bird flock target based on a confidence correction value to obtain the target confidence of each bird flock target in each frame of image.

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.

The invention relates to an LMX1B gene molecular marker primer related to egg weight character and application of the LMX1B gene molecular marker primer, and belongs to the technical field of biology. The molecular marker of the LMX1B gene is located at the 10705046th base of chromosome 17 of a chicken reference genome GRCg7b version, the base is mutated into T or G, and the genotypes are G / G, G / T and T / T. The egg weight of the G / G genotype chicken is higher than that of the G / T and T / T genotype individuals, and the egg weight of the G / T genotype chicken is higher than that of the T / T genotype individuals. According to the present invention, the forward and reverse primer pair is used to amplify the fragment containing the molecular marker site, and the amplification product is subjected to Sanger sequencing so as to rapidly and accurately identify the genotype of the individual; the molecular marker can be used as a genetic marker for chicken breeding to breed chicken with proper egg weight; the egg weight character can be efficiently and accurately detected, the uniformity of the egg weight of chicken flocks is improved, and the method has important value for chicken breeding and production.

The invention provides a Tibetan chicken intensive breeding health monitoring and early warning method and system, and relates to the technical field of computers, and the method comprises the steps: obtaining the three-dimensional motion and interval time sequence data of a Tibetan chicken flock in a breeding house, and the original chirp audio time sequence data; performing motion mode analysis according to the three-dimensional motion and the interval time sequence data to obtain a vector field sequence; carrying out stress level inference according to the vector field sequence to obtain a group recessive stress index; according to the group implicit stress index and the original chirp audio time sequence data, performing model construction to obtain a chirp-stress association model; acquiring real-time chirp audio data, and inputting the real-time chirp audio data into the chirp-stress correlation model for health state critical point prediction to obtain an instability precursor signal; and performing graded early warning according to the instability precursor signal to obtain a health early warning grade. According to the invention, prediction and early warning of health risks caused by group stress of the Tibetan chickens in an intensive breeding environment are realized.

A chicken flock average weight processing method comprises the following steps: a, removing unreasonable data samples from all weighed data samples in a chicken flock to obtain preliminarily denoised samples; b, finding a mean weight fuzzy value in a chicken flock by means of a k-means algorithm, and determining a maximum value and a minimum value of secondary screening; c, filtering the preliminarily denoised sample by means of the minimum value and the maximum value to obtain a data sample between the maximum value and the minimum value as an effective data sample; d, averaging the effective data samples to obtain an average weight initial value; and e, setting a weight coefficient to correct the average weight initial value to obtain an average weight final value. According to the processing method, unreasonable data read by the intelligent chicken scale are removed through the denoising and shake-removing processes, meanwhile, relatively effective samples are screened out, the relatively large-area effective samples are used for calculation and statistics of the average weight, the average weight of the chicken flocks can be estimated relatively accurately, and compared with manual weighing one by one, the average weight of the chicken flocks can be estimated more accurately. And the efficiency is greatly improved.

The invention provides a feeding station capable of accurately evaluating the social grade of chicken flocks, and belongs to the technical field of poultry farming, the feeding station comprises a frame, net racks are fixed in the left and right side walls of the frame, and a lifting door structure is arranged on the front side of the frame. According to the invention, through the arrangement of the transmission roller, the conveying belt, the cleaning linkage structure, the weight sensor, the foot ring scanner, the weighing sensor, the feed box and the built-in camera, the daily gain and daily feed intake of each chicken are accurately measured, the breeding efficiency is improved, the fighting behavior of the chicken flocks can be monitored in real time, the social grade of the chicken flocks can be evaluated conveniently, and the economic benefit of the chicken flocks is improved. Through transmission of the conveying belt, on one hand, chicken droppings can be conveniently cleaned, health management of chicken flocks is improved, disease transmission is reduced, on the other hand, chickens can be driven, and the phenomenon that the chickens lie down is avoided; the problems that in the prior art, chicken droppings are inconvenient to clean, chickens are inconvenient to drive, and fighting behaviors are inconvenient to monitor are solved.

The invention relates to the technical field of laying hen breeding, and discloses a laying hen breeding accurate feeding adjusting system based on big data cloud-side collaboration. The system comprises an environmental data acquisition module, a cloud edge collaborative analysis module, a multi-dimensional difference evaluation module, an abnormal area positioning module, a feeding strategy adjustment module and an encryption communication link module. The environment data acquisition module acquires chicken flock multi-source data streams through various sensors, and standard chicken flock data is generated after processing; a cloud-edge collaborative analysis module constructs a dynamic feed supply model according to the dynamic feed supply model and outputs theoretical feed demand; the multi-dimensional difference evaluation module performs multi-dimensional comparison on the theoretical value and the actual consumption value to generate a feed difference coefficient matrix; the abnormal area positioning module generates an abnormal feeding probability distribution diagram in combination with farm topological information; the feeding strategy adjusting module generates a self-adaptive feeding control signal based on the graph; the encryption communication link module transmits a control signal to an actuator through a quantum encryption channel. The system realizes accurate adjustment and safety control of feeding for laying hen breeding.

The embodiment of the utility model provides a bird repelling bomb which comprises a bomb shell, a bomb head and a lead, the bomb shell is a hollow cylindrical shell, and a first end of the bomb shell is provided with a solid lead agent to seal the end part; the interior of the cartridge case is filled with pushing powder; the bullet head is a hollow cylindrical shell, and the second end of the bullet head is connected with the second end of the cartridge case; a conical head is arranged at the first end of the bullet head, and the conical head is filled with clay to seal the first end of the bullet head; bird repelling powder and detonating explosive are filled in the bullet, and the bird repelling powder is divided into two parts by the detonating explosive. One part of the bird repelling powder is located between the conical head and the detonating explosive, and the other part of the bird repelling powder is located between the detonating explosive and the second end of the bullet; after the detonating explosive is detonated, the bird repelling powder can be uniformly thrown in the air, and meanwhile, the conical head is exploded to form fine particles to form throwing impact on bird flocks; and the lead penetrates into the detonating explosive of the bullet head from the solid fuse agent of the bullet case. The bird repelling bomb is uniform in throwing and good in bird repelling effect.