Cow lameness warning method and related equipment

Abstract

The present application provides a method and related equipment for early warning of lameness of dairy cows. The method comprises: obtaining an image of the bottom of the cow's hoof in a natural walking state, storing a first time value of obtaining the image of the bottom of the cow's hoof; obtaining the image of the bottom of the cow's hoof by an image acquisition device arranged at the bottom of one end in the length direction of a channel; obtaining ear tag data of the cow's hoof in a natural walking state, storing a second time value of obtaining the ear tag data of the cow's hoof; determining the identity information corresponding to the cow's hoof; extracting multiple features of the image of the bottom of the cow's hoof; fusing multiple features to obtain the fusion features of the image of the bottom of the cow's hoof; determining the state type of the cow's hoof according to the fusion features; wherein the state type includes abnormal or normal; the multiple features of the image of the bottom of the cow's hoof include the morphological features of the bottom of the cow's hoof, the texture features of the bottom of the cow's hoof and the color features of the bottom of the cow's hoof; determining the state of the cow's hoof according to the state type, and determining whether to output lameness early warning information according to the state of the cow's hoof. The state of the cow's hoof and the lameness risk can be accurately determined.

Description

Technical Field

[0001] The present application relates to the field of animal husbandry information technology, and in particular to a method for early warning of lameness of dairy cows and related equipment. Background Art

[0002] Cow lameness will affect the health and production performance of dairy cows. At present, the treatment of cow lameness mainly relies on intervention after the cows show lameness symptoms, which will cause more lasting effects on the health and production performance of dairy cows and is not conducive to the health and production performance of dairy cows. Therefore, there is an urgent need for a method that can warn of cow lameness before the cows show lameness symptoms. Summary of the invention

[0003] In view of this, the purpose of this application is to propose a dairy cow lameness early warning method and related equipment.

[0004] Based on the above purpose, the present application provides a method for early warning of lameness of dairy cows, comprising:

[0005] Acquire the bottom image of the cow's hoof in a natural walking state, and store the first time value of acquiring the bottom image of the cow's hoof; wherein the bottom image of the cow's hoof is acquired by an image acquisition device arranged at the bottom of one end in the length direction of the channel;

[0006] Acquire the ear tag data of the cow's hoof in a natural walking state, and store the second time value of acquiring the ear tag data of the cow's hoof;

[0007] In response to determining that the difference between the first time and the second time is a preset difference, determining identity information corresponding to the cow's hoof;

[0008] Extract multiple features of the cow hoof bottom image; fuse the multiple features of the cow hoof bottom image to obtain fused features of the cow hoof bottom image; determine the state type of the cow hoof according to the fused features; wherein the state type includes abnormal or normal; the multiple features of the cow hoof bottom image include morphological features of the cow hoof bottom, texture features of the cow hoof bottom and color features of the cow hoof bottom;

[0009] The state of the cow's hoof is determined according to the state type, and whether to output lameness warning information is determined according to the state of the cow's hoof.

[0010] In some embodiments, the channel is an outlet channel of a milking room; the width of the channel is sufficient to accommodate a single cow walking naturally; and the image acquisition device is arranged in the middle of the outlet channel in the width direction.

[0011] In some of the embodiments, the outlet channel is provided with a groove structure in the height direction, liquid is arranged in the groove structure, and the image acquisition device is arranged at the bottom of the outlet of the outlet channel.

[0012] In some embodiments, the image acquisition device includes a camera and a protective cover for enclosing the camera, and the protective cover is detachably mounted on the camera;

[0013] The image acquisition device also includes a scraper for cleaning the protective cover, and the scraper is hinged on the protective cover.

[0014] In some embodiments, determining the state of the cow's hoof according to the state type includes:

[0015] Stores the status type of the cow's hoof;

[0016] In response to determining that the status types of the cow's hooves within the preset number of days are all abnormal, it is determined that the cow's hoof status is abnormal, and a prompt message that the cow's hoof status is abnormal is output.

[0017] In some embodiments, determining whether to output lameness warning information according to the state of the cow's hoof includes:

[0018] In response to determining that the cattle hoof condition is abnormal, lameness warning information is output.

[0019] In some of the embodiments, the multiple features of the cow hoof bottom image are extracted; the multiple features of the cow hoof bottom image are fused to obtain the fused features of the cow hoof bottom image; and the state type of the cow hoof is determined according to the fused features through a pre-trained cow hoof state analysis model.

[0020] The embodiment of the present application also provides a dairy cow lameness warning device, comprising: a cow hoof bottom image acquisition module, a cow hoof ear tag data acquisition module, an identity information determination module, a state type determination module and a determination module;

[0021] The cow hoof bottom image acquisition module is used to acquire the cow hoof bottom image in a natural walking state and store the first time value of acquiring the cow hoof bottom image; wherein the cow hoof bottom image is acquired by an image acquisition device arranged at the bottom of one end in the length direction of the channel;

[0022] The cow hoof ear tag data acquisition module is used to acquire the cow hoof ear tag data in a natural walking state and store the second time value of acquiring the cow hoof ear tag data;

[0023] The identity information determination module is used to determine the identity information corresponding to the cow's hoof in response to determining that the difference between the first time and the second time is a preset difference;

[0024] The state type determination module is used to extract multiple features of the cow hoof bottom image; fuse the multiple features of the cow hoof bottom image to obtain the fusion features of the cow hoof bottom image; determine the state type of the cow hoof according to the fusion features; wherein the state type includes abnormal or normal; the multiple features of the cow hoof bottom image include the morphological features of the cow hoof bottom, the texture features of the cow hoof bottom and the color features of the cow hoof bottom;

[0025] The determination module is used to determine the state of the cow's hoof according to the state type, and determine whether to output lameness warning information according to the state of the cow's hoof.

[0026] An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the program.

[0027] The embodiment of the present application also provides a dairy cow lameness warning system, comprising a server deployed with the dairy cow lameness warning device as described above, an image acquisition device and an ear tag recognition device; wherein,

[0028] The image acquisition device is used to acquire an image of the bottom of a cow's hoof in a natural walking state, record a first time value of acquiring the image of the bottom of the cow's hoof; and send the image of the bottom of the cow's hoof and the first time value to the lameness warning device for dairy cows;

[0029] The ear tag recognition device is used to collect the hoof ear tag data of the dairy cow, record the second time value of obtaining the hoof ear tag data; and send the hoof ear tag data and the second time value to the dairy cow lameness early warning device.

[0030] In some of the embodiments, the dairy cow lameness warning system further includes a display device, and the display device is used to display at least one of the cow hoof status and lameness warning information.

[0031] As can be seen from the above, the dairy cow lameness warning method provided by the present application obtains an image of the bottom of the cow's hoof in a natural walking state, and stores a first time value for obtaining the image of the bottom of the cow's hoof; wherein the image of the bottom of the cow's hoof is obtained by an image acquisition device arranged at the bottom of one end of the length direction of a channel; the width of the channel is sufficient to accommodate the natural walking of a single cow; the ear tag data of the cow's hoof in a natural walking state is obtained, and the second time value for obtaining the ear tag data of the cow's hoof is stored; in response to determining that the difference between the first time and the second time is a preset difference, the identity information corresponding to the cow's hoof is determined; multiple features of the bottom image of the cow's hoof are extracted; and the multiple features of the bottom image of the cow's hoof are merged to obtain the bottom image of the cow's hoof. fusion features; determining the state type of the cow hoof according to the fusion features; wherein the state type includes abnormal or normal; the multiple features of the cow hoof bottom image include the morphological features of the cow hoof bottom, the texture features of the cow hoof bottom and the color features of the cow hoof bottom; determining the cow hoof state according to the state type, and determining whether to output lameness warning information according to the cow hoof state; being able to obtain the cow hoof image in a natural state, and accurately determine the state of the cow hoof through the fusion features after the fusion of multiple features in the cow hoof image, and finally determine whether there is a lameness risk according to the cow hoof state, and issue a lameness warning, thereby effectively reducing the stress of dairy cows and reducing the duration of lameness affecting the health and production performance of dairy cows. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 This is a schematic diagram of a flow chart of a method for early warning of lameness of dairy cows according to an embodiment of the present application;

[0034] Figure 2 A schematic diagram of the location of the image acquisition device according to an embodiment of the present application;

[0035] Figure 3 This is a schematic diagram of a lameness warning device for dairy cows according to an embodiment of the present application;

[0036] Figure 4 A schematic diagram of an electronic device according to an embodiment of the present application;

[0037] Figure 5 This is a schematic diagram of a dairy cow lameness warning system according to an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0039] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0040] At present, intervention is often initiated after analyzing the symptoms that have already appeared, such as the cow's walking posture and gait, and then confirming the lameness. This will obviously have a more lasting impact on the health and production performance of the cow, which is not conducive to the health and production performance of the cow. Therefore, it is very important to prevent lameness in dairy cows.

[0041] When the hoof condition of a cow is abnormal, it may cause lameness of the cow after it progresses to a certain extent. Therefore, by detecting the hoof condition, it can prevent lameness of the cow to a certain extent. In the related art, when detecting the hoof condition of a cow, it mainly relies on manual inspection, which is time-consuming, labor-intensive and inaccurate. This is because during manual inspection, it is necessary to passively lift the cow's hoof, collect the image of the bottom of the hoof, and detect the state of the bottom of the hoof based on the image of the bottom of the hoof. When the hoof is passively lifted, it is easy to scare the cow, which may cause a stress response in the cow. Therefore, how to obtain the image of the bottom of the hoof without scaring the cow, complying with animal welfare, and not affecting production has become an urgent problem to be solved.

[0042] Based on this, an embodiment of the present application provides a method for warning lameness of dairy cows. By setting an image acquisition device at the bottom of the exit of the natural departure passage (that is, the exit passage of the milking room) when the cow leaves the room naturally after milking, the bottom image of the cow's hoof is naturally obtained when the cow walks naturally. This can, to a certain extent, solve the problems of inaccurate and time-consuming and labor-intensive hoof status detection, as well as the problems of being detrimental to the health and production performance of the cow.

[0043] Figure 1 A flow chart of an exemplary method for early warning of lameness of dairy cows according to an embodiment of the present application is shown.

[0044] like Figure 1 As shown, the method for early warning of lameness of dairy cows provided in the embodiment of the present application may include:

[0045] S100, such as Figure 2 As shown, an image of the bottom of a cow's hoof in a natural walking state is obtained, and a first time value of obtaining the image of the bottom of the cow's hoof is stored; wherein the image of the bottom of the cow's hoof is obtained by an image acquisition device 92 arranged at the bottom of one end in the length direction of the channel 1; the width of the channel 1 is sufficient to accommodate a single cow walking naturally;

[0046] S200, obtaining ear tag data of a cow's hoof in a natural walking state, and storing a second time value of obtaining the ear tag data of the cow's hoof;

[0047] S300, in response to determining that the difference between the first time and the second time is a preset difference, determining identity information corresponding to the cow's hoof;

[0048] S400, extracting multiple features of the bottom image of the cow hoof;

[0049] S500, fusing multiple features of the cow hoof bottom image to obtain fused features of the cow hoof bottom image; determining the state type of the cow hoof according to the fused features; wherein the state type includes abnormal or normal; the multiple features of the cow hoof bottom image include morphological features of the cow hoof bottom, texture features of the cow hoof bottom and color features of the cow hoof bottom;

[0050] S600, determining a cow hoof status according to the status type, and determining whether to output lameness warning information according to the cow hoof status.

[0051] The method for early warning of lameness of dairy cows provided in an embodiment of the present application obtains an image of the bottom of the cow's hoof in a natural walking state, and stores a first time value of obtaining the image of the bottom of the cow's hoof; wherein the image of the bottom of the cow's hoof is obtained by an image acquisition device 92 arranged at the bottom of one end in the length direction of a channel 1; the width of the channel 1 is sufficient to accommodate the natural walking of a single cow; the ear tag data of the cow's hoof in a natural walking state is obtained, and a second time value of obtaining the ear tag data of the cow's hoof is stored; in response to determining that the difference between the first time and the second time is a preset difference, the identity information corresponding to the cow's hoof is determined; multiple features of the bottom image of the cow's hoof are extracted; and the multiple features of the bottom image of the cow's hoof are fused to obtain a Fusion features; determining the state type of the cow hoof according to the fusion features; wherein the state type includes abnormal or normal; the multiple features of the cow hoof bottom image include morphological features of the cow hoof bottom, texture features of the cow hoof bottom and color features of the cow hoof bottom; determining the cow hoof state according to the state type, and determining whether to output lameness warning information according to the cow hoof state; being able to obtain cow hoof images in a natural state, and accurately determine the state of the cow hoof through the fusion features after fusing multiple features in the cow hoof image, and finally determine whether there is a lameness risk according to the state of the cow hoof, and issue a lameness warning, thereby effectively reducing the stress of dairy cows and reducing the duration of lameness affecting the health and production performance of dairy cows.

[0052] In step S100, in some embodiments, continue to refer to Figure 2 As shown, the channel 1 can be the outlet channel of the milking room. The bottom of one end of the channel 1 in the length direction can be understood as the bottom of the entrance or exit of the channel 1. Further, the image acquisition device 92 can be arranged in the middle of the width direction of the outlet channel to increase the probability of acquiring the bottom image of the cow's hoof. Generally, a separate inlet channel and an inlet channel can be arranged in the milking room. Furthermore, the width of the outlet channel is sufficient to accommodate a single cow walking naturally to reduce stress.

[0053] This can better reduce the stress of cattle, increase the utilization rate of existing equipment on the ranch, and reduce costs. In addition, since cows are usually milked three times a day, the probability of collecting images of the bottom of the cow's hoof can be increased.

[0054] In some embodiments, the outlet channel may be a medicinal bath channel. The medicinal bath channel may contain medicinal liquid. The outlet channel may be provided with a groove structure in the height direction, and a liquid (i.e., medicinal liquid) is provided in the groove structure. The image acquisition device 92 may be provided at the bottom of the outlet of the outlet channel. In this way, the hooves of the cows can be cleaned while the hooves are being bathed in medicinal liquid, thereby removing debris at the bottom of the hooves, improving the clarity of the image at the bottom of the hooves, and improving the accuracy of the recognition of the hoof status to a certain extent, thereby improving the accuracy of the lameness warning.

[0055] In some embodiments, the image acquisition device 92 may include a camera and a protective cover for sealing the camera, and the protective cover may be detachably mounted on the camera. The protective cover may be a glass protective cover. By providing the protective cover, it is possible to prevent liquid medicine and other debris from directly splashing onto the camera, thereby improving the clarity of the image of the bottom of the cow's hoof to a certain extent.

[0056] In some embodiments, the image acquisition device 92 may further include a scraper for cleaning the protective cover, and the scraper may be hinged on the protective cover. In this way, the protective cover can be cleaned by the scraper to scrape off the sewage and other debris on the surface of the protective cover. It should be understood that the articulation between the scraper and the protective cover can be achieved by adopting an existing articulation structure, such as a protruding tenon and a corresponding groove interlocking with each other, and the embodiments of the present application do not involve improvements to the existing articulation structure.

[0057] Typically, after capturing the cow hoof bottom image, the image acquisition device 92 can synchronously record the first time value of capturing the cow hoof bottom image, and send the cow hoof bottom image and the first time value to the server 91 deployed with the cow lameness warning device 800.

[0058] In step S200, in some embodiments, the hoof ear tag data can be collected by an ear tag recognition device 93. The tag recognition device can be an RFID reader, which can usually be set on the side of the exit of the medicinal bath channel 1 (that is, the exit channel 1), for example, on the fence on the side, so that the ear tag information can be collected when the cow walks out of the exit channel 1, and the second time value of obtaining the hoof ear tag data can be recorded. Correspondingly, an RFID ear tag can be set on the ear of the cow.

[0059] Typically, the ear tag recognition device 93 can collect the cow's hoof ear tag data, and synchronously record the second time value of obtaining the hoof ear tag data, that is, the moment when the cow walks out of the exit passage; and send the hoof ear tag data and the second time value to the server 91 deployed with the cow lameness warning device 800.

[0060] In step S300, in some embodiments, the preset difference value can be set based on experience. Specifically, it can be determined based on the distance between the ear and the hoof of the cow and the speed of the cow passing through the medicinal bath. Generally, the coordinate values ​​of the ear tag recognition device 93 and the image acquisition device 92 in the width direction band of the outlet channel 1 can be the same. Exemplarily, the preset difference value can be 1-2 seconds to improve the accuracy of determining the identity information corresponding to the cow's hoof.

[0061] In step S400, in some embodiments, before extracting the multiple features of the cow hoof bottom image, the cow hoof bottom image may be preprocessed, for example, denoising, enhancement, geometric correction, etc. Preprocessing can improve the accuracy of subsequent extraction of multiple features of the cow hoof bottom image, and further improve the accuracy of subsequent state types.

[0062] In some embodiments, the extracting of multiple features of the cow hoof bottom image; fusing the multiple features of the cow hoof bottom image to obtain the fusion features of the cow hoof bottom image; and determining the state type of the cow hoof according to the fusion features are achieved by a pre-trained cow hoof state analysis model. The cow hoof state analysis model can be a ResNet deep learning model.

[0063] In some possible embodiments, the ResNet deep learning model. The ResNet deep learning model includes the basic network architecture of the ResNest deep learning model and texture analysis algorithms such as LBP and Gabor filters introduced in the basic network architecture, algorithms such as color histograms and color space conversion, and principal component analysis (PCA) algorithms. Among them, the relevant neural network layer in the basic network architecture of the ResNest deep learning model extracts information such as the edge and morphology of the bottom of the cow's hoof. Texture analysis algorithms such as LBP and Gabor filters can extract texture feature information of the bottom of the cow's hoof. Algorithms such as color histograms and color space conversion can extract color features of the bottom of the cow's hoof. The principal component analysis (PCA) algorithm can fuse the morphological features of the bottom of the cow's hoof, the texture features of the bottom of the cow's hoof, and the color features of the bottom of the cow's hoof to obtain the fusion features of the bottom image of the cow's hoof. Afterwards, the fused features can be mapped to the specific number of categories through the fully connected layer of the ResNest deep learning model, and the softmax function is used to perform probability calculation to determine the probability that the fused vector belongs to each category (that is, the normal state type or the abnormal state type), and finally output the predicted category.

[0064] In some possible embodiments, the ResNet deep learning model may also include the SE module and the CBAM module introduced in the basic network architecture, which can improve the selectivity of key areas in the feature extraction process, thereby improving the accuracy of the state type of the final output.

[0065] In some possible embodiments, the ResNet deep learning model may also include a dynamic threshold adjustment algorithm (Gaussian mixture model GMM) introduced in the basic network architecture to adaptively adjust the threshold of disease judgment according to the current environment and cattle conditions during the detection process to improve the generalization ability of the model.

[0066] In some possible embodiments, the training method of the ResNet deep learning model may include:

[0067] Obtain a training set of cow hoof bottom images. The training set of cow hoof bottom images can be obtained from historical detection data, or can be collected from a large number of pastures by the aforementioned cow hoof bottom image acquisition device 92. After collection, the cow hoof bottom images can be manually annotated according to the determination criteria of cow hoof disease to obtain label data of the cow hoof bottom images.

[0068] Build the ResNet deep learning model. For example, you can install it through pip: pip install git+https: / / github.com / zhanghang1989 / resnest, and then import it directly, for example: fromresnest.torch import resnest50 to create a resnest50 model.

[0069] Training model: The ResNet model is trained using the prepared training set of cow hoof bottom images. During the training process, the weights of the model are continuously adjusted through the back propagation algorithm to minimize the loss function between the predicted results and the true labels.

[0070] Optimization algorithm: Choose a suitable optimization algorithm, such as Stochastic Gradient Descent (SGD), Adagrad, Adadelta, etc., to update the weights of the model.

[0071] Hyperparameter adjustment: Hyperparameters include learning rate, batch size, number of training rounds (epoch), etc. It is necessary to find the optimal hyperparameter combination through experiments and adjustments to improve the performance of the model.

[0072] Model evaluation: Use the test set or validation set to evaluate the trained model. Common evaluation indicators include accuracy, recall, F1 value, etc.

[0073] In some embodiments, in step S500, determining the state of the cow's hoof according to the state type may include: storing the state type of the cow's hoof; in response to determining that the state types of the cow's hoof are all abnormal within a preset number of days, determining that the state of the cow's hoof is abnormal, and outputting a prompt message that the state of the cow's hoof is abnormal. The preset number of days may be 3 consecutive days, and the state type of the cow's hoof obtained within 3 consecutive days may be determined by the first time value corresponding to the bottom image of the cow's hoof to determine the state of the cow's hoof. In this way, the accuracy of the state of the cow's hoof can be improved to a certain extent to avoid misjudgment.

[0074] In some possible embodiments, information such as the lesion site and severity level of the cow's hoof condition may also be displayed in the prompt information of the abnormal cow's hoof condition.

[0075] In some embodiments, determining whether to output lameness warning information according to the state of the cow's hoof may include: outputting lameness warning information in response to determining that the state of the cow's hoof is abnormal.

[0076] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0077] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a dairy cow lameness warning device 800.

[0079] refer to Figure 3 The dairy cow lameness warning device 800 comprises: a cow hoof bottom image acquisition module 81, a cow hoof ear tag data acquisition module 82, an identity information determination module 83, a state type determination module 84 and a determination module 85;

[0080] The cow hoof bottom image acquisition module 81 is used to acquire the cow hoof bottom image in a natural walking state and store the first time value of acquiring the cow hoof bottom image; wherein the cow hoof bottom image is acquired by an image acquisition device 92 arranged at the bottom of one end in the length direction of the channel 1;

[0081] The cow hoof ear tag data acquisition module 82 is used to acquire the cow hoof ear tag data in a natural walking state, and store the second time value of acquiring the cow hoof ear tag data;

[0082] The identity information determination module 83 is used to determine the identity information corresponding to the cow hoof in response to determining that the difference between the first time and the second time is a preset difference;

[0083] The state type determination module 84 is used to extract multiple features of the cow hoof bottom image; fuse the multiple features of the cow hoof bottom image to obtain the fusion features of the cow hoof bottom image; determine the state type of the cow hoof according to the fusion features; wherein the state type includes abnormal or normal; the multiple features of the cow hoof bottom image include the morphological features of the cow hoof bottom, the texture features of the cow hoof bottom and the color features of the cow hoof bottom;

[0084] The determination module 85 is used to determine the state of the cow's hoof according to the state type, and determine whether to output lameness warning information according to the state of the cow's hoof.

[0085] In some embodiments, the channel 1 is an outlet channel 1 of a milking room; the width of the channel 1 is sufficient to accommodate a single cow walking naturally; and the image acquisition device 92 is disposed in the middle of the outlet channel 1 in the width direction.

[0086] In some of the embodiments, the outlet channel 1 is provided with a groove structure in the height direction, liquid is arranged in the groove structure, and the image acquisition device 92 is arranged at the bottom of the outlet of the outlet channel 1.

[0087] In some embodiments, the image acquisition device 92 includes a camera and a protective cover for enclosing the camera, and the protective cover is detachably mounted on the camera;

[0088] The image acquisition device 92 further comprises a scraper for cleaning the protective cover, wherein the scraper is hinged on the protective cover.

[0089] In some embodiments, the determining module 85 is used to determine the state of the cow's hoof according to the state type, including:

[0090] Stores the status type of the cow's hoof;

[0091] In response to determining that the status types of the cow's hooves within the preset number of days are all abnormal, it is determined that the cow's hoof status is abnormal, and a prompt message that the cow's hoof status is abnormal is output.

[0092] In some embodiments, the determining module 85 is used to determine whether to output lameness warning information according to the cow hoof state, including:

[0093] In response to determining that the cattle hoof condition is abnormal, lameness warning information is output.

[0094] In some embodiments, the state type determination module 84 is used to extract multiple features of the cow hoof bottom image; fuse the multiple features of the cow hoof bottom image to obtain the fused features of the cow hoof bottom image; determine the state type of the cow hoof according to the fused features, including: through a pre-trained cow hoof state analysis model.

[0095] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0096] The device of the above embodiment is used to implement the corresponding cow lameness warning method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0097] Based on the same inventive concept, corresponding to any of the above embodiments, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the cow lameness warning method described in any of the above embodiments when executing the program. Figure 3 The dairy cow lameness warning device 800.

[0098] Figure 4 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0099] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0100] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0101] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0102] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0103] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0104] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0105] The electronic device of the above embodiment is used to implement the corresponding cow lameness warning method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0106] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods / devices, the present application also provides a dairy cow lameness warning system.

[0107] refer to Figure 5 The cow lameness warning system comprises a server 91 equipped with the cow lameness warning device 800 as described above, an image acquisition device 92, an ear tag recognition device 93 and a display device 94; wherein,

[0108] The image acquisition device 92 is used to acquire the image of the bottom of the cow's hoof in a natural walking state, record the first time value of acquiring the image of the bottom of the cow's hoof; and send the image of the bottom of the cow's hoof and the first time value to the dairy cow lameness warning device 800;

[0109] The ear tag recognition device 93 is used to collect the hoof ear tag data of the cow, record the second time value of obtaining the hoof ear tag data; and send the hoof ear tag data and the second time value to the server;

[0110] The display device 94 is used to display at least one of the cow hoof status and lameness warning information.

[0111] In some embodiments, the channel 1 is an outlet channel 1 of a milking room; the width of the channel 1 is sufficient to accommodate a single cow walking naturally; and the image acquisition device 92 is disposed in the middle of the outlet channel 1 in the width direction.

[0112] In some of the embodiments, the outlet channel 1 is provided with a groove structure in the height direction, liquid is arranged in the groove structure, and the image acquisition device 92 is arranged at the bottom of the outlet of the outlet channel 1.

[0113] In some embodiments, the image acquisition device 92 includes a camera and a protective cover for enclosing the camera, and the protective cover is detachably mounted on the camera;

[0114] The image acquisition device 92 further comprises a scraper for cleaning the protective cover, wherein the scraper is hinged on the protective cover.

[0115] In some embodiments, the determining module is used to determine the state of the cow's hoof according to the state type, including:

[0116] Stores the status type of the cow's hoof;

[0117] In response to determining that the status types of the cow's hooves within the preset number of days are all abnormal, it is determined that the cow's hoof status is abnormal, and a prompt message that the cow's hoof status is abnormal is output.

[0118] In some embodiments, the determining module is used to determine whether to output lameness warning information according to the state of the cow's hoof, including:

[0119] In response to determining that the cattle hoof condition is abnormal, lameness warning information is output.

[0120] In some embodiments, the state type determination module is used to extract multiple features of the cow hoof bottom image; fuse the multiple features of the cow hoof bottom image to obtain fused features of the cow hoof bottom image; determine the state type of the cow hoof based on the fused features, including: through a pre-trained cow hoof state analysis model.

[0121] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0122] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the cow lameness warning method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0123] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0124] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0125] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0126] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A method for early warning of lameness of dairy cows, characterized in that: include: Acquire the bottom image of the cow's hoof in a natural walking state, and store the first time value of acquiring the bottom image of the cow's hoof; wherein the bottom image of the cow's hoof is acquired by an image acquisition device arranged at the bottom of one end of a passage in the length direction; the passage is the outlet passage of the milking room; the width of the passage is sufficient to accommodate a single cow walking naturally; the image acquisition device is arranged at the bottom of the outlet of the outlet passage; Acquire the ear tag data of the cow's hoof in a natural walking state, and store the second time value of acquiring the ear tag data of the cow's hoof; the ear tag data of the cow's hoof is collected by an ear tag recognition device; the ear tag recognition device is arranged on the side of the exit of the exit passage; In response to determining that the difference between the first time and the second time is a preset difference, determining the identity information corresponding to the cow's hoof according to the cow's hoof ear tag data; the preset difference is determined according to the interval between the cow's ear and the hoof and the speed at which the cow passes through the exit passage; Extract multiple features of the cow hoof bottom image; fuse the multiple features of the cow hoof bottom image to obtain fused features of the cow hoof bottom image; determine the state type of the cow hoof according to the fused features; wherein the state type includes abnormal or normal; the multiple features of the cow hoof bottom image include morphological features of the cow hoof bottom, texture features of the cow hoof bottom and color features of the cow hoof bottom; Determine the state of the cow's hoof according to the state type, and determine whether to output lameness warning information according to the state of the cow's hoof; The method comprises extracting multiple features of the bottom image of the cow hoof; fusing the multiple features of the bottom image of the cow hoof to obtain the fusion features of the bottom image of the cow hoof; and determining the state type of the cow hoof according to the fusion features by using a pre-trained cow hoof state analysis model. Determining the state of the cow's hoof according to the state type, and determining whether to output lameness warning information according to the state of the cow's hoof includes: storing the state type of the cow's hoof; in response to determining that the state types of the cow's hoof within a preset number of days are all abnormal, determining that the cow's hoof state is abnormal, outputting prompt information that the cow's hoof state is abnormal, and outputting lameness warning information.

2. The method for early warning of lameness of dairy cows according to claim 1, characterized in that: The image acquisition device is arranged in the middle of the outlet passage in the width direction.

3. The method for early warning of lameness of dairy cows according to claim 2, characterized in that: The outlet channel is provided with a groove structure in the height direction, and liquid is arranged in the groove structure.

4. The method for early warning of lameness of dairy cows according to any one of claims 1 to 3, characterized in that: The image acquisition device comprises a camera and a protective cover for enclosing the camera, wherein the protective cover is detachably mounted on the camera; The image acquisition device also includes a scraper for cleaning the protective cover, and the scraper is hinged on the protective cover.

5. A dairy cow lameness warning device, characterized in that: The dairy cow lameness warning device comprises: a cow hoof bottom image acquisition module, a cow hoof ear tag data acquisition module, an identity information determination module, a state type determination module and a determination module; The cow hoof bottom image acquisition module is used to acquire the cow hoof bottom image in a natural walking state and store the first time value of acquiring the cow hoof bottom image; wherein, the cow hoof bottom image is acquired by an image acquisition device arranged at the bottom of one end in the length direction of a channel; the channel is the outlet channel of the milking room; the width of the channel is sufficient to accommodate a single cow walking naturally; the image acquisition device is arranged at the bottom of the outlet of the outlet channel; The cow hoof ear tag data acquisition module is used to acquire the cow hoof ear tag data in a natural walking state and store the second time value of acquiring the cow hoof ear tag data; the cow hoof ear tag data is collected by an ear tag recognition device; the ear tag recognition device is arranged on the side of the exit of the exit channel; The identity information determination module is used to, in response to determining that the difference between the first time and the second time is a preset difference, determine the identity information corresponding to the cow's hoof according to the cow's hoof ear tag data; the preset difference is determined according to the interval between the cow's ear and the hoof and the speed at which the cow passes through the exit channel; The state type determination module is used to extract multiple features of the cow hoof bottom image; fuse the multiple features of the cow hoof bottom image to obtain the fusion features of the cow hoof bottom image; determine the state type of the cow hoof according to the fusion features; wherein the state type includes abnormal or normal; the multiple features of the cow hoof bottom image include the morphological features of the cow hoof bottom, the texture features of the cow hoof bottom and the color features of the cow hoof bottom; The determination module is used to determine the state of the cow's hoof according to the state type, and determine whether to output lameness warning information according to the state of the cow's hoof; The method comprises extracting multiple features of the bottom image of the cow hoof; fusing the multiple features of the bottom image of the cow hoof to obtain the fusion features of the bottom image of the cow hoof; and determining the state type of the cow hoof according to the fusion features by using a pre-trained cow hoof state analysis model. Determining the state of the cow's hoof according to the state type, and determining whether to output lameness warning information according to the state of the cow's hoof includes: storing the state type of the cow's hoof; in response to determining that the state types of the cow's hoof within a preset number of days are all abnormal, determining that the cow's hoof state is abnormal, outputting prompt information that the cow's hoof state is abnormal, and outputting lameness warning information.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 4 when executing the program.

7. A dairy cow lameness warning system, characterized in that: It comprises a server equipped with the lameness warning device for dairy cows as claimed in claim 5, an image acquisition device, and an ear tag recognition device; wherein, The image acquisition device is used to acquire an image of the bottom of a cow's hoof in a natural walking state, record a first time value of acquiring the image of the bottom of the cow's hoof; and send the image of the bottom of the cow's hoof and the first time value to the lameness warning device for dairy cows; The ear tag recognition device is used to collect the hoof ear tag data of the dairy cow, record the second time value of obtaining the hoof ear tag data; and send the hoof ear tag data and the second time value to the dairy cow lameness early warning device.

8. The cow lameness warning system according to claim 7, characterized in that: The dairy cow lameness warning system further comprises a display device, and the display device is used to display at least one of the cow hoof status and lameness warning information.

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