A feeding environment information prompt method, device, medium and electronic equipment

Abstract

The present invention discloses a method, device, medium, and electronic device for providing information about a livestock breeding environment. The method comprises: acquiring video data of the livestock breeding environment; wherein the video data covers the entire area of a livestock breeding environment unit; determining the degree of livestock crowding and the type of crowding event within the livestock breeding environment unit using a pre-trained neural network model; and generating information indicating the degree of crowding and the type of crowding event if the degree of crowding and the type of crowding event meet pre-set conditions. By employing the technical solution provided in this application, it is possible to provide notifications about special events occurring in the livestock breeding environment, thereby improving livestock breeding quality.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of digital auxiliary technology for large-scale livestock breeding, and in particular to a method, device, medium and electronic device for providing information about a breeding environment. Background Art

[0002] With the rapid development of science and technology, the application of Internet technology and smart terminals has penetrated into all areas of social life. In the process of livestock feeding, taking pigs as an example, changes in the temperature of the breeding environment will produce significant changes in the feed-to-meat ratio.

[0003] Moreover, since livestock can move freely in a breeding environment, pigs may sometimes be locally crowded due to eating, drinking water or other events. In this case, if the staff is not reminded in time, it will affect the health of the livestock, affect the changes in the livestock's body temperature, and induce potential pig diseases. In more serious cases, pigs may fight to death. Summary of the Invention

[0004] The embodiments of the present application provide a method, device, medium, and electronic device for providing information prompts for a breeding environment, which can provide reminders for special events that occur to livestock in the breeding environment, thereby improving the breeding quality of livestock.

[0005] In a first aspect, an embodiment of the present application provides a method for providing information about a feeding environment, the method comprising:

[0006] Acquire feeding environment video data; wherein the environment video data covers the entire range of the feeding environment unit;

[0007] determining the degree of crowding and the type of crowding events of livestock in the feeding environment unit by a pre-trained neural network model;

[0008] If the congestion level and congestion event type meet the preset conditions, congestion level and congestion event type prompt information is generated.

[0009] Furthermore, the degree of crowding and the type of crowding events of livestock in the feeding environment unit are determined by a pre-trained neural network model, including:

[0010] determining the distance between livestock in the housing environment unit;

[0011] Based on the distances, the Delaunay triangulation algorithm is used to determine the livestock crowding level.

[0012] Furthermore, the determining of the distance between the livestock in the feeding environment unit includes:

[0013] Using a target detection model, identifying livestock in the feeding environment unit and obtaining location information of the livestock;

[0014] The distance between the livestock is determined based on the location information of the livestock in the breeding environment unit. At the same time, each livestock is regarded as a point and two of them are connected to obtain an abstract geometric figure. The type of crowding event is predicted based on the geometric figure.

[0015] Furthermore, the degree of crowding and the type of crowding events of livestock in the feeding environment unit are determined by a pre-trained neural network model, including:

[0016] Determining the degree of crowding of livestock and types of crowding events, as well as crowding-inducing events, in the livestock feeding environment unit using a pre-trained neural network model;

[0017] Correspondingly, if the congestion level and congestion event type meet the preset conditions, a congestion level and congestion event type prompt information is generated, including:

[0018] If the congestion level and the type of congestion event meet the preset conditions, and according to the congestion-inducing event, prompt information of the congestion level and the type of congestion event is determined.

[0019] In a second aspect, an embodiment of the present application further provides an information prompting device for an online breeding environment, the device comprising:

[0020] A video acquisition module is used to acquire the video data of the breeding environment; wherein the environmental video data covers the entire range of the breeding environment unit;

[0021] a crowding degree and crowding event type determination module, configured to determine the crowding degree and crowding event type of the livestock in the feeding environment unit by using a pre-trained neural network model;

[0022] The prompt information generating module is used for generating prompt information of the congestion level and the congestion event type if the congestion level and the congestion event type meet the preset conditions.

[0023] Furthermore, the module for determining the congestion level and congestion event type includes:

[0024] a distance determination unit, configured to determine the distance of the livestock in the feeding environment unit;

[0025] The crowding degree and crowding event type calculation unit is used to determine the crowding degree of livestock according to the distance using a Delaunay triangulation algorithm.

[0026] Furthermore, the distance determination unit is specifically configured to:

[0027] Using a target detection model, identifying livestock in the feeding environment unit and obtaining location information of the livestock;

[0028] The distance between the livestock is determined based on the livestock position information of each livestock in the breeding environment unit.

[0029] Furthermore, the module for determining the congestion level and congestion event type includes:

[0030] an induced event determination unit, configured to determine the degree of crowding of livestock and the type of crowding event, as well as crowding-induced events, in the feeding environment unit using a pre-trained neural network model;

[0031] Correspondingly, the prompt information generation module includes:

[0032] The prompt information determining unit is configured to determine the prompt information of the congestion level and the congestion event type if the congestion level and the congestion event type meet preset conditions and according to the congestion inducing event.

[0033] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the information prompt method for the breeding environment as described in the embodiment of the present application.

[0034] In a fourth aspect, an embodiment of the present application provides a mobile electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for prompting the breeding environment information as described in the embodiment of the present application is implemented.

[0035] The technical solution provided in the embodiments of this application acquires video data of a feeding environment, wherein the video data covers the entire range of a feeding environment unit; determines the degree of livestock crowding and the type of crowding event within the feeding environment unit using a pre-trained neural network model; and generates a notification message regarding the degree of crowding and the type of crowding event if the degree of crowding and the type of crowding event meet preset conditions. By employing the technical solution provided in this application, it is possible to provide notifications regarding special events occurring to livestock within the feeding environment, thereby improving livestock breeding quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of the method for providing information about the feeding environment provided in Example 1 of the present application;

[0037] Figure 2 This is a schematic diagram of the structure of the information prompt device for the breeding environment provided in Example 2 of the present application;

[0038] Figure 3This is a structural diagram of an electronic device provided in Example 4 of the present application. DETAILED DESCRIPTION

[0039] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the present application and are not intended to limit the present application. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions of the present application, not all of the structures.

[0040] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the steps as sequential processes, many of the steps can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0041] Example 1

[0042] Figure 1 This is a flowchart of the information prompt method for the feeding environment provided in Example 1 of the present application. This embodiment can be applied to livestock breeding. The method can be executed by the information prompt device for the feeding environment provided in the embodiment of the present application. The device can be implemented by software and / or hardware and can be integrated into an electronic device.

[0043] like Figure 1 As shown, the feeding environment information prompting method includes:

[0044] S110, obtaining feeding environment video data; wherein the environment video data covers the entire range of the feeding environment unit.

[0045] Among them, livestock may include cattle, sheep, pigs, etc.

[0046] In this solution, a camera can be hung directly above the pen in a normal pig pen scene to record the situation of the pigs in the pen.

[0047] Among them, professional cameras are installed in reasonable locations to ensure coverage, and cameras are used to record the activities of pigs in the pig pens.

[0048] S120. Determine the crowding degree and crowding event type of the livestock in the feeding environment unit using a pre-trained neural network model.

[0049] Pre-trained neural network models can be based on a large number of images of pig pens. Since pigs move freely within the pens, the difference between the pigs' color and the background, or motion detection, can be used to determine that when the number of pigs per unit area reaches a certain level, a crowding situation will occur. The degree of crowding and the type of crowding event can be reflected by the number of pigs per unit area.

[0050] In this solution, optionally, determining the degree of crowding and the type of crowding events of livestock in the feeding environment unit by a pre-trained neural network model includes:

[0051] determining the distance between livestock in the housing environment unit;

[0052] Based on the distances, the Delaunay triangulation algorithm is used to determine the livestock crowding level.

[0053] Among them, the closer the distance between livestock, the higher the degree of crowding and the type of crowding events. The Delaunay triangulation algorithm can be used to quantify the degree of crowding and the type of crowding events to obtain the crowding value.

[0054] Delaunay triangulation is characterized by its ability to maximize the minimum angle, creating a "closest to regular" triangulation, and uniqueness (no four points can be co-circular). Delaunay triangulation is fundamental to representing three-dimensional shapes. Projections of objects can be used to create a two-dimensional visual image, and the two-dimensional Delaunay triangulation can be used to analyze and identify the object or compare it with the real thing. Delaunay triangulation serves as a bridge between computer vision and computer graphics.

[0055] Based on the above technical solution, optionally, determining the distance between the livestock in the feeding environment unit includes:

[0056] Using a target detection model, identifying livestock in the feeding environment unit and obtaining location information of the livestock;

[0057] The distance between the livestock is determined based on the location information of the livestock in the breeding environment unit. At the same time, each livestock is regarded as a point and two of them are connected to obtain an abstract geometric figure. The type of crowding event is predicted based on the geometric figure.

[0058] This solution reads camera images, uses an object detection model to detect pigs, and obtains livestock location information. The distance between each pig and the other pigs in the pen is calculated, and Delaunay triangulation is used to calculate the crowding level of the pigs. This setup accurately determines the degree of crowding within the pen and the type of crowding event.

[0059] S130: If the congestion level and congestion event type meet preset conditions, generate congestion level and congestion event type prompt information.

[0060] After a crowding event is determined to have occurred, if it reaches a certain level, a notification indicating the crowding level and event type can be generated to prompt staff to intervene to prevent livestock health from being affected. Images are used to illustrate the crowding level and event type, while video data (or a collection of continuous images) representing the crowding level and event type is used to illustrate the time of crowding induction.

[0061] In this solution, optionally, determining the degree of crowding and the type of crowding events of livestock in the feeding environment unit by a pre-trained neural network model includes:

[0062] Determining the degree of crowding of livestock and types of crowding events, as well as crowding-inducing events, in the livestock feeding environment unit using a pre-trained neural network model;

[0063] Correspondingly, if the congestion level and congestion event type meet the preset conditions, a congestion level and congestion event type prompt information is generated, including:

[0064] If the congestion level and the type of congestion event meet the preset conditions, and according to the congestion-inducing event, prompt information of the congestion level and the type of congestion event is determined.

[0065] Among them, neural networks are used to extract features and analyze the crowding conditions and event types of pig herds over a period of time, generating a crowding value and event type (overcooling, overheating, fighting), and finally prompting the breeder to intervene in the temperature control in the pen, whether the temperature is too high or too low.

[0066] Since the suitable temperature for pig growth is around 20℃, when the temperature drops by 1℃, 418.4kJ / day of energy is needed, and when the temperature rises by 1℃, 209.2kJ / day of energy is needed. That is, for every 1℃ increase or decrease in temperature, each pig will consume 15 to 33g more feed per day, thereby reducing the utilization rate of feed.

[0067] At the same time, overcrowding of the pigs in each pen will affect the pigs’ feed intake and growth rate. Generally, for pigs weighing less than 25 kg, the appropriate enclosure area for each pig is 0.25 m 2 For pigs weighing between 25 and 50 kg, the suitable enclosure area is 0.52 m 2 For pigs weighing 50 to 75 kg, the appropriate enclosure area is 0.7 m 2 For pigs weighing 75 to 100 kg, the appropriate enclosure area is 0.8 m 2 .

[0068] Therefore, both temperature and the degree of crowding and the type of crowding events are important monitoring indicators during livestock breeding.

[0069] In this solution, we define events requiring strong intervention based on accumulated data and analysis of pig herd behavior. We use three techniques, neural networks, Markov chain regression, and LDA, to classify and predict different events. Based on the level of crowding and the type of crowding event, we prompt the owner to take appropriate temperature intervention measures. For example, if the temperature is too low, adjust it. If fighting occurs, intervene and adjust the pens accordingly.

[0070] The technical solution provided in the embodiments of this application acquires video data of a feeding environment, wherein the video data covers the entire range of a feeding environment unit; determines the degree of livestock crowding and the type of crowding event within the feeding environment unit using a pre-trained neural network model; and generates a notification message regarding the degree of crowding and the type of crowding event if the degree of crowding and the type of crowding event meet preset conditions. By employing the technical solution provided in this application, it is possible to provide notifications regarding special events occurring to livestock within the feeding environment, thereby improving livestock breeding quality.

[0071] Example 2

[0072] Figure 2 This is a schematic diagram of the structure of the information prompt device for the breeding environment provided in Example 2 of this application. Figure 2 As shown, the information prompting device for the breeding environment includes:

[0073] The video acquisition module 210 is used to acquire the video data of the breeding environment; wherein the environmental video data covers the entire range of the breeding environment unit;

[0074] a crowding degree and crowding event type determination module 220, configured to determine the crowding degree and crowding event type of the livestock in the feeding environment unit by using a pre-trained neural network model;

[0075] The prompt information generating module 230 is configured to generate prompt information of the congestion level and the congestion event type if the congestion level and the congestion event type meet preset conditions.

[0076] Furthermore, the congestion level and congestion event type determination module 220 includes:

[0077] a distance determination unit, configured to determine the distance of the livestock in the feeding environment unit;

[0078] The crowding degree and crowding event type calculation unit is used to determine the crowding degree of livestock according to the distance using a Delaunay triangulation algorithm.

[0079] Furthermore, the distance determination unit is specifically configured to:

[0080] Using a target detection model, identifying livestock in the feeding environment unit and obtaining location information of the livestock;

[0081] The distance between the livestock is determined based on the livestock position information of each livestock in the breeding environment unit.

[0082] Furthermore, the congestion level and congestion event type determination module 220 includes:

[0083] an induced event determination unit, configured to determine the degree of crowding of livestock and the type of crowding event, as well as crowding-induced events, in the feeding environment unit using a pre-trained neural network model;

[0084] Accordingly, the prompt information generating module 230 includes:

[0085] The prompt information determining unit is configured to determine the prompt information of the congestion level and the congestion event type if the congestion level and the congestion event type meet preset conditions and according to the congestion inducing event.

[0086] The above-mentioned product can execute the method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method.

[0087] Example 3

[0088] The present application also provides a storage medium containing computer-executable instructions. When the computer-executable instructions are executed by a computer processor, the computer-executable instructions are used to execute a method for providing information about a feeding environment. The method includes:

[0089] Acquire feeding environment video data; wherein the environment video data covers the entire range of the feeding environment unit;

[0090] determining the degree of crowding and the type of crowding events of livestock in the feeding environment unit by a pre-trained neural network model;

[0091] If the congestion level and congestion event type meet the preset conditions, congestion level and congestion event type prompt information is generated.

[0092] Storage medium - any of various types of memory electronic devices or storage electronic devices. The term "storage medium" is intended to include: installation media, such as CD-ROMs, floppy disks, or tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disks or optical storage); registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the computer system in which the program is executed, or it may be located in a different second computer system that is connected to the computer system via a network (such as the Internet). The second computer system may provide program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (e.g., in different computer systems connected via a network). The storage medium may store program instructions (e.g., embodied as a computer program) that can be executed by one or more processors.

[0093] Of course, the storage medium containing computer-executable instructions provided in the embodiment of the present application is not limited to the information prompt operation of the online breeding environment as described above, and can also execute related operations in the information prompt method of the breeding environment provided in any embodiment of the present application.

[0094] Example 4

[0095] An embodiment of the present application provides an electronic device, into which the information prompt device for the breeding environment provided by the embodiment of the present application can be integrated. Figure 3 This is a schematic diagram of the structure of an electronic device provided in Example 4 of this application. Figure 3 As shown, this embodiment provides an electronic device 300, which includes: one or more processors 320; a storage device 310 for storing one or more programs. When the one or more programs are executed by the one or more processors 320, the one or more processors 320 implement the feeding environment information prompt method provided in the embodiment of the present application, which includes:

[0096] Acquire feeding environment video data; wherein the environment video data covers the entire range of the feeding environment unit;

[0097] determining the degree of crowding and the type of crowding events of livestock in the feeding environment unit by a pre-trained neural network model;

[0098] If the congestion level and congestion event type meet the preset conditions, congestion level and congestion event type prompt information is generated.

[0099] Of course, those skilled in the art will appreciate that the processor 320 may also implement the technical solution of the feeding environment information prompt method provided in any embodiment of the present application.

[0100] Figure 3 The electronic device 300 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0101] like Figure 3 As shown, the electronic device 300 includes a processor 320, a storage device 310, an input device 330, and an output device 340; the number of processors 320 in the electronic device can be one or more. Figure 3 In the figure, a processor 320 is used as an example; the processor 320, the storage device 310, the input device 330 and the output device 340 in the electronic device can be connected via a bus or other means. Figure 3 The connection via bus 350 is taken as an example.

[0102] The storage device 310 is a computer-readable storage medium that can be used to store software programs, computer-executable programs, and module units, such as program instructions corresponding to the breeding environment information prompt method in the embodiment of the present application.

[0103] The storage device 310 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the terminal. Furthermore, the storage device 310 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some instances, the storage device 310 may further include memory remotely located relative to the processor 320, and such remote memory may be connected via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0104] The input device 330 may be used to receive input numbers, character information or voice information, and generate key signal input related to user settings and function control of the electronic device. The output device 340 may include electronic devices such as a display screen and a speaker.

[0105] The electronic device provided in the embodiment of the present application can provide reminders for special events that occur to livestock in a breeding environment, thereby improving the breeding quality of livestock.

[0106] The feeding environment information display device, medium, and electronic device provided in the above embodiments can execute the feeding environment information display method provided in any embodiment of the present application, and have the corresponding functional modules and beneficial effects of executing the method. For technical details not fully described in the above embodiments, please refer to the feeding environment information display method provided in any embodiment of the present application.

[0107] Note that the above are only preferred embodiments of the present application and the technical principles employed. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present application. The scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for providing information about a breeding environment, characterized in that: include: Acquire feeding environment video data; wherein the environment video data covers the entire range of the feeding environment unit; The crowding level and crowding event type of livestock in the feeding environment unit are determined by a pre-trained neural network model, including: Using a target detection model, identifying livestock in the feeding environment unit and obtaining location information of the livestock; determining the distance between the livestock based on the location information of each livestock in the feeding environment unit; Based on the distance, the livestock crowding degree is determined using the Delaunay triangulation algorithm; Taking each livestock as a point, connecting two of them with lines to obtain an abstract geometric figure, and predicting the type of crowding event based on the geometric figure; Determining the degree of crowding of livestock and types of crowding events, as well as crowding-inducing events, in the livestock feeding environment unit using a pre-trained neural network model; Correspondingly, if the congestion level and congestion event type meet the preset conditions, a congestion level and congestion event type prompt information is generated, including: Based on accumulated data and pig herd habits, neural networks, Markov chain theorem and LDA technology are used to classify and predict different events, and breeders are prompted to take corresponding temperature intervention measures based on the degree of crowding and the type of crowding event.

2. A feeding environment information prompting device, characterized in that: include: A video acquisition module is used to acquire the video data of the breeding environment; wherein the environmental video data covers the entire range of the breeding environment unit; a crowding degree and crowding event type determination module, configured to determine the crowding degree and crowding event type of livestock in the feeding environment unit using a pre-trained neural network model, including: determining the distance between livestock in the feeding environment unit, and determining the crowding degree of the livestock based on the distance using a Delaunay triangulation algorithm; identifying the livestock in the feeding environment unit using a target detection model and obtaining livestock position information; determining the distance between the livestock based on the livestock position information of each livestock in the feeding environment unit, and simultaneously taking each livestock as a point and connecting two of them to obtain an abstract geometric figure, and predicting the type of crowding event based on the geometric figure; a prompt information generating module, configured to generate prompt information of the congestion level and the congestion event type if the congestion level and the congestion event type meet preset conditions; an induced event determination unit, configured to determine the degree of crowding of livestock and the type of crowding event, as well as crowding-induced events, in the feeding environment unit using a pre-trained neural network model; Correspondingly, the prompt information generation module includes: The prompt information determination unit is used to determine the prompt information of the crowding degree and the crowding event type if the crowding degree and the crowding event type meet the preset conditions and according to the crowding-inducing event; based on the accumulated data and the habits of the pig herd, use the neural network, Markov chain rule and LDA technology to classify and predict different events, and prompt the breeder to take corresponding intervention measures on the temperature according to the crowding degree and the crowding event type.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the feeding environment information prompting method according to claim 1 is implemented.

4. A mobile electronic device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein: When the processor executes the computer program, the feeding environment information prompting method according to claim 1 is implemented.

Images