Method and system for monitoring herd livestock

Abstract

The invention provides a herd livestock monitoring method and system, and relates to the technical field of livestock monitoring, and the method comprises the steps: determining a grazing region, and carrying out the grid division of the grazing region, and obtaining a plurality of grid regions; a corresponding communication unmanned aerial vehicle is configured for each grid area; acquiring positioning data periodically sent to the corresponding communication unmanned aerial vehicle by the necklace worn by the herd livestock; analyzing the positioning data, and judging whether an off-management event exists or not; if yes, determining a final position area of the herd livestock corresponding to the tube removal event; on the basis of the final position area, predicting a pipe separation advancing area; generating a search path based on the demanagement advancing area, and sending the search path to the search unmanned aerial vehicle; the searching unmanned aerial vehicle is controlled to search herd livestock in the demanagement advancing area according to the searching path, and a searching result is fed back; unmanned monitoring of the grazing herd livestock can be realized, and the livestock in the grazing herd can be timely found out and searched for the livestock in the grazing herd.

Description

Technical Field

[0001] The present invention relates to the technical field of livestock monitoring, and in particular to a method and system for monitoring herd livestock. Background Art

[0002] Limited by the extreme cold and natural conditions of the high altitude, livestock farming is extremely risky and economically inefficient. While residential areas have gained access to roads, water, electricity, and internet, large areas of pastureland remain without electricity and internet. Consequently, family-run pastures, characterized by settled grazing (rotational grazing within fixed pastures) and semi-settled nomadism (nomadism within large, shared pastures), remain the predominant livestock production models in the Tibetan plateau. These pastoral practices are limited by vast areas, long distances, and difficult mountain roads, making them difficult to locate lost livestock. Summary of the Invention

[0003] In response to the above-mentioned problems, the present invention proposes a method and system for monitoring herds of livestock, which solves the technical problems in the prior art that grazing breeding is faced with constraints such as wide areas, long distances, and dangerous mountain roads, which make it difficult to search for lost livestock and to conduct manual intervention. The method and system can realize unmanned monitoring of grazing herds of livestock, promptly detect herds of livestock that have escaped from control, and search for herds of livestock that have escaped from control.

[0004] An embodiment of the present invention provides a method for monitoring livestock in a herd, comprising:

[0005] Determine the grazing area and divide the grazing area into grids to obtain several grid areas;

[0006] Configure a corresponding communication drone for each grid area;

[0007] Obtain positioning data periodically sent by collars worn by livestock to corresponding communication drones;

[0008] Analyzing the positioning data to determine whether there is a pipe-out event;

[0009] If there is an escape event, determine the final location area of the herd livestock corresponding to the escape event;

[0010] Based on the final location area, predict the escape area;

[0011] Generate a search path based on the uncontrolled travel area and send it to the search drone;

[0012] The search drone is controlled to search for livestock in the uncontrolled travel area according to the search path, and the search results are fed back.

[0013] In some embodiments, the determining of the grazing area and dividing the grazing area into grids to obtain a plurality of grid areas include:

[0014] Determine the length and width of the grid area based on the communication range of the communication drone;

[0015] The grazing area is divided into grids according to the length and width of the grid areas, and the number of grid areas is determined.

[0016] In some embodiments, including:

[0017] The communication drone is configured at the center coordinates of the grid area.

[0018] In some embodiments, if there is an escape event, determining the final location area of the herd livestock corresponding to the escape event includes:

[0019] Preprocess the last set of collected positioning data;

[0020] Based on the pre-processed positioning data, the probability density function is used to estimate the range of movement of livestock before they are released from control:

[0021]

[0022] Where f(x,y) represents the probability density function of the herd livestock appearing at different locations (x,y) in the grazing area, N is the number of positioning data points before the release of management, (x i ,y i ) is the coordinate of the i-th positioning data point, δ is the Dirac function;

[0023] The high probability density area is determined as the final location area.

[0024] In some embodiments, predicting the off-control travel area based on the final location area includes:

[0025] Based on the final location area, determine the movement trajectory of the herd livestock before they are released from control;

[0026] Based on the movement trajectory and speed direction of the livestock before they escape, the escape area is predicted:

[0027]

[0028] Where A P It is the off-control travel area. is the velocity vector in the x direction, is the velocity vector in the y direction, t is the time, (x f ,y f ) is the final position coordinate before the tube is removed, T P is the forecast time range.

[0029] In some embodiments, including:

[0030] Determine the forecast time horizon based on the average movement speed of grazing livestock and the maximum search distance you want to cover:

[0031]

[0032] Where D max is the maximum search distance, and v is the average moving speed of grazing livestock.

[0033] In some embodiments, including:

[0034] The positioning data is obtained through the positioning collars worn by livestock, and the displacement and time interval between two adjacent positioning points are calculated to update the average movement speed:

[0035]

[0036] Where Δd i is the displacement in the i-th time period, Δt i The time interval in the i-th time period, n is the number of positioning data points.

[0037] In some embodiments, generating a search path based on the off-control travel area and sending the search path to the search drone includes:

[0038] Analyze the geographical scope of the out-of-control area and obtain key coordinate points on its boundary;

[0039] Divide the off-pipe travel area into a number of search grids, and determine the center coordinates of each search grid based on key coordinate points on the boundary of the off-pipe travel area;

[0040] Connect the center coordinates of each search grid in sequence to generate a search path and send it to the search drone.

[0041] An embodiment of the present invention provides a livestock monitoring system, comprising:

[0042] A division module is used to determine the grazing area and divide the grazing area into grids to obtain a number of grid areas;

[0043] Configuration module, used to configure a corresponding communication drone for each grid area;

[0044] An acquisition module is used to obtain positioning data periodically sent by collars worn by livestock to corresponding communication drones;

[0045] An analysis module is used to analyze the positioning data to determine whether there is a pipe-out event;

[0046] A determination module, configured to determine the final location area of the herd livestock corresponding to the escape event if an escape event occurs;

[0047] A prediction module, used to predict the off-control travel area based on the final location area;

[0048] A generation module is used to generate a search path based on the off-control travel area and send it to the search UAV;

[0049] The control module is used to control the search drone to search for livestock in the uncontrolled travel area according to the search path and to feed back the search results.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] By determining the grazing area and dividing the grazing area into grids, a number of grid areas are obtained; a corresponding communication drone is configured for each grid area; positioning data periodically sent by collars worn by livestock to the corresponding communication drone is obtained; the positioning data is analyzed to determine whether there is an escape event; if there is an escape event, the final location area of the livestock corresponding to the escape event is determined; based on the final location area, the escape travel area is predicted; based on the escape travel area, a search path is generated and sent to a search drone; the search drone is controlled to search for livestock in the escape travel area according to the search path, and the search results are fed back; unmanned monitoring of grazing livestock can be achieved, escaped livestock can be discovered in a timely manner, and escaped livestock can be searched. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The embodiments of the present invention are further described below with reference to the accompanying drawings:

[0053] Figure 1 A schematic diagram of a process for implementing a method for monitoring livestock herds provided by an embodiment of the present invention;

[0054] Figure 2 A schematic structural diagram of a livestock monitoring system provided by an embodiment of the present invention;

[0055] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0057] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0058] If similar descriptions of "first\second\third" appear in the application documents, the following explanation is added. In the following description, the terms "first\second\third" are only used to distinguish similar objects and do not represent a specific order of the objects. It can be understood that "first\second\third" can be interchanged with the specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention pertains. The terms used herein are for the purpose of describing embodiments of the present invention only and are not intended to limit the present invention.

[0060] Based on the problems existing in the related art, an embodiment of the present invention provides a method for monitoring herd livestock, and the execution subject of the monitoring method can be an electronic device. The electronic device can be various types of terminals such as laptops, tablet computers, desktop computers, set-top boxes, mobile devices (for example, mobile phones, portable music players, personal digital assistants, dedicated messaging devices, portable gaming devices), etc., and can also be implemented as a server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0061] In some embodiments, the functions implemented by the monitoring method provided by the embodiments of the present invention can be implemented by calling program codes by a processor of an electronic device, wherein the program codes can be stored in a computer storage medium.

[0062] The embodiment of the present invention provides a method for monitoring livestock in a herd. Figure 1 A schematic diagram of a method for monitoring livestock herds provided by an embodiment of the present invention is provided. Figure 1 As shown, including:

[0063] Step S1: determine the grazing area and divide the grazing area into grids to obtain several grid areas;

[0064] In some embodiments, step S1 includes:

[0065] Step S11: Determine the length and width of the grid area according to the communication range of the communication drone;

[0066] Step S12: Divide the grazing area into grids according to the length and width of the grid areas, and determine the number of grid areas.

[0067] In this embodiment of the present invention, the grazing area is typically set to a rectangular shape. The communication range of a communication drone is typically a circle with a certain radius, centered around itself. Therefore, the length and width of the grid area can be determined by drawing a square with the largest area within the circle. The length and width of the square serve as the length and width of the grid area. After determining the length and width of the grid area, the grazing area is divided into grids to determine the number of grid areas.

[0068] Step S2: assign a corresponding communication drone to each grid area;

[0069] In some embodiments, including:

[0070] The communication drone is configured at the center coordinates of the grid area.

[0071] In an embodiment of the present invention, the center coordinates of the grid area can be determined by the coordinates of the lower left corner and the upper right corner of the grid area, and then the communication drone can be configured at the center coordinates of the grid area so that the communication range of the communication drone fully covers the corresponding grid area.

[0072] Step S3: Acquire positioning data periodically sent by collars worn by livestock to corresponding communication drones;

[0073] In this embodiment of the present invention, a communication drone receives positioning data periodically transmitted from collars worn by livestock within a corresponding grid area. It is understood that the positioning data includes an identity identifier representing the livestock's identity, and the positioning data includes location information representing the livestock's coordinates. Each communication drone within the grid area stores the received positioning data, and a backend system periodically retrieves the positioning data of each communication drone for subsequent processing.

[0074] Step S4: Analyze the positioning data to determine whether there is a pipe-out event;

[0075] In this embodiment of the present invention, all positioning data is analyzed to determine whether there is positioning data corresponding to any livestock that has not been received. If there is positioning data corresponding to any livestock, it is determined that a loss of control event has occurred. At this point, an identity identifier corresponding to the loss of control event can be obtained, and then the last set of positioning data corresponding to the identity identifier can be retrieved to facilitate subsequent processing.

[0076] Step S5: If there is an escape event, determine the final location area of the herd livestock corresponding to the escape event;

[0077] In some embodiments, if there is an escape event, determining the final location area of the herd livestock corresponding to the escape event includes:

[0078] Step S51: pre-processing the last set of positioning data collected;

[0079] Step S52: Based on the pre-processed positioning data, the activity range of the herd livestock before being released from the control is evaluated using the probability density function:

[0080]

[0081] Where f(x,y) represents the probability density function of the herd livestock appearing at different locations (x,y) in the grazing area, N is the number of positioning data points before the release of management, (x i ,y i ) is the coordinate of the i-th positioning data point, δ is the Dirac function;

[0082] Step S53: Determine the high probability density area as the final location area.

[0083] In this embodiment of the present invention, because the communication ranges of communication drones may overlap or contain errors, the final set of collected positioning data must be preprocessed. This preprocessing includes deduplication and removal of outliers to ensure data accuracy. A probability density function is then used to evaluate the range of movement of the livestock before they were released from surveillance. Areas with high probability density are used as the final location areas to determine the livestock's movement trajectory before they were released from surveillance.

[0084] Step S6: Based on the final location area, predict the off-pipe travel area;

[0085] In some embodiments, step S6 includes:

[0086] Step S61: determining the movement trajectory of the herd livestock before they escape from the control according to the final location area;

[0087] Step S62: Based on the movement trajectory and speed direction of the livestock before they escape, predict the escape area:

[0088]

[0089] Where A P It is the off-control travel area. is the velocity vector in the x direction, is the velocity vector in the y direction, t is the time, (x f ,y f ) is the final position coordinate before the tube is removed, T P is the forecast time range.

[0090] In some embodiments, including:

[0091] Determine the forecast time horizon based on the average movement speed of grazing livestock and the maximum search distance you want to cover:

[0092]

[0093] Where D max is the maximum search distance, and v is the average moving speed of grazing livestock.

[0094] In some embodiments, including:

[0095] The positioning data is obtained through the positioning collars worn by livestock, and the displacement and time interval between two adjacent positioning points are calculated to update the average movement speed:

[0096]

[0097] Where Δd i is the displacement in the i-th time period, Δt i The time interval in the i-th time period, n is the number of positioning data points.

[0098] In embodiments of the present invention, the movement trajectory of livestock before they escape can be determined based on the final location area, i.e., the direction of escape can be determined. A predicted time range can be determined based on the average movement speed of the livestock and the maximum search distance desired to be covered. The average movement speed can be updated based on the displacement and time interval between two adjacent positioning points calculated from all positioning data. Once the escape direction, predicted time range, and average movement speed are determined, the escape area can be predicted.

[0099] Step S7: Generate a search path based on the off-control travel area and send it to the search UAV;

[0100] In some embodiments, generating a search path based on the off-control travel area and sending the search path to the search drone includes:

[0101] Analyze the geographical scope of the out-of-control area and obtain key coordinate points on its boundary;

[0102] Divide the off-pipe travel area into a number of search grids, and determine the center coordinates of each search grid based on key coordinate points on the boundary of the off-pipe travel area;

[0103] Connect the center coordinates of each search grid in sequence to generate a search path and send it to the search drone.

[0104] In an embodiment of the present invention, the off-control travel area is divided into several search grids, and then the center coordinates of each search grid are determined based on the key coordinate points on the boundary of the off-control travel area. The center coordinates of the search grids are connected in sequence to generate a search path, and the search path is sent to the search drone.

[0105] Step S8: Control the search drone to search for livestock in the uncontrolled travel area according to the search path, and feed back the search results.

[0106] In an embodiment of the present invention, a search drone searches for livestock within an area where they have escaped from control, following a search path. The drone can establish a communication connection with the collars worn by the livestock and receive positioning data from the collars. After receiving the positioning data, a first search result is generated, indicating that the livestock corresponding to the escape event has been found. If no positioning data is received after the search is complete, a second search result is generated, indicating that the livestock corresponding to the escape event has not been found. This enables unmanned monitoring of grazing livestock, allowing for the timely detection and search of escaped livestock.

[0107] Based on the foregoing embodiments, an embodiment of the present invention provides a herd livestock monitoring system. The modules included in the system, and the units included in each module, can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; during implementation, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0108] The embodiment of the present invention provides a livestock monitoring system. Figure 2 A schematic diagram of the structure of a fault detection device for a hydraulic turbine according to an embodiment of the present invention is shown in FIG. Figure 2 As shown, including:

[0109] A division module is used to determine the grazing area and divide the grazing area into grids to obtain a number of grid areas;

[0110] Configuration module, used to configure a corresponding communication drone for each grid area;

[0111] An acquisition module is used to obtain positioning data periodically sent by collars worn by livestock to corresponding communication drones;

[0112] An analysis module is used to analyze the positioning data to determine whether there is a pipe-out event;

[0113] A determination module, configured to determine the final location area of the herd livestock corresponding to the escape event if an escape event occurs;

[0114] A prediction module, used to predict the off-control travel area based on the final location area;

[0115] A generation module is used to generate a search path based on the off-control travel area and send it to the search UAV;

[0116] The control module is used to control the search drone to search for livestock in the uncontrolled travel area according to the search path and to feed back the search results.

[0117] In some embodiments, the partitioning module includes:

[0118] A first determining unit is configured to determine the length and width of the grid area according to the communication range of the communication drone;

[0119] The first division unit is configured to divide the grazing area into grids according to the length and width of the grid areas, and determine the number of grid areas.

[0120] In some embodiments, including:

[0121] The communication drone is configured at the center coordinates of the grid area.

[0122] In some embodiments, the determining module includes:

[0123] A preprocessing unit, used for preprocessing the last set of collected positioning data;

[0124] The evaluation unit is used to evaluate the activity range of the herd livestock before they are released from the control system by using a probability density function based on the pre-processed positioning data:

[0125]

[0126] Where f(x,y) represents the probability density function of the herd livestock appearing at different locations (x,y) in the grazing area, N is the number of positioning data points before the release of management, (x i ,y i ) is the coordinate of the i-th positioning data point, δ is the Dirac function;

[0127] The second determining unit is configured to determine the high probability density area as the final location area.

[0128] In some embodiments, the prediction module includes:

[0129] A third determining unit is used to determine the movement trajectory of the herd livestock before they escape from the control according to the final location area;

[0130] The prediction unit is used to predict the area where the herd of livestock will escape from the control according to their movement trajectory and speed direction before they escape from the control:

[0131]

[0132] Where A P It is the off-control travel area. is the velocity vector in the x direction, is the velocity vector in the y direction, t is the time, (x f ,y f ) is the final position coordinate before the tube is removed, T P is the forecast time range.

[0133] In some embodiments, including:

[0134] Determine the subunits for determining the forecast time horizon based on the average movement speed of grazing livestock and the maximum search distance you wish to cover:

[0135]

[0136] Where D max is the maximum search distance, and v is the average moving speed of grazing livestock.

[0137] In some embodiments, including:

[0138] The calculation subunit is used to obtain positioning data from the positioning collars worn by livestock, calculate the displacement and time interval between two adjacent positioning points, and update the average movement speed:

[0139]

[0140] Where Δd i is the displacement in the i-th time period, Δt i The time interval in the i-th time period, n is the number of positioning data points.

[0141] In some embodiments, the generating module includes:

[0142] An analysis unit is used to analyze the geographical scope of the out-of-control travel area and obtain key coordinate points on its boundary;

[0143] A second division unit is configured to divide the off-pipe travel area into a plurality of search grids, and determine the center coordinates of each search grid based on key coordinate points on the boundary of the off-pipe travel area;

[0144] The generation unit is used to connect the center coordinates of each search grid in sequence, generate a search path, and send it to the search drone.

[0145] It should be noted that, in the embodiment of the present invention, if the above-mentioned monitoring method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a magnetic disk or an optical disk. In this way, the embodiment of the present invention is not limited to any specific combination of hardware and software.

[0146] Accordingly, an embodiment of the present invention provides a storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the monitoring method provided in the above embodiment are implemented.

[0147] An embodiment of the present invention provides an electronic device; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present invention is shown in FIG. Figure 3 As shown, the electronic device 400 includes: a processor 401, at least one communication bus 402, a user interface 403, at least one external communication interface 404, and a memory 405. The communication bus 402 is configured to enable communication between these components. The user interface 403 may include a display screen, and the external communication interface 404 may include a standard wired interface and a wireless interface. The processor 401 is configured to execute the monitoring method program stored in the memory to implement the steps of the monitoring method provided in the above embodiment.

[0148] It should be noted that the descriptions of the above storage medium and electronic device embodiments are similar to the descriptions of the above method embodiments and have similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of the present invention, please refer to the descriptions of the method embodiments of the present invention for understanding.

[0149] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments.

[0150] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, object, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, object, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of other identical elements in the process, method, object, or apparatus comprising the element.

[0151] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0152] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0153] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0154] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), magnetic disks or optical disks, and other media that can store program codes.

[0155] Alternatively, if the integrated unit of the present invention is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes a number of instructions for causing a controller to execute all or part of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0156] The above description is merely an embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for monitoring livestock herds, characterized in that: include: Determine the grazing area and divide the grazing area into grids to obtain several grid areas; Configure a corresponding communication drone for each grid area; Obtain positioning data periodically sent by collars worn by livestock to corresponding communication drones; Analyzing the positioning data to determine whether there is a pipe-out event; If there is an escape event, determine the final location area of the herd livestock corresponding to the escape event; Based on the final location area, predict the escape area; Generate a search path based on the uncontrolled travel area and send it to the search drone; The search drone is controlled to search for livestock in the uncontrolled travel area according to the search path, and the search results are fed back.

2. The method for monitoring livestock according to claim 1, wherein: The grazing area is determined and divided into grids to obtain a number of grid areas, including: Determine the length and width of the grid area based on the communication range of the communication drone; The grazing area is divided into grids according to the length and width of the grid areas, and the number of grid areas is determined.

3. The method for monitoring livestock according to claim 1, wherein: include: The communication drone is configured at the center coordinates of the grid area.

4. The method for monitoring livestock according to claim 1, wherein: If there is an escape event, determining the final location area of the herd livestock corresponding to the escape event includes: Preprocess the last set of collected positioning data; Based on the pre-processed positioning data, the probability density function is used to estimate the range of movement of livestock before they are released from control: Where f(x,y) represents the probability density function of the herd livestock appearing at different locations (x,y) in the grazing area, N is the number of positioning data points before the release of management, (x i ,y i ) is the coordinate of the i-th positioning data point, δ is the Dirac function; The high probability density area is determined as the final location area.

5. The method for monitoring livestock according to claim 4, characterized in that: The method of predicting the off-control travel area based on the final location area includes: Based on the final location area, determine the movement trajectory of the herd livestock before they are released from control; Based on the movement trajectory and speed direction of the livestock before they escape, the escape area is predicted: Where A P It is the off-control travel area. is the velocity vector in the x direction, is the velocity vector in the y direction, t is the time, (x f ,y f ) is the final position coordinate before the tube is removed, T P is the forecast time range.

6. The method for monitoring livestock according to claim 5, characterized in that: include: Determine the forecast time horizon based on the average movement speed of grazing livestock and the maximum search distance you want to cover: Where D max is the maximum search distance, and v is the average moving speed of grazing livestock.

7. The method for monitoring livestock according to claim 5, characterized in that: include: The positioning data is obtained through the positioning collars worn by livestock, and the displacement and time interval between two adjacent positioning points are calculated to update the average movement speed: Where Δd i is the displacement in the i-th time period, Δt i The time interval in the i-th time period, n is the number of positioning data points.

8. The method for monitoring livestock herds according to claim 1, wherein: The generating of a search path based on the off-control travel area and sending the path to the search UAV includes: Analyze the geographical scope of the out-of-control area and obtain key coordinate points on its boundary; Divide the off-pipe travel area into a number of search grids, and determine the center coordinates of each search grid based on key coordinate points on the boundary of the off-pipe travel area; Connect the center coordinates of each search grid in sequence to generate a search path and send it to the search drone.

9. A livestock monitoring system, characterized in that: include: A division module is used to determine the grazing area and divide the grazing area into grids to obtain a number of grid areas; Configuration module, used to configure a corresponding communication drone for each grid area; An acquisition module is used to obtain positioning data periodically sent by collars worn by livestock to corresponding communication drones; An analysis module is used to analyze the positioning data to determine whether there is a pipe-out event; A determination module, configured to determine the final location area of the herd livestock corresponding to the escape event if an escape event occurs; A prediction module, used to predict the off-control travel area based on the final location area; A generation module is used to generate a search path based on the off-control travel area and send it to the search UAV; The control module is used to control the search drone to search for livestock in the uncontrolled travel area according to the search path and to feed back the search results.

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