Intelligent estrus identification method and system for animals

Abstract

The application discloses an intelligent estrus identification method and system, and belongs to the technical field of animal estrus identification. The method comprises the following steps: obtaining estrus indication data of animals, obtaining estrus indication reference data, and forming an estrus indication reference data-time change graph; constructing an estrus reference model according to the estrus conditions of the animals in a set time period; and comparing the estrus indication reference data-time change graph with the estrus reference model to predict the estrus conditions of a to-be-tested animal individual.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of animal estrus identification, and particularly relates to an intelligent animal estrus identification method and system. BACKGROUND

[0002] At present, the commonly used estrus identification method in actual production is still mainly the conventional estrus identification method, including external observation method, estrus test method, vaginal examination method, rectal examination method, bionics method and the like. These methods mainly rely on the observation and feeling ability of breeders, and thus the detection result is limited by the experience of breeders and the estrus performance intensity of female animals, and has strong subjectivity and inaccuracy. Observation of behavior performance is a common method, but it is easily affected by the feeding environment, individual differences and subjective judgment of human beings. Estrus identification using observation of behavior or other non-automatic methods requires a large amount of manpower and time input. This is a challenge for large-scale breeding farms or farms, which may lead to low efficiency and rising production cost. The operation of some estrus detection devices is complex, and requires personnel with professional knowledge and skills to operate and interpret the results. This may limit the use range of some breeders or farmers, and increase the demand for training and technical support.

[0003] In addition, with the rapid development of livestock breeding, the breeding methods and means for large animals such as cattle and sheep are constantly updated in breeding farms, but most breeding farms still use manual methods to manage sheep, do not use corresponding equipment to collect sheep daily activity data, and the management process is complicated, the breeding cost is high, and at the same time, the accuracy of the collected data is not compared. SUMMARY

[0004] In order to achieve the above purpose, the application is realized by the following technical scheme:

[0005] In the first aspect, the application provides an intelligent animal estrus identification method, comprising the following steps:

[0006] Obtaining estrus indication data of animals, obtaining estrus indication reference data, and forming an estrus indication reference data-time change graph;

[0007] According to the estrus condition of the animals in the set time period, an estrus reference model is constructed;

[0008] According to the comparison between the estrus indication reference data-time change graph and the estrus reference model, the estrus condition of the to-be-tested animal individual is predicted.

[0009] As a further technical scheme, the estrus indication data includes body temperature, exercise amount and feeding time, and the estrus indication reference data includes body temperature reference data, exercise amount reference data and feeding time reference data.

[0010] As a further technical solution, the estrus indication data is obtained by a plurality of detection devices, the same estrus indication data obtained by the plurality of detection devices is compared, abnormal data is eliminated, and the average value of the remaining data is taken as the estrus indication reference data.

[0011] The detection device includes a wearing detection module, an infrared detection module, and a molecular identity card, and each detection device communicates with the data processing terminal and transmits corresponding data to the data processing terminal.

[0012] As a further technical solution, the first estrus indication data of the animal collected by the wearing detection module is taken as the first detection result, and the first detection result is transmitted to the data processing terminal, and the data processing terminal generates a first estrus indication data-time change graph.

[0013] The second estrus indication data of the animal collected by the infrared detection module is taken as the second detection result, and the second detection result is transmitted to the data processing terminal, and the data processing terminal generates a second estrus indication data-time change graph.

[0014] The third estrus indication data of the animal collected by the molecular identity card is taken as the third detection result, and the third detection result is transmitted to the data processing terminal, and the data processing terminal generates a third estrus indication data-time change graph.

[0015] As a further technical solution, the process of obtaining the body temperature reference data is as follows:

[0016] The body temperature data measured in the first detection result, the second detection result, and the third detection result is processed in the data processing terminal, the body temperature-time change graph in the first estrus indication data-time change graph, the second estrus indication data-time change graph, and the third estrus indication data-time change graph is compared, the body temperature data change amplitude is set according to different use sites and environmental climate, the abnormal body temperature data is eliminated, the average value of the remaining body temperature data is taken as the body temperature reference data, and a body temperature reference data-time change graph is obtained.

[0017] As a further technical solution, the process of obtaining the feeding time reference data is as follows:

[0018] The feeding time data measured in the first detection result and the second detection result is processed in the data processing terminal, the feeding time-time change graph in the first estrus indication data-time change graph and the second estrus indication data-time change graph is compared, the feeding time data change amplitude is set according to different use sites and environmental climate, the abnormal feeding time data is eliminated, the average value of the remaining feeding time data is taken as the feeding time reference data, and a feeding time reference data-time change graph is obtained.

[0019] As a further technical solution, the process of obtaining the movement reference data is as follows:

[0020] The movement and movement trajectory data determined in the first detection result and the third detection result are processed in the data processing terminal, the movement-time change graph in the first estrus indication data-time change graph and the third estrus indication data-time change graph is compared, and the movement trajectory of the target animal is checked, wherein the movement trajectories of the first detection result and the third detection result should be consistent, which is the movement trajectory checking data, if there is a difference, check whether the equipment is lost or damaged, check whether the sheep is lost, set the movement data change range according to different use sites and environmental climate, remove the movement data with abnormal change range, and take the average value of the remaining movement data as the movement reference data, and obtain the movement reference data-time change graph.

[0021] As a further technical solution, the process of constructing the estrus reference model is as follows: in a set time period in different seasons, the estrus conditions of a plurality of animals are determined as the estrus reference model; the estrus condition of an animal is estrus or non-estrus; the estrus indication data corresponding to the estrus of the animal in the estrus reference model is stored;

[0022] The process of predicting the estrus condition of the individual animal to be measured is as follows:

[0023] The estrus indication reference data-time change graph of the same season and the estrus indication data corresponding to the estrus of the animal in the estrus reference model are compared, if the data in the corresponding interval of the estrus indication reference data-time change graph and the estrus indication data corresponding to the estrus of the animal in the estrus reference model can be matched, it indicates that the corresponding animal is in estrus state.

[0024] As a further technical solution, the wearing detection module is fixed on the animal body through a strap; the molecular identity card stores the DNA, RNA and body weight data of the target animal, a body weight scale is arranged at the animal path in the animal farm, the body weight change of the animal is monitored and the data is sent to the molecular identity card and the data processing terminal; the data processing terminal carries out health monitoring, movement evaluation, movement trajectory analysis and feeding time monitoring on the animal according to the estrus indication data.

[0025] In a second aspect, the present application also provides an intelligent animal estrus identification system, comprising:

[0026] The first module is used for obtaining the estrus indication data of the animal, obtaining the estrus indication reference data, and forming the estrus indication reference data-time change graph;

[0027] The second module is used for constructing the estrus reference model according to the estrus condition of the animal in a set time period;

[0028] The third module is configured to compare the estrus indication reference data-time variation diagram and the estrus reference model to predict the estrus condition of the animal individual.

[0029] The beneficial effects of the present application are as follows:

[0030] The intelligent animal estrus identification method can determine the body temperature, the amount of exercise, the exercise track, the behavior, the feeding time and other data of the target sheep from different angles through the combination of multiple devices, and then accurately determine the data by comparing the data collected by different devices, thereby solving the problem that the data cannot be determined whether it is correct or not due to the influence of too many external factors in the traditional single device determination, and improving the accuracy of the collected data.

[0031] The intelligent animal estrus identification method can quickly identify the estrus of the target animal by using the accurate data obtained through the combination of multiple devices, improve the accuracy of estrus identification, improve the efficiency of breeding, monitor the health condition of the animal, help the breeder to find and treat the disease or abnormal condition in time, improve the productivity and health level of the animal, and thereby reduce the labor cost.

[0032] The intelligent animal estrus identification method can more conveniently identify the feeding, body temperature change, exercise and other behavior characteristics of the animal individual, conveniently master the accurate position, body condition and activity information of each animal in real time, and perform estrus identification, disease monitoring and other treatments on the target animal, can find problems in time, participate in advance, efficiently manage each animal, greatly improve the work efficiency and standardized breeding of the animal, save a large amount of breeding cost, and has a wide market application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0033] The drawings accompanying the specification of the present application serve to provide a further understanding of the present application, and the illustrative embodiments of the present application and their descriptions serve to explain the present application, and do not constitute an improper limitation on the present application.

[0034] Figure 1 is a flowchart of the intelligent animal estrus identification method according to one or more embodiments of the present application;

[0035] In the drawings, the mutual distance or size is exaggerated to show the position of each part, and the schematic diagram is only used for illustration. DETAILED DESCRIPTION

[0036] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present application have the same meaning as generally understood by those skilled in the art to which the present application belongs.

[0037] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0038] For the convenience of description, if the terms of "upper", "lower", "left", "right" are used in the present application, they only mean the same direction as the upper, lower, left and right directions of the drawings themselves, and do not limit the structure, but only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0039] In a typical embodiment of the present application, as shown in Figure 1 An intelligent estrus identification method for animals is provided, comprising the following steps:

[0040] Obtaining estrus indication data of the animal, obtaining estrus indication reference data, and forming an estrus indication reference data-time change graph;

[0041] According to the estrus condition of the animal in the set time period, an estrus reference model is constructed;

[0042] According to the estrus indication reference data-time change graph and the estrus reference model, the estrus condition of the animal to be tested is predicted.

[0043] It should be noted that the animal in the present application can be sheep, cattle, pigs, etc.

[0044] The estrus indication data includes body temperature, exercise amount, feeding time, and the estrus indication reference data includes body temperature reference data, exercise amount reference data, and feeding time reference data.

[0045] In a preferred embodiment, the estrus indication data is obtained by multiple detection devices, the same estrus indication data obtained by the multiple detection devices is compared, abnormal data is eliminated, and the average value of the remaining data is taken as the estrus indication reference data.

[0046] The present application provides a method for detecting different physiological characteristics of animals from multiple angles based on modern biological methods and intelligent detection, screening and comparing the collected data, and automatically identifying the estrus of the target animal according to the collected data.

[0047] The detection device includes a wearing detection module, an infrared detection module, and a molecular identity card. Each detection device communicates with the data processing terminal and transmits corresponding data to the data processing terminal. The data processing terminal performs estrus identification on the target animal according to the collected data and performs visual processing on the collected data.

[0048] The data collection interval time of the wearing detection module, the infrared detection module, and the molecular identity card can be set according to different needs.

[0049] Based on the collected data such as the amount of exercise, the exercise trajectory, the feeding time, and the body temperature, a target result indicating whether the target animal is in estrus is generated. The data collected by the wearing detection module, the infrared detection module, and the molecular identity card are compared in the data processing terminal to ensure the accuracy of the target animal data monitoring, which serves as the main basis for estrus identification.

[0050] The first estrus indication data of the animal collected by the wearing detection module is taken as the first detection result, and the first detection result is transmitted to the data processing terminal. The data processing terminal generates a first estrus indication data-time change graph.

[0051] The second estrus indication data of the animal collected by the infrared detection module is taken as the second detection result, and the second detection result is transmitted to the data processing terminal. The data processing terminal generates a second estrus indication data-time change graph.

[0052] The third estrus indication data of the animal collected by the molecular identity card is taken as the third detection result, and the third detection result is transmitted to the data processing terminal. The data processing terminal generates a third estrus indication data-time change graph.

[0053] Specifically, the wearing detection module collects the body temperature, the amount of exercise, the feeding time, and the exercise trajectory of the animal. The data processing terminal generates a corresponding first estrus indication data-time change graph for each data, i.e., a first body temperature-time change graph, a first amount of exercise-time change graph, and a first feeding time-time change graph.

[0054] Specifically, the infrared detection module collects the body temperature and the feeding time of the animal. The data processing terminal generates a corresponding second estrus indication data-time change graph for each data, i.e., a second body temperature-time change graph and a second feeding time-time change graph.

[0055] Specifically, the molecular identity card collects the body temperature, the amount of exercise, and the exercise trajectory of the animal. The data processing terminal generates a corresponding third estrus indication data-time change graph for each data, i.e., a third body temperature-time change graph and a second amount of exercise-time change graph.

[0056] The estrus indication reference data includes body temperature reference data, feeding time reference data, and exercise amount reference data.

[0057] The body temperature reference data is obtained by the following process:

[0058] The body temperature data measured in the first detection result, the second detection result, and the third detection result is processed in the data processing terminal, the body temperature data collected by different detection devices is compared, the body temperature data with an abnormal change range is removed according to the change range of the body temperature data in different use sites and environmental climates, and the average value of the remaining body temperature data is taken as the body temperature reference data, so that the influence of different external factors on the obtained body temperature data is greatly reduced, and the accuracy of the data is improved.

[0059] The body temperature reference data obtained above is data in the same time period, and the above process is performed in different time periods to obtain multiple body temperature reference data in different time periods and generate a body temperature reference data-time change graph.

[0060] The comparison of the body temperature data can directly compare the body temperature data collected by different detection devices in the same time period, or compare the body temperature data change graphs collected by different detection devices.

[0061] Specifically, the comparison process of the body temperature data according to the body temperature data change graph is as follows: the body temperature-time change graphs in the first estrus indication data-time change graph, the second estrus indication data-time change graph, and the third estrus indication data-time change graph are compared, that is, the first body temperature-time change graph, the second body temperature-time change graph, and the third body temperature-time change graph are compared, the data with an abnormal change range is removed, and then the average value of the body temperature data collected by different detection devices is taken in each time period to obtain the body temperature reference data in each time period and generate a body temperature reference data-time change graph, which can be used for evaluating the state of the animal.

[0062] By comparing the data collected by different detection devices in each time period, the abnormal data change caused by external factors is avoided, and the accuracy of the final data is ensured.

[0063] The feeding time reference data is obtained by the following process:

[0064] The feeding time data measured in the first detection result and the second detection result is processed in the data processing terminal, the feeding time data collected by different detection devices is compared, the change range of the feeding time data is set according to different use sites and environmental climates, the feeding time data with an abnormal change range is removed, and the average value of the remaining feeding time data is taken as the feeding time reference data, so that the error is reduced and the accuracy and reliability of the data are improved.

[0065] The feeding time reference data obtained above are all data of the same time period. The above process is performed in different time periods to obtain multiple feeding time reference data of different time periods and generate a feeding time reference data-time variation graph.

[0066] The comparison of the feeding time data can directly compare the feeding time data collected by different detection devices in the same time period, or compare the feeding time data variation graphs collected by different detection devices.

[0067] Specifically, the comparison of the feeding time data according to the feeding time data variation graph is as follows: comparing the feeding time-time variation graphs in the first estrus indication data-time variation graph and the second estrus indication data-time variation graph, that is, comparing the first feeding time-time variation graph and the second feeding time-time variation graph, removing the data with abnormal variation amplitude, and then taking the average of the feeding time data collected by different detection devices in each time period to obtain the feeding time reference data of each time period and generate a feeding time reference data-time variation graph. The graph can be used for evaluating the state of the animal.

[0068] The process of obtaining the movement reference data is as follows:

[0069] The movement and movement trajectory data measured in the first detection result and the third detection result are processed in the data processing terminal. The movement and movement trajectory data collected by different detection devices are compared, and the movement trajectory and movement of the target animal are checked. The movement trajectories of the first detection result and the third detection result should be consistent, which is the movement trajectory checking data. If there is a difference, the equipment should be checked for loss or damage, and the sheep should be checked for loss. The movement data variation amplitude is set according to different use sites and environmental climate. After removing the movement data with abnormal variation amplitude, the average of the remaining movement data is taken as the movement reference data.

[0070] When the movement trajectory is different, the detection equipment is checked for loss. If it is caused by animal fighting, the animal is checked for injury. This can ensure the monitoring of the dynamic of each animal during grazing and prevent the animal from getting lost.

[0071] The movement reference data obtained above are all data of the same time period. The above process is performed in different time periods to obtain multiple movement reference data of different time periods and generate a movement reference data-time variation graph.

[0072] The comparison of the movement data can directly compare the movement data collected by different detection devices in the same time period, or compare the movement data variation graphs collected by different detection devices.

[0073] Specifically, the comparison process of the motion amount data according to the motion amount data change graph is: comparing the motion amount-time change graph in the first estrus indication data-time change graph and the third estrus indication data-time change graph, that is, comparing the first motion amount-time change graph and the second motion amount-time change graph, eliminating the data with abnormal change amplitude, and then taking the average value of the motion amount data collected by different detection devices in each time period to obtain the motion amount reference data of each time period, and generating a motion amount reference data-time change graph. The graph can be used for evaluation of the animal state.

[0074] The body temperature reference data-time change graph, the feeding time reference data-time change graph, and the motion amount reference data-time change graph obtained in the above process can be used for real-time monitoring of each animal in the farm, and stored in the data processing terminal and the molecular identity card as the electronic health record of the target animal.

[0075] The construction process of the estrus reference model is:

[0076] In the set time period of different seasons, the estrus conditions of multiple animals are determined as the estrus reference model.

[0077] Specifically, the set time period is the initial period of detection, usually 1-2 months.

[0078] Specifically, the estrus condition of the animal is estrus or non-estrus.

[0079] In this process, the estrus condition of the animal is confirmed by artificial; the body temperature, feeding time, and motion amount of the animal are collected synchronously, and the estrus indication data (body temperature, feeding time, and motion amount) corresponding to the estrus of the animal in the estrus reference model are stored to provide a reference for subsequent animal estrus prediction.

[0080] In this process, the estrus reference model can also be iteratively trained according to the estrus indication data of the animals in estrus in different seasons to more accurately predict.

[0081] The process of predicting the estrus condition of the individual animal to be tested is:

[0082] The estrus indication reference data-time change graph of the same season and the estrus indication data corresponding to the estrus of the animal in the estrus reference model are compared to predict the estrus condition of the individual animal to be tested. If the data in the corresponding interval of the estrus indication reference data-time change graph and the estrus indication data corresponding to the estrus of the animal in the estrus reference model can be matched, it indicates that the corresponding animal is in estrus.

[0083] Specifically, in the same season, the body temperature reference data-time change graph, the feeding time reference data-time change graph, and the exercise amount reference data-time change graph are respectively compared with the corresponding body temperature data, feeding time data, and exercise amount data of the animals in estrus in the estrus reference model to determine the matching of the body temperature reference data-time change graph, the feeding time reference data-time change graph, and the exercise amount reference reference data-time change graph with the corresponding body temperature data, feeding time data, and exercise amount data of the animals in estrus in the estrus reference model. If two or more of the body temperature reference data-time change graph, the feeding time reference data-time change graph, and the exercise amount reference data-time change graph respectively match the corresponding data of the animals in estrus in the estrus reference model, the corresponding animal is in estrus.

[0084] That is, if the data of the corresponding interval of the body temperature reference data-time change graph matches the body temperature data of the animals in estrus in the estrus reference model, the data of the corresponding interval of the feeding time reference data-time change graph matches the feeding time data of the animals in estrus in the estrus reference model, or the data of the corresponding interval of the body temperature reference data-time change graph matches the body temperature data of the animals in estrus in the estrus reference model, the data of the corresponding interval of the exercise amount reference data-time change graph matches the exercise amount data of the animals in estrus in the estrus reference model, or the data of the corresponding interval of the feeding time reference data-time change graph matches the feeding time data of the animals in estrus in the estrus reference model, the data of the corresponding interval of the exercise amount reference data-time change graph matches the exercise amount data of the animals in estrus in the estrus reference model, or the data of the corresponding interval of the body temperature reference data-time change graph matches the body temperature data of the animals in estrus in the estrus reference model, the data of the corresponding interval of the feeding time reference data-time change graph matches the feeding time data of the animals in estrus in the estrus reference model, and the data of the corresponding interval of the exercise amount reference data-time change graph matches the exercise amount data of the animals in estrus in the estrus reference model, the corresponding animal is in estrus.

[0085] In the present application, the data processing terminal can process the estrus indication data to obtain visualized data (such as a graph of each data changing with time), and based on the obtained visualized data, the animal can be monitored for health, evaluated for exercise amount, analyzed for exercise trajectory, and monitored for feeding time.

[0086] Through monitoring of body temperature, when the body temperature shows abnormal changes, it may be a signal that the animal has a disease or discomfort, potential health problems such as infection, fever, or other disease symptoms can be discovered in time, and measures can be taken in time for diagnosis and treatment, which helps to maintain the health status of the animal.

[0087] The activity level and exercise needs can be evaluated by the amount of exercise. Normal exercise amount helps the development of animal muscles, digestive system function and cardiovascular health. By monitoring the exercise amount of the animal, it can be determined whether the activity level of the animal is normal, so as to adjust the feeding management and provide appropriate exercise space.

[0088] Through exercise trajectory analysis, information about nutritional resources and feed distribution is provided. By obtaining the activity range of the animal at different times and places, the grassland utilization, feed distribution and grazing management can be better planned to optimize the feeding benefit and resource utilization.

[0089] Through feeding time monitoring, the feed intake and feeding benefit can be evaluated. Normal feeding time helps maintain the nutritional balance and digestive system function of the animal. By monitoring the feeding time of the animal, the feed distribution and supply strategy can be adjusted to ensure that the animal obtains sufficient feed and appropriate nutrition.

[0090] The following will be described in detail.

[0091] The detection module, infrared detection module and molecular identity card send data to the data processing terminal every set time. This part can modify some parameters through the Bluetooth module, such as modifying the interval time (sending frequency) of data sending.

[0092] The wearing detection module is mainly composed of a built-in detection element, a lithium ion battery and a protective shell. The protective shell is made of plastic, which is wrapped outside the built-in detection element and directly contacts the animal. When worn on the animal, it prevents bumps and uses skin-friendly lightweight materials. The lithium ion battery powers the built-in detection element. The built-in detection element mainly includes an mpu6050 six-axis sensor, a body temperature monitoring module and a Bluetooth information transmission module. The mpu6050 six-axis sensor can collect data such as animal exercise amount, exercise distance, exercise trajectory and feeding time according to the set requirements; the body temperature monitoring module can monitor the animal's body temperature in real time, and the data is transmitted to the data processing terminal through the Bluetooth information transmission module at the set transmission time.

[0093] The wearing detection module is fixed on the animal's body by a fixing band. The fixing band can be a woven band, which is fixedly connected with the protective shell to fix the wearing detection module at the specified position of the target animal and ensure the stability and safety of the equipment.

[0094] The infrared detection module can use an infrared detection camera, which mainly collects data such as the body temperature and feeding time of the target animal and uploads the data to the data processing terminal.

[0095] The molecular identity card can adopt an animal ear tag, which is mainly used for measuring the target animal temperature, motion amount, motion track, and storing target animal DNA, RNA, weight and the like; the temperature is collected every interval time according to the set temperature collection time, and the data is uploaded to the data processing terminal.

[0096] In a preferred embodiment, a weight scale matched with the molecular identity card is arranged at the animal path in the animal farm, when the target animal passes through the weight scale, the weight change is automatically monitored and the data is sent to the molecular identity card and the data processing terminal, and the target animal weight data is updated in real time.

[0097] In another typical embodiment of the present application, an intelligent animal estrus identification system is provided, comprising:

[0098] The first module is used for obtaining the estrus indication data of the animal, obtaining the estrus indication reference data, and forming an estrus indication reference data-time change graph;

[0099] The second module is used for constructing an estrus reference model according to the estrus condition of the animal in a set time period;

[0100] The third module is used for comparing the estrus indication reference data-time change graph and the estrus reference model, and predicting the estrus condition of the to-be-tested animal individual.

[0101] It should be noted that the specific implementation process of each module has been described in detail in the foregoing content, and will not be described in detail here.

[0102] In another typical embodiment of the present application, a terminal device is provided, comprising a server, the server comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the intelligent animal estrus identification method as described above when executing the program. For the sake of brevity, it will not be described here.

[0103] It should be understood that in the present embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, ready-to-program gate arrays FPGA or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like.

[0104] The memory can include read-only memory and random access memory, and provide instructions and data to the processor, and a part of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0105] In the implementation process, each step of the above method can be completed by integrated logic circuit of hardware in the processor or instructions in the form of software.

[0106] In another typical embodiment of the present application, a computer readable storage medium is provided, in which a plurality of instructions are stored, and the instructions are adapted to be loaded and executed by a processor of a terminal device to implement the intelligent animal estrus identification method.

[0107] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code.

[0108] The present application is described according to flowcharts and / or block diagrams of the method, device (system), and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the functions specified in the flowchart

[0109] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the functions specified in the flowchart

[0110] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the functions specified in the flowchart

[0111] The above merely provides the preferred embodiments of the present application, and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the principles and technical scope of the present application shall fall into the scope of the present application.

Claims

1. A method for identifying estrus in intelligent animals, characterized in that, The method comprises the following steps: Obtaining estrus indication data of animals to obtain estrus indication reference data and form an estrus indication reference data-time change graph; the estrus indication data is obtained by multiple detection devices, the same estrus indication data obtained by the multiple detection devices is compared, abnormal data is eliminated, and the average value of the remaining data is taken as the estrus indication reference data; The detection devices include a wearing detection module, an infrared detection module, and a molecular identity card, and each detection device communicates with a data processing terminal and transmits corresponding data to the data processing terminal. According to the estrus condition of the animals in a set time period, an estrus reference model is constructed; the animals are sheep, cattle, or pigs; The estrus condition of a to-be-tested animal individual is predicted by comparing the estrus indication reference data-time change graph and the estrus reference model; in the same season, the body temperature reference data-time change graph, the feeding time reference data-time change graph, and the exercise amount reference data-time change graph are respectively compared with the corresponding body temperature data, feeding time data, and exercise amount data of the animals in estrus in the estrus reference model to determine the matching conditions of the body temperature reference data-time change graph, the feeding time reference data-time change graph, and the exercise amount reference data-time change graph with the corresponding data of the animals in estrus in the estrus reference model; if two or more of the body temperature reference data-time change graph, the feeding time reference data-time change graph, and the exercise amount reference data-time change graph respectively match the corresponding data of the animals in estrus in the estrus reference model, the corresponding animals are in estrus.

2. The method of claim 1, wherein the method further comprises the step of: The estrus indication data includes body temperature, exercise amount, and feeding time, and the estrus indication reference data includes body temperature reference data, exercise amount reference data, and feeding time reference data. ​ 3. The method of claim 1, wherein the method further comprises the step of: The first estrus indication data of the animals collected by the wearing detection module is taken as the first detection result, and the first detection result is transmitted to the data processing terminal, and the data processing terminal generates a first estrus indication data-time change graph; ​ The second estrus indication data of the animals collected by the infrared detection module is taken as the second detection result, and the second detection result is transmitted to the data processing terminal, and the data processing terminal generates a second estrus indication data-time change graph; The third estrus indication data of the animals collected by the molecular identity card is taken as the third detection result, and the third detection result is transmitted to the data processing terminal, and the data processing terminal generates a third estrus indication data-time change graph.

4. The method of claim 3, wherein the method further comprises the step of: The process of obtaining the body temperature reference data is as follows: ​ The measured body temperature data in the first detection result, the second detection result, and the third detection result are processed in the data processing terminal, the body temperature-time change graphs in the first estrus indication data-time change graph, the second estrus indication data-time change graph, and the third estrus indication data-time change graph are compared, the body temperature data change range is set according to different use sites and environmental climates, the abnormal body temperature data is eliminated, the average value of the remaining body temperature data is taken as the body temperature reference data, and a body temperature reference data-time change graph is obtained.

5. The intelligent animal estrus detection method as described in claim 3, characterized in that, The process of obtaining the feeding time reference data is as follows: The feeding time data measured in the first detection result and the second detection result is processed in the data processing terminal, the feeding time-time change graph in the first estrus indication data-time change graph and the second estrus indication data-time change graph is compared, the feeding time data change range is set according to different use sites and environmental climates, the feeding time data with an abnormal change range is removed, and the average value of the remaining feeding time data is taken as the feeding time reference data, and a feeding time reference data-time change graph is obtained.

6. The intelligent animal estrus detection method as described in claim 3, characterized in that, The process of obtaining the movement amount reference data is as follows: The movement amount and the movement trajectory data measured in the first detection result and the third detection result are processed in the data processing terminal, the movement amount-time change graph in the first estrus indication data-time change graph and the third estrus indication data-time change graph is compared, the movement trajectory of the target animal is checked, wherein the movement trajectories of the first detection result and the third detection result should be consistent, and the movement trajectory checking data, if there is a difference, the equipment is checked whether it is lost or damaged, and whether the sheep is lost, and the movement amount data change range is set according to different use sites and environmental climates, the movement amount data with an abnormal change range is removed, and the average value of the remaining movement amount data is taken as the movement amount reference data, and a movement amount reference data-time change graph is obtained.

7. The method of claim 1, wherein the method further comprises the step of: The process of constructing the estrus reference model is as follows: in the set time period of different seasons, the estrus conditions of a plurality of animals are determined as the estrus reference model; the estrus condition of an animal is estrus or non-estrus; the estrus indication data corresponding to the estrus of the animal in the estrus reference model is stored; ​ The process of predicting the estrus condition of the target animal is as follows: The estrus indication reference data-time change graph of the same season and the estrus indication data corresponding to the estrus of the animal in the estrus reference model are compared, and if the data in the corresponding interval of the estrus indication reference data-time change graph can match the estrus indication data corresponding to the estrus of the animal in the estrus reference model, it indicates that the corresponding animal is in the estrus state.

8. The method of claim 1, wherein the method further comprises the step of: The wearing detection module is fixed on the animal body through a strap; the molecular identity card stores the DNA, RNA and weight data of the target animal, a weight scale is arranged at the animal path in the animal farm, the weight change of the animal is monitored and the data is sent to the molecular identity card and the data processing terminal; the data processing terminal performs health monitoring, movement amount evaluation, movement trajectory analysis and feeding time monitoring on the animal according to the estrus indication data. ​ 9. An intelligent animal estrus detection system, characterized by, It comprises: A first module is used for obtaining estrus indication data of an animal, obtaining estrus indication reference data, and forming an estrus indication reference data-time change graph; the estrus indication data is obtained by a plurality of detection devices, the same estrus indication data obtained by the plurality of detection devices is compared, the abnormal data is removed, and the average value of the remaining data is taken as the estrus indication reference data; The detection devices include a wearing detection module, an infrared detection module and a molecular identity card, and each detection device communicates with the data processing terminal and transmits corresponding data to the data processing terminal. The second module is configured to construct an estrus reference model according to the estrus condition of the animal in a set time period; the animal is a sheep, a cow or a pig; The third module is configured to compare the estrus reference model with the estrus indication reference data-time change graph to predict the estrus condition of the animal to be tested; in the same season, the body temperature reference data-time change graph, the feeding time reference data-time change graph and the exercise amount reference data-time change graph are respectively compared with the body temperature data, the feeding time data and the exercise amount data corresponding to the estrus of the animal in the estrus reference model to determine the matching conditions of the body temperature reference data-time change graph, the feeding time reference data-time change graph and the exercise amount reference data-time change graph with the body temperature data, the feeding time data and the exercise amount data corresponding to the estrus of the animal in the estrus reference model; if two or more of the body temperature reference data-time change graph, the feeding time reference data-time change graph and the exercise amount reference data-time change graph are matched with the corresponding data corresponding to the estrus of the animal in the estrus reference model, the corresponding animal is in the estrus state.

Images