Livestock health state monitoring method, device and equipment and storage medium

Abstract

The invention discloses a livestock health state monitoring method, apparatus and device, and a storage medium. The livestock health state monitoring method comprises the steps of obtaining sensor monitoring data in a livestock factory; constructing a sample data set according to the sensor monitoring data; and performing model pre-training and model tuning training on a pre-constructed monitoring network model by adopting the sensor sample data until a preset model training ending condition is met, and taking the trained monitoring network model as a livestock health monitoring model. By means of the technical scheme, the model performance of the livestock health monitoring model applied to different livestock factory areas is improved, and meanwhile the training efficiency of model training is improved.

Description

Technical Field

[0001] The present invention relates to the field of animal husbandry technology, and in particular to a method, device, equipment and storage medium for monitoring the health status of livestock. Background Art

[0002] Animal husbandry is an important part of agriculture and has a profound impact on the economy, society and environment. Especially in terms of food supply, animal husbandry provides a large amount of animal food such as meat, milk, eggs, etc., which is an indispensable part of people's daily diet.

[0003] The deterioration of livestock health status happens from time to time, which not only has a certain impact on livestock populations, but also causes huge economic losses. Therefore, livestock health monitoring is very important for animal husbandry. Summary of the invention

[0004] The present invention provides a method, device, equipment and storage medium for monitoring the health status of livestock, so as to improve the accuracy of identifying the health status of livestock.

[0005] According to one aspect of the present invention, there is provided a method for monitoring the health status of livestock, the method comprising:

[0006] Acquire sensor monitoring data in the livestock factory area; wherein the sensor monitoring data includes time-series motion data of livestock collected by body and leg sensors, and time-series environmental data in the livestock factory area collected by environmental sensors;

[0007] Constructing a sample data set according to the sensor monitoring data; the sensor sample data in the sample data set carries at least two sample labels, and the sample labels include a health label and a behavior pattern label;

[0008] The sensor sample data is used to perform model pre-training and model tuning training on a pre-built monitoring network model until a preset model training end condition is met, and the trained monitoring network model is used as a livestock health monitoring model;

[0009] The monitoring network model includes a physical sign monitoring model and a behavior monitoring model. The physical sign monitoring model is used to detect the physiological physical sign information of the livestock and identify abnormal health signs of the livestock. The behavior monitoring model is used to identify the behavior patterns of the livestock.

[0010] According to another aspect of the present invention, there is provided a device for monitoring the health status of livestock, the device comprising:

[0011] A data acquisition module is used to acquire sensor monitoring data in the livestock farm area; wherein the sensor monitoring data includes time-series motion data of livestock collected by body and leg sensors, and time-series environmental data in the livestock farm area collected by environmental sensors;

[0012] A data set construction module, used to construct a sample data set according to the sensor monitoring data; the sensor sample data in the sample data set carries at least two sample labels, and the sample labels include a health label and a behavior pattern label;

[0013] A model training module is used to use the sensor sample data to perform model pre-training and model tuning training on a pre-built monitoring network model until a preset model training end condition is met, and the trained monitoring network model is used as a livestock health monitoring model; wherein the monitoring network model includes a physical sign monitoring model and a behavior monitoring model, wherein the physical sign monitoring model is used to detect the physiological sign information of the livestock and identify abnormal health signs of the livestock, and the behavior monitoring model is used to identify the behavior patterns of the livestock.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for monitoring the health status of livestock described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for monitoring the health status of livestock described in any embodiment of the present invention when executed.

[0019] According to another aspect of the present invention, a computer program product is provided. The computer program product comprises a computer program. When the computer program is executed by a processor, the method for monitoring the health status of livestock according to any embodiment of the present invention is implemented.

[0020] The technical solution of the embodiment of the present invention improves the model performance of the livestock health monitoring model applied in different livestock farms by pre-training and tuning the monitoring network model according to the sensor monitoring data in the livestock farm, reduces the dependence on a large amount of label data during the model training process, and improves the training efficiency of the model training.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a method for monitoring the health status of livestock provided according to Embodiment 1 of the present invention;

[0024] Figure 2 is a flow chart of a method for monitoring the health status of livestock provided according to Embodiment 2 of the present invention;

[0025] Figure 3a is a flow chart of a method for monitoring the health status of livestock provided according to Embodiment 3 of the present invention;

[0026] Figure 3b is a schematic diagram of the structure of a core module of a sensor provided in Embodiment 3 of the present invention;

[0027] Figure 4 is a schematic structural diagram of a device for monitoring the health status of livestock provided according to a fourth embodiment of the present invention;

[0028] Figure 5 It is a structural schematic diagram of an electronic device for implementing the method for monitoring the health status of livestock according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Embodiment 1

[0032] Figure 1 A flowchart of a method for monitoring the health status of livestock is provided for the first embodiment of the present invention. This embodiment is applicable to the situation of monitoring the health of livestock. The method can be performed by a monitoring device for the health status of livestock. The monitoring device for the health status of livestock can be implemented in the form of hardware and / or software. The monitoring device for the health status of livestock can be configured in various general-purpose computing devices. Figure 1 As shown, the method includes:

[0033] S110, obtaining sensor monitoring data within the livestock factory area.

[0034] Among them, the sensor monitoring data may include the time-series movement data of livestock collected by body and leg sensors, and the time-series environmental data in the livestock farm area collected by environmental sensors.

[0035] In an embodiment of the present invention, the body and leg sensors and environmental sensors deployed in the livestock farm area can collect the time-series motion data of livestock and the time-series environmental data in the livestock farm area at a preset collection frequency, and the central server can periodically receive the sensor monitoring data collected by the body and leg sensors and the environmental sensors.

[0036] Optionally, the body and leg sensors can be installed on the tail or legs of livestock, and can be composed of a microcontroller, an acceleration sensor, a heart rate and blood oxygen sensor, a body temperature sensor, a display, and a battery management controller. They can be used to monitor the livestock's motion data (three-dimensional acceleration and rotational angular velocity, etc.), the livestock's body temperature, heart rate, and blood oxygen information, and the display of the body and leg sensors can synchronously display the sensor monitoring data collected by itself and its own power. Environmental sensors can be used to detect environmental information such as temperature, humidity, organic volatile compound concentration, carbon dioxide concentration, and pressure in the livestock's living environment. It should be noted that the technicians in the embodiments of the present invention can adaptively set the types of sensors required in the body and leg sensors and environmental sensors according to the types of health indicators, motion indicators, and environmental indicators to be monitored.

[0037] S120: construct a sample data set based on the sensor monitoring data.

[0038] Among them, the sensor sample data in the sample data set may carry at least two sample labels, and the sample labels may include health labels and behavior pattern labels, which are used to characterize the health information and behavior patterns of the livestock corresponding to the sensor sample data, such as behavior patterns such as walking, eating, ruminating and resting, and health information of whether the livestock is in a disease state.

[0039] Specifically, a sample data set can be constructed based on the sensor monitoring data collected by the sensors deployed in the animal husbandry plant. Optionally, the sensor sample data in the sample data set can be divided into training sample data and test sample data. For example, 70% of the sensor sample data in the sample data set can be used as training sample data, and 30% of the sensor sample data in the sample data set can be used as test sample data.

[0040] S130, using sensor sample data to perform model pre-training and model tuning training on the pre-built monitoring network model until the preset model training end conditions are met, and using the trained monitoring network model as a livestock health monitoring model.

[0041] The monitoring network model may include a physical sign monitoring model and a behavior monitoring model. The physical sign monitoring model may be used to detect the physiological signs of livestock and identify abnormal health signs of livestock. The behavior monitoring model may be used to identify the behavior patterns of livestock. Optionally, the monitoring network model may be a neural network model. Those skilled in the art may adaptively set the training method of the monitoring network model. For example, a support vector machine algorithm may be used to train the behavior monitoring model, and a random forest algorithm may be used to train the physical sign monitoring model.

[0042] In an embodiment of the present invention, a two-stage training process can be adopted for the monitoring network model. The first stage of the training process can be a pre-training process for the monitoring network model, and the monitoring network model is trained using large-scale general sensor sample data so that the monitoring network model learns a rich feature representation. Then, for the task type that needs to be monitored in a specific livestock farm area, the pre-trained monitoring network model is tuned and trained using the small-scale sensor sample data exclusive to the livestock farm, so that the tuned and trained monitoring network model can be more in line with the actual environmental information of the livestock farm area. Optionally, the model training end conditions of the pre-training stage and the tuning training stage can be adaptively set according to technical personnel in this field. The livestock health monitoring model is used to monitor the health status of the livestock in real time, so that the abnormal health status of the livestock can be monitored in a timely and accurate manner, and a notification is sent to the terminal device of the relevant staff to remind the relevant personnel to intervene and deal with it, thereby reducing losses.

[0043] The technical solution of the embodiment of the present invention improves the model performance of the livestock health monitoring model applied in different livestock farms by pre-training and tuning the monitoring network model according to the sensor monitoring data in the livestock farm, reduces the dependence on a large amount of label data during the model training process, and improves the training efficiency of the model training.

[0044] Embodiment 2

[0045] Figure 2 This is a flowchart of a method for monitoring the health status of livestock provided in the second embodiment of the present invention. This embodiment further refines the above embodiment and provides specific steps for using sensor sample data to perform model pre-training and model tuning training on a pre-built monitoring network model. It should be noted that for the undetailed parts of the embodiment of the present invention, please refer to the relevant descriptions of other embodiments, which will not be repeated here. Figure 2 As shown, the method includes:

[0046] S210, obtaining sensor monitoring data within the livestock factory area.

[0047] S220: construct a sample data set based on the sensor monitoring data.

[0048] S230, performing feature aggregation on the historically collected time-series kinematic data, time-series physiological data, and time-series environmental data according to the time window to obtain at least one first initial feature.

[0049] S240 , extracting features from data changes in each first initial feature to obtain a first standardized feature corresponding to each first initial feature.

[0050] S250. Pre-training a physical sign monitoring model and a behavior monitoring model respectively according to at least two sample labels of the first standardized feature.

[0051] Among them, the time-series motion data may include time-series vital sign data and time-series kinematic data. For example, the time-series vital sign data can be used to characterize the physiological state information of livestock, such as body temperature data, blood oxygen data, and heart rate data; the time-series kinematic data can be used to characterize the movement information of livestock, such as three-dimensional acceleration and rotational angular velocity, etc. The time-series kinematic data after feature aggregation may include movement characteristics such as the livestock's step frequency, stride, and movement direction.

[0052] In actual situations, the temporal significance of livestock's time-series motion data is not significant, but the time-series motion data within a fixed time period is of greater significance. Compared with the original sensor monitoring data at the minute or hour level, in the scenario where the model is built in units of days, the sensor monitoring data aggregated according to a certain time window can greatly reduce the data dimension. In practical applications, the time-series kinematic data, time-series physiological data, and time-series environmental data can be feature aggregated according to the time window to obtain their corresponding initial features. For example, 30 minutes can be used as the time window to perform feature aggregation on the time-series kinematic data and time-series physiological data; 1 hour can be used as the time window to perform feature aggregation on the time-series environmental data. Optionally, the setting of the time window can be adaptively set according to technical personnel in this field, and the time windows of different sensor monitoring data can be the same or different.

[0053] In an embodiment of the present invention, in the pre-training stage of the monitoring network model, the time series kinematic data, time series physiological data and time series environmental data collected in the same time and space in history can be respectively feature aggregated according to the time window to obtain the first initial features corresponding to each; and the data change in each initial feature is further feature extracted to obtain the first standardized feature corresponding to each first initial feature, for example, the feature information that can reflect the data change trend, such as the mean, extreme value or mutation feature in the first initial feature, is extracted by a preset feature extraction algorithm. According to at least two sample labels of the first standardized feature, the physical sign monitoring model and the behavior monitoring model are pre-trained respectively, so that the physical sign monitoring model and the behavior monitoring model can learn the correlation between the running information of the livestock and the physical sign information and the environmental information of the livestock, and by inputting multiple types of data features, the physical state of the livestock can be identified more accurately, so that the confidence of the trained model is higher. Optionally, the feature extraction algorithm can be adaptively set according to those skilled in the art, such as fast Fourier transform, integral-differential method, window function sliding average and variance analysis.

[0054] In the tuning and training stage of the detection network model, the time-series kinematic data, time-series physiological data and time-series environmental data collected in real time in the same time and space can be feature aggregated according to the time window to obtain the corresponding second initial features; and the data changes in each of the two initial features are further feature extracted to obtain the second standardized features corresponding to each second initial feature, and the physical sign monitoring model and the behavior monitoring model are tuned and trained according to at least two sample labels of the second standardized features. It should be noted that the historical sensor sample data required in the pre-training process of the monitoring network model can come from different livestock farms, while the real-time sensor sample data required in the tuning and training stage of the monitoring network model comes from a livestock farm to be monitored, that is, the sources of sensor sample data of the monitoring network model in the pre-training stage and the tuning and training stage are different.

[0055] The technical solution of the embodiment of the present invention collects multiple types of sensor data and involves the multiple types of sensor data in the training process of the livestock health monitoring model, so that the livestock health monitoring model can learn more feature information during the training process, thereby improving the precision of livestock health monitoring and the accuracy of the monitoring results.

[0056] Embodiment 3

[0057] Figure 3a This is a flow chart of a method for monitoring the health status of livestock provided in the second embodiment of the present invention. This embodiment further refines the above embodiment. It provides specific steps for obtaining sensor monitoring data in the livestock plant area. It should be noted that for the undetailed parts of the embodiment of the present invention, please refer to the relevant descriptions of other embodiments, which will not be repeated here. Figure 3a As shown, the method includes:

[0058] S310, receiving sensor monitoring data collected by body and leg sensors and environmental sensors deployed in the livestock factory area, performing data preprocessing operations on the received sensor monitoring data, and adding corresponding timestamp information to each sensor monitoring data.

[0059] S320: Add health labels and behavior pattern labels to each sensor monitoring data, and store the sensor monitoring data in a database.

[0060] In an embodiment of the present invention, after receiving the sensor monitoring data sent by the sensors deployed in the livestock factory, the central server will first perform data preprocessing operations on the sensor monitoring data, for example, data cleaning, data standardization and data normalization processing on the sensor monitoring data to improve the data quality of the sensor monitoring data, and then add timestamp information representing the collection time to the sensor detection data according to the collection time carried in the sensor monitoring data; then, the sensor monitoring data carrying the health label and behavior pattern label will be stored in the database for subsequent model training.

[0061] Optionally, in an embodiment of the present invention, the body and leg sensors and the environmental sensors can be wirelessly connected to the central server for communication. During the operation of the body and leg sensors and the environmental sensors, the communication connection status with each sensor can be detected, and the sensor configuration parameters of the sensor can be adjusted according to the communication connection status with the sensor; the communication connection status can include the connection signal strength, data packet loss rate and transmission rate, and the sensor configuration parameters can include the sensor acquisition frequency and the sensor transmission power. Exemplarily, in actual animal husbandry scenarios, the distribution range and activity status of livestock are not fixed. In order to stably obtain the sensor monitoring data collected for each livestock, the central server can monitor the connection status of the body and leg sensors in real time, monitor the connection status of the body and leg sensors deployed on the livestock according to the activity status of the livestock, and dynamically adjust the working configuration parameters of the body and leg sensors to adapt to different livestock distribution status in real time.

[0062] Optionally, in the embodiment of the present invention, the structural architecture of the core modules of the body and leg sensors and the environment sensors can be as follows: Figure 3bAs shown, the core modules of the body and leg sensors and the environmental sensors are basically the same, and both establish wireless connections with the central server through WiFi antennas, and the working status of the body and leg sensors or environmental sensors and the connection status with the central server can be displayed through indicator lights and OLED lights. According to the USB debugging interface, relevant staff can set the functions of the master control MCU (Master Control Unit), such as the display status setting of the indicator lights or OLED lights, the wireless connection setting with the central server, the charging and discharging setting of the power supply (lithium battery), and the setting of the RTC clock circuit (Real-Time Clock). It should be noted that for fixedly deployed sensors such as environmental sensors, their own core modules are fixed modules and will not be moved frequently. Therefore, for fixedly deployed core modules, external AC power can be used for power supply. It should be noted that, for body and leg sensors, the corresponding main control MCU also needs to be connected with different sensor modules, such as acceleration sensors, heart rate and blood oxygen sensors, body temperature sensors, etc.; for environmental sensors, the corresponding main control MCU not only needs to be connected with different sensor modules, such as temperature sensors, humidity sensors, organic volatile compound concentration sensors and carbon dioxide concentration sensors, etc., but also needs to be connected with actuator modules of different environmental actuators. The time series environmental data collected by the environmental sensor is also used to determine the opening state of at least one environmental actuator deployed in the livestock factory. For example, when the environmental sensor deployed in the livestock shed detects abnormal environmental values, the central server can send work instructions to the actuator module and issue an early warning, such as environmental actuator equipment such as sprinklers and fans.

[0063] S330: construct a sample data set based on the sensor monitoring data.

[0064] S340, using sensor sample data to perform model pre-training and model tuning training on the pre-built monitoring network model until the preset model training end conditions are met, and using the trained monitoring network model as a livestock health monitoring model.

[0065] The technical solution of the embodiment of the present invention improves the data quality of the sensor monitoring data by preprocessing the received sensor monitoring data, and lays a reliable data foundation for the subsequent training of the monitoring network model.

[0066] Embodiment 4

[0067] Figure 4 This is a schematic diagram of the structure of a device for monitoring the health status of livestock provided by Embodiment 3 of the present invention. Figure 4 As shown, the device comprises:

[0068] The data acquisition module 410 is used to acquire sensor monitoring data in the livestock farm area; wherein the sensor monitoring data includes the time-series motion data of the livestock collected by the body and leg sensors, and the time-series environmental data in the livestock farm area collected by the environmental sensors;

[0069] A data set construction module 420 is used to construct a sample data set according to the sensor monitoring data; the sensor sample data in the sample data set carries at least two sample labels, and the sample labels include a health label and a behavior pattern label;

[0070] The model training module 430 is used to use the sensor sample data to perform model pre-training and model tuning training on the pre-constructed monitoring network model until the preset model training end conditions are met, and the trained monitoring network model is used as the livestock health monitoring model; wherein the monitoring network model includes a physical sign monitoring model and a behavior monitoring model, the physical sign monitoring model is used to detect the physiological sign information of the livestock and identify abnormal health signs of the livestock, and the behavior monitoring model is used to identify the behavior patterns of the livestock.

[0071] The technical solution of the embodiment of the present invention improves the model performance of the livestock health monitoring model applied in different livestock farms by pre-training and tuning the monitoring network model according to the sensor monitoring data in the livestock farm, reduces the dependence on a large amount of label data during the model training process, and improves the training efficiency of the model training.

[0072] Optionally, the time-series motion data may include time-series vital sign data and time-series kinematics data.

[0073] Optionally, the model training module 430 includes:

[0074] The pre-training unit is specifically used to perform feature aggregation on the historically collected time-series kinematic data, time-series physiological data, and time-series environmental data according to the time window to obtain at least one first initial feature; perform feature extraction on the data change in each first initial feature to obtain a first standardized feature corresponding to each first initial feature; and pre-train the physical sign monitoring model and the behavior monitoring model according to at least two sample labels of the first standardized feature;

[0075] The tuning and training unit is specifically used to obtain the time-series kinematic data, time-series physiological data and time-series environmental data collected in real time by the sensors deployed in the livestock factory area, and aggregate them respectively according to the time window to obtain at least one second initial feature; extract the features of the data changes in each second initial feature respectively to obtain the second standardized features corresponding to each second initial feature; and perform tuning and training on the feature monitoring model and the behavior monitoring model respectively according to at least two sample labels of the second standardized features. Optionally, the data acquisition module 410 can be specifically used to: receive the sensor monitoring data collected by the body and leg sensors and environmental sensors deployed in the livestock factory area, perform data preprocessing operations on the received sensor monitoring data, and add corresponding timestamp information to each sensor monitoring data; add health labels and behavior pattern labels to each sensor data, and store the sensor data in a database.

[0076] Optionally, the body and leg sensors and the environmental sensors can be connected to the central server for communication via wireless means.

[0077] Optionally, the device further comprises:

[0078] The connection configuration update module is used to detect the communication connection status with each sensor and adjust the sensor configuration parameters of the sensor according to the communication connection status with the sensor; the communication connection status includes the connection signal strength, data packet loss rate and transmission rate, and the sensor configuration parameters include the sensor acquisition frequency and sensor transmission power.

[0079] Optionally, the time series environmental data can also be used to determine the activation state of at least one environmental actuator deployed in the livestock farm.

[0080] The livestock health status monitoring device provided in the embodiment of the present invention can execute the livestock health status monitoring method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0081] Embodiment 5

[0082] Figure 5 A schematic diagram of an electronic device 510 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0083] like Figure 5 As shown, the electronic device 510 includes at least one processor 511, and a memory connected to the at least one processor 511 in communication, such as a read-only memory (ROM) 512, a random access memory (RAM) 513, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 511 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 512 or the computer program loaded from the storage unit 518 to the random access memory (RAM) 513. In the RAM 513, various programs and data required for the operation of the electronic device 510 can also be stored. The processor 511, the ROM 512, and the RAM 513 are connected to each other via a bus 514. An input / output (I / O) interface 515 is also connected to the bus 514.

[0084] A number of components in the electronic device 510 are connected to the I / O interface 515, including: an input unit 516, such as a keyboard, a mouse, etc.; an output unit 517, such as various types of displays, speakers, etc.; a storage unit 518, such as a disk, an optical disk, etc.; and a communication unit 519, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 519 allows the electronic device 510 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0085] The processor 511 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 511 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 511 performs the various methods and processes described above, such as a method for monitoring the health status of livestock.

[0086] In some embodiments, the method for monitoring the health status of livestock can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 518. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 510 via the ROM 512 and / or the communication unit 519. When the computer program is loaded into the RAM 513 and executed by the processor 511, one or more steps of the method for monitoring the health status of livestock described above can be performed. Alternatively, in other embodiments, the processor 511 can be configured to perform the method for monitoring the health status of livestock in any other appropriate manner (e.g., by means of firmware).

[0087] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0088] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0089] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0090] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0091] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0092] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0093] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0094] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring the health status of livestock, characterized in that: include: Acquire sensor monitoring data in the livestock factory area; wherein the sensor monitoring data includes time-series motion data of livestock collected by body and leg sensors, and time-series environmental data in the livestock factory area collected by environmental sensors; Constructing a sample data set according to the sensor monitoring data; wherein the sensor sample data in the sample data set carries at least two sample labels, and the sample labels include a health label and a behavior pattern label; The sensor sample data is used to perform model pre-training and model tuning training on a pre-built monitoring network model until a preset model training end condition is met, and the trained monitoring network model is used as a livestock health monitoring model; The monitoring network model includes a physical sign monitoring model and a behavior monitoring model. The physical sign monitoring model is used to detect the physiological physical sign information of the livestock and identify abnormal health signs of the livestock. The behavior monitoring model is used to identify the behavior patterns of the livestock.

2. The method according to claim 1, characterized in that The time series motion data includes time series vital sign data and time series kinematics data; Accordingly, the sensor sample data is used to perform model pre-training on the pre-built monitoring network model, including: Performing feature aggregation on the historically collected time-series kinematic data, time-series physiological data, and time-series environmental data according to the time window to obtain at least one first initial feature; Extracting features of data changes in each first initial feature respectively to obtain a first standardized feature corresponding to each first initial feature; The physical sign monitoring model and the behavior monitoring model are pre-trained respectively according to at least two sample labels of the first standardized feature.

3. The method according to claim 2, characterized in that The sensor sample data is used to perform model tuning training on the pre-built monitoring network model, including: Acquire time-series kinematic data, time-series physiological data, and time-series environmental data collected in real time by sensors deployed in the animal husbandry plant, and aggregate them respectively according to time windows to obtain at least one second initial feature; Perform feature extraction on the data change in each second initial feature to obtain a second standardized feature corresponding to each second initial feature; The feature monitoring model and the behavior monitoring model are respectively fine-tuned and trained according to at least two sample labels of the second standardized feature.

4. The method according to claim 1, characterized in that: Obtain sensor monitoring data within the livestock factory area, including: Receive sensor monitoring data collected by body and leg sensors and environmental sensors deployed in the livestock factory area, perform data preprocessing operations on the received sensor monitoring data, and add corresponding timestamp information to each sensor monitoring data; Add health labels and behavior pattern labels to each sensor data, and store the sensor data in the database.

5. The method according to claim 1, characterized in that: The body and leg sensors and the environment sensors are connected to the central server in a wireless communication manner; Accordingly, the method further includes: Detect the communication connection status with each sensor, and adjust the sensor configuration parameters of the sensor according to the communication connection status with the sensor; the communication connection status includes connection signal strength, data packet loss rate and transmission rate, and the sensor configuration parameters include sensor acquisition frequency and sensor transmission power.

6. The method according to claim 1, characterized in that Also includes: The time series environmental data is used to determine the activation state of at least one environmental actuator deployed in the livestock farm.

7. A device for monitoring the health status of livestock, characterized in that: include: A data acquisition module is used to acquire sensor monitoring data in the livestock farm area; wherein the sensor monitoring data includes time-series motion data of livestock collected by body and leg sensors, and time-series environmental data in the livestock farm area collected by environmental sensors; A data set construction module, used to construct a sample data set according to the sensor monitoring data; the sensor sample data in the sample data set carries at least two sample labels, and the sample labels include a health label and a behavior pattern label; A model training module is used to use the sensor sample data to perform model pre-training and model tuning training on a pre-built monitoring network model until the preset model training end conditions are met, and the trained monitoring network model is used as a livestock health monitoring model; wherein the monitoring network model includes a physical sign monitoring model and a behavior monitoring model, the physical sign monitoring model is used to detect the physiological sign information of the livestock and identify abnormal health signs of the livestock, and the behavior monitoring model is used to identify the behavior patterns of the livestock.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for monitoring the health status of livestock according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for monitoring the health status of livestock according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the method for monitoring the health status of livestock as described in any one of claims 1 to 6.

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