Animal information processing method and device and network equipment

Abstract

The embodiment of the invention provides an animal information processing method and device and network equipment, and the method comprises the steps: obtaining the first video data of an animal in a whole period, and enabling the first video data to correspond to a disease model of the animal; according to the first video data, obtaining first annotation data to train an animal information identification model; the first annotation data comprises basic information, behavior characteristics, disease models and biological indexes; test video data are obtained and input into the animal information recognition model for recognition, a test information recognition result is obtained, and the test information recognition result comprises basic information, behavior characteristics, a disease model and biological indexes; analyzing behavior characteristics in the test information recognition result to screen action sequences with the accuracy lower than a preset threshold value, and after correction, training the animal information recognition model to determine a trained animal information recognition model.

Description

Technical Field

[0001] The present application relates to the field of computer information technology, and more specifically, to an animal information processing method, device and network equipment. Background Art

[0002] With the development of posture estimation and deep learning, animal behavior analysis systems have been continuously generated and developed in recent years, mainly through extracting features from videos for behavior analysis.

[0003] However, existing solutions usually only analyze animal behavior based on videos and are unable to obtain other information. Summary of the invention

[0004] The embodiments of the present application provide an animal information processing method, device and network equipment, which can obtain video data of the entire cycle of an animal, and obtain corresponding basic information, behavioral characteristics, disease models, biological indicators, etc. as annotations of the video data to train a recognition model, and use the trained recognition model to directly obtain the basic information, behavioral characteristics, disease models, and biological indicators of the animal through the video.

[0005] The technical solution is as follows:

[0006] In a first aspect, the present application provides an animal information processing method, the method comprising: obtaining first video data of an animal's entire cycle, the first video data corresponding to the animal's disease model, the video data comprising a first video corresponding to the animal's spontaneous behavior, and a second video corresponding to the animal's behavior test; based on the first video data, obtaining first annotated data to train an animal information recognition model; the first annotated data comprising: basic information, behavioral characteristics, disease models, and biological indicators; obtaining test video data, and inputting it into the animal information recognition model for recognition to obtain a test information recognition result, the test information recognition result comprising basic information, behavioral characteristics, disease models, and biological indicators; analyzing the behavioral characteristics in the test information recognition result to screen out action sequences whose accuracy is lower than a preset threshold, and after correction, training the animal information recognition model to determine a trained animal information recognition model.

[0007] Furthermore, before obtaining the video annotation data, the method also includes: inputting the video data into a behavior analysis model, determining the behavior analysis results, and removing low-quality videos from the video data based on the behavior analysis results; the method also includes: obtaining the video data to be analyzed and inputting it into a trained animal information recognition model to determine the analysis results of the animal, wherein the analysis results include basic information, behavioral characteristics, disease models, and biological indicators.

[0008] Furthermore, the step of training the animal information recognition model includes: obtaining second video data of the animal and corresponding annotations, the second video data including videos of disease-free animals, and the second video data including a third video corresponding to the spontaneous behavior of the animal and a fourth video corresponding to the animal behavior test; obtaining the third video and the annotations corresponding to the third video, and training the animal information recognition model; obtaining the fourth video and the annotations corresponding to the fourth video, and training the animal information recognition model trained with the third video; obtaining the first video and the annotations corresponding to the first video, and training the animal information recognition model trained with the fourth video; obtaining the second video and the annotations corresponding to the second video, and training the animal information recognition model trained with the first video.

[0009] Furthermore, the step of training the animal information recognition model includes: obtaining a fifth video and the annotations corresponding to the fifth video, the annotations corresponding to the fifth video are obtained by analyzing the fifth video; obtaining a sixth video and the annotations corresponding to the sixth video, the annotations corresponding to the sixth video are obtained after collecting data on the animal through an auxiliary collection device; training the animal information recognition model based on the fifth video and its annotations, and training the animal information recognition model trained with the fifth video based on the sixth video and its annotations.

[0010] Furthermore, the basic information includes animal species, gender, age, and weight; the behavioral characteristics include behavioral definition, duration, and behavioral parameters; the disease model includes natural animals, or disease model animals, course of disease, and main manifestations; the biological indicators include heart rate, blood pressure, respiratory rate, and EEG.

[0011] Furthermore, in the first video data of the entire cycle of the animal: the first video recording time is 1 hour, and the second video recording time is 5-15 minutes; the frequency of video collection for animals with a lifespan of less than 3 years is once a week; the frequency of collection for animals over 3 years is once a month; video recording is divided into single perspective and multi-perspective; multi-perspective consists of 4 surrounding cameras and 1 top camera; single perspective includes a top camera; basic information is collected when obtaining the first video data to form the first annotation data of the first video data based on the basic information.

[0012] Furthermore, the first annotation data also includes sound information and ambient temperature information.

[0013] Furthermore, the behavioral characteristics in the test information recognition results are analyzed to screen out action sequences with an accuracy rate lower than a preset threshold, and after correction, the animal information recognition model is trained to determine a trained animal information recognition model, including: analyzing the behavioral characteristics in the test information recognition results based on a manual screening method to screen out action sequences with an accuracy rate lower than a preset threshold; filtering and manually correcting the screened action sequences to determine test update data, and adjusting the animal information recognition model based on the test update data to determine a trained animal information recognition model.

[0014] In a second aspect, the present application provides an animal information processing device, which includes: a video data acquisition module, which is used to acquire first video data of the animal's entire cycle, the first video data corresponds to the animal's disease model, and the video data includes a first video corresponding to the animal's spontaneous behavior and a second video corresponding to the animal's behavior test; a recognition model training module, which is used to acquire first labeled data based on the first video data to train an animal information recognition model; the first labeled data includes: basic information, behavioral characteristics, disease models, and biological indicators; a test data acquisition module, which is used to acquire test video data and input it into the animal information recognition model for recognition to obtain a test information recognition result, and the test information recognition result includes basic information, behavioral characteristics, disease models, and biological indicators; a test result analysis module, which is used to analyze the behavioral characteristics in the test information recognition result to screen out action sequences with an accuracy rate lower than a preset threshold, and after correction, train the animal information recognition model to determine a trained animal information recognition model.

[0015] In a third aspect, the present application provides a network device comprising: a memory, a transceiver, and a processor; wherein the memory is used to store a computer program; the transceiver is used to send and receive data under the control of the processor; and the processor is used to read the computer program in the memory and execute the method described in the first aspect.

[0016] In a fourth aspect, the present application provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in the first aspect.

[0017] The beneficial effects of the technical solution provided by this application are:

[0018] The solution of the present application can be applied in animal information recognition scenarios, and can obtain the basic information, behavioral information, and disease information of animals based on animal videos. Specifically, the solution can obtain video data of the entire animal cycle, and obtain the corresponding basic information, behavioral characteristics, disease models, biological indicators, etc. as annotations of the video data to train the animal information recognition model, and use the trained animal information recognition model to directly obtain the basic information, behavioral characteristics, disease models, and biological indicators of animals through videos, so as to realize the functions of obtaining basic information of animals and diagnosing diseases. Specifically, this solution can obtain the first video data of the entire cycle of the animal, the first video data corresponds to the disease model of the animal, and the video data includes a first video corresponding to the spontaneous behavior of the animal and a second video corresponding to the animal behavior test; based on the first video data, first annotation data is obtained to train the animal information recognition model; the first annotation data includes: basic information, behavioral characteristics, disease model, biological indicators; after training the animal information recognition model, test video data can be obtained and input into the animal information recognition model for recognition to obtain test information recognition results, and the test information recognition results include basic information, behavioral characteristics, disease model, and biological indicators; after obtaining the test information recognition results, if all the test information recognition results are corrected, the workload is large, so this solution can only analyze the behavioral characteristics in the test information recognition results, and screen the action sequences with an accuracy rate lower than the preset threshold, and after correction, train the animal information recognition model based on the corrected test information to determine the trained animal information recognition model. After the animal information recognition model is trained, the video data to be analyzed can be obtained and input into the trained animal information recognition model to determine the analysis results of the animal, which include basic information, behavioral characteristics, disease models, and biological indicators. This solution can not only analyze the behavior of animals based on video data, but also obtain other information about animals, which can be more convenient for subsequent processing. In addition, the information collected by this solution can also be stored in a database for analysis or training of other models. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in describing the embodiments of the present application are briefly introduced below.

[0020] Figure 1 is a flowchart of an animal information processing method according to an embodiment of the present application;

[0021] Figure 2 is a schematic diagram of the structure of an animal information processing device according to an embodiment of the present application;

[0022] Figure 3 It is a structural block diagram of a network device according to an embodiment of the present application;

[0023] Figure 4 This is a structural block diagram of a user device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout identify the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0025] It will be understood by those skilled in the art that, unless specifically stated, the singular forms "one", "said", and "the" used herein may also include plural forms, and "multiple" refers to two or more, and other quantifiers are similar. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may be intermediate elements. In addition, the "connection" or "coupling" used here may include wireless connection or wireless coupling. The term "and / or" used here describes the association relationship of associated objects, indicating that there may be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0026] The solution of the present application can be applied in animal information recognition scenarios, and can obtain the basic information, behavioral information, and disease information of animals based on animal videos. Specifically, this solution can obtain video data of the entire cycle of animals, and obtain the corresponding basic information, behavioral characteristics, disease models, biological indicators, etc. as annotations of video data to train animal information recognition models, and use the trained animal information recognition model to directly obtain the basic information, behavioral characteristics, disease models, and biological indicators of animals through videos to achieve functions such as obtaining basic information of animals and diagnosing diseases. In addition, the information collected by this solution can also be stored in a database for analysis or training of other models.

[0027] The following is an overall introduction to the scheme of this application:

[0028] This solution aims to solve the problems of complicated processes and incomplete information in current animal behavior analysis. With the development of posture estimation and deep learning, animal behavior analysis systems have been continuously generated and developed in recent years, mainly by extracting the features of body points of interest from videos for behavior analysis. For researchers in the biological field who do not have a computer foundation, this method has a high learning cost and a complex processing process, and the information obtained is limited to behavioral information, and the accuracy needs to be improved. This makes the time and labor costs invested by researchers inconsistent with the richness and accuracy of the information obtained. This solution uses visual understanding technology based on a full life cycle database to directly obtain the basic information, behavioral information, disease information, and biological indicators of animals through videos, greatly reducing the time researchers spend processing videos and obtaining more target information.

[0029] The existing behavior analysis technology is mainly divided into two technical routes. The first is to extract key points for behavior clustering analysis, clustering analysis models such as Keypoint-MoSeq, Behavior Atlas, DeepLabCut, etc. After obtaining the video, the researchers manually annotated the key points, trained the model, supervised or unsupervised behavior clustering, and annotated the behavior to obtain the behavior sequence. A database based on this technology has been established, allowing researchers to directly parse the video to obtain the behavior sequence. The second is to analyze behavioral data through video understanding technology, deep neural networks such as DeepEthogram, and use supervised machine learning networks to classify the original video stream into continuous behavior sequences.

[0030] This solution establishes a database based on visual understanding to reduce labor costs. First, it uses visual understanding to annotate through language, which reduces the difficulty of operation. Secondly, it collects behavioral videos of the life cycle of common experimental animal groups and trains them to form a large multimodal model. The model can automatically identify the appearance characteristics of animals, reducing the tedious process of manual labeling. As the database gradually becomes richer, the model can identify various commonly used experimental animals in most scenarios.

[0031] This solution enriches the full life cycle database to improve the universality of the method, and establishes a full life cycle database of commonly used experimental animals, which can make this method applicable to most application scenarios of experimental animals. In addition to spontaneous behavior, commonly used task behaviors (such as behavioral tests) and carrying equipment can maintain a high accuracy rate.

[0032] This solution uses high-quality video training models analyzed by existing technologies to improve accuracy. It will use existing technologies, Deeplabcut and Behaviour Atlas, to conduct preliminary analysis of the videos, combined with manual assistance, to screen high-quality videos and include them in the database to ensure the accuracy of the model. Then, the universality of the model will be ensured by enriching the training content.

[0033] This solution uses algorithms and devices to assist in obtaining comprehensive information. In addition to video information, this solution integrates the basic information, behavioral characteristics, disease models, biological indicators and other information of animals into the database. Assisted by relevant algorithms and auxiliary devices, researchers can obtain the maximum amount of animal information as possible.

[0034] The basic content of the technical solution of this program: This program uses a multimodal large language model to analyze the long-term behavior video of common experimental animals throughout their life cycle, and generates text descriptions of the animals' basic information, behavioral characteristics, disease models, and biological indicators. The technical solution includes five parts: data collection, model training, model adjustment, data analysis, and system construction.

[0035] Data collection: The animals used for data collection include common animals such as mice, rats, dogs, monkeys, and their disease models. Videos of the entire life cycle of more than 10 animals were collected for each disease model. The spontaneous behavior was recorded for 1 hour, and the behavioral test was recorded for 5-15 minutes. The frequency of video collection for animals with a lifespan of less than 3 years was once a week; the frequency of collection for animals over 3 years was once a month. Basic information such as body weight, heart rate, and respiratory rate was also collected. Video recording is divided into single-view and multi-view (in principle, multi-view is used). The multi-view consists of 4 surrounding cameras and 1 top camera (single-view only has a top camera). A checkerboard was used for proofreading during collection. After collection, DeepLabCut and Behaviour Atlas were first used for preliminary analysis to exclude videos with confusing behavioral classifications.

[0036] Model training: After eliminating the confusing videos, refer to the preliminary results of behavioral analysis to annotate the video content. The annotated content includes four parts: basic information, behavioral characteristics, disease model, and biological indicators. Basic information includes animal species, gender, age, and weight. Behavioral characteristics include behavioral definition, duration, and behavioral parameters. Disease models include natural animals or disease model animals, course of disease, and main manifestations. Biological indicators include heart rate, blood pressure, respiratory rate, and EEG. For example: mice, 8 weeks, 22g; rest 30s, sniff 10s, climb 3s, look around 4s, average speed 20mm / s,; anesthetized mice, 15min after anesthesia, movement speed less than 25mm / s; respiratory rate 85 times / min, heart rate 486 times / min, oxygen consumption 1230mm2 / g live weight, ventilation 14ml / min, tidal volume 0.11ml, systolic blood pressure 99mmHg, diastolic blood pressure 66mmHg.

[0037] Model adjustment: After training the model using the dataset, collect new videos as test sets and evaluate the model accuracy. Manually screen action sequences with accuracy below 80% to form a short-term segment dataset for filtering correction and manual correction.

[0038] Data collection and model training are gradually added to the database in the order of spontaneous behavior of natural animals first, then task behavior of natural animals (including social behavior), then spontaneous behavior and animal behavior of disease models, and finally auxiliary devices (such as light recorders, vital signs monitors, various invasive or non-invasive instruments, etc.).

[0039] System construction: Design a complete data analysis process, including basic information, behavioral information, disease models, and biological indicators, as well as their correlation and spatial feature distribution. Build a complete system to guide researchers to record videos, parse videos, analyze data, and export results.

[0040] Based on the above embodiments, the present application also provides an animal information processing method, such as Figure 1 As shown, the method includes:

[0041] Step 102, obtain the first video data of the animal's full cycle, the first video data corresponds to the animal's disease model, and the video data includes a first video corresponding to the animal's spontaneous behavior and a second video corresponding to the animal's behavioral test. As an optional embodiment, in the first video data of the animal's full cycle: the first video recording time is 1 hour, and the second video recording time is 5-15 minutes; the frequency of video collection for animals with a lifespan of less than 3 years is once a week; the frequency of collection for animals over 3 years is once a month; video recording is divided into single-view and multi-view; multi-view consists of 4 surrounding cameras and 1 top camera; single-view includes a top camera; basic information is collected when obtaining the first video data to form the first annotation data of the first video data based on the basic information. This solution can use long-term video to analyze basic information, behavioral information, diseases, and biological characteristics, and can reduce the content of annotation.

[0042] Step 104: Based on the first video data, obtain the first annotated data to train the animal information recognition model; the first annotated data includes: basic information, behavioral characteristics, disease model, and biological indicators. As an optional embodiment, the basic information includes animal species, gender, age, and weight; the behavioral characteristics include behavioral definition, duration, and behavioral parameters; the disease model includes natural animals, or disease model animals, course of disease, and main manifestations; the biological indicators include heart rate, blood pressure, respiratory rate, and EEG. As an optional embodiment, the first annotated data also includes sound information and ambient temperature information.

[0043] Step 106: Acquire test video data and input it into an animal information recognition model for recognition to obtain a test information recognition result, which includes basic information, behavioral characteristics, disease models, and biological indicators.

[0044] Step 108, analyzing the behavioral features in the test information recognition results to screen out action sequences with accuracy rates lower than a preset threshold, and after correction, training the animal information recognition model to determine a trained animal information recognition model.

[0045] The implementation of the embodiments of the present application is similar to that of the above embodiments. The specific implementation can refer to the specific implementation of the above embodiments, which will not be repeated here.

[0046] The solution of the present application can be applied in animal information recognition scenarios, and can obtain the basic information, behavioral information, and disease information of animals based on animal videos. Specifically, the solution can obtain video data of the entire animal cycle, and obtain the corresponding basic information, behavioral characteristics, disease models, biological indicators, etc. as annotations of the video data to train the animal information recognition model, and use the trained animal information recognition model to directly obtain the basic information, behavioral characteristics, disease models, and biological indicators of animals through videos, so as to realize the functions of obtaining basic information of animals and diagnosing diseases. Specifically, this solution can obtain the first video data of the entire cycle of the animal, the first video data corresponds to the disease model of the animal, and the video data includes a first video corresponding to the spontaneous behavior of the animal and a second video corresponding to the animal behavior test; based on the first video data, first annotation data is obtained to train the animal information recognition model; the first annotation data includes: basic information, behavioral characteristics, disease model, biological indicators; after training the animal information recognition model, test video data can be obtained and input into the animal information recognition model for recognition to obtain test information recognition results, and the test information recognition results include basic information, behavioral characteristics, disease model, and biological indicators; after obtaining the test information recognition results, if all the test information recognition results are corrected, the workload is large, so this solution can only analyze the behavioral characteristics in the test information recognition results, and screen the action sequences with an accuracy rate lower than the preset threshold, and after correction, train the animal information recognition model based on the corrected test information to determine the trained animal information recognition model.

[0047] Before the video data is annotated, the scheme can input the video data into the behavior analysis model to analyze the behavior, so as to remove the low-quality videos with chaotic animal behavior. In addition, after the animal information recognition model is trained, the scheme can obtain the video data to be analyzed, and input it into the trained animal information recognition model to determine the analysis results of the animal, and the analysis results include basic information, behavioral characteristics, disease models, and biological indicators. Based on the video data, the scheme can not only analyze the behavior of the animal, but also obtain other information of the animal, which can be more convenient for subsequent processing. Specifically, as an optional embodiment, before obtaining the video annotation data, the method also includes: inputting the video data into the behavior analysis model, determining the behavior analysis results, and removing low-quality videos from the video data based on the behavior analysis results; the method also includes: obtaining the video data to be analyzed, and inputting it into the trained animal information recognition model to determine the analysis results of the animal, and the analysis results include basic information, behavioral characteristics, disease models, and biological indicators. The training process of the model can include: inputting the data into the model, the model obtains the analysis results, and adjusts the model parameters according to the difference between the analysis results and the annotations, so as to obtain a trained model.

[0048] In the process of training the animal information recognition model, this solution can preliminarily train the model based on the existing videos and annotations of disease-free animals, and then train the model based on the videos and annotations of diseased animals. Specifically, as an optional embodiment, the step of training the animal information recognition model includes: obtaining the second video data of the animal and the corresponding annotations, the second video data includes the video of the disease-free animal, and the second video data includes the third video corresponding to the spontaneous behavior of the animal and the fourth video corresponding to the animal behavior test; obtaining the third video and the annotations corresponding to the third video, and training the animal information recognition model; obtaining the fourth video and the annotations corresponding to the fourth video, and training the animal information recognition model after the third video training; obtaining the first video and the annotations corresponding to the first video, and training the animal information recognition model after the fourth video training; obtaining the second video and the annotations corresponding to the second video, and training the animal information recognition model after the first video training. Taking data for model training in the set order can improve the recognition accuracy of the model.

[0049] The existing video data has different annotation sources, some are manual, some are obtained based on video analysis, and some are obtained based on acquisition equipment. Therefore, this solution can first train the model based on low-quality annotations (such as those obtained through video analysis and may be inaccurate) and videos, and then fine-tune the model by manual annotations or annotations obtained based on acquisition equipment. Specifically, as an optional embodiment, the step of training the animal information recognition model includes: obtaining the fifth video and the annotations corresponding to the fifth video, the annotations corresponding to the fifth video are obtained by analyzing the fifth video; obtaining the sixth video and the annotations corresponding to the sixth video, the annotations corresponding to the sixth video are obtained after the auxiliary acquisition device collects data on the animal; training the animal information recognition model based on the fifth video and its annotations, and training the animal information recognition model trained with the fifth video based on the sixth video and its annotations.

[0050] After obtaining the test information recognition results, if all the test information recognition results are corrected, the workload is large. Therefore, this solution can only analyze the behavioral characteristics in the test information recognition results. Specifically, as an optional embodiment, the behavioral characteristics in the test information recognition results are analyzed to screen out action sequences with an accuracy rate lower than a preset threshold, and after correction, the animal information recognition model is trained to determine the trained animal information recognition model, including: analyzing the behavioral characteristics in the test information recognition results according to a manual screening method to screen out action sequences with an accuracy rate lower than a preset threshold; filtering and correcting the screened action sequences and manually correcting them to determine the test update data, and adjusting the animal information recognition model according to the test update data to determine the trained animal information recognition model.

[0051] Based on the above embodiments, the present application also provides an animal information processing device, such as Figure 2 As shown, the device comprises:

[0052] The video data acquisition module 202 is used to acquire first video data of the entire cycle of the animal, wherein the first video data corresponds to the disease model of the animal, and the video data includes a first video corresponding to the spontaneous behavior of the animal and a second video corresponding to the behavior test of the animal.

[0053] The recognition model training module 204 is used to obtain first annotation data based on the first video data to train the animal information recognition model; the first annotation data includes: basic information, behavioral characteristics, disease model, and biological indicators.

[0054] The test data acquisition module 206 is used to acquire test video data and input it into the animal information recognition model for recognition to obtain test information recognition results, which include basic information, behavioral characteristics, disease models, and biological indicators.

[0055] The test result analysis module 208 is used to analyze the behavioral features in the test information recognition results to screen out action sequences with accuracy rates lower than a preset threshold, and after correction, train the animal information recognition model to determine a trained animal information recognition model.

[0056] The implementation of the embodiments of the present application is similar to that of the above embodiments. The specific implementation can refer to the specific implementation of the above embodiments, which will not be repeated here.

[0057] The solution of the present application can be applied in animal information recognition scenarios, and can obtain the basic information, behavioral information, and disease information of animals based on animal videos. Specifically, the solution can obtain video data of the entire animal cycle, and obtain the corresponding basic information, behavioral characteristics, disease models, biological indicators, etc. as annotations of the video data to train the animal information recognition model, and use the trained animal information recognition model to directly obtain the basic information, behavioral characteristics, disease models, and biological indicators of animals through videos, so as to realize the functions of obtaining basic information of animals and diagnosing diseases. Specifically, this solution can obtain the first video data of the entire cycle of the animal, the first video data corresponds to the disease model of the animal, and the video data includes a first video corresponding to the spontaneous behavior of the animal and a second video corresponding to the animal behavior test; based on the first video data, first annotation data is obtained to train the animal information recognition model; the first annotation data includes: basic information, behavioral characteristics, disease model, biological indicators; after training the animal information recognition model, test video data can be obtained and input into the animal information recognition model for recognition to obtain test information recognition results, and the test information recognition results include basic information, behavioral characteristics, disease model, and biological indicators; after obtaining the test information recognition results, if all the test information recognition results are corrected, the workload is large, so this solution can only analyze the behavioral characteristics in the test information recognition results, and screen the action sequences with an accuracy rate lower than the preset threshold, and after correction, train the animal information recognition model based on the corrected test information to determine the trained animal information recognition model. After the animal information recognition model is trained, the video data to be analyzed can be obtained and input into the trained animal information recognition model to determine the analysis results of the animal, which include basic information, behavioral characteristics, disease models, and biological indicators. This solution can not only analyze the behavior of animals based on video data, but also obtain other information about animals, which can be more convenient for subsequent processing.

[0058] It should be noted that the division of units and / or modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional unit and / or module in each embodiment of the present application may be integrated into a processing unit and / or module, or each unit and / or module may exist physically separately, or two or more units and / or modules may be integrated into one unit and / or module. The above-mentioned integrated units and / or modules may be implemented in the form of hardware or in the form of software functional units and / or modules.

[0059] If the integrated units and / or modules are implemented in the form of software functional units and / or modules and sold or used as independent products, they can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the relevant technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0060] In addition, the data transmission device and the data transmission method provided in the above embodiments are based on the same application concept. Since the principles of solving problems by the method and the device are similar, the implementation of the device and the method can refer to each other, and the repeated parts will not be repeated.

[0061] Figure 3 A structural block diagram of a network device is shown according to an exemplary embodiment.

[0062] like Figure 3 As shown, the network device 1100 includes at least: a processor 1110 , a memory 1120 , and a transceiver 1130 .

[0063] The transceiver 1130 is used to receive and send data under the control of the processor 1110 .

[0064] exist Figure 3In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by processor 1110 and memory represented by memory 1120. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 1130 may be a plurality of components, namely, a transmitter and a receiver, providing units and / or modules for communicating with various other devices on a transmission medium, which may include a wireless channel, a wired channel, an optical cable, and other transmission media.

[0065] The processor 1110 is responsible for managing the bus architecture and general processing, and the memory 1120 can store data used by the processor 1110 when performing operations.

[0066] Optionally, the processor 1110 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or a complex programmable logic device (CPLD), and the processor 1110 may also adopt a multi-core architecture. The processor 1110 and the memory 1120 may also be physically arranged separately.

[0067] The processor 1110 calls the computer program stored in the memory 1120 to execute any one of the methods for allocating a cell radio network temporary identifier provided in the above embodiments of the present application according to the obtained executable instructions.

[0068] Figure 4 A structural block diagram of a user equipment is shown according to an exemplary embodiment.

[0069] like Figure 4 As shown, the user equipment 1300 includes at least: a processor 1310 , a memory 1320 , and a transceiver 1330 .

[0070] The transceiver 1330 is used to receive and send data under the control of the processor 1310 .

[0071] exist Figure 4In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by processor 1310 and various circuits of memory represented by memory 1320 are linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 1330 may be a plurality of components, namely, a transmitter and a receiver, providing units and / or modules for communicating with various other devices on a transmission medium, including transmission media such as wireless channels, wired channels, and optical cables. For different user devices, the user interface 1340 may also be an interface capable of externally and internally connecting required devices, and the connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, and the like.

[0072] The processor 1310 is responsible for managing the bus architecture and general processing, and the memory 1320 can store data used by the processor 1310 when performing operations.

[0073] Optionally, the processor 1310 may be a CPU (central processing unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or a CPLD (Complex Programmable Logic Device), and the processor 1310 may also adopt a multi-core architecture. The processor 1310 and the memory 1320 may also be physically arranged separately.

[0074] The processor 1310 calls the computer program stored in the memory 1320 to execute any one of the methods for allocating a cell radio network temporary identifier provided in the above embodiments of the present application according to the obtained executable instructions.

[0075] It should be noted here that the above-mentioned device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0076] In addition, a storage medium is provided in an embodiment of the present application, on which a computer program is stored, and when the computer program is executed by the processor, the data transmission method in the above embodiments is implemented. The storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic storage (such as floppy disk, hard disk, tape, magneto-optical disk (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid state drive (SSD)), etc.

[0077] In an embodiment of the present application, a program product is provided, for example, the program product is an FPGA chip or a DSP chip, and the program product includes executable instructions, and the executable instructions are stored in a storage medium. The processor reads the executable instructions from the storage medium, so that when the executable instructions are executed by the processor, the data transmission method in each of the above embodiments is implemented.

[0078] The solution of the present application can be applied in animal information recognition scenarios, and can obtain the basic information, behavioral information, and disease information of animals based on animal videos. Specifically, the solution can obtain video data of the entire animal cycle, and obtain the corresponding basic information, behavioral characteristics, disease models, biological indicators, etc. as annotations of the video data to train the animal information recognition model, and use the trained animal information recognition model to directly obtain the basic information, behavioral characteristics, disease models, and biological indicators of animals through videos, so as to realize the functions of obtaining basic information of animals and diagnosing diseases. Specifically, this solution can obtain the first video data of the entire cycle of the animal, the first video data corresponds to the disease model of the animal, and the video data includes a first video corresponding to the spontaneous behavior of the animal and a second video corresponding to the animal behavior test; based on the first video data, first annotation data is obtained to train the animal information recognition model; the first annotation data includes: basic information, behavioral characteristics, disease model, biological indicators; after training the animal information recognition model, test video data can be obtained and input into the animal information recognition model for recognition to obtain test information recognition results, and the test information recognition results include basic information, behavioral characteristics, disease model, and biological indicators; after obtaining the test information recognition results, if all the test information recognition results are corrected, the workload is large, so this solution can only analyze the behavioral characteristics in the test information recognition results, and screen the action sequences with an accuracy rate lower than the preset threshold, and after correction, train the animal information recognition model based on the corrected test information to determine the trained animal information recognition model. After the animal information recognition model is trained, the video data to be analyzed can be obtained and input into the trained animal information recognition model to determine the analysis results of the animal, which include basic information, behavioral characteristics, disease models, and biological indicators. This solution can not only analyze the behavior of animals based on video data, but also obtain other information about animals, which can be more convenient for subsequent processing.

[0079] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0080] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer executable instructions. These computer executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0081] These processor executable instructions may also be stored in a processor readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the processor readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0082] These processor-executable instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0083] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0084] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for processing animal information, characterized in that: The method comprises: Acquire first video data of the entire cycle of the animal, wherein the first video data corresponds to the disease model of the animal, and the video data includes a first video corresponding to the spontaneous behavior of the animal and a second video corresponding to the behavior test of the animal; According to the first video data, first annotated data is obtained to train an animal information recognition model; the first annotated data includes: basic information, behavioral characteristics, disease model, and biological indicators; Acquire test video data and input it into an animal information recognition model for recognition to obtain test information recognition results, which include basic information, behavioral characteristics, disease models, and biological indicators; The behavioral characteristics in the test information recognition results are analyzed to screen out action sequences with accuracy rates lower than a preset threshold, and after correction, the animal information recognition model is trained to determine a trained animal information recognition model.

2. The method according to claim 1, characterized in that Before acquiring the video annotation data, the method further includes: Input the video data into the behavior analysis model, determine the behavior analysis results, and remove low-quality videos from the video data based on the behavior analysis results; The method further comprises: The video data to be analyzed is obtained and input into a trained animal information recognition model to determine the analysis results of the animal, which include basic information, behavioral characteristics, disease models, and biological indicators.

3. The method according to claim 1, characterized in that The steps of training the animal information recognition model include: Acquire second video data of the animal and corresponding annotations, wherein the second video data includes a video of a disease-free animal, and the second video data includes a third video corresponding to the spontaneous behavior of the animal and a fourth video corresponding to a behavior test of the animal; Obtaining a third video and a label corresponding to the third video, and training an animal information recognition model; Obtaining a fourth video and a label corresponding to the fourth video, and training an animal information recognition model trained with the third video; Obtaining the first video and the annotations corresponding to the first video, and training the animal information recognition model trained by the fourth video; A second video and a label corresponding to the second video are obtained, and the animal information recognition model trained with the first video is trained.

4. The method according to claim 1, characterized in that The steps of training the animal information recognition model include: Obtaining a fifth video and a label corresponding to the fifth video, where the label corresponding to the fifth video is obtained after analyzing the fifth video; Obtaining a sixth video and a label corresponding to the sixth video, wherein the label corresponding to the sixth video is obtained after collecting data on the animal through an auxiliary collection device; The animal information recognition model is trained based on the fifth video and its annotations, and the animal information recognition model trained with the fifth video is trained based on the sixth video and its annotations.

5. The method according to claim 1, characterized in that The basic information includes animal species, gender, age, and weight; the behavioral characteristics include behavioral definition, duration, and behavioral parameters; the disease model includes natural animals or disease model animals, course of disease, and main manifestations; the biological indicators include heart rate, blood pressure, respiratory rate, and EEG.

6. The method according to claim 1, characterized in that In the first video data of the animal's full cycle: the first video recording time is 1 hour, and the second video recording time is 5-15 minutes; the frequency of video collection for animals with a lifespan of less than 3 years is once a week; the frequency of collection for animals over 3 years is once a month; video recording is divided into single-view and multi-view; the multi-view consists of 4 surrounding cameras and 1 top camera; The single viewing angle includes a top camera; basic information is collected when acquiring the first video data to form first annotation data of the first video data based on the basic information.

7. The method according to claim 1, characterized in that The first annotation data also includes sound information and ambient temperature information.

8. The method according to claim 1, characterized in that The behavioral characteristics in the test information recognition results are analyzed to screen the action sequences with accuracy rates lower than a preset threshold, and after correction, the animal information recognition model is trained to determine a trained animal information recognition model, including: According to the manual screening method, the behavioral characteristics in the test information recognition results are analyzed to screen the action sequences whose accuracy is lower than the preset threshold; The filtered action sequences are subjected to filtering correction and manual correction to determine the test update data, and the animal information recognition model is adjusted according to the test update data to determine the trained animal information recognition model.

9. An animal information processing device, characterized in that: The device comprises: A video data acquisition module, used to acquire first video data of the entire cycle of the animal, wherein the first video data corresponds to the disease model of the animal, and the video data includes a first video corresponding to the spontaneous behavior of the animal and a second video corresponding to the behavior test of the animal; The recognition model training module is used to obtain first labeled data based on the first video data to train the animal information recognition model; the first labeled data includes: basic information, behavioral characteristics, disease model, biological indicators; The test data acquisition module is used to acquire test video data and input it into the animal information recognition model for recognition to obtain test information recognition results, which include basic information, behavioral characteristics, disease models, and biological indicators; The test result analysis module is used to analyze the behavioral characteristics in the test information recognition results to screen out action sequences with accuracy rates lower than a preset threshold, and after correction, train the animal information recognition model to determine a trained animal information recognition model.

10. A network device, characterized in that: include: A memory, a transceiver, and a processor; wherein the memory is used to store a computer program; the transceiver is used to send and receive data under the control of the processor; The processor is used to read the computer program in the memory and execute the method according to claims 1-8.

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