CN114883010A - Livestock survival state judging method and device, storage medium and terminal equipment - Google Patents
Livestock survival state judging method and device, storage medium and terminal equipment Download PDFInfo
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Abstract
The embodiment of the application provides a livestock survival state judging method, a livestock survival state judging device, a storage medium and terminal equipment, wherein the method comprises the following steps: acquiring a plurality of text data of a target to be detected in a preset time period; carrying out state discrimination operation on the target data through a pre-trained state discrimination model to obtain the existence state of the target to be detected; and judging whether to send out reminding information according to the deposit and death state. The embodiment of the application can reduce the labor cost and improve the timeliness and the accuracy of supervision of the livestock survival state.
Description
Technical Field
The application relates to the technical field of electronic communication, in particular to the technical field of livestock survival state judgment, and particularly relates to a method, a device, a storage medium and a terminal device for judging livestock survival states.
Background
In recent years, the overall development trend of livestock breeding in China is that the number of livestock breeding households is reduced, the production scale is gradually enlarged, the traditional artificial breeding mode cannot meet the existing production scale, and the informatization of livestock breeding is urgent.
At present, the health state of the pigs is observed and judged mainly by adopting a traditional manual observation method in China, time and labor are wasted, efficiency is not high, and some abnormal behavior expressions in the breeding process are easily ignored due to the time and energy limitation of people, so that serious economic loss is caused. In addition, the livestock supply is increased year by year, and the death rate of livestock is increased due to the fact that the livestock is easy to get ill, so that how to effectively supervise the livestock is achieved, and the phenomenon that large-scale epidemic diseases or unqualified meat flows into the market due to the fact that the livestock die of diseases are prevented from occurring, and the problem is faced by current supervision departments.
Disclosure of Invention
The embodiment of the application provides a method, a device, a storage medium and a terminal device for judging the livestock survival state, which can reduce labor cost and improve timeliness and accuracy of supervision on the livestock survival state.
An aspect of an embodiment of the present application provides a method for determining livestock survival status, including:
acquiring a plurality of text data of a target to be detected in a preset time period;
carrying out state discrimination operation on the target data through a pre-trained state discrimination model to obtain the existence state of the target to be detected;
and judging whether to send out reminding information according to the deposit and death state.
In the livestock survival status determination method according to the embodiment of the application, before the status determination operation is performed on the plurality of target data through a pre-trained status determination model, the method further includes:
acquiring a training sample of a state discrimination model to be trained, wherein the training sample comprises a text data group provided with a label, the text data group comprises a plurality of text data with the same identification, and the label is used for indicating the survival and death state of the text data group;
performing feature extraction on a plurality of text data of the same group of text data in the training sample through the state discrimination model to be trained to obtain first text feature vectors corresponding to the plurality of text data;
judging the existence state of a text data set in the training sample based on the first text feature vector through the state discrimination model to be trained to obtain a first recognition result of the text data set;
and adjusting parameters of the state discrimination model to be trained based on the first recognition result and the label of the text data set to obtain the pre-trained state discrimination model.
In the livestock survival state discrimination method according to the embodiment of the present application, after obtaining the pre-trained state discrimination model, the method further includes:
performing feature evaluation operation on the text feature vectors corresponding to the plurality of text data in the same text data group through a pre-trained feature evaluation model respectively to obtain a comprehensive score of each text feature vector;
filtering the interference text data corresponding to the text feature vector with the comprehensive score lower than a preset threshold value from the training sample;
and iteratively training the state discrimination model to be trained based on the training sample for filtering the interference text data.
In the method for determining livestock survival status according to the embodiment of the present application, the performing feature evaluation operation on the text feature vectors corresponding to the plurality of text data in the same group of text data sets respectively through a pre-trained feature evaluation model to obtain the comprehensive score of each text feature vector includes:
and respectively inputting text feature vectors corresponding to a plurality of text data in the same group of text data groups into a random forest model, and respectively calculating the Kernel index score of each text feature vector to obtain the comprehensive score of each text feature vector.
In the method for determining livestock survival status in the embodiment of the application, the performing status determination operation on the plurality of target data through a pre-trained status determination model to obtain the survival status of the target to be detected includes:
performing feature extraction on the plurality of text data through the pre-trained state discrimination model to obtain second text feature vectors corresponding to the plurality of text data;
and judging the existence states of the plurality of text data based on the second text feature vector through the pre-trained state discrimination model to obtain a second recognition result of the plurality of text data.
In the method for determining livestock survival status according to the embodiment of the present application, the obtaining of the plurality of text data of the target to be detected in the preset time period includes:
acquiring original data of the target to be detected in a preset time period in a timing mode;
and performing data processing according to the original data to obtain the plurality of text data.
In the method for determining livestock survival and death states in the embodiment of the application, the periodically acquiring the original data of the target to be detected in a preset time period includes:
detecting whether the original data has a missing value in a preset time period;
and if the missing value exists in the preset time period, selecting the median of all the original data in the preset time period and writing the median into the position corresponding to the missing value.
Correspondingly, another aspect of the embodiments of the present application further provides a livestock survival status determination device, including:
the acquisition module is used for acquiring a plurality of text data of the target to be detected within a preset time period;
the judging module is used for carrying out state judging operation on the target data through a pre-trained state judging model to obtain the existence state of the target to be detected;
and the judging module is used for judging whether to send out reminding information according to the deposit and death state.
Accordingly, another aspect of the embodiments of the present application further provides a storage medium, where the storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the livestock in-existence status determination method.
Correspondingly, the embodiment of the application also provides terminal equipment, which comprises a processor and a memory, wherein the memory stores a plurality of instructions, and the processor loads the instructions to execute the livestock survival state judgment method.
The embodiment of the application provides a method, a device, a storage medium and a terminal device for judging livestock survival and death states, wherein the method comprises the steps of acquiring a plurality of text data of a target to be detected in a preset time period; carrying out state discrimination operation on the target data through a pre-trained state discrimination model to obtain the existence state of the target to be detected; and judging whether to send out reminding information according to the deposit and death state. The embodiment of the application can reduce the labor cost and improve the timeliness and the accuracy of supervision of the livestock survival state.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic flow chart of a livestock survival status determination method according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a livestock survival status determination device according to an embodiment of the present application.
Fig. 3 is another schematic structural diagram of a livestock survival status determination device according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without inventive step, are within the scope of the present application.
The embodiment of the application provides a livestock survival state judging method which can be applied to terminal equipment. The terminal equipment can be a television, a smart phone, a tablet computer and the like.
In order to know the health state of livestock, the traditional manual observation method is mostly adopted for observation and judgment at present, so that the time and labor are wasted, the efficiency is not high, and some abnormal behavior expressions in the breeding process are easily ignored due to the limitation of time and energy of people, so that the serious economic loss is caused. In addition, the livestock supply is increased year by year, and the death rate of livestock is increased due to the fact that the livestock is easy to get ill, so that how to effectively supervise the livestock is achieved, and the phenomenon that large-scale epidemic diseases or unqualified meat flows into the market due to the fact that the livestock die of diseases are prevented from occurring, and the problem is faced by current supervision departments. In order to solve the above technical problem, an embodiment of the present application provides a method for determining livestock survival status. By utilizing the livestock survival state judging method provided by the embodiment of the application, the timeliness and the accuracy of the supervision of the livestock survival state can be improved while the labor cost can be reduced.
Referring to fig. 1, fig. 1 is a schematic flow chart of a livestock survival status determination method according to an embodiment of the present application. The livestock survival state judging method is applied to terminal equipment, and can comprise the following steps:
In this embodiment, the target to be measured refers to a livestock, and the survival state of the animal can be represented by vital signs, such as body temperature and motion amount. In order to identify the existence state of the target to be detected more scientifically and reasonably through the vital signs, the original data of the target to be detected within a preset time period (for example, 2 hours) is acquired regularly (for example, 1 minute), namely, the vital signs, and the data acquisition device can be installed on the body of the target to be detected in the original data acquisition mode. In the scheme, the vital signs specifically refer to the body temperature and the exercise amount of the target to be measured. After the original data of the target to be detected in the preset time period are acquired, data processing is performed according to the original data to obtain a plurality of text data which can be strongly associated with the memory status of the target to be detected, for example, the original data in the preset time period are subjected to averaging, ratio, change trend and feature combination to obtain 33 text data listed in table 1.
It is to be explained that the mean value is the average value of all values in the period of time;
the proportion is the ratio between the number of the data meeting the condition in the time period and the number of all the data;
the combination between the features is an operation between the two features;
the trend is calculated as follows:
constructing a sample for the values in the time period;
((t i ,x i ) 1,2,3,. multidot.m, wherein t is i ＝i，x i A value representing at the ith time instant;
using a straight-line fitting function h θ (x)＝θ 0 +θ 1 x；
For the objective function
Using least squares to make J (theta) 0 ,θ 1 ) At minimum, theta at this time 1 Is the magnitude of the trend.
TABLE 1
Specifically, the original data of the target to be measured in a preset time period is obtained at regular time (for example, 1 minute), and data processing is performed according to the original data to obtain a plurality of text data.
In some embodiments, the number of the text feature vectors in the input state discrimination model needs to be strictly executed according to requirements, but data loss is easily caused in the process of data transmission of the original data, so that the text feature vectors in the subsequent input state discrimination model are incomplete, and therefore, in the embodiment, whether missing values exist in the original data within a preset time period is detected; and if the missing value exists in the preset time period, selecting the median of all the original data in the preset time period and writing the median into the position corresponding to the missing value.
And 102, carrying out state discrimination operation on the target data through a pre-trained state discrimination model to obtain the existence state of the target to be detected.
In this embodiment, the pre-trained state discrimination model can discriminate the existence state, i.e. survival or death, of the target to be measured corresponding to the plurality of target data according to the plurality of target data.
It should be noted that the state discrimination model may be obtained by training based on models such as a decision tree, a gradient boosting tree, an SVM, naive bayes, and logistic regression, and the random forest model has the best performance through comparing the training results of the models, so the state discrimination model is obtained by preferably further training the random forest model in this embodiment.
The training process of the state discrimination model comprises the following steps:
the method comprises the steps of obtaining a training sample of a state discrimination model to be trained, wherein the training sample comprises a text data group provided with a label, the text data group comprises a plurality of text data with the same identification, the label is used for indicating the existence state of the text data group, the labeled data are subjected to hierarchical sampling, 80% of the labeled data are used as a training set, 20% of the labeled data are used as a test set, and the training set is used for model training. Before model training, the samples are standardized to eliminate the influence caused by different data dimensions. Common data normalization methods are min-max normalization, z-score normalization, nonlinear normalization, and the like;
performing feature extraction on a plurality of text data of the same group of text data in the training sample through the state discrimination model to be trained to obtain first text feature vectors corresponding to the plurality of text data;
judging the existence state of a text data set in the training sample based on the first text feature vector through the state discrimination model to be trained to obtain a first recognition result of the text data set;
and adjusting parameters of the state discrimination model to be trained based on the first recognition result and the label of the text data set to obtain the pre-trained state discrimination model.
In some embodiments, since not all text data can be used as training samples for improving the discrimination accuracy of the state discrimination model, and there may be some text data with weaker importance compared with other text data, in order to further improve the discrimination accuracy of the state discrimination model and reduce the complexity of model training, in this embodiment, a plurality of text data constructed in advance are screened to obtain more representative text data.
Specifically, feature evaluation operation is carried out on text feature vectors corresponding to a plurality of text data in the same group of text data respectively through a pre-trained feature evaluation model, so that a comprehensive score of each text feature vector is obtained; filtering interference text data corresponding to the text feature vectors with the comprehensive scores lower than a preset threshold value from the training samples; and iteratively training the state discrimination model to be trained based on the training sample for filtering the interference text data.
The feature evaluation model can be obtained based on tree model training, in other embodiments, a variance selection method, a correlation coefficient method and the like can be adopted, feature importance evaluation is performed on the text data through the feature evaluation model, some text data with importance obviously lower than that of other text data are excluded, complexity of the model is reduced, 19 features are selected finally, and then parameter adjustment and training are performed.
Taking a tree model as an example, the text data screening process is further explained:
using the Gini index to score the comprehensive score VIM Gini Vim (variable import measures) represents the variable importance score, Gini represents the kini index (GI), assuming that there are m text feature vectors: x 1 ,X 2 ,X 3 ,..,X c Now, each text feature vector X is calculated j (ii) a King index score of VIM Gini I.e. the j-th feature is the average of the variables of the splitting purities of all decision tree nodes in the random forest.
The Gini index is calculated as:
wherein K represents K categories, p mk Indicating the proportion of class k in node m.
Intuitively, two samples are randomly extracted from the node m, and the categories mark the probability of inconsistency.
Characteristic X j The importance of the node m, i.e., the Gini index change amount before and after the node m branches, is:
VIM jm Gini ＝GI m -GI l -GI r
wherein, GI l And GI r Respectively representing the Gini indexes of two new nodes after branching.
If the feature X j The node appearing in decision tree i is set M, then X j The importance in the ith tree is:
VIM tj Gini ＝∑ m∈M VIM jm Gini
assuming a total of n trees in a random forest, then:
finally, the obtained keny index score is normalized
After the pre-trained state discrimination model is obtained, feature extraction is carried out on the plurality of text data through the state discrimination model to be trained to obtain second text feature vectors corresponding to the plurality of text data; and judging the existence states of the plurality of text data based on the second text feature vector through the state discrimination model to be trained to obtain a second recognition result (survival or death) of the plurality of text data.
And 103, judging whether to send out reminding information according to the deposit and death state.
In this embodiment, the existence state of the target to be detected is determined according to the recognition result obtained by the state discrimination model, and if the dead target to be detected appears, the worker is timely notified to go to check the dead target to be detected.
In order to avoid increasing workload of workers due to misjudgment of the state discrimination model, in some embodiments, when the first recognition result of the target to be detected is a death state, the target to be detected is marked as a to-be-processed state, the reminding information is not sent out temporarily, the text data of the target to be detected is obtained again after 10 minutes, recognition is carried out again through the state discrimination model, if the recognition result is also death, the target to be detected is judged as the death state, the reminding information is sent out in time, and if not, misjudgment is judged as a survival state.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
In particular implementation, the present application is not limited by the execution sequence of the described steps, and some steps may be performed in other sequences or simultaneously without conflict.
According to the method for judging the livestock survival state, the plurality of text data of the target to be detected in the preset time period are obtained; carrying out state discrimination operation on the target data through a pre-trained state discrimination model to obtain the existence state of the target to be detected; and judging whether to send out reminding information according to the deposit and death state. The embodiment of the application can reduce the labor cost and improve the timeliness and the accuracy of supervision of the livestock survival state.
The embodiment of the application also provides a livestock survival state judging device which can be integrated in the terminal equipment. The terminal equipment can be a television, a smart phone, a tablet computer and the like.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a livestock survival status determination device according to an embodiment of the present application. The livestock survival status determination device 30 may include:
the acquiring module 31 is configured to acquire a plurality of text data of a target to be detected within a preset time period;
the judging module 32 is configured to perform a state judging operation on the plurality of target data through a pre-trained state judging model to obtain a survival state of the target to be detected;
and the judging module 33 is configured to judge whether to send out a reminding message according to the deposit and death state.
In some embodiments, the apparatus further includes a training module, configured to obtain a training sample of a state discrimination model to be trained, where the training sample includes a text data set provided with a tag, the text data set includes a plurality of text data with the same identifier, and the tag is used to indicate an existence state of the text data set; performing feature extraction on a plurality of text data of the same group of text data in the training sample through the state discrimination model to be trained to obtain first text feature vectors corresponding to the plurality of text data; judging the existence state of a text data set in the training sample based on the first text feature vector through the state discrimination model to be trained to obtain a first recognition result of the text data set; and adjusting parameters of the state discrimination model to be trained based on the first recognition result and the label of the text data set to obtain the pre-trained state discrimination model.
In some embodiments, the apparatus further includes a calculation module, configured to perform feature evaluation operations on text feature vectors corresponding to multiple text data in the same group of text data groups respectively through a pre-trained feature evaluation model, so as to obtain a comprehensive score of each text feature vector; filtering the interference text data corresponding to the text feature vector with the comprehensive score lower than a preset threshold value from the training sample; and iteratively training the state discrimination model based on the training sample for filtering the interference text data.
In some embodiments, the calculation module is configured to input text feature vectors corresponding to a plurality of text data in the same group of text data groups to a random forest model, and calculate a kini index score of each text feature vector, so as to obtain a comprehensive score of each text feature vector.
In some embodiments, the determination module is configured to perform feature extraction on the plurality of text data through the state determination model to be trained to obtain second text feature vectors corresponding to the plurality of text data; and judging the existence states of the plurality of text data based on the second text feature vector through the state discrimination model to be trained to obtain a second recognition result of the plurality of text data.
In some embodiments, the obtaining module is configured to obtain, at regular time, original data of the target to be measured in a preset time period; and performing data processing according to the original data to obtain the plurality of text data.
In some embodiments, the obtaining module is configured to detect whether missing values exist in the raw data within a preset time period; and if the missing value exists in the preset time period, selecting the median of all the original data in the preset time period and writing the median into the position corresponding to the missing value.
In specific implementation, the modules may be implemented as independent entities, or may be combined arbitrarily and implemented as one or several entities.
As can be seen from the above, the livestock survival state determination device 30 provided in the embodiment of the present application obtains a plurality of text data of the target to be detected within the preset time period through the obtaining module 31; the judging module 32 performs state judging operation on the target data through a pre-trained state judging model to obtain the existence state of the target to be detected; the judging module 33 judges whether to send out a reminding message according to the deposit and death state.
Referring to fig. 3, fig. 3 is another schematic structural diagram of a livestock death status determination apparatus according to an embodiment of the present application, the livestock death status determination apparatus 30 includes a memory 120, one or more processors 180, and one or more applications, wherein the one or more applications are stored in the memory 120 and configured to be executed by the processor 180; the processor 180 may include an acquisition module 31, a discrimination module 32, and a determination module 33. For example, the structures and connection relationships of the above components may be as follows:
the memory 120 may be used to store applications and data. The memory 120 stores applications containing executable code. The application programs may constitute various functional modules. The processor 180 executes various functional applications and data processing by running the application programs stored in the memory 120. Further, the memory 120 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 120 may also include a memory controller to provide the processor 180 with access to the memory 120.
The processor 180 is a control center of the device, connects various parts of the entire terminal using various interfaces and lines, performs various functions of the device and processes data by running or executing an application program stored in the memory 120 and calling data stored in the memory 120, thereby monitoring the entire device. Optionally, processor 180 may include one or more processing cores; preferably, the processor 180 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, an application program, and the like.
Specifically, in this embodiment, the processor 180 loads the executable code corresponding to the process of one or more application programs into the memory 120 according to the following instructions, and the processor 180 runs the application programs stored in the memory 120, thereby implementing various functions:
the acquiring module 31 is configured to acquire a plurality of text data of a target to be detected within a preset time period;
the judging module 32 is configured to perform a state judging operation on the plurality of target data through a pre-trained state judging model to obtain a survival state of the target to be detected;
and the judging module 33 is configured to judge whether to send out a reminding message according to the deposit and death state.
In some embodiments, the apparatus further includes a training module, configured to obtain a training sample of a state discrimination model to be trained, where the training sample includes a text data set provided with a tag, the text data set includes a plurality of text data with the same identifier, and the tag is used to indicate an existence state of the text data set; performing feature extraction on a plurality of text data of the same group of text data in the training sample through the state discrimination model to be trained to obtain first text feature vectors corresponding to the plurality of text data; judging the existence state of a text data set in the training sample based on the first text feature vector through the state discrimination model to be trained to obtain a first recognition result of the text data set; and adjusting parameters of the state discrimination model to be trained based on the first recognition result and the label of the text data set to obtain the pre-trained state discrimination model.
In some embodiments, the apparatus further includes a calculation module, configured to perform feature evaluation operations on text feature vectors corresponding to multiple text data in the same group of text data groups through a pre-trained feature evaluation model, respectively, to obtain a comprehensive score of each text feature vector; filtering the interference text data corresponding to the text feature vector with the comprehensive score lower than a preset threshold value from the training sample; and iteratively training the state discrimination model based on the training sample for filtering the interference text data.
In some embodiments, the calculation module is configured to input text feature vectors corresponding to multiple text data in the same group of text data groups to a random forest model, and calculate a kini index score of each text feature vector, so as to obtain a comprehensive score of each text feature vector.
In some embodiments, the determination module is configured to perform feature extraction on the plurality of text data through the state determination model to be trained to obtain second text feature vectors corresponding to the plurality of text data; and judging the existence states of the plurality of text data based on the second text feature vector through the state discrimination model to be trained to obtain a second recognition result of the plurality of text data.
In some embodiments, the obtaining module is configured to obtain, at regular time, original data of the target to be measured in a preset time period; and performing data processing according to the original data to obtain the plurality of text data.
In some embodiments, the obtaining module is configured to detect whether missing values exist in the raw data within a preset time period; and if the missing value exists in the preset time period, selecting the median of all the original data in the preset time period and writing the median into the position corresponding to the missing value.
The embodiment of the application further provides the terminal equipment. The terminal equipment can be equipment such as a smart phone, a computer and a tablet computer.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a terminal device provided in the embodiment of the present application, where the terminal device may be used to implement the livestock survival status determination method provided in the foregoing embodiment. The terminal device 1200 may be a television, a smart phone, or a tablet computer.
As shown in fig. 4, the terminal device 1200 may include an RF (Radio Frequency) circuit 110, a memory 120 including one or more computer-readable storage media (only one shown in the figure), an input unit 130, a display unit 140, a sensor 150, an audio circuit 160, a transmission module 170, a processor 180 including one or more processing cores (only one shown in the figure), and a power supply 190. Those skilled in the art will appreciate that the terminal device 1200 configuration shown in fig. 4 does not constitute a limitation of terminal device 1200, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components. Wherein:
the RF circuit 110 is used for receiving and transmitting electromagnetic waves, and performs interconversion between the electromagnetic waves and electrical signals, so as to communicate with a communication network or other devices. The RF circuitry 110 may include various existing circuit elements for performing these functions, such as an antenna, a radio frequency transceiver, a digital signal processor, an encryption/decryption chip, a Subscriber Identity Module (SIM) card, memory, and so forth. The RF circuitry 110 may communicate with various networks such as the internet, an intranet, a wireless network, or with other devices over a wireless network.
The memory 120 can be used for storing software programs and modules, such as program instructions/modules corresponding to the livestock survival state determination method in the above embodiment, and the processor 180 executes various functional applications and data processing by operating the software programs and modules stored in the memory 120, can automatically select a vibration reminding mode according to the current scene where the terminal device is located to determine the livestock survival state, can ensure that scenes such as a conference and the like are not disturbed, can ensure that a user can sense an incoming call, and improves the intelligence of the terminal device. Memory 120 may include high speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 120 may further include memory located remotely from the processor 180, which may be connected to the terminal device 1200 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input unit 130 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, input unit 130 may include a touch-sensitive surface 131 as well as other input devices 132. The touch-sensitive surface 131, also referred to as a touch display screen or a touch pad, may collect touch operations by a user on or near the touch-sensitive surface 131 (e.g., operations by a user on or near the touch-sensitive surface 131 using a finger, a stylus, or any other suitable object or attachment), and drive the corresponding connection device according to a predetermined program. Alternatively, the touch sensitive surface 131 may comprise two parts, a touch detection means and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 180, and can receive and execute commands sent by the processor 180. Additionally, the touch-sensitive surface 131 may be implemented using various types of resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch-sensitive surface 131, the input unit 130 may also include other input devices 132. In particular, other input devices 132 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.
The display unit 140 may be used to display information input by or provided to a user and various graphic user interfaces of the terminal apparatus 1200, which may be configured by graphics, text, icons, video, and any combination thereof. The Display unit 140 may include a Display panel 141, and optionally, the Display panel 141 may be configured in the form of an LCD (Liquid Crystal Display), an OLED (Organic Light-Emitting Diode), or the like. Further, the touch-sensitive surface 131 may cover the display panel 141, and when a touch operation is detected on or near the touch-sensitive surface 131, the touch operation is transmitted to the processor 180 to determine the type of the touch event, and then the processor 180 provides a corresponding visual output on the display panel 141 according to the type of the touch event. Although in FIG. 4, touch-sensitive surface 131 and display panel 141 are shown as two separate components to implement input and output functions, in some embodiments, touch-sensitive surface 131 may be integrated with display panel 141 to implement input and output functions.
The terminal device 1200 may also include at least one sensor 150, such as light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel 141 according to the brightness of ambient light, and a proximity sensor that may turn off the display panel 141 and/or the backlight when the terminal device 1200 is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when the mobile phone is stationary, and can be used for applications of recognizing the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which may be further configured in the terminal device 1200, detailed descriptions thereof are omitted.
The audio circuitry 160, speaker 161, microphone 162 may provide an audio interface between the user and the terminal device 1200. The audio circuit 160 may transmit the electrical signal converted from the received audio data to the speaker 161, and convert the electrical signal into a sound signal for output by the speaker 161; on the other hand, the microphone 162 converts the collected sound signal into an electric signal, converts the electric signal into audio data after being received by the audio circuit 160, and then outputs the audio data to the processor 180 for processing, and then to the RF circuit 110 to be transmitted to, for example, another terminal, or outputs the audio data to the memory 120 for further processing. The audio circuitry 160 may also include an earbud jack to provide communication of peripheral headphones with the terminal device 1200.
The terminal device 1200, which may assist the user in sending and receiving e-mails, browsing web pages, accessing streaming media, etc., through the transmission module 170 (e.g., Wi-Fi module), provides the user with wireless broadband internet access. Although fig. 4 shows the transmission module 170, it is understood that it does not belong to the essential constitution of the terminal device 1200, and may be omitted entirely as needed within a range not changing the essence of the invention.
The processor 180 is a control center of the terminal device 1200, connects various parts of the entire mobile phone by using various interfaces and lines, and performs various functions of the terminal device 1200 and processes data by running or executing software programs and/or modules stored in the memory 120 and calling data stored in the memory 120, thereby performing overall monitoring of the mobile phone. Optionally, processor 180 may include one or more processing cores; in some embodiments, the processor 180 may integrate an application processor, which primarily handles operating systems, user interfaces, applications, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 180.
Terminal device 1200 also includes a power supply 190 for powering the various components, which in some embodiments may be logically coupled to processor 180 via a power management system to manage power discharge and power consumption via the power management system. The power supply 190 may also include any component including one or more of a dc or ac power source, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
Although not shown, the terminal device 1200 may further include a camera (e.g., a front camera, a rear camera), a bluetooth module, and the like, which are not described in detail herein. Specifically, in this embodiment, the display unit 140 of the terminal device 1200 is a touch screen display, and the terminal device 1200 further includes a memory 120, and one or more programs, wherein the one or more programs are stored in the memory 120, and the one or more programs configured to be executed by the one or more processors 180 include instructions for:
the method comprises the steps of obtaining an instruction, wherein the instruction is used for obtaining a plurality of text data of a target to be detected in a preset time period;
a judging instruction, configured to perform state judging operation on the multiple target data through a pre-trained state judging model to obtain a survival state of the target to be detected;
and the judging instruction is used for judging whether to send out reminding information or not according to the deposit and death state.
In some embodiments, the program further includes a training instruction for obtaining a training sample of a state discrimination model to be trained, where the training sample includes a text data set provided with a label, the text data set includes a plurality of text data with the same identifier, and the label is used to indicate an existence state of the text data set; performing feature extraction on a plurality of text data of the same group of text data in the training sample through the state discrimination model to be trained to obtain first text feature vectors corresponding to the plurality of text data; judging the existence state of a text data set in the training sample based on the first text feature vector through the state discrimination model to be trained to obtain a first recognition result of the text data set; and adjusting parameters of the state discrimination model to be trained based on the first recognition result and the label of the text data set to obtain the pre-trained state discrimination model.
In some embodiments, the program further includes a calculation instruction, configured to perform feature evaluation operations on text feature vectors corresponding to multiple pieces of text data in the same group of text data groups respectively through a pre-trained feature evaluation model, so as to obtain a comprehensive score of each text feature vector; filtering the interference text data corresponding to the text feature vector with the comprehensive score lower than a preset threshold value from the training sample; and iteratively training the state discrimination model based on the training sample for filtering the interference text data.
In some embodiments, the calculating instructions are configured to input text feature vectors corresponding to a plurality of text data in the same group of text data groups to a random forest model, and calculate a kini index score of each text feature vector, so as to obtain a comprehensive score of each text feature vector.
In some embodiments, the determining instruction is configured to perform feature extraction on the plurality of text data through the state determining model to be trained to obtain second text feature vectors corresponding to the plurality of text data; and judging the existence states of the plurality of text data based on the second text feature vector through the state discrimination model to be trained to obtain a second recognition result of the plurality of text data.
In some embodiments, the obtaining instruction is configured to obtain, at regular time, original data of the target to be measured in a preset time period; and performing data processing according to the original data to obtain the plurality of text data.
In some embodiments, the obtaining instruction is configured to detect whether missing values exist in the raw data within a preset time period; and if the missing value exists in the preset time period, selecting the median of all the original data in the preset time period and writing the median into the position corresponding to the missing value.
The embodiment of the application also provides the terminal equipment. The terminal equipment can be a television, a smart phone, a tablet computer and the like.
As can be seen from the above, an embodiment of the present application provides a terminal device 1200, where the terminal device 1200 executes the following steps:
an embodiment of the present application further provides a storage medium, where a computer program is stored in the storage medium, and when the computer program runs on a computer, the computer executes the livestock survival status determination method according to any one of the above embodiments.
It should be noted that, for the livestock death state determination method described in the present application, it can be understood by a person skilled in the art that all or part of the process for implementing the livestock death state determination method described in the embodiment of the present application may be implemented by controlling related hardware through a computer program, where the computer program may be stored in a computer readable storage medium, such as a memory of a terminal device, and executed by at least one processor in the terminal device, and the process of the embodiment of the livestock death state determination method may be included in the execution process. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.
For the livestock survival state judging device in the embodiment of the application, each functional module can be integrated in one processing chip, each module can exist independently and physically, and two or more modules can be integrated in one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium, such as a read-only memory, a magnetic or optical disk, or the like.
The livestock survival state determination method, the livestock survival state determination device, the storage medium and the terminal device provided by the embodiment of the application are described in detail above. The principle and the implementation of the present application are explained herein by applying specific examples, and the above description of the embodiments is only used to help understand the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A livestock survival state discrimination method is characterized by comprising the following steps:
acquiring a plurality of text data of a target to be detected in a preset time period;
carrying out state discrimination operation on the target data through a pre-trained state discrimination model to obtain the existence state of the target to be detected;
and judging whether to send out reminding information according to the deposit and death state.
2. The livestock presence status discrimination method of claim 1, wherein before said performing a status discrimination operation on said plurality of target data through a pre-trained status discrimination model, said method further comprises:
acquiring a training sample of a state discrimination model to be trained, wherein the training sample comprises a text data group provided with a label, the text data group comprises a plurality of text data with the same identification, and the label is used for indicating the survival and death state of the text data group;
performing feature extraction on a plurality of text data of the same group of text data in the training sample through the state discrimination model to be trained to obtain first text feature vectors corresponding to the plurality of text data;
judging the existence state of a text data set in the training sample based on the first text feature vector through the state discrimination model to be trained to obtain a first recognition result of the text data set;
and adjusting parameters of the state discrimination model to be trained based on the first recognition result and the label of the text data group to obtain the pre-trained state discrimination model.
3. The livestock mortality status determination method of claim 2, wherein after said obtaining said pre-trained status determination model, said method further comprises:
respectively carrying out feature evaluation operation on text feature vectors corresponding to a plurality of text data in the same text data group through a pre-trained feature evaluation model to obtain a comprehensive score of each text feature vector;
filtering the interference text data corresponding to the text feature vector with the comprehensive score lower than a preset threshold value from the training sample;
and iteratively training the state discrimination model to be trained based on the training sample for filtering the interference text data.
4. The livestock survival state discrimination method of claim 3, wherein said performing feature evaluation operations on the text feature vectors corresponding to a plurality of text data in the same set of text data through a pre-trained feature evaluation model to obtain the comprehensive score of each text feature vector comprises:
and respectively inputting text feature vectors corresponding to a plurality of text data in the same group of text data groups into a random forest model, and respectively calculating the Kernel index score of each text feature vector to obtain the comprehensive score of each text feature vector.
5. The livestock survival state discrimination method of claim 1, wherein the performing a state discrimination operation on the plurality of target data through a pre-trained state discrimination model to obtain the survival state of the target to be detected comprises:
performing feature extraction on the plurality of text data through the pre-trained state discrimination model to obtain second text feature vectors corresponding to the plurality of text data;
and judging the existence states of the plurality of text data based on the second text feature vector through the pre-trained state discrimination model to obtain a second recognition result of the plurality of text data.
6. The livestock survival state discrimination method of claim 1, wherein the acquiring of the plurality of text data of the target to be measured in the preset time period comprises:
acquiring original data of the target to be detected in a preset time period in a timing mode;
and carrying out data processing according to the original data to obtain the plurality of text data.
7. The livestock survival state discrimination method of claim 6, wherein the periodically acquiring the original data of the target to be detected in a preset time period comprises:
detecting whether the original data has a missing value in a preset time period;
and if the missing value exists in the preset time period, selecting the median of all the original data in the preset time period and writing the median into the position corresponding to the missing value.
8. A livestock survival state discrimination device is characterized by comprising:
the acquisition module is used for acquiring a plurality of text data of the target to be detected within a preset time period;
the judging module is used for carrying out state judging operation on the target data through a pre-trained state judging model to obtain the existence state of the target to be detected;
and the judging module is used for judging whether to send out reminding information according to the deposit and death state.
9. A computer readable storage medium storing instructions adapted to be loaded by a processor to perform the livestock mortality status determination method of any one of claims 1 to 7.
10. A terminal device, comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor loads the instructions to execute the livestock survival status discrimination method according to any one of claims 1 to 7.
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