CN210275519U - Wearable monitoring device and monitoring system for cow ingestion behavior and ingestion amount - Google Patents
Wearable monitoring device and monitoring system for cow ingestion behavior and ingestion amount Download PDFInfo
- Publication number
- CN210275519U CN210275519U CN201920926237.0U CN201920926237U CN210275519U CN 210275519 U CN210275519 U CN 210275519U CN 201920926237 U CN201920926237 U CN 201920926237U CN 210275519 U CN210275519 U CN 210275519U
- Authority
- CN
- China
- Prior art keywords
- cow
- ingestion
- forehead
- nape
- acceleration information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the utility model provides a wearable monitoring devices and monitoring system of milk cow ingestion behavior and feed intake, locate the head of milk cow with every device cover, gather the acceleration information of milk cow ingestion behavior through the sensor that the buccal part or nasal part contact of device and milk cow; meanwhile, a sensor of the device is electrically connected with the repeater, the data storage module and the data processing module in sequence, acceleration information collected by the sensor is transmitted to the data storage module through the repeater for data processing and storage, and the data processing module monitors and analyzes the ingestion behavior and the ingestion amount of the dairy cow by calling the acceleration information in the data storage module. The embodiment of the utility model provides an realized every milk cow behavior of feeding and the automatic monitoring of individual feed intake among the milk cow breeding process, do benefit to the thing networking data platform based on milk cow behavior of feeding and individual feed intake, realize remote transmission, analysis and the management of data, improved the digitization and the intelligent level that the milk cow accurately fed.
Description
Technical Field
The utility model relates to a poultry technical field especially relates to a wearable monitoring devices and monitoring system of milk cow behavior of eating and food intake.
Background
With the improvement of national living standard and the adjustment of dietary structure, the consumption of dairy products is increasing day by day, and further the rapid development of the cow breeding industry in China is driven. At present, the modern dairy cow breeding industry is developing towards the direction of informatization, and the requirements for intelligent monitoring and digital accurate feeding of production information of dairy cows are continuously rising.
The feed intake is an important reference parameter for evaluating the nutrient and energy intake of livestock, and is also a key index for reflecting the health condition of the dairy cows and the level of productivity. As early as the 70's of the 20 th century, stockmen discovered that feed intake was one of the major limiting factors affecting animal growth and development and lactation, and for lactating cows, the peak of feed intake was often delayed by about 6 weeks from the peak of milk production, since high milk production is usually accompanied by high energy consumption, and lactating cows need energy compensation by increasing feed intake. Therefore, monitoring the feed intake change of the lactating cows in real time and supplementing the lactating cows in time are important means for ensuring sufficient nutrient intake of the lactating cows and maintaining the milk production peak; for replacement cattle, the acquisition of a larger muscle and bone growth rate from the weaning stage to the mating stage is an important factor for determining the first calving time, excessive energy can cause the parenchymal tissue to be fatted to shorten the optimal development time of mammary gland tissue, and the deposition of fat in the mammary gland tissue influences the future milk production capacity of the replacement cattle, so that the feed taken by the replacement cattle needs to be properly controlled, and the weight growth speed of the replacement cattle is not higher than 0.9 kg/day. In addition, the feed intake of cows is significantly reduced under heat stress or pathological conditions, while the feed intake of cows is increased under cold stress to maintain normal body temperature. Therefore, the monitoring of the feed intake and the intake condition of nutrient substances plays a crucial role in the regulation and control of the growth and the production performance of the dairy cows in each stage, and the real-time monitoring of the feed intake has great significance in improving the production benefit of the dairy cows.
At present, the feed intake of the dairy cows is mainly monitored by artificial experience estimation, the average feed intake of each cow is estimated through the feed consumption of the whole house in a period of time, but the artificial experience estimation has too high dependence on the experience, the feed intake change of the individual dairy cows cannot be accurately obtained, and the accurate guidance of the nutrition regulation and control in the production in time or the elimination of the dairy cows with low feed conversion rate is difficult. Or, there are also some intelligent feeding equipment that reforms transform the trough and neck flail area, calculate the feed intake through the change of weighing method record milk cow before and after the ingestion, but the weighing method needs to reform transform every trough and neck flail, leads to once only input cost too high, and is unfavorable for using the TMR car to spill the material automatically. In addition, in the existing research, students research wearable devices based on sound sensors, for example, monitoring tests are performed on the ingestion behaviors of the cows through the sound sensors, and the ingestion is calculated through the analysis of the ingestion behaviors of the cows; or the rumination sound of the milk cow is collected through the low-power-consumption wearable sound pressure sensor, and then the ingestion time, the ingestion rate and the ingestion times of the milk cow are obtained through a sound recognition algorithm, so that the ingestion amount of the milk cow is obtained. However, wearable devices based on sound sensors face the problems of high background noise, low sound monitoring precision and incapability of accurately acquiring the feed intake of dairy cows, and the cost of the sensors is high.
The above-mentioned various methods for acquiring the feed intake of dairy cows at present have the problem that the individual feed intake cannot be accurately and conveniently acquired, so a monitoring device and a monitoring system for accurately and conveniently acquiring the individual feed intake behavior and feed intake of dairy cows are urgently needed.
SUMMERY OF THE UTILITY MODEL
In order to solve present various methods to milk cow food intake collection and to have the problem that can't accurately, conveniently acquire individual food intake, the embodiment of the utility model provides a wearable monitoring devices and monitoring system of milk cow food intake action and food intake.
In a first aspect, the embodiment of the utility model provides a wearable monitoring devices of milk cow behaviour of food intake and food intake, the head of arbitrary one milk cow is located to every device cover, and the device includes: the forehead band, the nape band, the forehead buckle, the nape buckle, the movable rope, the first side connecting band, the second side connecting band and the sensor; the forehead band is clamped on the forehead of the cow through a forehead buckle, and the nape band is clamped on the nape of the cow through a nape buckle; the first side connecting belt and the second side connecting belt are respectively contacted with two cheeks of the cow; one end of the first side connecting belt is fixedly connected with the forehead belt through a forehead buckle, and the other end of the first side connecting belt is fixedly connected with the nape belt through a nape buckle; one end of the second side connecting belt is fixedly connected with the forehead belt through a forehead buckle, and the other end of the second side connecting belt is fixedly connected with the nape belt through a nape buckle; the first side connecting belt and the second side connecting belt are respectively used for tightening the forehead belt and the nape belt on two cheeks of the cow; the sensor is arranged on the first side connecting belt or the second side connecting belt and is contacted with the cheek part of the cow, or the sensor is arranged on the forehead belt and is contacted with the nose part of the cow; the sensor is used for acquiring acceleration information of the cow feeding behavior; the movable rope is contacted with the lower jaw of the cow; one end of the movable rope is movably connected with the forehead belt, and the other end of the movable rope is movably connected with the nape belt; the movable rope is used for moving on the forehead belt and the nape belt and enabling the forehead buckle and the nape buckle to be tightened on the lower jaw of the cow together.
Preferably, the sensor is a three-axis acceleration sensor, and the detection precision of the sensor is not lower than 1m/s2。
Preferably, the sensor is also used to collect the individual identification code of the cow.
Preferably, the forehead buckle, the nape buckle, the movable rope, the first side connecting band and the second side connecting band are all made of elastic nylon materials.
Preferably, the forehead clip and the nape clip are both detachable metal rings.
Preferably, the number of the forehead and nape buckles is at least one respectively.
In a second aspect, an embodiment of the present invention provides a system for monitoring the ingestion behavior and the ingestion amount of a milk cow, which includes a wearable monitoring device for monitoring the ingestion behavior and the ingestion amount of the milk cow, and a relay, a data storage module and a data processing module which are electrically connected with the device in sequence; accordingly, the repeater is connected to the sensor of the device; the device acquires acceleration information of the ingestion behavior of the dairy cow through a sensor and transmits the acceleration information to the repeater; the repeater transmits the acceleration information to the data storage module; the data storage module is used for storing acceleration information and transmitting the acceleration information to the data processing module; the data processing module is used for monitoring the ingestion behavior of the dairy cow according to the acceleration information and acquiring the individual ingestion amount.
Preferably, the data storage module comprises a filtering unit, and the filtering unit is used for performing filtering analysis on the acceleration information.
Preferably, the number of the devices and the number of the repeaters are respectively multiple, one repeater corresponds to multiple devices, and any one device is connected with the corresponding repeater.
Preferably, the device is connected with the repeater, the data storage module and the data processing module in sequence in a wired or wireless mode.
The embodiment of the utility model provides a wearable monitoring device and monitoring system of milk cow behavior of ingesting and food intake, through locating each device cover on the head of milk cow, wherein, the forehead band-pass is located on the forehead of milk cow through forehead buckle card, the nape band-pass is located on the nape of milk cow through nape buckle card; the first side connecting belt and the second side connecting belt are respectively contacted with two cheeks of the cow; one end of the first side connecting belt is fixedly connected with the forehead belt through a forehead buckle, and the other end of the first side connecting belt is fixedly connected with the nape belt through a nape buckle; one end of the second side connecting belt is fixedly connected with the forehead belt through a forehead buckle, and the other end of the second side connecting belt is fixedly connected with the nape belt through a nape buckle; the first side connecting belt and the second side connecting belt are respectively used for tightening the forehead belt and the nape belt on two cheeks of the cow; the sensor is arranged on the first side connecting belt or the second side connecting belt and is contacted with the cheek part of the cow, or the sensor is arranged on the forehead belt and is contacted with the nose part of the cow; the sensor is used for acquiring acceleration information of the cow feeding behavior; the movable rope is contacted with the lower jaw of the cow; one end of the movable rope is movably connected with the forehead belt, and the other end of the movable rope is movably connected with the nape belt; the movable rope is used for moving on the forehead belt and the nape belt and enabling the forehead buckle and the nape buckle to be tightened on the lower jaw of the cow together.
Meanwhile, a sensor of the device is electrically connected with the repeater, the data storage module and the data processing module in sequence, acceleration information collected by the sensor is transmitted to the data storage module through the repeater for data processing and storage, and the data processing module monitors and analyzes the ingestion behavior and the ingestion amount of the dairy cow by calling the acceleration information in the data storage module.
The embodiment of the utility model provides an realized every milk cow behavior of feeding and the automatic monitoring of individual feed intake among the milk cow breeding process, do benefit to the thing networking data platform based on milk cow behavior of feeding and individual feed intake, realize remote transmission, analysis and the management of data, improved the digitization and the intelligent level that the milk cow accurately fed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a wearable monitoring device for milk cow feeding behavior and feeding intake in an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a system for monitoring the ingestion behavior and the ingestion amount of the dairy cow in the embodiment of the present invention;
fig. 3 is a schematic flow chart of data processing of the system for monitoring the ingestion behavior and the ingestion amount of the dairy cow according to the embodiment of the present invention;
fig. 4 is a schematic flow chart of filtering of the filtering unit in the data storage module of the system for monitoring the ingestion behavior and the ingestion amount of the dairy cow according to the embodiment of the present invention;
wherein:
1. forehead buckle 2, sensor 3, nape buckle
4. Movable cord 5, forehead strap 6, nape strap
7. First side connecting belt 8, second side connecting belt 9 and repeater
10. A data storage module 11 and a data processing module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by a person skilled in the art without creative efforts belong to the protection scope of the present invention.
In the process of feeding of the dairy cow, the muscles on the two sides of the cheek part drive the lower jaw to generate regular reciprocating motion, so that the acceleration change caused by the fluctuation motion of the muscles on the two sides of the cheek part of the dairy cow can be measured, and the related parameters and the feed intake of the feeding behavior of the dairy cow can be further analyzed.
Fig. 1 is the utility model discloses the wearable monitoring devices's of milk cow behaviour of food intake and food intake structural schematic diagram, as shown in fig. 1, the embodiment of the utility model provides a wearable monitoring devices of milk cow behaviour of food intake and food intake, the head of arbitrary one milk cow is located to every device cover, and the device includes: the forehead band 5, the nape band 6, the forehead buckle 1, the nape buckle 3, the movable rope 4, the first side connecting band 7, the second side connecting band 8 and the sensor 2; wherein, the forehead band 5 is clamped on the forehead of the cow through a forehead buckle 1, and the nape band 6 is clamped on the nape of the cow through a nape buckle 3; the first side connecting belt 7 and the second side connecting belt 8 are respectively contacted with two cheeks of the cow; one end of the first side connecting belt 7 is fixedly connected with the forehead belt 5 through a forehead buckle 1, and the other end of the first side connecting belt is fixedly connected with the nape belt 6 through a nape buckle 3; one end of the second side connecting belt 8 is fixedly connected with the forehead belt 5 through a forehead buckle 1, and the other end is fixedly connected with the nape belt 6 through a nape buckle 3; the first side connecting belt 7 and the second side connecting belt 8 are respectively used for tightening the forehead belt 5 and the nape belt 6 together at two cheeks of the cow; the sensor 2 is arranged on the first side connecting belt 7 or the second side connecting belt 8 and is contacted with the left cheek or the right cheek of the cow, or the sensor 2 is arranged on the upper part of the forehead belt 5 and is contacted with the nose of the cow; the sensor 2 is used for acquiring acceleration information of the cow feeding behavior; the movable rope 4 is contacted with the lower jaw of the cow; one end of the movable rope 4 is movably connected with the forehead belt 5, and the other end is movably connected with the nape belt 6; the movable rope 4 is used for moving on the forehead belt 5 and the nape belt 6 and tightening the forehead buckle 1 and the nape buckle 3 on the lower jaw of the cow together.
Specifically, each device is used for monitoring the ingestion behavior and the ingestion amount of any cow, so that in practical application, each device is sleeved on the head of the cow to be monitored, wherein the forehead band 5 is clamped on the forehead of the cow through the forehead buckle 1, and the nape band 6 is clamped on the nape of the cow through the nape buckle 3; a first side connecting belt 7 and a second side connecting belt 8 which are respectively contacted with two cheeks of the cow are fixedly connected between the forehead belt 5 and the nape belt 6; a movable rope 4 contacted with the lower jaw of the cow is connected between the forehead belt 5 and the nape belt 6, and two ends of the movable rope 4 can respectively move left and right at the lower parts of the forehead belt 5 and the nape belt 6 so as to adjust the adaptability between the device and the head of the cow, so that the device is properly sleeved on the head of the cow.
Meanwhile, the sensor 2 is arranged on the first side connecting band 7 or the second side connecting band 8 and is contacted with the masseter of the left cheek or the right cheek of the cow, or the sensor 2 is arranged on the upper part of the forehead band 5 and is contacted with the nose lip levator of the upper part of the nose of the cow; acceleration information of acceleration change caused by fluctuation of muscles on two sides of a cheek of the cow when the cow eats is collected through the sensor 2, so that relevant parameters and food intake of the cow in a feeding behavior can be analyzed according to the acceleration information.
It should be noted that the number of the sensors 2 in each apparatus is one or more.
The embodiment of the utility model provides a pair of wearable monitoring devices of milk cow ingestion action and ingestion volume establishes this wearable monitoring devices through the head at every milk cow conveniently to gather the milk cow at the ingestion in-process, the acceleration information of the acceleration change that the muscle fluctuation motion of the buccal part both sides of milk cow arouses, so that according to the relevant parameter and the ingestion volume of acceleration information analysis milk cow ingestion action.
Further, the sensor 2 is a triaxial acceleration sensor, which is capacitive, inductive, strain, piezoresistive, piezoelectric. The detection precision of the sensor 2 is not less than 0.1g, g is the gravity constant of 9.8N/KG, so the detection precision of the sensor 2 is not less than 1m/s2。
It should be noted that the sensor 2 can also be used to identify the individual identification code of the cow, so as to distinguish the serial number of the cow that the device is used to monitor, and thus identify different cows.
Further, in order to facilitate the device to conveniently sleeve the head of the cow and make the cow comfortable to wear for a long time, the forehead buckle 1, the nape buckle 3, the movable rope 4, the first side connecting band 7 and the second side connecting band 8 are all made of elastic nylon materials.
It should be noted that the forehead buckle 1 and the nape buckle 3 are both detachable metal rings, so as to facilitate detaching the device from the head of the cow.
Further, the number of the forehead buckle 1 and the nape buckle 3 is at least one respectively, so that the device can be conveniently worn and detached. For example, two forehead buckles 1 may be symmetrically disposed on the left cheek and the right cheek of the cow, or two nape buckles 3 may be symmetrically disposed on both sides of the nape of the cow.
Fig. 2 is a schematic structural diagram of a system for monitoring ingestion behavior and ingestion amount of a cow according to an embodiment of the present invention, as shown in fig. 1 and fig. 2, an embodiment of the present invention provides a system for monitoring ingestion behavior and ingestion amount of a cow, which includes a wearable monitoring device for monitoring ingestion behavior and ingestion amount of a cow, and a relay 9, a data storage module 10 and a data processing module 11 electrically connected to the device in sequence; accordingly, the relay 9 is connected to the sensor 2 of the device; the device acquires acceleration information of the cow feeding behavior through the sensor 2 and transmits the acceleration information to the repeater 9; the repeater 9 transmits the acceleration information to the data storage module 10; the data storage module 10 is used for storing acceleration information and transmitting the acceleration information to the data processing module 11; the data processing module 11 is used for monitoring the ingestion behavior of the cow according to the acceleration information and acquiring the individual ingestion amount.
Specifically, the device is worn on the head of a cow to be monitored, acceleration information of the cow feeding behavior acquired by a sensor of the device is transmitted to the data storage module 10 through the repeater 9 for data processing and storage, and the feeding behavior and the feed intake of the cow are monitored and analyzed by calling the acceleration information in the data storage module 10 through the data processing module 11.
The embodiment of the utility model provides a monitoring system of milk cow behavior of eating and feed intake, integrated triaxial acceleration sensor, the thing networking, signal processing, and technologies such as artificial intelligence, the construction systematization, the quantification, standardized information acquisition device, and constitute thing networking data platform's software and hardware environment, realize the real-time automatic monitoring of milk cow individual behavior of eating and feed intake, realize the teletransmission of data, analysis and management, improve accurate digitization and the intelligent level of feeding of milk cow in the animal husbandry production. In the implementation process, wear wearable acceleration sensor device for the milk cow, utilize data receiver, relay router and long-range wireless network module to build the thing networking systems, also can utilize the current thing networking systems environment of plant, the analysis of indexes such as behavior of feeding, ingestion time, food intake provides the basis of quantification for the accurate evaluation such as feeding, health status, breed environment comfort level of milk cow to reach the purpose that promotes modern animal husbandry milk cow breeding production efficiency and quality.
Based on the above embodiment, the data storage module 10 includes a filtering unit, and the filtering unit is configured to perform filtering analysis on the acceleration information, and the specific filtering process is described in detail below with reference to the embodiment of fig. 4.
Further, the number of the devices and the number of the repeaters 9 are each plural, one repeater 9 corresponds to plural devices, and any one device is connected to its corresponding repeater 9.
Further, the device is connected with the repeater 9, the data storage module 10 and the data processing module 11 in sequence in a wired or wireless manner, and the specific connection mode can be set according to the actual situation.
The specific process of applying the monitoring system for the ingestion behavior and the feed intake of the dairy cow to perform data processing so as to analyze the ingestion behavior and the feed intake of the dairy cow is given below.
The feeding behavior of the cow mainly comprises a rolling state and a chewing state because the cow does not have the palate incisor teeth, and the feed is rolled by the tongue and then chewed or chewed while rolling so as to eat the feed. In the embodiment of the utility model, the state of chewing while rolling food is also regarded as the rolling food state; meanwhile, a continuous chewing state is defined as a chewing action, a continuous or discontinuous section of coiling action but adjacent coiling action is defined as a coiling action, and a complete ingestion process is defined from the beginning of the coiling action to the next coiling action, so that multiple chewing states can be generated in each ingestion process.
It should be known that, the milk cow is in the process of food intake, still includes head motion, and the jaw incisor is surely got and is rolled up food or chew the non-food process such as the short break of process midway, consequently the embodiment of the utility model discloses divide into the food intake action of milk cow and be used for the roll food state and the state of chewing of actual food to and be not used for the non-food state of actual food.
Fig. 3 is the utility model discloses milk cow ingestion behavior and the monitoring system's of food intake data processing's of food intake flow schematic diagram, as shown in fig. 3, the embodiment of the utility model provides a milk cow ingestion behavior and the monitoring system's of food intake data processing flow includes:
and S1, acquiring acceleration information of the ingestion behaviors of a plurality of cows.
Specifically, the specific state of the ingestion behavior of the cow can be determined according to the acceleration information of the ingestion behavior of the cow, and in step S1, the acceleration information of the ingestion behaviors of a plurality of cows is acquired as a basis for determining the specific state of the ingestion behavior of the cow, so that the subsequent steps are performed according to the acceleration information.
It should be noted that the unit of the acceleration information is the gravity constant g, which is 9.8N/Kg.
S2, inputting the acceleration information into a first preset neural network, and outputting the type of the ingestion behavior corresponding to the acceleration information, wherein the type comprises a roll feeding state, a chewing state and a non-feeding state for feeding; and the first preset neural network is obtained after training according to the first sample acceleration information and the type of the ingestion behavior corresponding to the first sample acceleration information.
Specifically, in step S2, acquiring acceleration information of ingestion behaviors of a plurality of cows as first sample acceleration information, and acquiring a live video to determine the ingestion behavior corresponding to the first sample acceleration information, thereby determining the category of the ingestion behavior corresponding to the first sample acceleration information; categories of feeding behavior include a roll state and a chew state for eating, and a non-eating state not for eating.
Training according to the first sample acceleration information and the type of the ingestion behavior corresponding to the first sample acceleration information to obtain a first preset neural network; the acceleration information is input into a first preset neural network, and the categories of the ingestion behaviors corresponding to the acceleration information are output, wherein the categories comprise a roll feeding state and a chewing state for feeding and a non-feeding state.
And S3, acquiring the duration for eating in the ingestion behavior in the preset time period according to the duration of the roll state and the chewing state in the preset time period.
Specifically, in step S3, the duration of the roll state, the duration of the chewing state and the time of the non-eating state in the ingestion behavior within the preset time period are respectively acquired according to the classification of the ingestion behavior in step S2; according to the duration time of the roll state and the duration time of the chewing state, the duration time for eating in the ingestion behavior within the preset time period can be acquired.
S4, inputting the acceleration information corresponding to the coiling state and the chewing state into a second preset neural network, and outputting an ingestion rate estimation value of the ingestion behavior corresponding to the acceleration information corresponding to the coiling state and the chewing state; the second preset neural network is obtained after training according to the second sample acceleration information and the ingestion rate value of the ingestion behavior corresponding to the second sample acceleration information; the second sample acceleration information is the acceleration information corresponding to the type classified by the first preset neural network, namely the volume state and the chewing state.
Specifically, in step S4, classifying the acceleration information of the ingestion behaviors of a plurality of cows through a first preset neural network, and identifying the category of the ingestion behaviors; and taking the acceleration information of which the type of the ingestion behavior is the roll state and the chewing state as second sample acceleration information, simultaneously actually measuring on site to obtain the ingestion rate value of the ingestion behavior corresponding to the second sample acceleration information, and training a second preset neural network according to the second sample acceleration information and the ingestion rate value corresponding to the second sample acceleration information.
And further, inputting the acceleration information corresponding to the coiling state and the chewing state obtained after the classification by the first preset neural network into a second preset neural network, and outputting an estimated value of the ingestion rate of the ingestion behavior corresponding to the acceleration information corresponding to the coiling state and the chewing state. The feeding rate estimation value of the feeding behavior is a comprehensive feeding rate estimation value of the roll feeding state and the feeding rate estimation value of the chewing state.
It should be noted that the first sample acceleration information and the second sample acceleration information are obtained by acquiring acceleration information of the ingestion behaviors of a plurality of cows; the first sample acceleration information and the second sample acceleration information may be the same or different sample information, and are respectively used for training the first preset neural network and the second preset neural network.
The second sample acceleration information is obtained by classifying the acceleration information of the ingestion behaviors of the cows through a first preset neural network and identifying the acceleration information corresponding to the curling state and the chewing state in the acceleration information of the categories of the ingestion behaviors.
And S5, acquiring the individual feed intake of each cow in a preset time period according to the feed intake rate estimation value of the feed intake behavior and the duration time for feeding in the feed intake behavior.
Specifically, in step S5, the individual feed intake of each cow in the preset period is obtained according to the feed intake rate estimation value of the feeding behavior obtained in step S4 and the duration for eating in the feeding behavior in the preset period obtained in step S3.
To sum up, the embodiment of the utility model provides a milk cow behaviour of food intake and the monitoring system's of food intake data processing flow does: based on the first preset neural network, according to the acceleration information of the ingestion behaviors of the cows, the corresponding types of the ingestion behaviors can be determined to be a roll feeding state or a chewing state for eating or a non-eating state for not eating; then in a preset time period, the duration time for eating in the ingestion behavior in the preset time period can be obtained according to the duration time of the roll eating state and the chewing state respectively; meanwhile, based on a second preset neural network, a corresponding feeding rate estimation value can be obtained according to the acceleration information; further, according to the duration for eating in the ingestion behavior and the ingestion rate estimation value of the ingestion behavior in the preset time period, the individual ingestion amount of the cow in the preset time period is obtained.
It should be noted that, in the embodiment of the present invention, the first preset neural network and the second preset neural network are both BP neural networks; the hidden layer of the first preset neural network is nine layers, and the hidden layer of the second preset neural network is ten layers.
Fig. 4 is the utility model discloses the flow diagram of filtering unit filtering in the data storage module of the monitoring system of milk cow ingestion action and food intake, as shown in fig. 4, acquire the acceleration information of a plurality of milk cow ingestion actions, and with acceleration information input to between the first neural network of predetermineeing, still include: and performing signal preprocessing on the acceleration information, wherein the signal preprocessing comprises extreme value denoising, wavelet denoising and Kalman filtering on the acceleration information in sequence.
Specifically, t is acquirediOriginal acceleration information X of ingestion behaviors of a plurality of dairy cows at any moment0(ti) Then on the original acceleration information X0(ti) And (4) carrying out a series of signal preprocessing to enable the relation curve of the original acceleration information and the time to be smooth and burr-free.
First, for the original acceleration information X0(ti) Carrying out extremum denoising: respectively obtaining a plurality of peak values and a plurality of valley values of the acceleration information in any time period, replacing harmonic mean values of the peak values with highest peak values, replacing harmonic mean values of the valley values with lowest valley values, and obtaining the acceleration information X 'with the extreme value denoised'0(ti)。
Then, the acceleration information X 'after the noise removal of the extreme value is carried out'0(ti) Performing wavelet denoising: performing wavelet transformation on the acceleration information subjected to the extreme value denoising to obtain a plurality of wavelet decomposition coefficients; performing threshold processing and parameter adjustment on the wavelet decomposition coefficient to obtain a wavelet coefficient, so that the difference between the wavelet decomposition coefficient and the wavelet coefficient is as small as possible; then wavelet reconstruction is carried out on the wavelet coefficient to obtain extremum de-noising and acceleration after wavelet de-noisingDegree information X ″)0(ti)。
Finally, the acceleration information X' after the extreme value denoising and the wavelet denoising is carried out0(ti) Performing Kalman filtering: obtaining acceleration information X' after extreme value denoising and wavelet denoising0(ti) Estimated value, measured value, covariance, and filter gain value of (a); acquiring acceleration information X after extreme value denoising, wavelet denoising and Kalman filtering according to an estimated value, an actual measurement value, covariance and a filter gain valuep(ti). Note that the kalman filter is performed by a nonlinear kalman filter.
Further, the formula for obtaining the estimated value is:
X”estimating(ti)＝φX″0(ti)+τW(ti-1)
Wherein, X "Estimating(ti) Is X0(ti) At tiTime of day estimate, X ″0(ti) Is tiAcceleration information after moment extreme value denoising and wavelet denoising is obtained, phi is a state equation transfer matrix, and tau is a noise driving matrix; w (t)i-1) Is ti-1Process noise at time, W (t)i-1) Both the mean and the error of (a) are 0.
Further, the formula for obtaining the measured value is:
Y(ti-1)＝HX″0(ti)+V(ti-1)
wherein, Y (t)i-1) Is X0(ti) At ti-1Actual time value, X ″0(ti) Is tiDenoising acceleration information after the moment extreme value and wavelet denoising, wherein H is an observation equation transfer matrix; v (t)i-1) Is Gaussian white noise, V (t)i-1) Is 0.
Further, the formula for obtaining the covariance is:
P(ti)＝φ(ti)P(ti-1)φT(ti)
wherein, P (t)i) Is tiCovariance of the moment, P (t)i-1) Is ti-1Covariance of time of day, phi (t)i) Is tiEquation of state transition matrix at time, phiT(ti) Is phi (t)i) The transposed matrix of (2).
Further, the formula for obtaining the filter gain value is:
K(ti-1)＝P(ti)HT(ti)[H(ti)P(ti)HT(ti)+R]
wherein, K (t)i-1) Is ti-1The value of the filter gain at time, P (t)i) Is tiCovariance of time, R is sensor error, H (t)i) Is tiTransfer matrix of observation equation of time, HT(ti) Is H (t)i) The transposed matrix of (2).
The acceleration information is acquired by clinging to the cheek or the nose of the cow through a triaxial acceleration sensor, and specifically acquired by clinging to masseter muscles on two sides of the cheek or the nasolabial levator on the upper part of the nose of the cow; the acceleration information obtained by the three-axis acceleration sensor comprises X-axis acceleration information, Y-axis acceleration information, Z-axis acceleration information and three-axis acceleration and information. Therefore, the acceleration information after signal preprocessing, i.e., the acceleration information X after extremum de-noising, wavelet de-noising and Kalman filteringp(ti) Inputting the data into a first preset neural network to obtain the classification of the ingestion behaviors, specifically, respectively using the original X-axis acceleration information X01(ti) Y-axis acceleration information X02(ti) Z-axis acceleration information X03(ti) And three-axis acceleration and information X04(ti) Signal preprocessing is carried out to obtain acceleration information X after corresponding signal preprocessingp1(ti)、Xp2(ti)、Xp3(ti) And Xp4(ti) Inputting the data into a first preset neural network, and then obtaining the classification of the ingestion behaviors: rolled state O1Masticatory state O2And non-ingestion state O3。
Based on the above embodiment, according to the assumption that there are k times of chewing behaviors between the ith food rolling state and the (i + 1) th food rolling state within the preset time period, it can be known that the duration for eating in the ingestion behavior within the preset time period is obtained according to the durations of the food rolling state and the chewing state within the preset time period, and the method specifically includes:
wherein, the AFD is the duration of the ingestion behavior within a preset time period, and the unit is minutes; Δ t01iThe duration of the ith food rolling state, i is the number of food rolling states, m is the total number of food rolling states, and delta t01i2kThe duration of the kth chewing state after the ith food coiling state is defined, k is the number of times of the chewing state from the ith food coiling state to the (i + 1) th food coiling state, and n is the total number of times of the chewing state from the ith food coiling state to the (i + 1) th food coiling state.
It should be noted that i, m, k, n are all natural numbers greater than 0.
Based on the above embodiment, before outputting the feeding rate estimation value of the feeding behavior corresponding to the acceleration information corresponding to the roll state and the chewing state, the method further includes: acquiring the chewing frequency of the cow according to the frequency spectrum of the X-axis acceleration information corresponding to the chewing state in the preset time period; inputting X-axis acceleration information, Y-axis acceleration information, Z-axis acceleration information and three-axis acceleration sum corresponding to the roll-eating state, X-axis acceleration information, Y-axis acceleration information, Z-axis acceleration information and three-axis acceleration sum corresponding to the chewing state, and chewing frequency into a second preset neural network.
For example, X-axis acceleration information corresponding to the ith wrapping state is defined as X01i1Y-axis acceleration information is X01i2Z-axis acceleration information is X01i3Three axes acceleration sum to X01i4(ii) a Between the ith food coiling state and the (i + 1) th food coiling state, the X-axis acceleration information corresponding to the kth chewing state is X02ik1Y-axis acceleration information is X02ik2Z-axis acceleration information is X02ik3Three axes acceleration sum to X02ik4(ii) a X-axis acceleration according to chewing stateDegree information, chewing frequency obtained as F01i2k。
Corresponding X-axis acceleration information X of the ith package state01i1Y-axis acceleration information X01i2Z-axis acceleration information X01i3Three axis acceleration and X01i4(ii) a X-axis acceleration information X corresponding to kth chewing state02ik1Y-axis acceleration information X02ik2Z-axis acceleration information X02ik3Three axis acceleration and X02ik4(ii) a According to X-axis acceleration information corresponding to the chewing state, the chewing frequency F is obtained01i2kThe data are input into a second preset neural network, so that an estimated ingestion rate value of the ingestion behavior is output.
Based on the above embodiment, the duration of the ith feeding behavior can be obtained by adding the duration of the ith feeding state to the duration of the k chewing states between the ith feeding state and the (i + 1) th feeding state, and the individual food intake of the ith feeding behavior can be obtained by multiplying the duration of the ith feeding behavior by the food intake rate estimation value of the ith feeding behavior. By analogy, the individual food intake of the whole food intake behavior can be obtained.
Therefore, according to the estimated ingestion rate value of the ingestion behavior and the duration time for eating in the ingestion behavior, obtaining the individual ingestion amount of each cow in the preset time period specifically comprises:
wherein, DFI is individual feed intake in kilograms within a preset time period; siFor feeding rate estimation, Δ t01iThe duration of the ith food rolling state, i is the number of food rolling states, m is the total number of food rolling states, and delta t01i2kThe duration of the kth chewing state after the ith food rolling state is defined, k is the chewing frequency between the ith food rolling state and the (i + 1) th food rolling state, and n is the total chewing frequency between the ith food rolling state and the (i + 1) th food rolling state.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention in its corresponding aspects.
Claims (10)
1. The utility model provides a wearable monitoring devices of milk cow behaviour of food intake and food intake, its characterized in that, every the head of arbitrary milk cow is located to the device cover, the device includes: the forehead band, the nape band, the forehead buckle, the nape buckle, the movable rope, the first side connecting band, the second side connecting band and the sensor;
the forehead band is clamped on the forehead of the cow through the forehead buckle, and the nape band is clamped on the nape of the cow through the nape buckle;
the first side connecting belt and the second side connecting belt are respectively contacted with two cheeks of the cow; one end of the first side connecting belt is fixedly connected with the forehead belt through the forehead buckle, and the other end of the first side connecting belt is fixedly connected with the nape belt through the nape buckle; one end of the second side connecting belt is fixedly connected with the forehead belt through the forehead buckle, and the other end of the second side connecting belt is fixedly connected with the nape belt through the nape buckle; the first side connecting belt and the second side connecting belt are respectively used for enabling the forehead belt and the nape belt to be tightened on two cheeks of the cow; the sensor is arranged on the first side connecting belt or the second side connecting belt and is in contact with the cheek part of the cow, or the sensor is arranged on the forehead belt and is in contact with the nose part of the cow; the sensor is used for acquiring acceleration information of the cow feeding behavior;
the movable rope is in contact with the lower jaw of the cow; one end of the movable rope is movably connected with the forehead belt, and the other end of the movable rope is movably connected with the nape belt; the movable rope is used for moving on the forehead belt and the nape belt and enabling the forehead buckle and the nape buckle to be tightened on the lower jaw of the cow together.
2. The wearable monitoring device for the ingestion behavior and the ingestion amount of the dairy cow of claim 1, wherein the sensor is a three-axis acceleration sensor, and the detection precision of the sensor is not lower than 1m/s2。
3. The wearable monitoring device of cow feeding behavior and feed intake of claim 1, wherein the sensor is further used for collecting the individual identification code of the cow.
4. The wearable monitoring device for the ingestion behavior and the ingestion amount of the dairy cow of claim 1, wherein the forehead buckle, the nape buckle, the movable rope, the first side connecting band and the second side connecting band are all made of elastic nylon materials.
5. The wearable monitoring device of cow feeding behavior and feed intake of claim 1, wherein the forehead buckle and the nape buckle are both detachable metal rings.
6. The wearable monitoring device of cow feeding behavior and feed intake of claim 1, wherein the number of forehead buckles and nape buckles is at least one respectively.
7. A monitoring system for the ingestion behavior and the ingestion amount of a cow is characterized by comprising a wearable monitoring device for the ingestion behavior and the ingestion amount of the cow as claimed in any one of claims 1 to 6, and a repeater, a data storage module and a data processing module which are electrically connected with the device in turn; accordingly, the repeater is connected to the sensor of the device;
the device acquires acceleration information of the cow feeding behavior through the sensor and transmits the acceleration information to the repeater; the repeater transmits the acceleration information to the data storage module; the data storage module is used for storing the acceleration information and transmitting the acceleration information to the data processing module; and the data processing module is used for monitoring the ingestion behavior of the dairy cow according to the acceleration information and acquiring the individual ingestion amount.
8. The system for monitoring the ingestion behavior and the ingestion amount of the dairy cow according to claim 7, wherein the data storage module comprises a filtering unit, and the filtering unit is used for performing filtering analysis on the acceleration information.
9. The system for monitoring the feeding behavior and the feed intake of the dairy cow of claim 7, wherein the number of the devices and the number of the repeaters are multiple, and any one of the devices is connected to the corresponding repeater.
10. The system for monitoring the ingestion behavior and the ingestion amount of the dairy cow according to claim 7, wherein the device is connected with the repeater, the data storage module and the data processing module in sequence in a wired or wireless manner.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201920926237.0U CN210275519U (en) | 2019-06-19 | 2019-06-19 | Wearable monitoring device and monitoring system for cow ingestion behavior and ingestion amount |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201920926237.0U CN210275519U (en) | 2019-06-19 | 2019-06-19 | Wearable monitoring device and monitoring system for cow ingestion behavior and ingestion amount |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN210275519U true CN210275519U (en) | 2020-04-10 |
Family
ID=70097872
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201920926237.0U Active CN210275519U (en) | 2019-06-19 | 2019-06-19 | Wearable monitoring device and monitoring system for cow ingestion behavior and ingestion amount |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN210275519U (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110169374A (en) * | 2019-06-19 | 2019-08-27 | 北京农业信息技术研究中心 | A kind of wearable monitoring device and monitoring system of cow feeding behavior and feed intake |
| CN110169374B (en) * | 2019-06-19 | 2024-05-10 | 北京农业信息技术研究中心 | Wearable monitoring device and monitoring system for dairy cow feeding behavior and feeding capacity |
-
2019
- 2019-06-19 CN CN201920926237.0U patent/CN210275519U/en active Active
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110169374A (en) * | 2019-06-19 | 2019-08-27 | 北京农业信息技术研究中心 | A kind of wearable monitoring device and monitoring system of cow feeding behavior and feed intake |
| CN110169374B (en) * | 2019-06-19 | 2024-05-10 | 北京农业信息技术研究中心 | Wearable monitoring device and monitoring system for dairy cow feeding behavior and feeding capacity |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110169375B (en) | Method and device for monitoring ingestion behavior and ingestion amount of dairy cows | |
| Costa et al. | Symposium review: Precision technologies for dairy calves and management applications | |
| JP2017060407A (en) | System and method for health care of ruminant | |
| CA2874567C (en) | System and method for in-rumen monitoring | |
| EP2563221B1 (en) | Apparatus and method for detecting disease in dairy animals | |
| EP3188592B1 (en) | Arrangement and method for measuring rumination of an animal | |
| JP2002253075A (en) | Device and method for milking animal and device for monitoring animal | |
| US20230270077A1 (en) | Method and system for determining phase transition in young animal | |
| CN116934088B (en) | Intelligent pigeon breeding management method and system based on analysis model | |
| CN106993546A (en) | A kind of cow oestrus monitoring method based on the time of ruminating and activity | |
| CN116627082A (en) | Intelligent management system for live pig breeding suitable for stock quantity checking | |
| CN210275519U (en) | Wearable monitoring device and monitoring system for cow ingestion behavior and ingestion amount | |
| CN111916096B (en) | Tibetan sheep grazing behavior monitoring method based on raspberry pie | |
| Campos et al. | Short-term fibre intake estimation in goats using surface electromyography of the masseter muscle | |
| CN107410082A (en) | Recognition methods is ruminated based on ruminant noseband pressure change | |
| KR101975932B1 (en) | Method for analysing condition of companion animal | |
| CN110169374B (en) | Wearable monitoring device and monitoring system for dairy cow feeding behavior and feeding capacity | |
| Blomberg | Automatic registration of dairy cows grazing behaviour on pasture | |
| de Campos et al. | Surface electromyography measurements for ingestive behaviour identification on goats | |
| Janzekovic et al. | Polar sport tester for cattle heart rate measurements | |
| CN115777560A (en) | Intelligent sow feeding system based on machine vision analysis technology | |
| CN110169374A (en) | A kind of wearable monitoring device and monitoring system of cow feeding behavior and feed intake | |
| DE102014003846A1 (en) | Device for recording animal-specific behavioral parameters of animals | |
| Alsina-Pagès et al. | Dairy Cattle Welfare through Acoustic Analysis: Preliminary results of acoustic environment description | |
| CN111122902A (en) | Intelligent monitoring method for abnormal situation of cattle rumination based on wireless network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| GR01 | Patent grant | ||
| GR01 | Patent grant |