WO2019218602A1 - Système de l'internet des objets appliqué au comportement de vaches et à leurs caractéristiques physiologiques et procédé de fonctionnement associé - Google Patents

Système de l'internet des objets appliqué au comportement de vaches et à leurs caractéristiques physiologiques et procédé de fonctionnement associé Download PDF

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WO2019218602A1
WO2019218602A1 PCT/CN2018/112990 CN2018112990W WO2019218602A1 WO 2019218602 A1 WO2019218602 A1 WO 2019218602A1 CN 2018112990 W CN2018112990 W CN 2018112990W WO 2019218602 A1 WO2019218602 A1 WO 2019218602A1
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cow
module
data
chip
diff
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董世杰
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深圳市双福世联科技有限责任公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

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  • the invention belongs to the technical field of aquaculture monitoring system, and particularly relates to a dairy cow behavior and physiological characteristic internet of things system and a working method thereof.
  • ______Animal husbandry is the core industry of modern agriculture. Dairy cows can convert feed into milk, thus providing nutritious drinking supplies to the society.
  • the dairy industry is an efficient, sustainable, and highly industrially related industry with an important strategic position in China's economic development. Therefore, it is of great significance for the development of the dairy industry to improve the dietary structure of residents, improve the health of the whole people, promote the adjustment of rural industrial structure and the coordinated development of urban and rural areas, bring new income growth points to farmers, and promote the development of national economy-related industries.
  • the behavior and physiological characteristics of the cow can be used to determine the physical condition of the cow.
  • the specific monitoring methods may be as follows:
  • Cows have an average estrus cycle of 21 days, with physical and behavioral changes in estrus.
  • Physiological changes include redness of the vulva, discharge of fluid from the vagina, and elevation of body temperature. Behavioral changes are anxious, alert and sensitive, and the amount of activity increases, and it will climb across other cows and so on.
  • Manual monitoring is generally based on the naked eye, and then check the development of follicles to determine estrus and ovulation, thus determining the time of artificial insemination, but manual monitoring is not only time-consuming and laborious, but also not accurate.
  • Automatic monitoring generally uses a pedometer to detect the number of steps per hour, then compares the daily base and issues an alarm after a certain amount is exceeded.
  • the pedometer used in the early days was a purely mechanical device, and the cow was crowned every step of the way.
  • accelerometers have been miniaturized to chip sizes, and electronic pedometers have become commonplace.
  • the electronic pedometer can be placed on the ankle, knees, neck, and ears.
  • the electronic pedometer is simple in algorithm.
  • due to the impact of installation individuals also have differences, winter and summer are also very different, can not detect the silent estrus, and the detection rate has certain limitations.
  • the monitoring of the disease is generally first observed manually, and then the body temperature is measured for the suspected diseased cow, and the initial diagnosis such as heartbeat is checked.
  • the initial diagnosis such as heartbeat is checked.
  • manual observation is clearly difficult to adapt to needs.
  • a survey by veterinarians found that early detection of disease can reduce economic losses, while manual testing does not allow early detection of disease.
  • Cows have many behavioral and physiological changes before calving. Before calving, the cattle will eat less, the number of lying and standing will increase, the body temperature will drop, etc. These are the basic characteristics. It has always relied on pregnancy records and visual observation to roughly determine the date of labor, and then do column management. But this approach not only wastes labor costs, but is also inefficient.
  • the bulletin number CN107593498A discloses a real-time monitoring system and method for cow's body temperature.
  • the cow body temperature data is collected by the PT1000 platinum resistor as a temperature measuring probe.
  • the voltage generated at both ends is transmitted to the temperature processing platform through the analog-to-digital converter.
  • the temperature acquisition process is: the temperature sensor is implanted in the cow vagina, the temperature measuring probe is in contact with the cow's vagina, and the whole circuit module is connected to the probe, including the constant current source. , analog to digital converter, MCU, wireless communication module.
  • the temperature information measured by the temperature sensor is pre-processed by the STM32 microcontroller to realize wireless communication with the user's computer, and finally the temperature in the cow's vagina is displayed on the computer; the invention can measure the true body temperature of the cow and monitor the temperature information.
  • Accurate and real-time monitoring of cow's body temperature information its structure is simple, easy to operate, and low in price.
  • the above-mentioned cow body temperature real-time monitoring system and method can only measure body temperature, and can not comprehensively and accurately analyze and judge the behavior and physiological changes of the cow, and the measurement method is vaginal measurement, which can only be performed on cows, and cannot Comprehensive detection of the situation of dairy cows throughout the breeding industry.
  • the primary object of the present invention is to provide a dairy cow behavior and physiological characteristic Internet of Things system and a working method thereof, which can combine the movement and body temperature of a cow to comprehensively determine the estrus of a cow, Physiological and behavioral conditions such as labor, disease, etc., using artificial intelligence algorithms for machine learning and classification, thus overcoming the shortcomings of the single pedometer calculation method, improving the estrus, labor, disease discovery rate and judgment reliability of dairy cows.
  • the management level of the dairy farming industry has been improved, thus improving the economic benefits of the dairy farming industry.
  • Another object of the present invention is to provide a behavioral and physiological characteristic Internet of Things system for dairy cows and a working method thereof.
  • the system and the working method thereof are simple in operation, accurate in data processing, good in use effect, and easy to widely popularize.
  • the invention provides a behavioral and physiological characteristic Internet of things system for dairy cows, comprising:
  • the entity monitoring module is configured to monitor a cow's motion signal and a body temperature signal
  • a data collection module configured to collect and process signal data monitored by the entity monitoring module
  • inter-network connection module configured to transmit signal data collected and processed by the data collection module
  • a network cloud module wherein the network cloud module is provided with an artificial intelligence algorithm, and the network cloud module is configured to receive signal data transmitted by the network connection module and then analyze and determine a specific physiological and behavioral condition of the cow;
  • a terminal control module configured to display a specific physiological and behavioral condition of the cow after analysis and judgment by the network cloud module
  • the entity monitoring module is in communication with the data collection module, the data collection module is in communication with the network connection module, and the network connection module is in communication with the network cloud module, and the network cloud module is The terminal control module is in communication connection.
  • the system is designed with a low-power wide-area Internet of Things consisting of two or more levels of wireless networks, including a primary network and a secondary network, the primary network consisting of an entity monitoring module and a data collection module.
  • the primary network consisting of an entity monitoring module and a data collection module.
  • the secondary network is composed of a data collection module and an inter-network connection module, and the secondary network may also be an ad hoc network solution, that is, A plurality of data collection modules form a link to the inter-network connection module.
  • the secondary network is responsible for uploading the data uploaded by all the data collection modules to the network cloud module through the network connection module, and the network cloud module of the v.
  • the entire system is not a stand-alone system, but a cross-regional wide-area IoT architecture that manages information from different countries or regions.
  • the secondary network wide area Internet of Things is designed to solve scenarios of various scales, and also forms big data in the network cloud; the system can combine the movement and body temperature of the cows to comprehensively judge the estrus of the cow through the above settings.
  • Physiological and behavioral conditions such as labor, disease, etc., using artificial intelligence algorithms for machine learning and classification, thereby overcoming the shortcomings of the single pedometer calculation method, improving the estrus, labor, disease discovery rate and judgment reliability of dairy cows. It also improved the management level of the dairy farming industry, thereby improving the economic benefits of the dairy farming industry.
  • the entity monitoring module includes a complex array of ear tags, each of which includes a plurality of ear tags, and the plurality of ear tags are fixed in cow ears of different countries or regions;
  • the data collection module includes a plurality of data collectors corresponding to the complex array ear tag group, the number of the plurality of data collectors being consistent with the number of groups of the complex array ear tag group, the plurality of data collectors Separatingly connected to the ear tags in the complex array ear tag group;
  • the inter-network connection module is a gateway, and the plurality of data collectors are all connected to the gateway;
  • the network cloud module includes a cloud database, and the cloud database is provided with an artificial intelligence algorithm.
  • the terminal control module is an electronic device, and the electronic device is provided with an APP, and the network cloud module passes the cloud database.
  • Receiving the signal data transmitted by the gateway and then analyzing and determining the specific physiological and behavioral conditions of the cow the terminal control module displays the cloud database to analyze the specific physiological and behavioral status of the determined cow through the APP.
  • the system can combine the movement and body temperature of the cow through the ear tag to comprehensively judge the physiological and behavioral conditions of the cow's estrus, labor, disease, etc., and through the artificial intelligence algorithm set in the cloud database for machine learning and classification, thereby overcoming the single meter.
  • the shortcomings of the calculation method of the stepper improve the estrus, labor, disease discovery rate and judgment reliability of the cow, and also improve the management level of the dairy farming industry, thereby improving the economic benefits of the dairy farming industry.
  • the ear tag includes a first system on chip, a sensing module, and a battery, the first system on chip is electrically connected to the battery, and the sensing module is electrically connected to the first system on the chip.
  • the sensing module includes a motion sensor and a body temperature sensor, wherein the motion sensor and the body temperature sensor are electrically connected to the first on-chip system, and the first system on chip is communicatively coupled to the data collector.
  • the ear tag is installed in the ear of the cow, and the eardrum temperature can be continuously measured for a long time, so the comparison object can be the historical temperature of the cow, thereby reducing the accuracy requirement of the absolute temperature measurement, and the characteristics of the eardrum temperature.
  • the comparison object can be the historical temperature of the cow, thereby reducing the accuracy requirement of the absolute temperature measurement, and the characteristics of the eardrum temperature. It is easy to install and can realize large-scale use of cows. It includes body temperature sensor and motion sensor to make the judgment better. At the same time, combined with the change of body temperature in physiological changes, it makes the judgment of estrus more accurate, and the body temperature information can also give cows. Disease and labor alarm.
  • the data collector includes a second system on chip, a third system on chip, and a power supply system, and the second system on chip and the system on the third system are electrically connected to the power supply system, and the second chip is on the second chip.
  • the system is in communication with the third on-chip system
  • the second system on chip is in communication with a first on-chip system of the electronic stud
  • the third system on chip is communicatively coupled to the gateway.
  • the second system on chip and the third system on chip perform bridge communication through a serial port connection, and the above setting can make the signal data more convenient to process the set.
  • the gateway includes a fourth system on chip, an ARM system chip, the fourth on-chip system is electrically connected to the ARM system chip, and the fourth on-chip system is in communication connection with the third on-chip system, The fourth on-chip system is in communication with the network cloud module.
  • the gateway runs the Linux operating system, and the instruction set of the ARM system chip is relatively simple, so the power consumption is low and the cost is low.
  • the gateway is a plurality of gateways, and the plurality of gateways are distributed in different countries or regions
  • the cloud database includes a plurality of cloud servers that receive data of gateways of different countries or regions
  • the electronic device may be The mobile phone, the tablet, and the computer, the APP is a user interface disposed on the electronic device.
  • the invention also provides a working method of a cow behavior and physiological characteristic internet of things system, comprising the following steps:
  • Step 1 monitoring and calculating body temperature data and motion data, including steps 11, step 12;
  • Step 11 the ear tag monitors the body temperature signal of the cow, and transmits the body temperature signal to the data collector, and the data collector sends the body temperature data processed by the body temperature signal to the gateway, and the gateway collects the data and sends the data to the cloud database for storage. , recording temperature Ti;
  • Step 12 The ear tag monitors the motion signal of the cow, and transmits the motion signal to the data collector, and the data collector sends the motion data processed by the motion signal to the gateway, and the gateway collects the data and sends the data to the cloud database for storage. Calculating the slot activity amount Lti by an artificial intelligence algorithm;
  • Step 2 calculating a historical baseline, calculating the average value of the measured temperature signal data, setting the historical baseline Tb; calculating the average of the measured motion signal data, and setting the historical baseline Ltb;
  • Step 3 comparing data, including step 31, step 32;
  • Step 31 subtract Ti from Tb, if Ti minus Tb is greater than 0.5, then count plus 1, continue to calculate the next Ti minus Tb, if Ti minus Tb is less than or equal to 0.5, the count is cleared, return to step 11;
  • Step 32 the Lti is divided by Ltb, if Lti divided by Ltb is greater than 0.5, the count is incremented by 1, and the next Lti is divided by Ltb, if Lti divided by Ltb is less than or equal to 0.5, the count is cleared, and the process returns to step 12;
  • Step 4 determining the specific situation of the cow, when the count of step 31 is less than or equal to 3, return to step 11; when the count of step 32 is less than or equal to 3, return to step 12; when the count of step 31 and step 32 are greater than 3
  • the count of step 31 is greater than 3 and the count of step 32 is 0, it is determined that the cow is sick; when the count of step 31 is 0 and the count of step 32 is greater than 3, it is determined that the cow is about to be calf ;
  • step 5 when it is determined that the cow is estrus, sick, or calving, the alarm information is sent to the APP.
  • the cow's movement and body temperature can be accurately and efficiently combined to comprehensively determine the physiological and behavioral conditions of the cow's estrus, labor, disease, etc., and the artificial intelligence algorithm set by the cloud database is used for machine learning and Classification, thus overcoming the shortcomings of the single pedometer calculation method, improving the estrus, labor, disease discovery rate and judgment reliability of the cow, and improving the management level of the dairy farming industry, thereby improving the economic benefits of the dairy farming industry. .
  • step 12 the algorithm for calculating the artificial intelligence of the slot activity amount Lti includes the following steps:
  • Step 121 appropriately select a sliding window in the cloud database, and calculate a sampling data mean value of each sliding window;
  • Step 123 Select an initial center point C i :sl i , ma i , ha i , and use the maximum activity amount of the first group of data, and select the center point of the third division;
  • step 124 for the new activity quantity x i+1 (sum diff value), the calculation determines which cluster belongs to, and the judgment conditions are as follows:
  • Step 125 recalculate the center point
  • HA All belong to HA, then
  • Step 126 calculating the slot activity amount l t .
  • the artificial intelligence algorithm can calculate the activity time slot activity of the cow more accurately and efficiently, and the machine learning and classification are performed by the artificial intelligence algorithm, thereby overcoming the shortcomings of the single pedometer calculation mode. It improves the estrus, labor, disease discovery rate and judgment reliability of dairy cows, and also improves the management level of dairy farming industry, thus improving the economic benefits of dairy farming.
  • k is a weighting coefficient
  • step 126 where q mat , q slt and q hat are respectively the numbers falling in the SL, MA and HA classifications at a certain time, k1, k2, k3 are weighting coefficients, and t represents time slots.
  • the time slot indicator is a statistical indicator that represents some total value of all motions.
  • the artificial intelligence algorithm of the system does not adopt a single pedometer method, but defines an activity amount including all motions, and then uses an artificial intelligence algorithm to classify and extract a time.
  • the relevant quantity value thereby simplifying the amount of uploaded data, and combining the body temperature measurement data in the network cloud to judge the behavior and physiological condition of the cow through the artificial intelligence algorithm, and improve the management level and benefit of the animal husbandry.
  • the invention has the beneficial effects that: compared with the prior art, in the invention, the system can combine the movement and body temperature of the cow to comprehensively determine the physiological and behavioral conditions of the cow's estrus, labor, disease, etc., using artificial intelligence.
  • the algorithm performs machine learning and classification, which overcomes the shortcomings of the single pedometer calculation method, improves the estrus, labor, disease discovery rate and judgment reliability of the cow, and improves the management level of the dairy farming industry, thus improving the The economic benefits of dairy farming.
  • FIG. 1 is a schematic view showing the framework of a behavioral and physiological characteristic Internet of Things system for a cow according to the present invention.
  • Embodiment 1 of a dairy cow behavior and physiological characteristic Internet of Things system of the present invention.
  • Embodiment 3 is a schematic view of Embodiment 2 of a behavioral and physiological characteristic Internet of Things system of a cow according to the present invention.
  • FIG. 4 is a schematic flow chart of a working method of a dairy cow behavior and physiological characteristic Internet of Things system according to the present invention.
  • the present invention provides a dairy animal behavior and physiological characteristics Internet of Things system, including:
  • the entity monitoring module is configured to monitor a cow's motion signal and body temperature signal
  • the data collection module is configured to collect and process signal data monitored by the entity monitoring module
  • An inter-network connection module configured to transmit signal data collected and processed by the data collection module;
  • the network cloud module is provided with an artificial intelligence algorithm in the network cloud module, and the network cloud module is configured to receive signal data transmitted by the network connection module and then analyze and determine the specific physiological and behavioral status of the cow;
  • terminal control module configured to display specific physiological and behavioral conditions of the cow after analysis and judgment by the network cloud module;
  • the entity monitoring module is in communication with the data collection module, the data collection module is in communication with the network connection module, the network connection module is in communication with the network cloud module, and the network cloud module is in communication with the terminal control module.
  • the system is designed with a low-power wide-area Internet of Things consisting of two or more levels of wireless networks, including a primary network and a secondary network, and the primary network is composed of an entity monitoring module and a data collection module, and is responsible for collecting The IoT node installed on each cow's ear, that is, the sensor data collected by the entity monitoring module; the secondary network is composed of a data collection module and an inter-network connection module, and the secondary network may also be an ad hoc network solution, that is, multiple The data collection module forms a link to the inter-network connection module.
  • the secondary network is responsible for uploading the data uploaded by all the data collection modules to the network cloud module through the network connection module, and the network cloud module of the v.
  • the entire system is not a stand-alone system, but a cross-regional wide-area IoT architecture that manages information from different countries or regions.
  • the secondary network wide area Internet of Things is designed to solve scenarios of various scales, and also forms big data in the network cloud; the system can combine the movement and body temperature of the cows to comprehensively judge the estrus of the cow through the above settings.
  • Physiological and behavioral conditions such as labor, disease, etc., using artificial intelligence algorithms for machine learning and classification, thereby overcoming the shortcomings of the single pedometer calculation method, improving the estrus, labor, disease discovery rate and judgment reliability of dairy cows. It also improved the management level of the dairy farming industry, thereby improving the economic benefits of the dairy farming industry.
  • the entity monitoring module includes a complex array ear tag group, the complex array ear tag group includes a plurality of ear tags, and the plurality of ear tags are fixed in the cow ears of different countries or regions;
  • the data collection module includes a plurality of data collectors corresponding to the complex array ear tag group, the number of the plurality of data collectors is consistent with the number of groups of the complex array ear tag group, and the plurality of data collectors and the complex array ear tags respectively Ear tag communication connection within the group;
  • the inter-network connection module is a gateway, and a plurality of data collectors are all connected with the gateway;
  • the network cloud module includes a cloud database, an algorithm for setting artificial intelligence in the cloud database, the terminal control module is an electronic device, and the APP is set on the electronic device, and the network cloud module receives the signal data transmitted by the gateway through the cloud database, and then analyzes and determines the cow's The specific physiological and behavioral conditions, the terminal control module through the APP display cloud database to analyze the specific physiological and behavioral status of the cow.
  • the system can combine the movement and body temperature of the cow through the ear tag to comprehensively judge the physiological and behavioral conditions of the cow's estrus, labor, disease, etc., and through the artificial intelligence algorithm set in the cloud database for machine learning and classification, thereby overcoming the single meter.
  • the shortcomings of the calculation method of the stepper improve the estrus, labor, disease discovery rate and judgment reliability of the cow, and also improve the management level of the dairy farming industry, thereby improving the economic benefits of the dairy farming industry.
  • the ear tag includes a first system on chip, a sensing module, and a battery.
  • the first system on the chip is electrically connected to the battery
  • the sensing module is electrically connected to the first system on the chip
  • the sensing module includes motion.
  • the sensor, the body temperature sensor, the motion sensor and the body temperature sensor are electrically connected to the first on-chip system, and the first on-chip system is in communication with the data collector.
  • the ear tag is installed in the ear of the cow, and the eardrum temperature can be continuously measured for a long time, so the comparison object can be the historical temperature of the cow, thereby reducing the accuracy requirement of the absolute temperature measurement, and the eardrum temperature is easy to be characterized.
  • Installation can achieve large-scale use of dairy cows, including body temperature sensor and motion sensor, so that the judgment effect is better, combined with changes in body temperature in physiological changes, making the judgment of estrus more accurate, body temperature information can also give disease of dairy cows And the labor alarm.
  • the data collector includes a second system on chip, a third system on chip, and a power supply system.
  • the second system on the chip and the system on the third chip are electrically connected to the power supply system, and the second system on the chip communicates with the system on the third system.
  • the second on-chip system is in communication with the first on-chip system of the electronic earrings, and the third on-chip system is in communication with the gateway.
  • the second on-chip system and the third on-chip system perform bridge communication through a serial port connection, and the above settings can make the signal data more convenient to process the set.
  • the gateway includes a fourth system on chip, an ARM system chip, the fourth system is electrically connected to the ARM system chip, the fourth system is connected to the third system, and the fourth system communicates with the network cloud module. connection.
  • the gateway runs the Linux operating system, and the instruction set of the ARM system chip is relatively simple, so the power consumption is low and the cost is low.
  • the gateway is a plurality of gateways, and the plurality of gateways are distributed in different countries or regions.
  • the cloud database includes a plurality of cloud servers that receive data of gateways in different countries or regions, and the electronic device may be a mobile phone, a tablet, or a computer.
  • APP is a user interface set on the electronic device.
  • the invention also provides a working method of a cow behavior and physiological characteristic internet of things system, comprising the following steps:
  • Step 1 monitoring and calculating body temperature data and motion data, including steps 11, step 12;
  • Step 11 the ear tag monitors the body temperature signal of the cow, transmits the body temperature signal to the data collector, and the data collector sends the body temperature data processed by the body temperature signal to the gateway, and the gateway collects the data and sends it to the cloud database for storage, and records the temperature Ti. ;
  • Step 12 The ear tag monitors the motion signal of the cow, transmits the motion signal to the data collector, and the data collector sends the motion data processed by the motion signal to the gateway, and the gateway collects the data and sends it to the cloud database for storage, through artificial intelligence.
  • the algorithm calculates the slot activity amount Lti;
  • Step 2 calculating a historical baseline, calculating the average value of the measured temperature signal data, setting the historical baseline Tb; calculating the average of the measured motion signal data, and setting the historical baseline Ltb;
  • Step 3 comparing data, including step 31, step 32;
  • Step 31 subtract Ti from Tb, if Ti minus Tb is greater than 0.5, then count plus 1, continue to calculate the next Ti minus Tb, if Ti minus Tb is less than or equal to 0.5, the count is cleared, return to step 11;
  • Step 32 divide Lti by Ltb, if Lti divided by Ltb is greater than 0.5, the count is incremented by 1, and the next Lti is divided by Ltb; if Lti divided by Ltb is less than or equal to 0.5, the count is cleared, and the process returns to step 12;
  • Step 4 determining the specific situation of the cow, when the count of step 31 is less than or equal to 3, return to step 11; when the count of step 32 is less than or equal to 3, return to step 12; when the count of step 31 and step 32 are greater than 3
  • the count of step 31 is greater than 3 and the count of step 32 is 0, it is determined that the cow is sick; when the count of step 31 is 0 and the count of step 32 is greater than 3, it is determined that the cow is about to be calf ;
  • step 5 when it is determined that the cow is estrus, sick, or calving, the alarm information is sent to the APP.
  • the cow's movement and body temperature can be accurately and efficiently combined to comprehensively determine the physiological and behavioral conditions of the cow's estrus, labor, disease, etc., and the artificial intelligence algorithm set by the cloud database is used for machine learning and Classification, thus overcoming the shortcomings of the single pedometer calculation method, improving the estrus, labor, disease discovery rate and judgment reliability of the cow, and improving the management level of the dairy farming industry, thereby improving the economic benefits of the dairy farming industry. .
  • step 12 the algorithm for calculating the artificial intelligence of the slot activity amount Lti includes the following steps:
  • Step 121 appropriately select a sliding window in the cloud database, and calculate a sampling data mean value of each sliding window;
  • Step 123 Select an initial center point C i :sl i , ma i , ha i , and use the maximum activity amount of the first group of data, and select the center point of the third division;
  • step 124 for the new activity quantity x i+1 (sum diff value), the calculation determines which cluster belongs to, and the judgment conditions are as follows:
  • Step 125 recalculate the center point
  • HA All belong to HA, then
  • Step 126 calculating the slot activity amount l t .
  • the artificial intelligence algorithm can calculate the activity time slot activity of the cow more accurately and efficiently, and the machine learning and classification are performed by the artificial intelligence algorithm, thereby overcoming the shortcomings of the single pedometer calculation mode. It improves the estrus, labor, disease discovery rate and judgment reliability of dairy cows, and also improves the management level of dairy farming industry, thus improving the economic benefits of dairy farming.
  • step 125 k is a weighting coefficient.
  • step 126 where q mat , q slt , and q hat are the numbers falling in the SL, MA, and HA classifications at a certain time, k1, k2, and k3 are weighting coefficients, and t represents time slots. .
  • the time slot indicator is a statistical indicator that represents some total value of all motions.
  • the artificial intelligence algorithm of the system does not adopt a single pedometer method, but defines an activity amount including all motions, and then uses an artificial intelligence algorithm to classify and extract a time.
  • the relevant quantity value thereby simplifying the amount of uploaded data, and combining the body temperature measurement data in the network cloud to judge the behavior and physiological condition of the cow through the artificial intelligence algorithm, and improve the management level and benefit of the animal husbandry.
  • the movement belongs to the behavior attribute
  • the body temperature belongs to the physiological attribute.
  • the behavior and physiological state of the normal cow should be changed within a certain range. If the range is beyond this range, a comprehensive analysis is needed to determine which state the cow is in. .
  • the comprehensive judgment of estrus detection is shown in Figure 2.
  • the body temperature of a Holstein cow has increased by 0.5 degrees for 3 hours. It may be estrus, it may be ill, or it may be other stress reactions, if at the same time.
  • the amount of exercise has also increased by 50% for 3 hours, and when it is in the estrus phase, the probability of estrus is high and the probability of getting sick will be small.
  • the invention has the beneficial effects that: compared with the prior art, in the invention, the system can combine the movement and body temperature of the cow to comprehensively determine the physiological and behavioral conditions of the cow's estrus, labor, disease, etc., using artificial intelligence.
  • the algorithm performs machine learning and classification, which overcomes the shortcomings of the single pedometer calculation method, improves the estrus, labor, disease discovery rate and judgment reliability of the cow, and improves the management level of the dairy farming industry, thus improving the The economic benefits of dairy farming.

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  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

L'invention concerne un système de l'Internet des objets appliqué au comportement de vaches et à leurs caractéristiques physiologiques et un procédé de fonctionnement associé. Le système comprend : un module de suivi d'entité, permettant de suivre un signal de mouvement et un signal de température d'une vache ; un module de collecte de données, permettant de collecter et de traiter des données de signal suivies par le module de suivi d'entité ; une machine de connexion inter-réseaux, permettant de transmettre les données de signal collectées et traitées par le module de collecte de données ; un module de nuage de réseau, équipé d'un algorithme d'intelligence artificielle et permettant de recevoir les données de signal transmises par le module de connexion inter-réseaux, et d'analyser et de déterminer des conditions physiologiques et comportementales spécifiques de la vache ; et un module terminal de commande, permettant d'afficher les conditions physiologiques et comportementales spécifiques de la vache, analysées et déterminées par le module de nuage de réseau. Les conditions physiologiques et comportementales, telles que l'œstrus, une période et une maladie de parturition d'une vache peuvent être déterminées de manière complète en combinaison avec le mouvement et avec la température de la vache ; le taux de découverte et la fiabilité de détermination de l'œstrus, de la période de parturition et de la maladie de la vache peuvent être augmentés ; le niveau de gestion de l'industrie laitière est amélioré ; et ainsi, les atouts économiques de l'industrie laitière sont accrus.
PCT/CN2018/112990 2018-05-18 2018-10-31 Système de l'internet des objets appliqué au comportement de vaches et à leurs caractéristiques physiologiques et procédé de fonctionnement associé WO2019218602A1 (fr)

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