A pig breeding behavior monitoring system for the entire breeding cycle

Abstract

The invention discloses a pig breeding full-cycle breeding behavior monitoring system, relates to the technical field of pig breeding, and solves the technical problems that the health status of pigs cannot be managed visually and the pigs with high monitoring coefficients cannot be intelligently identified for key monitoring. The system comprises: a feeding evaluation module for collecting feeding data of pigs within a preset time period to evaluate the monitoring coefficient GH; an instruction issuing module for automatically adjusting the information collection frequency of the corresponding pigs according to the monitoring coefficient GH, reasonably arranging monitoring resources, and improving monitoring efficiency; in response to the information collection instruction, the pig state collection module is used to collect the motion state parameter information of the pigs, and the motion state parameter information includes the heartbeat data, body temperature data and posture data of the pigs; the data analysis module realizes the monitoring of the pig state based on the analysis and processing of the motion state parameter information, realizes the timely management of the health of the pigs, reduces the pig mortality rate, and improves the efficiency of the pig farm.

Description

Technical Field

[0001] The present invention relates to the technical field of pig breeding, and in particular to a pig breeding full-cycle breeding behavior monitoring system. Background Art

[0002] With the development of animal husbandry, the large-scale pig farms and pig farming areas have increased rapidly, and the application of smart pig farms has become more and more popular. Smart pig farms are based on the professional needs of environmental emergencies and the concept of full-process control of environmental emergency events. They integrate cloud computing, big data, integration, mobility and other innovations. They can establish breeding production standards, planning management, breeding process management and real-time data statistical analysis platforms without geographical restrictions for pig farming companies, which are more easily adapted to users' existing business working methods and business processes; and establish an environmental early warning emergency management platform that combines information technology, communication technology and GIS technology, so as to achieve rapid, timely and accurate response and processing of emergencies in the environment.

[0003] However, the existing smart pig farm breeding behavior monitoring systems mainly stay at the stage of collecting and storing data. Most of them do not conduct more in-depth analysis and mining of the data, and cannot perform data visualization management of the health of pigs; at the same time, they are unable to intelligently identify pigs with high monitoring coefficients for key monitoring, so as to reasonably arrange monitoring resources and improve monitoring efficiency; based on the above shortcomings, the present invention proposes a pig breeding full-cycle breeding behavior monitoring system. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a pig breeding behavior monitoring system for the entire breeding cycle.

[0005] To achieve the above-mentioned purpose, the first aspect of the present invention provides a pig farming full-cycle breeding behavior monitoring system, including a pig feeding module, a feeding evaluation module, an instruction issuing module, a database, a pig status acquisition module and a data analysis module;

[0006] The pig feeding module includes a feeder, which is used to sense the pig's touching behavior on the feeder in real time, and obtain the corresponding pig's eating data according to the change of feed weight data before and after in the feeder;

[0007] The feeding evaluation module is connected to the pig feeding module, and is used to collect the feeding data of the pigs within a preset time period to evaluate the monitoring coefficient GH, and upload the monitoring coefficient GH of the pigs to the monitoring center; the monitoring coefficient GH is used to reflect the abnormal degree of feeding of the pigs;

[0008] After receiving the monitoring coefficient GH of the live pig, the monitoring center drives the instruction issuing module to issue the information collection instruction to the live pig status collecting module according to the preset rules; the preset rules are specifically:

[0009] Obtaining a mapping relationship table of monitoring coefficient range and acquisition frequency pre-stored in a database;

[0010] Based on the mapping relationship table, the acquisition frequency corresponding to the monitoring coefficient GH in the mapping relationship table is determined and marked as Hi; the instruction issuing module is used to issue information collection instructions according to the acquisition frequency Hi;

[0011] In response to the information collection instruction, the pig state collection module is used to collect the motion state parameter information of the corresponding pig and transmit the collected motion state parameter information to the monitoring center; the motion state parameter information includes the heartbeat data, body temperature data and posture data of the pig;

[0012] The monitoring center is used to transmit the received motion state parameter information to the data analysis module; the data analysis module realizes the monitoring of the pig status based on the analysis and processing of the motion state parameter information.

[0013] Furthermore, the specific evaluation steps of the eating evaluation module are as follows:

[0014] For the same live pig, within a preset time period, the total number of times the live pig eats is counted as C1;

[0015] Obtain the growth stage of the pig, and determine the corresponding feeding threshold as LT according to the growth stage; the growth stage includes a piglet stage, a medium pig stage, and a large pig stage;

[0016] The amount of food consumed each time is marked as Li; the amount of food consumed Li is compared with the eating threshold LT;

[0017] If the food intake Li> the food intake threshold LT and Li-LT> the set value, it means that the pig is in a state of overeating, and Li is marked as the overeating value at this time; the difference between all overeating values ​​Li and the food intake threshold LT is calculated and summed to obtain the first overeating coefficient BL;

[0018] If the food intake Li is less than the food intake threshold LT and LT-Li is greater than the set value, it indicates that the pig is in anorexia state, and Li is marked as anorexia value at this time; all anorexia values ​​Li and the food intake threshold LT are calculated by difference and summed to obtain the first anorexia coefficient YL;

[0019] The number of times the pigs are in an abnormal eating state is counted as abnormal frequency P1; the abnormal eating state includes overeating state and anorexia state; using the formula The monitoring coefficient GH of the live pig is calculated, wherein g1, g2, and g3 are all preset coefficient factors.

[0020] Furthermore, the feeding threshold is actual body weight × coefficient, the coefficient is 0.05 in the piglet stage, 0.04 in the middle pig stage, and 0.03 in the large pig stage.

[0021] Furthermore, the data analysis module analyzes and processes the heartbeat data and body temperature data in the following manner:

[0022] Obtain the heartbeat data and body temperature data in the motion state parameter information, and compare them with the standard heartbeat data and standard body temperature data stored in the database to obtain the corresponding heartbeat difference and body temperature difference;

[0023] Obtain the heartbeat difference and body temperature difference that are greater than zero, and calculate the corresponding pig's movement influence coefficient by combining the influence factors of various state parameters on the pig's health status stored in the database;

[0024] The calculated movement influence coefficient is compared with the preset influence threshold; if the movement influence coefficient ≥ the preset influence threshold, it is determined that the health status of the pigs at this time is abnormal, and an abnormal alarm is issued.

[0025] Furthermore, the data analysis module also includes:

[0026] Compare the pig's heartbeat data with the average heartbeat data of the last five days to get the corresponding heartbeat difference; compare the pig's body temperature with the average body temperature of the last five days to get the corresponding body temperature difference.

[0027] Furthermore, the data analysis module analyzes and processes the posture data including:

[0028] Obtaining posture data in the motion state parameter information, inputting the posture data into the abnormal state detection model, obtaining the output result, so as to determine whether the health status of the pig is abnormal;

[0029] When the output result is 1, it is determined that the health status of the corresponding pig is abnormal, and an abnormal alarm is issued; when the output result is 0, it is determined that the health status of the corresponding pig is normal.

[0030] Furthermore, the abnormal state detection model is established based on the Bi-LSTM model of the Attention mechanism and is trained using historical posture data of live pigs.

[0031] Furthermore, the pig status acquisition module includes a heartbeat sensor, a temperature sensor and a posture acquisition sensor; the posture acquisition sensor is a micro inertial sensor that integrates a three-axis acceleration, a three-axis gyroscope and a three-axis magnetic sensor.

[0032] Furthermore, the micro inertial sensor is a 9-axis micro inertial sensor MPU-9250.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. The feeding evaluation module of the present invention is used to collect the feeding data of the pigs within a preset time period to evaluate the monitoring coefficient GH, and the monitoring coefficient GH is used to reflect the abnormality of the pigs' feeding; the instruction issuing module is used to automatically adjust the information collection frequency of the corresponding pigs according to the monitoring coefficient GH, so as to realize automatic monitoring of the pig status, and at the same time, it can intelligently identify the pigs with high monitoring coefficients GH for key monitoring, reasonably arrange monitoring resources, greatly improve the efficiency of pig breeding, and reduce labor costs;

[0035] 2. In response to the information collection instruction, the pig status collection module is used to collect the motion status parameter information of the corresponding pig; the data analysis module realizes the monitoring of the pig status based on the analysis and processing of the motion status parameter information, and calculates the motion influence coefficient of the corresponding pig in combination with the heartbeat data and the body temperature data. If the motion influence coefficient is ≥ the preset influence threshold, it is determined that the health status of the pig is abnormal at this time, and an abnormal alarm is issued; at the same time, the posture data is input into the abnormal state detection model to obtain the output result to determine whether the health status of the pig is abnormal, so as to display the health status of the pig in real time, realize timely management of the health of the pig, reduce the mortality rate of the pig, and improve the efficiency of the pig farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 The present invention is a system block diagram of a pig breeding full-cycle breeding behavior monitoring system.

[0038] Figure 2 This is a flow chart of the method for analyzing and processing heartbeat data and body temperature data of the present invention.

[0039] Figure 3 This is a flow chart of the posture data analysis and processing method of the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] See also Figures 1 to 3 , the first aspect of the present invention provides a pig farming full-cycle breeding behavior monitoring system, including a pig feeding module, a feeding evaluation module, a monitoring center, an instruction issuing module, a pig status collection module, a database and a data analysis module;

[0042] Considering that pigs have very fixed habits, body temperature and food intake are the main observation indicators of pigs. When pigs are in estrus or in poor physical condition, they will be directly reflected;

[0043] Therefore, the pigs’ eating data is used as the first analysis indicator, and the collection and analysis of other status parameters are driven according to the analysis results of the eating data, which greatly reduces the analysis workload of the staff and improves the monitoring efficiency;

[0044] The pig feeding module includes a feeder, which is used to sense the pig's touch behavior on the feeder in real time to obtain the pig's eating data. The specific working steps are as follows:

[0045] When the pig's touching behavior on the feeder is sensed, it is matched with the pig's ear tag to obtain the pig's number, and the touching behavior and the pig's number are merged and recorded; each pig has a unique ear tag;

[0046] The feeding data of the corresponding live pigs is obtained according to the change of the feed weight data before and after the feeder; it should be understood that the feed weight data before and after the feeder can be collected by the load sensor;

[0047] The feeding evaluation module is connected to the pig feeding module to collect the feeding data of the pigs within a preset time period to evaluate the monitoring coefficient GH. The specific evaluation steps are as follows:

[0048] For the same pig, collect the pig's eating data within a preset time period;

[0049] The total number of times the pigs eat is counted as C1, and the amount of food eaten each time is marked as Li; where i = 1, 2, ..., n; i represents the i-th eating time; the unit of Li is kg;

[0050] Obtain the growth stage of the pig, and determine the corresponding feeding threshold as LT according to the growth stage; the growth stage includes the piglet stage, the medium pig stage and the large pig stage;

[0051] In this embodiment, the normal body temperature of pigs is 38-39.5°C, and the standard food intake is the actual body weight × coefficient. The coefficient is 0.05 in the piglet stage, 0.04 in the middle pig stage, and 0.03 in the large pig stage.

[0052] For example: if a pig is in the piglet stage and weighs 20kg, then the feeding threshold = weight × coefficient = 20kg × 0.05 = 1kg; or if a pig is in the middle pig stage and weighs 50kg, then the feeding threshold = weight × coefficient = 50kg × 0.04 = 2kg;

[0053] Compare the food intake Li with the food intake threshold LT; if the food intake Li> the food intake threshold LT, and Li-LT> the set value, it means that the pig is in a state of overeating, and Li is marked as the overeating value;

[0054] The overeating value Li and the eating threshold LT are calculated by difference to obtain the overeating value, and all overeating values ​​are accumulated to obtain the first overeating coefficient BL; the set value is preset by the administrator, for example, 0.9 kg;

[0055] If the feeding amount Li is less than the feeding threshold LT, and LT-Li is greater than the set value, it means that the pig is in anorexia state, and Li is marked as the anorexia value at this time; the feeding threshold LT and the anorexia value Li are calculated to obtain the feeding difference, and all the feeding differences are accumulated to obtain the first anorexia coefficient YL;

[0056] The number of times the pigs are in an abnormal eating state is counted as the abnormal frequency P1; the abnormal eating state includes the overeating state and the anorexia state; for example, if the pigs are in the overeating state 5 times and the anorexia state 3 times, the abnormal frequency P1 = 5 times + 3 times = 8 times;

[0057] Using the formula The monitoring coefficient GH of live pigs is calculated, where g1, g2, and g3 are all preset coefficient factors;

[0058] In this embodiment, the monitoring coefficient GH is used to reflect the degree of abnormal eating of pigs. The larger the monitoring coefficient GH is, the more serious the abnormal eating of pigs is. At this time, it is necessary to focus on monitoring the pigs and increase the frequency of collecting and analyzing other status parameters of the pigs, so as to accurately understand the health status of the pigs and find sick pigs in time.

[0059] The feeding evaluation module is used to upload the monitoring coefficient GH of the pigs to the monitoring center, and the monitoring center is used to drive the instruction issuing module to issue information collection instructions to the pig status collection module according to preset rules;

[0060] The specific working steps of the instruction issuing module include:

[0061] Obtain a mapping relationship table of monitoring coefficient range and collection frequency pre-stored in the database; the larger the monitoring coefficient GH is, the higher the collection frequency is and the shorter the collection interval is;

[0062] Based on the mapping relationship table, determine the monitoring coefficient range interval where the monitoring coefficient GH is located in the corresponding mapping relationship table, and then determine the corresponding collection frequency as Hi; the instruction issuing module is used to issue information collection instructions to the pig status collection module according to the collection frequency Hi;

[0063] The present invention can automatically adjust the information collection frequency of the corresponding pigs according to the monitoring coefficient GH of the pigs, realize automatic monitoring of the status of the pigs, and can intelligently identify the pigs with high monitoring coefficients GH for key monitoring, reasonably arrange monitoring resources, greatly improve the efficiency of pig breeding, and reduce labor costs;

[0064] In response to the information collection instruction, the pig state collection module is used to collect the motion state parameter information of the pig and transmit the collected motion state parameter information to the monitoring center;

[0065] In this embodiment, the pig status acquisition module includes a heartbeat sensor, a temperature sensor and a posture acquisition sensor; the heartbeat sensor is used to collect the heartbeat data of the pig, the temperature sensor is used to collect the body temperature data of the pig, and the posture acquisition sensor is used to collect the posture data of the pig;

[0066] Specifically, the attitude acquisition sensor is a micro-inertial sensor that integrates a three-axis acceleration, a three-axis gyroscope, and a three-axis magnetic sensor; the micro-inertial sensor is a 9-axis micro-inertial sensor MPU-9250;

[0067] A further technical solution is that the heartbeat sensor, temperature sensor and posture acquisition sensor will be calibrated according to the pig farm environment before installation, and the relevant parameters will be adjusted according to the pig farm environment to improve data accuracy;

[0068] The monitoring center is used to transmit the received motion state parameter information to the data analysis module; the data analysis module realizes the monitoring of the pig state based on the analysis and processing of the motion state parameter information;

[0069] like Figure 2 As shown, the data analysis module analyzes and processes the heartbeat data and body temperature data in the following manner:

[0070] Obtain the heartbeat data and body temperature data in the motion state parameter information, and compare them with the standard heartbeat data and standard body temperature data stored in the database to obtain the corresponding heartbeat difference and body temperature difference;

[0071] Alternatively, the heartbeat data of the live pig is compared with the average heartbeat data of the last five days to obtain the corresponding heartbeat difference; the body temperature of the live pig is compared with the average body temperature of the last five days to obtain the corresponding body temperature difference;

[0072] For example: when the pig's body temperature is 2 degrees Celsius higher than the average body temperature of the last 5 days, the temperature difference is 2; when the pig's body temperature is 1 degree Celsius lower than the average body temperature of the last 5 days, the temperature difference is -1; if the heartbeat difference and the body temperature difference are less than or equal to zero, it means that the pig's current heartbeat data and body temperature data do not affect the pig's health status;

[0073] Obtain the heartbeat difference and body temperature difference that are greater than zero, and calculate the corresponding pig's exercise influence coefficient by combining the influence factors of various state parameters on the pig's health status stored in the database; exercise influence coefficient = heartbeat difference × heartbeat influence factor + body temperature difference × body temperature influence factor;

[0074] The calculated movement influence coefficient is compared with the preset influence threshold; if the movement influence coefficient is ≥ the preset influence threshold, it is determined that the health status of the pigs is abnormal at this time, and an abnormal alarm is issued;

[0075] like Figure 3 As shown, the data analysis module analyzes and processes the posture data including:

[0076] Obtaining posture data in the motion state parameter information, inputting the posture data into the abnormal state detection model, obtaining the output result, so as to determine whether the health status of the pig is abnormal;

[0077] Among them, the abnormal state detection model is established based on the Bi-LSTM model of the Attention mechanism and is trained using the historical posture data of live pigs;

[0078] When the output result is 1, it is determined that the health status of the corresponding pig is abnormal, and an abnormal alarm is issued; when the output result is 0, it is determined that the health status of the corresponding pig is normal;

[0079] This application analyzes and calculates the motion state parameter information collected by the pig state collection module through the algorithm model arranged by the data analysis module, thereby displaying the health status of the pigs in real time, realizing timely management of the pig health, reducing the pig mortality rate, and improving the efficiency of the pig farm.

[0080] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0081] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0082] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A pig breeding full cycle breeding behavior monitoring system, characterized in that: It includes pig feeding module, feeding evaluation module, instruction issuing module, pig status collection module and data analysis module; The pig feeding module includes a feeder, which is used to sense the pig's touching behavior on the feeder in real time, and obtain the corresponding pig's eating data according to the change of feed weight data before and after in the feeder; The feeding evaluation module is connected to the pig feeding module, and is used to collect the feeding data of the pigs within a preset time period to evaluate the monitoring coefficient GH, and upload the monitoring coefficient GH of the pigs to the monitoring center; the monitoring coefficient GH is used to reflect the abnormal degree of feeding of the pigs; The specific evaluation steps of the eating evaluation module are as follows: For the same live pig, within a preset time period, the total number of times the live pig eats is counted as C1; Obtain the growth stage of the pig, and determine the corresponding feeding threshold as LT according to the growth stage; the growth stage includes a piglet stage, a medium pig stage, and a large pig stage; The amount of food consumed each time is marked as Li; the amount of food consumed Li is compared with the eating threshold LT; If the food intake Li> the food intake threshold LT and Li-LT> the set value, it means that the pig is in a state of overeating, and Li is marked as the overeating value at this time; the difference between all overeating values ​​Li and the food intake threshold LT is calculated and summed to obtain the first overeating coefficient BL; If the food intake Li is less than the food intake threshold LT and LT-Li is greater than the set value, it indicates that the pig is in anorexia state, and Li is marked as anorexia value at this time; all anorexia values ​​Li and the food intake threshold LT are calculated by difference and summed to obtain the first anorexia coefficient YL; The number of times the pigs are in an abnormal eating state is counted as abnormal frequency P1; the abnormal eating state includes overeating state and anorexia state; using the formula The monitoring coefficient GH of the live pig is calculated, wherein g1, g2, and g3 are all preset coefficient factors; After receiving the monitoring coefficient GH of the live pig, the monitoring center drives the instruction issuing module to issue the information collection instruction to the live pig status collecting module according to the preset rules; the preset rules are specifically: Obtaining a mapping relationship table of monitoring coefficient range and acquisition frequency pre-stored in a database; Based on the mapping relationship table, determine the acquisition frequency corresponding to the monitoring coefficient GH in the mapping relationship table and mark it as Hi; The instruction issuing module is used to issue information collection instructions according to the collection frequency Hi; In response to the information collection instruction, the pig state collection module is used to collect the motion state parameter information of the corresponding pig and transmit the collected motion state parameter information to the monitoring center; the motion state parameter information includes the heartbeat data, body temperature data and posture data of the pig; The monitoring center is used to transmit the received motion state parameter information to the data analysis module; the data analysis module realizes the monitoring of the pig status based on the analysis and processing of the motion state parameter information.

2. A pig farming full cycle breeding behavior monitoring system according to claim 1, characterized in that: The feeding threshold is actual body weight × coefficient. The coefficient is 0.05 in the piglet stage, 0.04 in the middle pig stage, and 0.03 in the large pig stage.

3. A pig farming full cycle breeding behavior monitoring system according to claim 1, characterized in that: The data analysis module analyzes and processes the heartbeat data and body temperature data in the following manner: Obtain the heartbeat data and body temperature data in the motion state parameter information, and compare them with the standard heartbeat data and standard body temperature data stored in the database to obtain the corresponding heartbeat difference and body temperature difference; Obtain the heartbeat difference and body temperature difference that are greater than zero, and calculate the corresponding pig's movement influence coefficient by combining the influence factors of various state parameters on the pig's health status stored in the database; The calculated movement influence coefficient is compared with the preset influence threshold; if the movement influence coefficient ≥ the preset influence threshold, it is determined that the health status of the pigs at this time is abnormal, and an abnormal alarm is issued.

4. A pig farming full cycle breeding behavior monitoring system according to claim 3, characterized in that: The data analysis module also includes: Compare the pig's heartbeat data with the average heartbeat data of the last five days to get the corresponding heartbeat difference; compare the pig's body temperature with the average body temperature of the last five days to get the corresponding body temperature difference.

5. A pig farming full cycle breeding behavior monitoring system according to claim 1, characterized in that: The data analysis module analyzes and processes the posture data including: Obtaining posture data in the motion state parameter information, inputting the posture data into the abnormal state detection model, obtaining the output result, so as to determine whether the health status of the pig is abnormal; When the output result is 1, it is determined that the health status of the corresponding pig is abnormal, and an abnormal alarm is issued; when the output result is 0, it is determined that the health status of the corresponding pig is normal.

6. A pig farming full cycle breeding behavior monitoring system according to claim 5, characterized in that: The abnormal state detection model is established based on the Bi-LSTM model of the Attention mechanism and is trained using the historical posture data of live pigs.

7. A pig farming full cycle breeding behavior monitoring system according to claim 1, characterized in that: The pig status acquisition module includes a heartbeat sensor, a temperature sensor and a posture acquisition sensor; the posture acquisition sensor is a micro inertial sensor integrating a three-axis acceleration, a three-axis gyroscope and a three-axis magnetic sensor.

8. A pig farming full cycle breeding behavior monitoring system according to claim 7, characterized in that: The micro inertial sensor is a 9-axis micro inertial sensor MPU-9250.

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