Integrated intelligent digital pig breeding management system and method

Abstract

The invention relates to the field of live pig breeding, and discloses an integrated intelligent digital live pig breeding management system and method.The management system comprises a numbering module, a health monitoring module, a breeding data module and a data analysis module, and unique numbers are generated for all sows through the numbering module; monitoring the physiological status corresponding to each numbered sow through a health monitoring module to obtain monitoring data of each numbered sow; inputting breeding data corresponding to each numbered sow through a breeding data module; and finally, analyzing the monitoring data and breeding data corresponding to each numbered sow through a data analysis module, rating each sow, and judging whether breeding can be continued or not according to a rating result. Sows with poor breeding ability are timely eliminated, and the breeding efficiency of the sows is improved; the loss in breeding is reduced.

Description

Technical Field

[0001] The present invention relates to the field of pig breeding, and in particular to an integrated intelligent digital pig breeding management system and method. Background Art

[0002] With the rapid development of pig farming and modern information technology, in some large pig farms, the Internet of Things technology and big data technology are used to monitor the pigs and pig houses in real time, realize precise feeding, environmental control and health monitoring, so as to achieve comprehensive monitoring and management of the pig farming process and improve farming efficiency.

[0003] Sow breeding is a very important link in pig farming. Chinese Patent 202410096729.7 discloses a data management system and method for analyzing sow breeding efficiency. The breeding efficiency of sows can be obtained through an overall analysis of the breeding environment and breeding materials.

[0004] However, the breeding efficiency of sows is related to the breeding quality of the sow itself. If the sow itself has a poor physique, the piglets it breeds may also have a poor physique. Not only does it require a lot of manpower and material resources to treat the sows and piglets, but the staff cannot accurately select suitable sows for continued breeding, resulting in reduced breeding efficiency. Summary of the invention

[0005] The purpose of the present invention is to provide an integrated intelligent digital pig breeding management system and method to solve the following technical problems:

[0006] How to determine whether a sow is suitable for continued breeding.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] An integrated intelligent digital pig breeding management system, the management system comprising:

[0009] The numbering module is used to generate a unique number for each sow, which is m;

[0010] The health monitoring module is used to monitor the physiological status of each sow with a corresponding number and obtain the monitoring data of each sow with a corresponding number;

[0011] The breeding data module is used to input the breeding data corresponding to each sow number;

[0012] The data analysis module is used to analyze the monitoring data and breeding data corresponding to each sow number, rate each sow, and determine whether breeding can continue based on the rating results.

[0013] As a further solution of the present invention: the monitoring data includes the temperature and weight of the sow;

[0014] The breeding data included the time of birth, the total number of pregnant piglets, the number of surviving piglets and the weight of each piglet.

[0015] As a further solution of the present invention: the analysis process of the analysis module is:

[0016] S10: Obtaining the sow postpartum health index by analyzing the temperature and weight of each sow after parturition;

[0017] S20: Obtain the piglet health index by analyzing the production time of each sow, the total number of pregnant piglets, the number of surviving piglets and the weight of each piglet;

[0018] S30: Obtaining a comprehensive reproductive index of each sow by analyzing the sow postpartum health index and piglet health index of each sow;

[0019] S40: Rating each sow by analyzing its comprehensive reproductive index.

[0020] As a further solution of the present invention: in step S10, the sow postpartum health index includes:

[0021]

[0022] Calculation of sow postpartum health index Q m ;

[0023] Where, f(X) is the judgment function. When X≥0, f(X)=X; when X<0, f(X)=0; Q0 is the basic health index of sows; t m is the production time of the sow numbered m; Δt is the preset duration; T m (t) is the temperature change curve of the real-time temperature of the sow numbered m over time; T0 is the preset temperature; W1 is the preset allowable error value; G m (t m G +Δt) is the weight of the sow numbered m at a preset time Δt after giving birth; m (t m ) is the initial weight of the sow numbered m at the time of delivery; G1 is the basic weight reduction within the preset time Δt after delivery; X is the total number of piglets in this pregnancy of the sow numbered m; σ X is the sow weight adjustment coefficient; v1 is the first weight coefficient; δ2 is the second weight coefficient; C1 is the first preset constant; C2 is the second preset constant.

[0024] As a further solution of the present invention: in step S20, the piglet health index includes:

[0025]

[0026] Calculation of piglet health index P m ;

[0027] Where Y is the number of surviving piglets left by the sow numbered m after a preset time Δt after giving birth, y∈Y; P0 is the basic health index of the piglets; G y (t m G +Δt) is the weight of the yth healthy piglet produced by the sow numbered m at a preset time Δt after production; y (t m ) is the initial weight of the yth healthy piglet produced by the sow numbered m at the time of production; ρ X is the piglet weight adjustment coefficient; G0 is the basic weight increase within the preset time Δt after production; γ1 is the third weight coefficient; γ2 is the fourth weight coefficient; C3 is the third preset constant; C4 is the fourth preset constant.

[0028] As a further solution of the present invention: in step S30, the comprehensive breeding index includes the following: m =P m Q m Calculate the comprehensive reproductive index U of sow numbered m m .

[0029] As a further solution of the present invention: in step S40, the process of grading sows is as follows:

[0030] The comprehensive reproductive index U of the sow numbered m m Compare with the preset threshold [R1, R2]; R2>P0Q0>R1;

[0031] When U m When ≥R2, the rating result is high-quality sow;

[0032] When R2>U m When ≥R1, the rating result is first-class sow;

[0033] When R1>U m , the rating result is level 2 sow.

[0034] An integrated intelligent digital pig farming management method, the management method comprising the following steps:

[0035] S1: Generate a unique number for each sow through the numbering module;

[0036] S2: Monitor the physiological status of the sows with different numbers through the health monitoring module to obtain the monitoring data of the sows with different numbers;

[0037] S3: Enter the breeding data corresponding to each sow number through the breeding data module;

[0038] S4: The monitoring data and breeding data corresponding to each sow number are analyzed through the data analysis module, each sow is rated, and it is determined whether the breeding can continue based on the rating results.

[0039] Beneficial effects of the present invention:

[0040] (1) The present invention generates a unique number for each sow through a numbering module, and then monitors the physiological state of each numbered sow through a health monitoring module to obtain monitoring data of each numbered sow; then the breeding data corresponding to each numbered sow is input through a breeding data module; finally, the monitoring data and breeding data corresponding to each numbered sow are analyzed through a data analysis module, and each sow is rated, and whether it can continue to breed is determined according to the rating results; sows with poor breeding ability are eliminated in time to improve sow breeding efficiency; and losses in breeding are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] Figure 1 A system module framework diagram of an embodiment of the present invention;

[0043] Figure 2 The present invention is a method flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0045] See also Figure 1 As shown, in one embodiment, an integrated intelligent digital pig breeding management system is provided, comprising:

[0046] The numbering module is used to generate a unique number for each sow, which is m;

[0047] The health monitoring module is used to monitor the physiological status of each sow with a corresponding number and obtain the monitoring data of each sow with a corresponding number;

[0048] The breeding data module is used to input the breeding data corresponding to each sow number;

[0049] The data analysis module is used to analyze the monitoring data and breeding data corresponding to each sow number, rate each sow, and determine whether breeding can continue based on the rating results;

[0050] Through the above technical scheme, this embodiment generates a unique number m for each sow through the numbering module; then monitors the physiological state of the sows with each number through the health monitoring module to obtain the monitoring data of the sows with each number; then enters the breeding data corresponding to the sows with each number through the breeding data module; finally, analyzes the monitoring data and breeding data corresponding to the sows with each number through the data analysis module, rates each sow, and determines whether it can continue to breed based on the rating results; timely eliminates sows with poor breeding ability, improves sow breeding efficiency, and reduces losses.

[0051] As an embodiment of the present invention, the monitoring data includes the temperature and weight of the sow;

[0052] The breeding data include the time of production, the total number of pregnant piglets, the number of surviving piglets and the weight of each piglet;

[0053] Through the above technical scheme, the methods for obtaining the temperature of the sow, the weight of the sow, the delivery time, the total number of pregnant piglets, the number of surviving piglets and the weight of each piglet in this embodiment are all existing technologies and will not be described in detail here.

[0054] As an implementation manner of the present invention, the analysis process of the analysis module is:

[0055] S10: Obtaining the sow postpartum health index by analyzing the temperature and weight of each sow after parturition;

[0056] S20: Obtain the piglet health index by analyzing the production time of each sow, the total number of pregnant piglets, the number of surviving piglets and the weight of each piglet;

[0057] S30: Obtaining a comprehensive reproductive index of each sow by analyzing the sow postpartum health index and piglet health index of each sow;

[0058] S40: Rating each sow by analyzing its comprehensive reproductive index;

[0059] Through the above technical scheme, this embodiment first obtains the sow postpartum health index by analyzing the temperature and weight of each sow after delivery; then obtains the piglet health index by analyzing the weight and health status of the piglets born by each sow; then obtains the comprehensive breeding index of each sow by analyzing the sow postpartum health index and the piglet health index of each sow; finally, each sow is rated by analyzing the comprehensive breeding index of each sow; sows with poor breeding ability are eliminated through the rating results of the sow's breeding ability, sow breeding efficiency is improved, and losses in breeding are reduced.

[0060] As an embodiment of the present invention, in step S10, the sow postpartum health index includes:

[0061]

[0062] Calculation of sow postpartum health index Q m ;

[0063] Where, f(X) is the judgment function. When X≥0, f(X)=X; when X<0, f(X)=0; Q0 is the basic health index of sows; t m is the production time of the sow numbered m; Δt is the preset duration; T m (t) is the temperature change curve of the real-time temperature of the sow numbered m over time; T0 is the preset temperature; W1 is the preset allowable error value; G m (t m G +Δt) is the weight of the sow numbered m at a preset time Δt after giving birth; m (t m ) is the initial weight of the sow numbered m at the time of delivery; G1 is the basic weight reduction within the preset time Δt after delivery; X is the total number of piglets in this pregnancy of the sow numbered m; σ X is the sow weight adjustment coefficient; δ1 is the first weight coefficient; δ2 is the second weight coefficient; C1 is the first preset constant; C2 is the second preset constant;

[0064] Through the above technical solution, this embodiment The accumulated temperature of the sow numbered m for a preset time period Δt after giving birth; The average temperature of the sow numbered m for a preset time period Δt after giving birth; The absolute value of the difference between the average temperature of the sow numbered m and the preset temperature for the preset time Δt after giving birth; is the difference between the absolute value of the difference between the average temperature of the sow numbered m and the preset temperature for the preset time Δt after giving birth and the preset allowable error value; at this time, X in the judgment function refers to when , it means that the absolute value of the difference between the average temperature of the sow numbered m and the preset temperature after the preset time Δt exceeds the preset allowable error value difference; at this time, it means that the sow's temperature is abnormal and her body condition is poor. Therefore, the absolute value of the difference between the average temperature of the sow numbered m and the preset temperature after the preset time Δt and the preset allowable error value is greater than the preset allowable error value. The larger the sow's postpartum health index Q m The smaller; |G m (t m +Δt)-G m (t m)| is the actual weight loss of the sow numbered m after giving birth for a preset time period of Δt; σ X G1 is the preset weight loss of the sow numbered m after giving birth to X piglets within the preset time Δt; |G m (t m +Δt)-G m (t m )|-σ X G1 is the difference between the actual weight loss of x piglets produced by the sow numbered m and the preset weight loss; when |G m (t m +Δt)-G m (t m )|-σ X When G1>0, it means that the actual weight loss of the sow numbered m producing X piglets is greater than the preset weight loss; the greater the difference between the actual weight loss of the sow numbered m producing X piglets and the preset weight loss, the worse the sow's postpartum condition is, and the higher the sow's postpartum health index Q m The smaller;

[0065] It should be noted that the sow basic health index Q0, the preset allowable error value T0, the preset allowable error value W1, the basic weight reduction G1, the preset time Δt, the first weight coefficient δ1, the second weight coefficient δ2, the first preset constant C1 and the second preset constant C2 are preset values, and the sow weight adjustment coefficient ρ numbered m is X Determined according to the total number of piglets in this pregnancy of the sow numbered m; different total numbers of piglets correspond to a sow weight adjustment coefficient σ X The preset value is obtained based on experience and will not be described in detail here;

[0066] As an embodiment of the present invention, in step S20, the piglet health index includes:

[0067]

[0068] Calculation of piglet health index P m ;

[0069] Where Y is the number of surviving piglets left by the sow numbered m after a preset time Δt after giving birth, y∈Y; P0 is the basic health index of the piglets; G y (t m G +Δt) is the weight of the yth healthy piglet produced by the sow numbered m at a preset time Δt after production; y (t m ) is the initial weight of the yth healthy piglet produced by the sow numbered m at the time of production; ρ Xis the piglet weight adjustment coefficient; G0 is the basic weight increase within the preset time Δt after production; γ1 is the third weight coefficient; γ2 is the fourth weight coefficient; C3 is the third preset constant; C4 is the fourth preset constant;

[0070] Through the above technical solution, this embodiment G y (t m +Δt)-G y (t m ) is the weight increase of the yth healthy piglet produced by the sow numbered m within a preset time Δt after production; is the average weight gain of Y healthy piglets produced by sow numbered m; X G0 is the preset weight gain of healthy piglets produced by the sow numbered m; is the difference between the average weight gain of Y healthy piglets produced by the sow numbered m and the preset weight gain; When , it means that the average weight increase of Y healthy piglets produced by the sow numbered m is greater than the preset weight increase; the greater the difference between the average weight increase of Y healthy piglets produced by the sow numbered m and the preset weight increase, the healthier the piglets are and the greater the piglet health index is; is the standard deviation of the weight growth of Y healthy piglets produced by the sow numbered m. The smaller the standard deviation of the weight growth of Y healthy piglets produced by the sow numbered m, the more stable the health of the piglets produced by the sow numbered m, which means that the piglets need to grow better and the piglet health index is greater; It is the ratio of the number of surviving piglets produced by the sow numbered m to the total number of pregnant piglets. The larger the ratio of the number of surviving piglets produced by the sow numbered m to the total number of pregnant piglets, the higher the quality of the piglets produced by the sow numbered m, and therefore the greater the piglet health index.

[0071] It should be noted that the preset time Δt, the basic health index of the piglet P0, the basic weight increase G0 within the preset time Δt after production, the third weight coefficient γ1, the fourth weight coefficient γ2, the third preset constant C3 and the fourth preset constant C4 are preset values, and the piglet weight adjustment coefficient ρ X Determined according to the total number of piglets in this pregnancy of the sow numbered m; different total numbers of piglets correspond to a piglet weight adjustment coefficient ρ X The preset values ​​are obtained based on experience and will not be described in detail.

[0072] As an embodiment of the present invention, in step S30, the comprehensive breeding index includes the following: m =P m Q m Calculate the comprehensive reproductive index U of sow numbered m m;

[0073] Through the above technical solution, the postpartum health index Q of the sow numbered m in this embodiment is m The larger the value is, the healthier the sow numbered m is after giving birth and the more suitable it is for continued breeding. The comprehensive breeding index U m The larger the piglet health index P is, the m The larger the value, the healthier the piglets produced by the sow numbered m are after birth and the more suitable they are for continued breeding. The comprehensive breeding index U of the sow numbered m is m The bigger.

[0074] As an embodiment of the present invention, in step S40, the process of rating sows is as follows:

[0075] The comprehensive reproductive index U of the sow numbered m m Compare with the preset threshold [R1, R2]; R2>P0Q0>R1;

[0076] When U m When ≥R2, the rating result is high-quality sow;

[0077] When R2>U m When ≥R1, the rating result is first-class sow;

[0078] When R1>U m When , the rating result is level 2 sow;

[0079] Through the above technical solution, this embodiment calculates the comprehensive reproductive index U of the sow numbered m. m and compared with the preset threshold [R1, R2]; when U m ≥R2, it means that the sow is healthy, and the piglets it breeds have a high survival rate and high breeding quality, so it has strong breeding ability and is rated as a high-quality sow; when R2>U m ≥R1, it means that the sow is in good health, and the piglets it breeds have a high survival rate and average breeding quality. Therefore, the rating result is a first-class sow; when R1>U m When the sow is rated as level 2, it means that the sow is in poor health, and the piglets it breeds have a low survival rate and low breeding quality, so its breeding ability is weak, and the rating result is level 2 sow; it is not suitable for further breeding; through the rating results of the sow's breeding ability, sows with poor breeding ability can be eliminated to improve the sow's breeding efficiency and reduce the loss in breeding;

[0080] It should be noted that the preset thresholds [R1, R2] are preset values, which are obtained based on experience and will not be described in detail.

[0081] See also Figure 2 As shown, an integrated intelligent digital pig farming management method, the management method comprises the following steps:

[0082] S1: Generate a unique number for each sow through the numbering module;

[0083] S2: Monitor the physiological status of the sows with different numbers through the health monitoring module to obtain the monitoring data of the sows with different numbers;

[0084] S3: Enter the breeding data corresponding to each sow number through the breeding data module;

[0085] S4: Analyze the monitoring data and breeding data corresponding to each sow number through the data analysis module, rate each sow, and determine whether to continue breeding based on the rating results.

[0086] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An integrated intelligent digital pig breeding management system, characterized in that: The management system comprises: The numbering module is used to generate a unique number for each sow, which is m; The health monitoring module is used to monitor the physiological status of each sow with a corresponding number and obtain the monitoring data of each sow with a corresponding number; The breeding data module is used to input the breeding data corresponding to each sow number; The data analysis module is used to analyze the monitoring data and breeding data corresponding to each sow number, rate each sow, and determine whether breeding can continue based on the rating results.

2. The integrated intelligent digital pig breeding management system according to claim 1 is characterized in that: The monitoring data include the temperature of the sow and the weight of the sow; The breeding data included the time of birth, the total number of pregnant piglets, the number of surviving piglets and the weight of each piglet.

3. The integrated intelligent digital pig breeding management system according to claim 2 is characterized in that: The analysis process of the analysis module is as follows: S10: Obtaining the sow postpartum health index by analyzing the temperature and weight of each sow after parturition; S20: Obtain the piglet health index by analyzing the production time of each sow, the total number of pregnant piglets, the number of surviving piglets and the weight of each piglet; S30: Obtaining a comprehensive reproductive index of each sow by analyzing the sow postpartum health index and piglet health index of each sow; S40: Rating each sow by analyzing its comprehensive reproductive index.

4. The integrated intelligent digital pig breeding management system according to claim 3 is characterized in that: In step S10, the sow postpartum health index includes: Calculation of sow postpartum health index Q m ; Where, f(X) is the judgment function. When X≥0, f(X)=X; when X<0, f(X)=0; Q0 is the basic health index of sows; t m is the production time of the sow numbered m; Δt is the preset duration; T m (t) is the temperature change curve of the real-time temperature of the sow numbered m over time; T0 is the preset temperature; W1 is the preset allowable error value; G m (t m G +Δt) is the weight of the sow numbered m at a preset time Δt after giving birth; m (t m ) is the initial weight of the sow numbered m at the time of delivery; G1 is the basic weight reduction within the preset time Δt after delivery; X is the total number of piglets in this pregnancy of the sow numbered m; σ X is the sow weight adjustment coefficient; δ1 is the first weight coefficient; δ2 is the second weight coefficient; C1 is the first preset constant; C2 is the second preset constant.

5. The integrated intelligent digital pig breeding management system according to claim 4 is characterized in that: In step S20, the piglet health index includes: Calculation of piglet health index P m ; Where Y is the number of surviving piglets left by the sow numbered m after a preset time Δt after giving birth, y∈Y; P0 is the basic health index of the piglets; G y (t m G +Δt) is the weight of the yth healthy piglet produced by the sow numbered m at a preset time Δt after production; y (t m ) is the initial weight of the yth healthy piglet produced by the sow numbered m at the time of production; ρ X is the piglet weight adjustment coefficient; G0 is the basic weight increase within the preset time Δt after production; γ1 is the third weight coefficient; γ2 is the fourth weight coefficient; C3 is the third preset constant; C4 is the fourth preset constant.

6. The integrated intelligent digital pig breeding management system according to claim 5 is characterized in that: In step S30, the comprehensive breeding index includes the following: m =P m Q m Calculate the comprehensive reproductive index U of the sow numbered m m .

7. The integrated intelligent digital pig breeding management system according to claim 6 is characterized in that: In step S40, the process of rating sows is as follows: The comprehensive reproductive index U of the sow numbered m m Compare with the preset threshold [R1, R2]; R2>P0Q0>R1; When U m When ≥R2, the rating result is high-quality sow; When R2>U m When ≥R1, the rating result is first-class sow; When R1>U m , the rating result is level 2 sow.

8. An integrated intelligent digital pig breeding management method, applicable to an integrated intelligent digital pig breeding management system according to any one of claims 1 to 7, characterized in that: The management method comprises the following steps: S1: Generate a unique number for each sow through the numbering module; S2: Monitor the physiological status of the sows with different numbers through the health monitoring module to obtain the monitoring data of the sows with different numbers; S3: Enter the breeding data corresponding to each sow number through the breeding data module; S4: Analyze the monitoring data and breeding data corresponding to each sow number through the data analysis module, rate each sow, and determine whether to continue breeding based on the rating results.

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