A global digital intelligent breeding system for pigs
By promoting the whole-region digital intelligent breeding system in pig breeding, the problem of unscientific local pig breeding management has been solved, the accuracy and sharing of breeding data have been achieved, and the quality and market competitiveness of breeding pigs have been improved.
Patent Information
- Application Number
- CN202510054980.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-14
- Publication Date
- 2025-05-09
- Estimated Expiration
- 2045-01-14
AI Technical Summary
In pig breeding, local pigs lack scientific breeding management systems, resulting in the lack of a scientific breeding management system, which leads to insufficient exploitation and utilization of advantageous traits, high breeding costs, low production performance and economic benefits, and insufficient market competitiveness. At the same time, data between enterprises or units is not shared, and the accuracy and details of breeding data records are difficult to guarantee, which affects the assessment of genetic accuracy.
Provide a pig whole-domain digital intelligent breeding system, including a production chain entry subsystem, a pig association factor evaluation subsystem and a genome detection subsystem. The system collects pig breeding data through modular design, and verifies the accuracy and representativeness of breeding data through dominant shape population database and genome detection, achieving data sharing and accuracy guarantee.
Through the digital breeding management system, the accuracy and sharing of breeding data will be improved, the efficiency of breeding screening is enhanced, the quality of breeding pigs will be improved, and the needs of modern pigs in terms of reproductive ability, growth rate, etc., while reducing breeding costs.
Smart Images
Figure CN119477589B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of digital intelligent technology for pigs, and in particular to a global digital intelligent breeding system for pigs. Background Art
[0002] In pig farming, local pigs have significant advantages in meat quality and flavor compared to modern pigs (such as Kele pigs, Qianbei black pigs and other local pigs), but have disadvantages of low fertility, growth rate and lean meat rate. The reason is that local pigs lack a scientific breeding management system in the traditional breeding process, which makes local pigs insufficient in exploring and utilizing their superior traits and the cost of breeding feed is high, which leads to reduced comprehensive production performance and breeding economic benefits, and weak market competitiveness.
[0003] The difficulty in establishing a breeding management system is that data is often not shared between enterprises or units. Each enterprise or unit often uses different software to record breeding data, and the measuring instruments and methods used by each enterprise or unit to measure breeding data are not unified, which often makes it difficult to ensure the accuracy and detail of data records, increasing the difficulty of subsequent genetic accuracy assessment. Therefore, the present invention provides a digital intelligent breeding system to provide a guarantee for the transmission of breeding data, improve the accuracy of breeding data, and improve the efficiency of breeding screening and the quality of breeding pigs. Summary of the invention
[0004] To solve the above problems, the present invention provides a global digital intelligent breeding system for pigs, which provides guarantee for the transmission of breeding data, improves the accuracy of breeding data, and thus improves the breeding screening efficiency and the quality of breeding pigs.
[0005] In order to achieve the above-mentioned object, the technical solution of the present invention is as follows: a global digital intelligent breeding system for pigs, including a production chain entry subsystem, a pig correlation factor evaluation subsystem and a genome detection subsystem for recording and managing the corresponding pig breeding data of each region;
[0006] The production chain entry subsystem includes pig growth record module, sow record module, pig transfer record module and slaughter evaluation record module;
[0007] The pig growth record module is used to record the breeding data of pigs in different growth cycles of birth, growth and slaughter. The breeding data includes weight change data, cycle time, disease data, feed usage data and growth environment data;
[0008] The sow recording module is used to record the sow pregnancy data from the sow estrus to the piglet slaughter period. The sow pregnancy data includes the sow estrus time, sow weight change data, piglet number and slaughtered piglet weight data;
[0009] The pig transfer record module is used to record the identities of pigs of different generations and the corresponding pen data of the pig identities;
[0010] The slaughter evaluation record module is used to record the carcass quality evaluation of pigs when they are slaughtered;
[0011] The pig-related factor evaluation subsystem is used to establish several different dominant shape population databases, and distinguish the pig breeding data of each region according to the dominant shape population databases; then sort the breeding data based on the correlation between the dominant shape in the dominant shape population data and the breeding data, sow pregnancy data, pen data and carcass quality evaluation, and obtain comparative breeding data with representative dominant shapes; the pig-related factor evaluation subsystem then sends detailed supplementary instructions to the production chain entry subsystem based on the abundance of the comparative breeding data, and verifies the accuracy of the comparative breeding data based on the difference between the breeding data of the production chain entry subsystem in the current region and the production chain entry subsystem in other regions;
[0012] The genome detection subsystem is used to detect genetic variation and gene expression levels of the pig genome, and to verify the dominant shape based on the expression level of the pig genome.
[0013] The technical principle of the above scheme is as follows: Through the pig growth record module, sow record module, pig transfer record module and slaughter evaluation record module, the pig breeding data of different growth cycles from birth, growth and slaughter are collected digitally. It provides the basic conditions for establishing a breeding system, and then through the establishment of a dominant shape population database, the preliminary division of regions for different production chain input subsystems is achieved, and the accuracy of breeding data is verified according to the abundance and difference.
[0014] The above scheme has the following beneficial effects:
[0015] 1. This plan establishes a digital breeding management system through separate collection of different modules, realizes data sharing between different regions (enterprises), so as to establish a scientific breeding management system to meet the needs of modern pigs in terms of fertility, growth rate, etc. while retaining the advantageous shapes of local pigs.
[0016] 2. In this solution, in the process of uploading pig breeding data to different production chain entry subsystems, due to the different management levels of enterprises or the rationality of enterprise systems, breeding data is often missing. By first selecting comparative breeding data with representative advantageous shapes, and then supplementing and judging the accuracy of the breeding data, the accuracy of the breeding data is guaranteed. Then, through precise genetic confirmation, the quality of the breeding pigs in reflecting the advantageous shapes is verified, and duplication of work is reduced to improve the efficiency of breeding screening.
[0017] Furthermore, the growth environment data includes temperature, humidity, harmful gases and light.
[0018] Beneficial effects: By judging from temperature, humidity, harmful gases and light, an adaptive growth environment can be provided for pigs to ensure their healthy growth.
[0019] Furthermore, ketone body quality evaluation is divided into different qualities according to body weight, lean meat percentage and fat thickness.
[0020] Beneficial effects: Meat quality is evaluated by weight, lean meat percentage and fat thickness to determine whether the meat flavor traits of local pigs have been retained during the breeding process and improved accordingly, providing a basis for subsequent breeding adjustments.
[0021] Furthermore, the dominant shape population database is established by using different dominant shapes of toxicity resistance, meat quality, disease resistance and feed requirements to establish the dominant shape population database.
[0022] Beneficial effects: By establishing a database of different dominant populations, we can enrich a variety of breeding materials and provide basic conditions for the subsequent breeding of local pigs. It is also convenient to distinguish regional pigs and provide adjustments for subsequent traceability work.
[0023] Furthermore, the pig-related factor assessment subsystem includes a basic division module, a ranking comparison module, and a verification module;
[0024] The basic division module is used to establish a corresponding dominant shape population database based on different dominant shapes, and to mark the pig breeding data in the current area based on the ketone body quality evaluation, and then send the marked pig breeding data to the sorting comparison module and the verification module;
[0025] The sorting and comparison module is used to screen the ketone body quality evaluation based on the dominant shape with external manifestations, obtain the pig breeding data with the same ketone body quality evaluation mark, and then sort the ketone body quality evaluation corresponding to the pig breeding data in each region to obtain the comparative breeding data with representative dominant shapes, and then determine whether the abundance degree associated with the comparative breeding data exceeds the first standard value. If so, send a verification instruction to the verification module, if not, send a report detailed supplementary instruction to the corresponding production chain entry subsystem of the region;
[0026] The verification module is used to obtain comparative breeding data with representative dominant shapes based on verification instructions, and then obtain pig breeding data with dominant shapes with the same external performance in different regions to calculate the degree of difference, and compare the degree of difference with the second standard value. If the degree of difference is greater than the second standard value, a report with detailed supplementary instructions is sent to the production chain entry subsystem of the region; if the degree of difference is less than the second standard value, a secondary verification instruction is sent to the genome detection subsystem, and the genome detection subsystem obtains the expression level of the pig genome based on the secondary verification instruction to determine the dominant shape.
[0027] Beneficial effects: The pig breeding data is sorted through the sorting and comparison module to make the comparison breeding data representative, providing a basis for the associated data that reflects the dominant shape. The breeding data is obtained in detail to ensure the stable acquisition of the breeding data and provide a basis for verifying the accuracy of the data. At the same time, other non-essential data is not uploaded to reduce the processing volume of breeding data, thereby improving the efficiency of breeding screening.
[0028] Furthermore, the verification module is used to add an invisible shape verification mark to the production chain entry subsystem of the corresponding region when the difference is less than the second standard value;
[0029] The genome detection subsystem is also used to verify the production chain entry subsystem based on the invisible shape, and then determine the dominant shape of internal expression based on the expression level of the pig genome in the region of the production chain entry subsystem.
[0030] Beneficial effects: By supplementing the complete data production chain entry subsystem to measure the expression level of the pig genome, the accuracy and completeness of the breeding data are guaranteed, so as to facilitate the establishment of an internal performance dominant shape population database. During the internal performance dominant shape analysis process, there is no need to obtain data again, thereby improving the breeding screening efficiency and the quality of the breeding pigs.
[0031] Furthermore, the verification module is also used to obtain comparative breeding data with representative dominant shapes based on the verification instructions, and then obtain pig breeding data with dominant shapes with the same external performance in different regions for calculating the degree of difference. When the degree of difference is less than a second standard value, the component composition is obtained based on the comparative breeding data with representative dominant shapes, and the pig breeding data with dominant shapes with the same external performance are filtered out according to the type and proportion of the component composition, so as to obtain pig breeding data consistent with the type and proportion of the component composition for calculating the degree of difference.
[0032] Beneficial effects: By filtering out the pig breeding data with dominant shapes of the same external performance, the accuracy of the comparison process is guaranteed, the amount of calculation of subsequent breeding data is reduced, and the breeding screening efficiency and the quality of breeding pigs are improved.
[0033] Furthermore, the richness of the breeding data association was compared and determined by the degree of missingness of sow pregnancy data, pen data, breeding data and carcass quality evaluation associated with different growth cycles of pigs from birth, growth and slaughter.
[0034] Beneficial effect: It is determined through the integrity of the data of the entire growth stage of pigs from birth, growth and slaughter, covering the pig breeding data of the entire stage of the pig farming process. The richness is calculated based on this, which can well ensure the integrity of the data.
[0035] Furthermore, the dominant shapes expressed internally in the dominant shape population database include disease resistance and toxicity resistance; the dominant shapes expressed externally in the dominant shape population database include meat quality and feed requirements.
[0036] Beneficial effects: Specific division is made through internal and external performance to facilitate subsequent calculation of the degree of difference, and pig breeding data is divided based on the expression of dominant shapes to facilitate verification of the degree of enrichment through external performance.
[0037] Furthermore, the pig-related factor evaluation subsystem is also used to record the cumulative number of times the production chain entry subsystems in each region receive detailed supplementary instructions within a preset breeding cycle, and calculate the average number of times all production chain entry subsystems receive detailed supplementary instructions; then compare the cumulative number with the average number, if the cumulative number is greater than the average number, add a ranking reduction mark to the corresponding production chain entry subsystem, if the cumulative number is less than the average number, add a ranking normal mark to the corresponding production chain entry subsystem, the pig-related factor evaluation subsystem sorts the pig breeding data of each region based on the ranking reduction mark and the ranking normal mark, and obtains comparative breeding data with representative advantageous shapes.
[0038] Beneficial effect: By adding sorting marks, the production chain entry subsystem is reduced to carry out subsequent comparison of comparative breeding data with representative advantageous shapes, thereby reducing the process of adjusting and re-acquiring data, reducing the waiting time for data supplementation, and thus improving breeding screening efficiency and breeding pig quality.
[0039] Furthermore, the pig-associated factor evaluation subsystem is also used to, in the next breeding cycle, when the pig breeding data of the production chain entry subsystem with the added ranking reduction mark is used again as the comparison breeding data with representative advantageous shapes, the pig-associated factor evaluation subsystem is based on whether the abundance degree associated with the comparison breeding data exceeds the first standard value. If so, a normal ranking mark is added to the corresponding production chain entry subsystem; if not, a secondary ranking reduction mark is added to the corresponding production chain entry subsystem.
[0040] Beneficial effect: By verifying the corresponding abundance level of the production chain entry subsystem in advance, it is determined whether the production chain entry subsystem should be rectified, and then the rectification is verified based on the abundance level. The trust of the subsequent production chain entry subsystem is readjusted to ensure the accuracy of the breeding data.
[0041] Furthermore, the pig-related factor evaluation subsystem also includes a cost evaluation module, which is used for pig breeding data entered into the subsystem based on the same production chain, sow pregnancy data, pen data, breeding data and carcass quality evaluation based on different growth cycles of pigs from birth, growth and slaughter;
[0042] First, confirm the identity of the live pigs according to the pen data, and then retrieve the corresponding sow pregnancy data and breeding data according to the pen data; calculate the cost of a single piglet according to the sow pregnancy data; calculate the daily maintenance cost, feed cost and meat-to-feed ratio according to the breeding data; then calculate the breeding cost of the production chain entry subsystem based on the piglet cost, daily maintenance cost, feed cost and meat-to-feed ratio.
[0043] Beneficial effects: Through the calculation of breeding costs, a basis is provided for determining the correlation between the dominant shape of pigs and breeding costs. Then, through the comparison of the correlation between the dominant shape and breeding costs in different regions, the cost increase or decrease in each link can be determined through comparison, thereby reducing the overall cost increase in the breeding process.
[0044] Furthermore, the pig-related factor assessment subsystem is also used to classify the comparative breeding data with representative advantageous shapes in different regions according to different growth environment data, after verifying the accuracy of the comparative breeding data by comparing the breeding data between the production chain entry subsystem of the current region and the production chain entry subsystem of other regions.
[0045] Beneficial effect: By reclassifying according to different growth environment data, the interference of different growth environments in the breeding screening process can be reduced, the accuracy of breeding data can be improved, and the subsequent analysis and processing of breeding data can be facilitated.
[0046] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0047] Figure 1 This is a schematic diagram of the structure of an embodiment of the global digital intelligent breeding system for pigs of the present invention;
[0048] Figure 2 It is a schematic diagram of the process of an embodiment of the global digital intelligent breeding system for pigs of the present invention; DETAILED DESCRIPTION
[0049] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. 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.
[0050] The following is further described in detail through specific implementation methods:
[0051] Embodiment 1:
[0052] As attached Figure 1 to Figure 2 Shown: A global digital intelligent pig breeding system, including a production chain entry subsystem for recording and managing corresponding pig breeding data in each region, a pig correlation factor evaluation subsystem and a genome detection subsystem.
[0053] The production chain entry subsystem includes pig growth record module, sow record module, pig transfer record module and slaughter evaluation record module.
[0054] The pig growth record module is used to record the breeding data of pigs in different growth cycles of birth, growth and slaughter. The breeding data includes weight change data, cycle time, disease data, feed usage data and growth environment data; and the growth environment data includes temperature, humidity, harmful gases and light.
[0055] For example, the suitable environmental temperature for pigs varies according to the growth stage. Too high or too low temperature will affect the pigs' feed intake, feed conversion rate, growth rate and health status. Humidity has an important impact on the pigs' body temperature regulation, health and production performance. Too high or too low humidity may weaken the pigs' resistance and increase the incidence rate. In the process of pig breeding, the main harmful gases in the pig house include ammonia, hydrogen sulfide and carbon dioxide. Excessive concentrations of these gases will damage the pigs' respiratory system and cause respiratory diseases. The appropriate lighting time, light intensity and light frequency in the pig house are conducive to the growth and health of pigs. However, too much light may cause breeding problems. Therefore, judging from temperature, humidity, harmful gases and light can provide pigs with an adaptive growth environment and ensure the healthy growth of pigs. At the same time, in the subsequent reclassification process through different growth environment data, the interference of different growth environments in the breeding screening process can be reduced to improve the accuracy of breeding data and facilitate the subsequent analysis and processing of breeding data.
[0056] The sow recording module is used to record the sow pregnancy data from the sow estrus to the piglet slaughter period. The sow pregnancy data includes the sow estrus time, sow weight change data, piglet number and slaughtered piglet weight data.
[0057] The pig transfer record module is used to record the identities of pigs of different generations, and at the same time record the pen data corresponding to the pig identities.
[0058] The slaughter evaluation record module is used to record the carcass quality evaluation of live pigs when they are slaughtered. The carcass quality evaluation is divided into different qualities according to body weight, lean meat rate and fat thickness.
[0059] For example, meat quality is evaluated by weight, lean meat percentage and fat thickness to determine whether the meat flavor traits of local pigs are retained during the breeding process and improved accordingly, providing a basis for subsequent breeding adjustments. At the same time, the pig growth record module, sow record module, pig transfer record module and slaughter evaluation record module are used to realize the digital collection of pig breeding data from birth, growth and slaughter in different growth cycles. Data sharing between different regions (enterprises) is realized to facilitate the establishment of a scientific breeding management system to meet the needs of modern pigs in terms of fertility and growth rate while retaining the advantageous shapes of local pigs.
[0060] The pig-related factor assessment subsystem includes a basic division module, a sorting and comparison module, and a verification module.
[0061] The basic division module is used to establish a corresponding dominant shape population database based on different dominant shapes. The dominant shape population database is established based on different dominant shapes of toxicity resistance, meat quality, disease resistance and feed requirements. The dominant shapes of the internal performance in the dominant shape population database include disease resistance and toxicity resistance; the dominant shapes of the external performance in the dominant shape population database include meat quality and feed requirements. At the same time, the pig breeding data of the current area is marked based on the ketone quality evaluation, and then the marked pig breeding data is sent to the sorting comparison module and the verification module.
[0062] For example, the local breeding resource base group of enterotoxigenic Escherichia coli established by toxicity resistance; the local pig breeding group of high intramuscular fat established by meat quality; the respiratory disease resistance breeding resource base group established by disease resistance and the local pig breeding resource group of roughage tolerance established by feed demand, etc., are established through different dominant population databases, which not only enriches a variety of breeding materials and provides basic conditions for the subsequent cultivation of local pigs. It is also convenient to distinguish regional pigs and provide adjustments for subsequent traceability work.
[0063] The sorting and comparison module is used to screen the carcass quality evaluation based on the dominant shape with external manifestations, obtain the pig breeding data with the same carcass quality evaluation mark, and then sort according to the carcass quality evaluation size corresponding to the pig breeding data in each region, obtain the comparative breeding data with representative dominant shapes, and determine the richness of the comparative breeding data association based on the missing degree of sow pregnancy data, pen data, breeding data and carcass quality evaluation associated with different growth cycles of pigs from birth, growth and slaughter. The sorting and comparison module then determines whether the abundance of the comparative breeding data association exceeds the first standard value. If so, it sends a verification instruction to the verification module. If not, it sends a report detailed supplementary instruction to the corresponding production chain entry subsystem of the region.
[0064] The verification module is used to obtain comparative breeding data with representative dominant shapes based on the verification instructions, and then obtain pig breeding data with dominant shapes of the same external performance in different regions to calculate the difference degree, and compare the difference degree with the second standard value. If the difference degree is greater than the second standard value, a detailed supplementary report instruction is sent to the production chain entry subsystem of the region; if the difference degree is less than the second standard value, an invisible shape verification mark is added to the production chain entry subsystem of the corresponding region, and then a secondary verification instruction is sent to the genome detection subsystem. The genome detection subsystem obtains the expression level of the pig genome based on the secondary verification instruction to determine the dominant shape. At the same time, the verification module classifies the comparative breeding data with representative dominant shapes in different regions according to different growth environment data.
[0065] For example, in the process of uploading pig breeding data to different production chain entry subsystems, the lack of breeding data is often caused by different enterprise management levels or the rationality of enterprise systems. The pig breeding data is sorted through the sorting and comparison module to make the comparison breeding data representative, provide a basis for the associated data that reflects the dominant shape, and obtain the breeding data in detail to ensure the stable acquisition of breeding data and provide a basis for verifying the accuracy of the data; at the same time, other non-essential data is not uploaded to reduce the processing volume of breeding data and improve the efficiency of breeding screening. Then, the data is classified again according to the different growth environment data to reduce the interference of different growth environments in the breeding screening process, so as to improve the accuracy of breeding data and facilitate the subsequent analysis and processing of breeding data.
[0066] The genome detection subsystem is used to detect the genetic variation and gene expression level of the pig genome, and verify the dominant form based on the expression level of the pig genome. The genome detection subsystem is based on the production chain entry subsystem of the invisible form verification mark, and then determines the dominant form of the internal expression based on the expression level of the pig genome in the region of the production chain entry subsystem.
[0067] For example, by supplementing the complete production chain input subsystem with data to measure the expression level of the pig genome, the accuracy and completeness of the breeding data can be guaranteed, so as to establish an internal dominant shape population database. In the process of internal dominant shape analysis, there is no need to obtain data again, thereby improving the breeding screening efficiency and the quality of breeding pigs. The established breeding management system can improve the economic performance of local pig breeds on the basis of maintaining inherent dominant traits.
[0068] Embodiment 2:
[0069] The difference from Example 1 is that the verification module is also used to obtain comparative breeding data with representative dominant shapes based on the verification instructions, and then obtain pig breeding data with dominant shapes with the same external performance in different regions for difference calculation. When the difference is less than the second standard value, the component composition is obtained based on the comparative breeding data with representative dominant shapes, and the pig breeding data with dominant shapes with the same external performance are filtered out according to the type and proportion of the component composition, so as to obtain pig breeding data consistent with the type and proportion of the component composition for difference calculation.
[0070] For example, inconsistent pig breeding data are different, which may be caused by recording bias or other factors, and have low accuracy, so they need to be filtered out. The pig breeding data with the same external dominant shape are filtered out to ensure accuracy during the comparison process, reduce the amount of calculation of subsequent breeding data, and thus improve breeding screening efficiency and breeding pig quality.
[0071] Embodiment 3:
[0072] The difference from Example 2 is that the pig-related factor evaluation subsystem is also used to record the cumulative number of times the production chain entry subsystems in each region receive detailed supplementary instructions within a preset breeding cycle, and calculate the average number of times all production chain entry subsystems receive detailed supplementary instructions; then compare the cumulative number with the average number, if the cumulative number is greater than the average number, add a ranking reduction mark to the corresponding production chain entry subsystem, if the cumulative number is less than the average number, add a ranking normal mark to the corresponding production chain entry subsystem, and the pig-related factor evaluation subsystem sorts the pig breeding data of each region based on the ranking reduction mark and the ranking normal mark to obtain comparative breeding data with representative advantageous shapes.
[0073] In the next breeding cycle, when the pig breeding data of the production chain entry subsystem with the added ranking reduction mark is used again as the comparison breeding data with representative dominant shapes, the pig association factor evaluation subsystem will determine whether the abundance degree associated with the comparison breeding data exceeds the first standard value. If so, a normal ranking mark will be added to the corresponding production chain entry subsystem; if not, a secondary ranking reduction mark will be added to the corresponding production chain entry subsystem.
[0074] For example, by comparing the cumulative number of times and the average number of times, it is possible to determine whether the abundance of pig breeding data uploaded by the production chain entry subsystem meets actual needs. By adding sorting marks, the production chain entry subsystem can reduce the subsequent comparison of comparative breeding data with representative advantageous shapes, thereby reducing the process of adjusting and re-acquiring data, reducing the waiting time for data supplementation, and thus improving breeding screening efficiency and breeding pig quality.
[0075] At the same time, during different breeding cycles, the corresponding abundance level of the production chain entry subsystem will be verified in advance to determine whether the production chain entry subsystem should be rectified, and then the rectification situation will be verified based on the abundance level. The trust situation of the subsequent production chain entry subsystem will be readjusted to ensure the accuracy of the breeding data.
[0076] Embodiment 4:
[0077] The difference from Example 3 is that the pig-related factor evaluation subsystem also includes a cost evaluation module. The cost rating module is used to evaluate the pig breeding data of the same production chain entry subsystem, based on the sow pregnancy data, pen data, breeding data and ketone body quality associated with the different growth cycles of the pig from birth, growth and slaughter; the cost evaluation module first confirms the identity of the pig according to the pen data, and then retrieves the corresponding sow pregnancy data and breeding data according to the pen data; calculates the cost of a single piglet according to the sow pregnancy data; calculates the daily maintenance cost, feed cost and meat-to-feed ratio according to the breeding data; and then comprehensively calculates the breeding cost of the production chain entry subsystem based on the piglet cost, daily maintenance cost, feed cost and meat-to-feed ratio.
[0078] For example, the breeding cost is determined by the growth cycle of pigs, and the cost of different links can be understood by dividing them according to different growth cycles. The calculation of breeding costs provides a basis for determining the correlation between the dominant shape of pigs and breeding costs, and then the correlation between the dominant shape and breeding costs in different regions is compared. By comparing, the cost increase or decrease in each link can be determined, thereby reducing the overall cost increase in the breeding process.
[0079] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.
Claims
1. A global digital intelligent breeding system for pigs, characterized in that: It includes a production chain entry subsystem, a pig-related factor evaluation subsystem and a genome detection subsystem for recording and managing pig breeding data corresponding to each region; The production chain entry subsystem includes pig growth record module, sow record module, pig transfer record module and slaughter evaluation record module; The pig growth record module is used to record the breeding data of pigs in different growth cycles of birth, growth and slaughter. The breeding data includes weight change data, cycle time, disease data, feed usage data and growth environment data; The sow recording module is used to record the sow pregnancy data from the sow estrus to the piglet slaughter period. The sow pregnancy data includes the sow estrus time, sow weight change data, piglet number and slaughtered piglet weight data; The pig transfer record module is used to record the identities of pigs of different generations and the corresponding pen data of the pig identities; The slaughter evaluation record module is used to record the carcass quality evaluation of pigs when they are slaughtered; The pig correlation factor evaluation subsystem is used to establish several different dominant shape population databases, and distinguish the pig breeding data of various regions according to the dominant shape population databases; then sort the breeding data based on the correlation between the dominant shape in the dominant shape population data and breeding data, sow pregnancy data, pen data and ketone quality evaluation, and obtain comparative breeding data with representative dominant shapes; The pig-related factor assessment subsystem then sends detailed supplementary instructions to the production chain entry subsystem based on the abundance of the comparative breeding data, and verifies the accuracy of the comparative breeding data based on the difference between the breeding data of the production chain entry subsystem in the current region and the production chain entry subsystem in other regions; The pig-related factor assessment subsystem includes a basic division module, a ranking comparison module, and a verification module; The basic division module is used to establish a corresponding dominant shape population database based on different dominant shapes, and to mark the pig breeding data in the current area based on the ketone body quality evaluation, and then send the marked pig breeding data to the sorting comparison module and the verification module; The sorting and comparison module is used to screen the ketone body quality evaluation based on the dominant shape with external manifestations, obtain the pig breeding data with the same ketone body quality evaluation mark, and then sort the ketone body quality evaluation corresponding to the pig breeding data in each region to obtain the comparative breeding data with representative dominant shapes, and then determine whether the abundance degree associated with the comparative breeding data exceeds the first standard value. If so, send a verification instruction to the verification module, if not, send a report detailed supplementary instruction to the corresponding production chain entry subsystem of the region; The verification module is used to obtain comparative breeding data with representative dominant shapes based on the verification instructions, and then obtain pig breeding data with dominant shapes of the same external performance in different regions to calculate the degree of difference, and compare the degree of difference with the second standard value. If the degree of difference is greater than the second standard value, an invisible shape verification mark is added to the production chain entry subsystem of the corresponding region; and then a detailed supplementary instruction of the report is sent to the production chain entry subsystem of the region; if the degree of difference is less than the second standard value, a secondary verification instruction is sent to the genome detection subsystem, and the genome detection subsystem obtains the expression level of the pig genome based on the secondary verification instruction to determine the dominant shape; The genome detection subsystem is used to detect genetic variations and gene expression levels of the pig genome, and to verify the dominant form based on the expression level of the pig genome; the genome detection subsystem is also used to verify the production chain entry subsystem based on the invisible form verification mark, and then determine the dominant form of internal expression based on the expression level of the pig genome in the region of the production chain entry subsystem.
2. The global digital intelligent pig breeding system according to claim 1 is characterized in that: Growth environment data includes temperature, humidity, harmful gases and light.
3. The global digital intelligent pig breeding system according to claim 2 is characterized in that: Ketone body quality evaluation is divided into different qualities according to weight, lean meat percentage and fat thickness.
4. The global digital intelligent pig breeding system according to claim 3 is characterized in that: The dominant shape population database is established by using different dominant shapes of toxicity resistance, meat quality, disease resistance and feed requirements to establish the dominant shape population database.
5. The global digital intelligent breeding system for pigs according to claim 4 is characterized in that: The verification module is also used to obtain comparative breeding data with representative dominant shapes based on verification instructions, and then obtain pig breeding data with dominant shapes with the same external performance in different regions for difference calculation. When the difference is less than a second standard value, the component composition is obtained based on the comparative breeding data with representative dominant shapes, and the pig breeding data with dominant shapes with the same external performance are filtered out according to the type and proportion of the component composition, so as to obtain pig breeding data consistent with the type and proportion of the component composition for difference calculation.
6. The global digital intelligent pig breeding system according to claim 5 is characterized in that: The richness of the breeding data association is determined by the degree of missing sow pregnancy data, pen data, breeding data and carcass quality evaluation associated with the different growth cycles of pigs from birth, growth and slaughter.
7. The global digital intelligent pig breeding system according to claim 6 is characterized in that: The dominant shapes expressed internally in the dominant shape population database include disease resistance and toxicity resistance; the dominant shapes expressed externally in the dominant shape population database include meat quality and feed requirements.
8. The global digital intelligent pig breeding system according to claim 7, characterized in that: The pig-related factor evaluation subsystem is also used to record the cumulative number of times the production chain entry subsystems in each region receive detailed supplementary instructions within a preset breeding cycle, and calculate the average number of times all production chain entry subsystems receive detailed supplementary instructions; then compare the cumulative number with the average number, if the cumulative number is greater than the average number, add a ranking reduction mark to the corresponding production chain entry subsystem, if the cumulative number is less than the average number, add a ranking normal mark to the corresponding production chain entry subsystem, the pig-related factor evaluation subsystem sorts the pig breeding data of each region based on the ranking reduction mark and the ranking normal mark, and obtains comparative breeding data with representative advantageous shapes.
9. The global digital intelligent pig breeding system according to claim 8, characterized in that: The pig-associated factor evaluation subsystem is also used to add a normal ranking mark to the corresponding production chain entry subsystem in the next breeding cycle. If the pig breeding data of the production chain entry subsystem with a ranking reduction mark is used again as comparative breeding data with a representative dominant shape, the pig-associated factor evaluation subsystem is used to determine whether the abundance degree associated with the comparative breeding data exceeds the first standard value. If so, the normal ranking mark is added to the corresponding production chain entry subsystem. If not, the secondary ranking reduction mark is added to the corresponding production chain entry subsystem.
10. The global digital intelligent pig breeding system according to claim 9, characterized in that: The pig-related factor evaluation subsystem also includes a cost evaluation module. The cost rating module is used to evaluate the pig breeding data entered into the subsystem based on the same production chain, sow pregnancy data, pen data, breeding data and carcass quality evaluation based on the different growth cycles of pigs from birth, growth and slaughter; First, confirm the identity of the live pigs according to the pen data, and then retrieve the corresponding sow pregnancy data and breeding data according to the pen data; calculate the cost of a single piglet according to the sow pregnancy data; calculate the daily maintenance cost, feed cost and meat-to-feed ratio according to the breeding data; then calculate the breeding cost of the production chain entry subsystem based on the piglet cost, daily maintenance cost, feed cost and meat-to-feed ratio.
11. The global digital intelligent pig breeding system according to claim 10, characterized in that: The pig-related factor assessment subsystem is also used to classify the comparative breeding data with representative advantageous shapes in different regions according to different growth environment data, after verifying the accuracy of the comparative breeding data by comparing the breeding data between the production chain entry subsystem of the current region and the production chain entry subsystem of other regions.
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