A method for intelligent optimization and adjustment of feed mixing parameters for livestock young

Abstract

The application discloses a kind of feed mixing parameter intelligent optimization adjustment method for livestock young, it is related to feed production technical field, and young growth information is collected to feed component parameter;Virtual monitoring space is constructed, and young growth information is converted, and growth monitoring change graph is obtained, and standard month data are homomorphic transposition by growth monitoring change graph, and obtain comprehensive growth monitoring graph;Difference determination is carried out to comprehensive growth monitoring graph, and threshold comparison result is obtained, and component extraction is carried out to feeding feed by threshold comparison result, and obtain excessive feeding feed data;According to excessive feeding feed data, young feed of next feeding node is proportioned and controlled, and dispensing feeding feed is obtained, and according to dispensing feeding feed, simulation feeding optimization is carried out in virtual monitoring space, and the best feeding feed proportion is obtained;Greatly improve the growth rate and health level of livestock young, improve feed utilization, reduce feed waste.

Description

Technical Field

[0001] This invention relates to the field of feed production technology, specifically a method for intelligent optimization and adjustment of feed mixing parameters for young livestock. Background Technology

[0002] With the rapid development of animal husbandry, the feeding and management of young livestock are receiving increasing attention. Young livestock have high nutritional requirements for feed during their growth and development, and these requirements vary depending on the species and growth stage. Therefore, providing young livestock with a scientific and reasonable feed mixture ratio is of great significance for their growth and development.

[0003] However, traditional feed mixing methods usually rely on manual proportioning and mixing. Livestock calves have high nutritional requirements for feed, and the proportioning of feed nutrients based on manual experience lacks precision, which can easily lead to nutritional imbalances and affect the growth and development of calves. At the same time, there is also a certain degree of blindness in the feed mixing process, which can easily lead to feed waste and increase breeding costs.

[0004] This invention aims to provide an intelligent optimization and adjustment method for feed mixing parameters for livestock calves. By collecting real-time data on the feed fed to livestock calves, extracting real-time growth data, analyzing the growth progress at each growth stage, and simulating and adjusting feed parameters in a constructed virtual space based on the growth progress, the method selects the feed with the best formulation, thereby achieving precise control of feed nutrient composition, improving the growth rate and health of livestock calves, reducing breeding costs, and contributing to the development of animal husbandry. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent optimization and adjustment method for feed mixing parameters for livestock calves, in order to solve the problems of unbalanced feed nutrient ratio, slow growth rate and low feed conversion efficiency mentioned in the background art.

[0006] A method for intelligent optimization and adjustment of feed mixing parameters for young livestock includes the following steps: Step S1: Collect feed composition parameters and juvenile growth information, including real-time growth data and standard age data, and mark the collection time; Step S2: Construct a virtual monitoring space, sort and transform the cub growth information to obtain a real-time growth sequence, construct a growth monitoring change map based on the real-time growth sequence, and perform homomorphic transpose on the standard age data through the growth monitoring change map to obtain a comprehensive growth monitoring map; Step S3: Mark nodes on the comprehensive growth monitoring map to obtain comprehensive capture nodes. Perform standard difference statistics on the comprehensive capture nodes to obtain comparison difference values. Based on the comparison difference values, determine abnormal growth and obtain threshold comparison results. Extract the components of the feed based on the threshold comparison results to obtain overfeeding feed data. Step S4: Adjust the feed ratio of the cubs at the next feeding node based on the overfeeding data to obtain the adjusted feed. Simulate feeding in the virtual monitoring space based on the adjusted feed and optimize the feeding process to obtain the best feed ratio.

[0007] Preferably, the process of collecting feed composition parameters and larval growth information includes: Set up feed testing terminals and livestock data collection terminals; The feed composition parameters are obtained by capturing the components of the feed through the feed detection terminal; Information on livestock calves is collected through a livestock data collection terminal to obtain calves' growth information, and the collected calves' growth information is time-stamped to obtain the collection time.

[0008] Preferably, the process of constructing a virtual monitoring space includes: A virtual monitoring space is constructed for livestock calves. The livestock calves are uploaded to the virtual monitoring space, and the calves are converted into virtual models through the virtual monitoring space. The real-time growth data is sorted by time to obtain the real-time growth sequence.

[0009] Preferably, the process of obtaining a comprehensive growth monitoring map includes: A two-dimensional rectangular coordinate system is constructed based on the acquisition time and the real-time growth sequence. A growth dynamic curve is generated based on the real-time growth sequence. The obtained growth dynamic curve is uploaded to the two-dimensional rectangular coordinate system to obtain a growth monitoring change map. Standard age data is uploaded to the growth monitoring change map. The standard age data is then homogenized using the growth monitoring change map to obtain the standard age growth curve. The growth monitoring change map that includes the standard age growth curve is then marked as the comprehensive growth monitoring map.

[0010] Preferably, the process of obtaining the comparison difference value includes: Set a time sliding axis, upload the time sliding axis to the comprehensive growth monitoring map, and capture nodes in the comprehensive growth monitoring map through the time sliding axis to obtain comprehensive capture nodes; The starting point of the integrated growth monitoring map is marked based on the acquisition time to obtain the capture origin. The time sliding axis is moved to the capture origin. Based on the capture origin, the integrated growth monitoring map is compared and observed through the integrated capture node to obtain the comparison difference value.

[0011] Preferably, the process of extracting components from feed based on threshold comparison results includes: A difference threshold is set based on the cub growth information. The difference value is compared with the difference threshold to obtain the threshold comparison result. The threshold comparison result includes excessive growth nodes and qualified growth nodes. Obtain the threshold comparison result of the capture origin. When the threshold comparison result is an overgrowth node, obtain the feed composition parameters of the feed corresponding to the overgrowth node and record them as overfeed feed data.

[0012] Preferably, the process of obtaining formulated feed includes: Data is extracted from the comprehensive growth monitoring map based on the excessive growth nodes to obtain abnormal growth data. The components of the excessive feed data are then compared with the abnormal growth data to obtain the abnormal influencing components. The composition of feed is adjusted based on the abnormal influencing components to obtain formulated feed.

[0013] Preferably, the process for obtaining the optimal feed formulation includes: The prepared feed is uploaded to the virtual monitoring space. The virtual monitoring space then simulates feeding the virtual cub model based on the prepared feed and collects real-time growth data of the virtual cub model during the simulated feeding process, which is recorded as the growth data to be detected. By performing homomorphic comparisons of the growth data to be tested using comprehensive growth monitoring charts, the results of regulated feeding are obtained, including effective feeding regulation and ineffective feeding regulation. The results of the regulated feeding are obtained by adjusting the feed formulation to achieve effective feeding regulation. The adjusted feed formulation is then compared and matched based on the virtual monitoring space to obtain the optimal feed ratio.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. Collect real-time growth data of livestock calves and feed composition parameters of the feed for livestock calves. By comparing the real-time growth data with standard growth requirements, abnormal growth stages can be identified. Based on the abnormal growth stages, feed composition parameters can be obtained to promptly detect growth abnormalities and take corrective measures. At the same time, feed composition parameters can be adjusted according to the actual growth of the calves to achieve personalized feeding and meet the nutritional needs of different calves.

[0015] 2. By adjusting the feed composition parameters corresponding to the monitored abnormal growth conditions, the composition of the feed can be adjusted to obtain regulated feed. This can identify nutrients that may be lacking or excessive in the feed, thereby optimizing the feed formula and improving the nutritional value of the feed.

[0016] 3. Construct a virtual monitoring space to simulate feeding livestock calves based on the obtained regulated feed, precisely control feed composition, monitor the simulated feeding process, and analyze the growth of virtual calves after feeding them regulated feed to obtain the optimal feed ratio. This helps calves grow faster and healthier, improves farm production efficiency, reduces unnecessary feed waste and lowers breeding costs through precise control of feed composition, and reduces disease treatment costs and labor costs caused by malnutrition through early nutritional intervention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, a method for intelligent optimization and adjustment of feed mixing parameters for livestock calves includes the following steps: Step S1: Collect feed composition parameters and juvenile growth information, including real-time growth data and standard age data, and mark the collection time; Step S2: Construct a virtual monitoring space, sort and transform the cub growth information to obtain a real-time growth sequence, construct a growth monitoring change map based on the real-time growth sequence, and perform homomorphic transpose on the standard age data through the growth monitoring change map to obtain a comprehensive growth monitoring map; Step S3: Mark nodes on the comprehensive growth monitoring map to obtain comprehensive capture nodes. Perform standard difference statistics on the comprehensive capture nodes to obtain comparison difference values. Based on the comparison difference values, determine abnormal growth and obtain threshold comparison results. Extract the components of the feed based on the threshold comparison results to obtain overfeeding feed data. Step S4: Adjust the feed ratio of the cubs at the next feeding node based on the overfeeding data to obtain the adjusted feed. Simulate feeding in the virtual monitoring space based on the adjusted feed and optimize the feeding process to obtain the best feed ratio.

[0021] It should be further explained that, in the specific implementation process, the juvenile stage of livestock is a critical period for livestock growth, and the mixing ratio of daily feed consumption is particularly important. Adjusting the feed to be most suitable for juvenile livestock, ensuring they receive balanced nutrition and promote healthy growth, requires effective adjustment of feed mixing parameters to support their healthy growth. The primary step is to collect feed composition parameters and juvenile growth information. The specific process includes: Set up feed testing terminals and livestock data collection terminals; The feed collection terminal is used to collect comprehensive information about the feed used to feed young livestock, such as nutrient content and mixing state. The livestock collection terminal is used to collect information about the young livestock's physical condition and growth status. The feed composition parameters are obtained by capturing the components of the feed through the feed detection terminal; Furthermore, the feeding feed refers to the feed ingested by feeding livestock calves, which is a feed that meets the growth requirements of livestock calves. The component capture refers to obtaining the content of the components contained in the feeding feed and obtaining feed component parameters. The feed component parameters include nutrient components, feed particle shape, moisture content, digestibility, and feed suitability stage. Among them, the nutrient components include protein level, energy content, vitamins and minerals. The feed suitability stage refers to the growth stage of livestock calves that can be eaten. For example, feed a is suitable for lamb calves aged 1-2 months but not for camel calves aged 1-2 months. Information on livestock calves is collected through a livestock data collection terminal to obtain calves' growth information, and the collected calves' growth information is time-stamped to obtain the collection time; The cub growth information includes real-time growth data and standard age data. The real-time growth data includes cub nutritional status, feed consumption data, health status data, and growth environment data. The cub nutritional status includes, but is not limited to, cub species, age, weight, growth stage, and expected growth rate. The feed consumption data includes, but is not limited to, cub feed intake and feed conversion rate. The health status data includes cub health status, morbidity, and mortality. The growth environment data includes, but is not limited to, the temperature, humidity, and light of the environment in which the cubs are located. The standard age data represents the physical growth data that livestock cubs should meet at the corresponding growth stage. For example, the standard weight and body shape that cubs should meet at the first month of age, and the standard weight and body shape that cubs should meet at the second month of age.

[0022] A virtual monitoring space is constructed, and the growth information of the cubs is sorted and transformed to obtain a real-time growth sequence. The specific process includes: A virtual monitoring space is constructed for livestock calves. The virtual monitoring space is a virtual space used to monitor the growth of livestock calves. By converting the livestock calves into the virtual monitoring space for virtual comparison, it is possible to obtain whether the current growth status of the livestock calves meets the growth requirements of the standard age. The obtained livestock calves are uploaded to the virtual monitoring space, and the livestock calves are converted into virtual calves through the virtual monitoring space to obtain virtual calves models; The aforementioned isomorphic transformation refers to converting the image of livestock calves into a virtual calves image through a virtual monitoring space. Specifically, it involves constructing a three-dimensional virtual model of the livestock calves using their physical characteristics and behavioral patterns. This virtual model is then endowed with the corresponding behaviors, movements, and growth patterns of the calves, resulting in a virtual calves model. This virtual calves model is then integrated with the virtual environment in the virtual monitoring space, achieving natural interaction and display. In particular, the virtual calves model possesses the same mobility, physical condition, and real-time nutritional status as real livestock calves at the same time. The virtual calves facilitate physical analysis to determine whether their nutritional needs and growth stage meet the requirements of their standard age. Acquire cub growth information, sort the real-time growth data in the acquired cub growth information by time, and obtain a real-time growth sequence; The time sorting refers to sorting the real-time growth data according to the time sequence of collection to obtain the real-time growth sequence, that is, sorting the real-time growth data according to the collection time. Furthermore, based on the larval nutritional status, feed consumption data, health status data, and growth environment data included in the real-time growth data, the real-time growth sequence includes the larval nutritional status sequence, feed consumption sequence, health status sequence, and growth environment sequence.

[0023] A growth monitoring change map is constructed based on real-time growth sequences. The standard age-based data are then homomorphically transposed using this growth monitoring change map to obtain a comprehensive growth monitoring map. The specific process includes: Based on the acquisition time, a two-dimensional rectangular coordinate system is constructed according to the obtained real-time growth sequence. A growth dynamic curve is generated according to the obtained real-time growth sequence. The obtained growth dynamic curve is uploaded to the two-dimensional rectangular coordinate system to obtain a growth monitoring change map. The growth dynamic curve represents the change curve formed by the real-time growth sequence. Based on the acquisition time, the curve is obtained by connecting the real-time growth data corresponding to each acquisition time. According to the larval nutrition status sequence, feed consumption sequence, health status sequence and growth environment sequence included in the real-time growth sequence, the growth dynamic curve includes the larval nutrition status curve, feed consumption curve, health status curve and growth environment curve. The obtained standard age data is uploaded to the growth monitoring change map. The standard age data is homogenized through the growth monitoring change map to obtain the standard age growth curve. The growth monitoring change map including the standard age growth curve is marked as the comprehensive growth monitoring map. Furthermore, the homogeneous conversion refers to converting the obtained standard-age data into a dynamic curve form that is the same as the real-time growth data, i.e., a standard-age growth curve. The resulting comprehensive growth monitoring chart includes a growth dynamic curve and a standard-age growth curve. For the same collection time, the value of each growth dynamic curve can have a corresponding collection time value on the standard-age growth curve. That is, each growth data of livestock calves can obtain corresponding standard-age growth data, indicating that there are corresponding standard growth data at different growth stages. Only by meeting the standard growth data of the corresponding stage can livestock calves grow healthily and avoid slow growth due to lack of nutrition, thereby affecting livestock harvest.

[0024] The integrated growth monitoring map is marked with nodes to obtain integrated capture nodes. Standard difference statistics are then performed on these integrated capture nodes to obtain the comparison difference value. The specific process includes: A time sliding axis is set, which is a straight line perpendicular to the horizontal axis of the comprehensive growth monitoring chart, and can be translated left and right on the horizontal axis; The obtained time sliding axis is uploaded to the comprehensive growth monitoring map. The comprehensive growth monitoring map is captured by the time sliding axis to obtain comprehensive capture nodes, which include real-time growth nodes and standard month age nodes. Furthermore, the node capture refers to the point where the time sliding axis and the curve are captured in the comprehensive growth monitoring chart, which is the comprehensive capture node. The intersection of the time sliding axis and the growth dynamic curve is recorded as the real-time growth node, and the intersection of the time sliding axis and the standard age growth curve is recorded as the standard age node. The comprehensive capture node can obtain a corresponding value on the vertical axis. The value of the real-time growth node represents the real-time growth data corresponding to the growth dynamic curve, and the value of the standard age node represents the standard age data corresponding to the standard age growth curve. The starting point of the acquired integrated growth monitoring map is marked based on the acquisition time to obtain the capture origin. The acquired time sliding axis is moved to the capture origin. Based on the capture origin, the integrated growth monitoring map is compared and observed through the integrated capture node to obtain the comparison difference value. It should be further explained that, in the specific implementation process, the capture origin refers to the starting point of the curve in the comprehensive growth monitoring chart, and the analog observation refers to comparing the difference between the node values ​​of the real-time growth node and the standard age node at the capture origin, which is the comparison difference value. The obtained comparison difference value is marked as BD, where BD = SH - BZ, SH represents the value of the growth dynamic curve corresponding to the real-time growth node, and BZ represents the value of the standard age growth curve corresponding to the standard age node.

[0025] Abnormal growth is determined based on the comparison difference value, and a threshold comparison result is obtained. The composition of the feed is extracted based on the threshold comparison result to obtain data on overfeeding. The specific process includes: A difference threshold is set based on the cub growth information. The difference threshold is a pre-set threshold range, including an upper limit and a lower limit. It is a threshold used to determine whether the difference between the cub growth data and the standard growth data meets the requirements of the safe fluctuation range. The difference value is compared with the difference threshold to obtain the threshold comparison result, which includes overgrowth nodes and qualified growth nodes. The reference comparison means comparing the difference threshold with the comparison difference value. When the comparison difference value is greater than the upper limit of difference or less than the lower limit of difference, the real-time growth node corresponding to the comparison difference value is recorded as the difference growth node, indicating that the growth data of the livestock calves at this time point exceeds the standard growth requirements, and the growth progress of the livestock calves needs to be controlled by adjusting the feed mixing parameters. When the comparison difference value is greater than or equal to the lower limit of difference and less than or equal to the upper limit of difference, the real-time growth node corresponding to the comparison difference value is recorded as the qualified growth node, indicating that the growth data of the livestock calves at this time point meets the standard growth requirements. Obtain the threshold comparison result of the capture origin. When the threshold comparison result is an overgrowth node, obtain the feed composition parameters of the feed corresponding to the overgrowth node, and record it as overfeed data. This indicates that in the growth stage of livestock calves, there is a growth data at a growth node that is judged to be abnormal, that is, exceeding the standard growth requirements or not meeting the standard growth requirements. It is necessary to adjust the parameters of the feed to adapt to the nutritional needs of the abnormally growing livestock calves. It is necessary to analyze the feed composition parameters at this time to obtain the nutrients that the livestock calves lack or have excessive nutrients, and generate an adjusted mixed feed ratio to improve the abnormal growth of the livestock calves. When the threshold comparison result is a qualified growth node, it means that the growth data of the livestock calves at that time point meets the requirements of standard growth. Then, the moving time sliding axis is moved from the capture origin to the next collection time. Analogous observation is conducted at the next collection time to obtain the comparison difference value for anomaly judgment. If the threshold comparison result is still a qualified growth node, the moving time sliding axis continues, and the threshold comparison result is obtained repeatedly at each collection time until the endpoint of the comprehensive growth monitoring chart is reached. The composition statistics of the feed for the threshold comparison result of the excessive growth node are performed to obtain the excessive feed data. The composition of the excessive feed data is adjusted by mixing to obtain the feed mixture ratio with the best feeding effect.

[0026] Based on the overfeeding data, the feed ratio for the calves at the next feeding stage is adjusted to obtain a formulated feed. The specific process includes: Data on abnormal growth is obtained by extracting data from the comprehensive growth monitoring map based on excessive growth nodes. It needs to be further explained that, in the specific implementation process, the data extraction refers to the type of real-time growth data at the point where all threshold comparison results indicate an overgrowth node. This means that, based on the real-time growth data including the fowl's nutritional status, feed consumption, health status, and growth environment, each type of real-time growth data has a corresponding threshold comparison result. Therefore, at the capture origin, real-time growth data for each type of threshold comparison result indicating an overgrowth node can be obtained. Based on different abnormal real-time growth data, a comprehensive analysis can be conducted to determine the nutritional components lacking or excessive in the livestock fowl at the capture origin. This allows for component analysis of the overfeeding data to obtain the component parameters causing abnormal conditions in the livestock fowl, facilitating feed mixing and adjustment to obtain a mixed feed beneficial to the growth of the livestock fowl. For example, at the capture origin, if the threshold comparison results for fowl weight data and fowl feed intake indicate an overgrowth node, while the threshold comparison results for the remaining real-time growth data indicate a qualified growth node, then the fowl weight data and fowl feed intake are recorded as abnormal growth data. Based on abnormal growth data, the composition of the obtained overfeeding feed data was compared to identify the components that caused the abnormalities. The component comparison refers to analyzing the nutritional components of the feed that affect the transformation of real-time growth data into abnormal growth data in the overfeeding feed data, based on the type of real-time growth data included in the abnormal growth data, and obtaining the mixing ratio of the nutritional components to obtain abnormal nutritional parameters. These parameters represent the abnormal component parameters of the feed that cause abnormal growth in livestock calves, i.e., what kind of feed mixing ratio caused the unqualified growth of livestock calves. Furthermore, based on the nutrient composition of the overfeeding data, the corresponding growth requirements of the livestock fowl can be obtained. That is, which nutrients can control which aspects of the livestock fowl's nutrition. Then, based on the corresponding effects, the nutrient composition corresponding to the abnormal growth data of the livestock fowl at the capture origin can be obtained, which is the abnormal influencing component. For example, if the weight data of the livestock fowl is abnormal growth data, then the nutrient composition causing the weight to be below standard and the growth to be slow is related to the protein content in the feed. So, if the component analysis is performed here, the abnormal influencing component is found to be the protein content. Therefore, the protein content of the feed needs to be adjusted to obtain a feed suitable for supplementing the fowl with protein. The composition of the feed is adjusted based on the abnormal influencing components obtained to obtain a formulated feed. It should be further explained that, in the specific implementation process, the component adjustment refers to adjusting the component parameters of the feed according to the abnormal influencing components. Referring to the impact of various nutrients in the feed on the growth of livestock calves, corresponding adjustments are made, and the parameters of the nutrients after each adjustment are recorded to obtain the corresponding formulated feed. Specifically, each component adjustment changes only one abnormal influencing component, and the proportion of the adjusted component parameter changes is small, thus obtaining a large number of adjusted formulated feeds. This allows for intelligent simulation of the virtual calves model in the virtual monitoring space to find the most suitable feed mixture ratio for improving the growth of livestock calves. For example, the first component adjustment increases the protein content by m, the second by 2m, the third by 3m, ..., the kth adjustment increases the vitamin A content by y, the (k+1)th adjustment increases the vitamin A content by 2y, ...

[0027] In a virtual monitoring space, simulated feeding is conducted based on the formulated feed, and the feeding process is optimized to obtain the best feed ratio. The specific process includes: The obtained formulated feed is uploaded to the virtual monitoring space. The virtual monitoring space simulates feeding the virtual cub model according to the formulated feed, and collects the real-time growth data of the virtual cub model during the simulated feeding process, which is recorded as the growth data to be detected. Furthermore, the simulated feeding refers to the virtual feeding of a virtual model of livestock calves in a virtual monitoring space. That is, the virtual calves are virtually fed by feeding them formulated feed, and the real-time growth data of the virtual calves during the virtual feeding process is collected, which is the growth data to be detected. In particular, the composition of the feed can be adjusted to obtain different formulated feeds. For each formulated feed, simulated feeding is carried out in the virtual monitoring space to obtain the most suitable formulated feed. The obtained growth data to be tested is homomorphically compared by a comprehensive growth monitoring chart to obtain the results of the feeding adjustment, which include effective feeding adjustment and ineffective feeding adjustment. It should be further explained that, in the specific implementation process, the homomorphic comparison expression processes the collected growth data to be detected into a curve form that is the same as the growth dynamic curve in the comprehensive growth monitoring chart, and compares the growth dynamic curve of the growth data to be detected with the standard age growth curve at the same collection time to obtain the comparison difference value, which is recorded as the comparison difference value to be detected. When the absolute value of the comparison difference value to be detected is smaller than the comparison difference value before simulated feeding, it indicates that the adjusted feed has the effect of improving the growth of livestock calves. Then, the feed can be adjusted with the same ingredients but different dosages to obtain the most suitable feed for the growth and development of livestock calves. That is, the feeding adjustment result is effective feeding adjustment. If the absolute value of the difference between the test and comparison values ​​increases or remains unchanged compared to the difference before simulated feeding, it means that the adjusted feed has no effect on improving the growth of the livestock fowl. In this case, it is necessary to change the different components or dosages of the feed to obtain the most suitable feed for the growth and development of the livestock fowl. That is, the result of the feeding adjustment at this time is an ineffective feeding adjustment. The formulated feed for effective feeding regulation is obtained by comparing and adapting the obtained formulated feed based on the virtual monitoring space to obtain the optimal feed ratio. It needs to be further explained that, in the specific implementation process, the adaptation comparison refers to the statistical analysis of all formulated feeds with effective feeding adjustment results in the virtual monitoring space, comparing the test comparison difference values ​​of the formulated feeds with effective feeding adjustment, converting all test comparison difference values ​​into absolute values ​​to obtain preliminary comparison values, sorting the obtained preliminary comparison values ​​in ascending order, obtaining the formulated feed corresponding to the first ranked preliminary comparison value, recording it as the optimal feed, and obtaining the nutrient composition ratio parameters of the optimal feed to obtain the optimal feed ratio. The feed for the livestock calves corresponding to the virtual calves model is mixed according to the optimal feed ratio to obtain the final feed. The livestock calves are fed at the next feeding time, so that the livestock calves receive the best supplementary feed at the next feeding time when their growth is not up to standard. The calves can quickly replenish the nutrients they lacked, thereby accelerating their growth rate and restoring them to a normal growth trajectory as soon as possible.

[0028] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for intelligent optimization and adjustment of feed mixing parameters for young livestock, characterized in that, Includes the following steps: Step S1: Collect feed composition parameters and juvenile growth information, including real-time growth data and standard age data, and mark the collection time; Step S2: Construct a virtual monitoring space, sort and transform the cub growth information to obtain a real-time growth sequence, construct a growth monitoring change map based on the real-time growth sequence, and perform homomorphic transpose on the standard age data through the growth monitoring change map to obtain a comprehensive growth monitoring map; Step S3: Mark nodes on the comprehensive growth monitoring map to obtain comprehensive capture nodes. Perform standard difference statistics on the comprehensive capture nodes to obtain comparison difference values. Based on the comparison difference values, determine abnormal growth and obtain threshold comparison results. Extract the components of the feed based on the threshold comparison results to obtain overfeeding feed data. Step S4: Adjust the feed ratio of the cubs at the next feeding node based on the overfeeding data to obtain the adjusted feed. Simulate feeding in the virtual monitoring space based on the adjusted feed and optimize the feeding process to obtain the best feed ratio.

2. The intelligent optimization and adjustment method for feed mixing parameters for young livestock according to claim 1, characterized in that, The process of collecting feed composition parameters and larval growth information includes: Set up feed testing terminals and livestock data collection terminals; The feed composition parameters are obtained by capturing the components of the feed through the feed detection terminal; Information on livestock calves is collected through a livestock data collection terminal to obtain calves' growth information, and the collected calves' growth information is time-stamped to obtain the collection time.

3. The intelligent optimization and adjustment method for feed mixing parameters for young livestock according to claim 1, characterized in that, The process of constructing a virtual monitoring space includes: A virtual monitoring space is constructed for livestock calves. The livestock calves are uploaded to the virtual monitoring space, and the calves are converted into virtual models through the virtual monitoring space. The real-time growth data is sorted by time to obtain the real-time growth sequence.

4. The intelligent optimization and adjustment method for feed mixing parameters for young livestock according to claim 1, characterized in that, The process of obtaining a comprehensive growth monitoring map includes: A two-dimensional rectangular coordinate system is constructed based on the acquisition time and the real-time growth sequence. A growth dynamic curve is generated based on the real-time growth sequence. The obtained growth dynamic curve is uploaded to the two-dimensional rectangular coordinate system to obtain a growth monitoring change map. Standard age data is uploaded to the growth monitoring change map. The standard age data is then homogenized using the growth monitoring change map to obtain the standard age growth curve. The growth monitoring change map that includes the standard age growth curve is then marked as the comprehensive growth monitoring map.

5. The intelligent optimization and adjustment method for feed mixing parameters for young livestock according to claim 1, characterized in that, The process of obtaining the comparison difference value includes: Set a time sliding axis, upload the time sliding axis to the comprehensive growth monitoring map, and capture nodes in the comprehensive growth monitoring map through the time sliding axis to obtain comprehensive capture nodes; The starting point of the integrated growth monitoring map is marked based on the acquisition time to obtain the capture origin. The time sliding axis is moved to the capture origin. Based on the capture origin, the integrated growth monitoring map is compared and observed through the integrated capture node to obtain the comparison difference value.

6. The intelligent optimization and adjustment method for feed mixing parameters for young livestock according to claim 5, characterized in that, The process of extracting components from feed based on threshold comparison results includes: A difference threshold is set based on the cub growth information. The difference value is compared with the difference threshold to obtain the threshold comparison result. The threshold comparison result includes excessive growth nodes and qualified growth nodes. Obtain the threshold comparison result of the capture origin. When the threshold comparison result is an overgrowth node, obtain the feed composition parameters of the feed corresponding to the overgrowth node and record them as overfeed feed data.

7. The intelligent optimization and adjustment method for feed mixing parameters for young livestock according to claim 6, characterized in that, The process of obtaining formulated feed includes: Data is extracted from the comprehensive growth monitoring map based on the excessive growth nodes to obtain abnormal growth data. The components of the excessive feed data are then compared with the abnormal growth data to obtain the abnormal influencing components. The composition of feed is adjusted based on the abnormal influencing components to obtain formulated feed.

8. The intelligent optimization and adjustment method for feed mixing parameters for livestock calves according to claim 3, characterized in that, The process of obtaining the optimal feed formulation includes: The prepared feed is uploaded to the virtual monitoring space. The virtual monitoring space then simulates feeding the virtual cub model based on the prepared feed and collects real-time growth data of the virtual cub model during the simulated feeding process, which is recorded as the growth data to be detected. By performing homomorphic comparisons of the growth data to be tested using comprehensive growth monitoring charts, the results of regulated feeding are obtained, including effective feeding regulation and ineffective feeding regulation. The results of the regulated feeding are obtained by adjusting the feed formulation to achieve effective feeding regulation. The adjusted feed formulation is then compared and matched based on the virtual monitoring space to obtain the optimal feed ratio.

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