Feed feeding control system and method based on data analysis

Abstract

The invention discloses a feed feeding control system and method based on data analysis, and relates to the technical field of feed feeding, and the method comprises the steps: obtaining feed feeding data and corresponding livestock growth data according to a historical feed feeding cycle; analyzing the feed feeding data, and extracting a matching sequence of historical feed feeding data; combining the corresponding livestock growth data to establish an associated data group; constructing a feed ratio prediction model based on a support vector regression method; performing feed preparation and feeding according to the real-time matching sequence; analyzing an adaptive value of the real-time matching sequence of the current feed feeding period according to the real-time monitoring data set, and judging the continuous condition of the current feed feeding period according to the adaptive value; and constructing an update cycle matching model based on the update cycle matching set, and analyzing the total number of days of the subsequent feed feeding cycle according to the update cycle matching model. The rationality of feed feeding is improved, and intelligent and reasonable feeding is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of feed feeding, and in particular to a feed feeding control system and method based on data analysis. Background Art

[0002] With the rapid development of the domestic economy and people's increasing attention to ecological environment protection, automatic feeding technology has emerged. Many domestic companies have invested in the research and development and production of automatic feeding technology and applied it to farms. At present, automatic feeding technology has been widely used in poultry, livestock, aquatic products and other fields.

[0003] At present, automatic feeding technology often does not update the feed ratio. Livestock will adapt to the feed with the same ratio, which can easily lead to a decrease in feed conversion rate. If the feed ratio is updated regularly, the decrease in conversion rate can be reduced to a certain extent, but the preset update time is often set manually, which is not conducive to realizing intelligent and reasonable feeding.

[0004] Therefore, the present invention discloses a feed feeding control system and method based on data analysis to solve the above problems. Summary of the invention

[0005] The object of the present invention is to provide a feed feeding control system and method based on data analysis to solve the problems raised in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solution: a feed feeding control method based on data analysis, the method comprising the following steps: S1: Obtain feed feeding data and corresponding livestock growth data according to historical feed feeding cycles; analyze the feed feeding data and extract the ratio sequence of historical feed feeding data; S2: Based on the ratio sequence of historical feed feeding data, the corresponding livestock growth data are combined to form a related data group; the Gaussian kernel function in the Kriging interpolation method is used as the kernel function to perform high-dimensional space linear processing on the related data group; and a feed ratio prediction model is constructed based on the support vector regression method; S3: Analyze the real-time ratio sequence corresponding to the current feed feeding cycle, and perform feed configuration and feeding according to the real-time ratio sequence; analyze the adaptation value of the real-time ratio sequence of the current feed feeding cycle according to the real-time monitoring data set, and judge the duration of the current feed feeding cycle according to the adaptation value; S4: Generate an update cycle matching set according to the updated feed feeding cycle and the corresponding initial weight of the livestock cycle; construct an update cycle matching model based on the update cycle matching set, and analyze the total number of days of the subsequent feed feeding cycle according to the update cycle matching model.

[0007] According to the above scheme, in S1, A historical feed feeding cycle corresponds to a set of feed feeding data; the duration of the historical feed feeding cycle is preset by the system; the feed feeding data includes feed ingredient names and feed weight data; the livestock growth data includes the total weight of livestock in the breeding pen; According to the feed feeding data, the feed weight data is extracted to form a ratio sequence, and the ratio sequence of the i-th historical feed feeding cycle is recorded as PS i ={PS （i，1） , P.S. （i，2） , …, PS （i，j） , …, PS （i，J）}; where PS （i，j） Represents the weight of the jth ingredient in the i-th historical feed feeding cycle; i∈[1,I]; j∈[1,J]; where I represents the total number of historical feed feeding cycles; J represents the total number of ingredients.

[0008] By analyzing the feed feeding data and extracting the corresponding ratio sequence, the numerical relationship in the feed ratio can be effectively displayed, providing effective data support for subsequent analysis; According to the above scheme, S2 includes the following contents: S201: Associating the ratio sequence of the i-th historical feed feeding cycle with the initial weight of the corresponding livestock cycle to form an associated data set denoted as (PS i ,MG i MG i represents the initial weight of livestock cycle corresponding to the ratio sequence of the i-th historical feed feeding cycle; the initial weight of livestock cycle represents the average weight of livestock in the monitored breeding pen at the beginning of the historical feed feeding cycle; S202: using the Gaussian kernel function in the Kriging interpolation method as the kernel function to perform high-dimensional spatial linear processing on the associated data set; and constructing a feed ratio prediction model based on the support vector regression method.

[0009] This application uses the Gaussian kernel function in the Kriging interpolation method as the kernel function to perform high-dimensional spatial linear processing on the associated data set; effectively solves complex regression problems and improves the accuracy of overall prediction; constructs a feed ratio prediction model based on the support vector regression method, which can perform regression fitting on the data, has strong generalization ability, effectively handles nonlinear and high-dimensional problems, and further improves the accuracy of the prediction model; According to the above scheme, S3 includes the following: S301: On the first day of the current feeding cycle, livestock growth data of livestock in the monitored breeding pen are obtained, the initial weight of the livestock cycle in the monitored breeding pen is calculated, the initial weight of the livestock cycle is brought into the feed ratio prediction model to obtain a real-time ratio sequence, and feed is configured and fed according to the real-time ratio sequence; S302: In the current feeding cycle, the livestock growth data of the livestock in the monitored breeding pen are acquired in real time, and the corresponding livestock cycle weight is calculated; the livestock cycle weight at the same time of the day is extracted to calculate the livestock cycle weight, and a real-time monitoring data set is formed, which is recorded as M={M1, M2, ..., M q , …, M Q}; where M q It is equal to the livestock cycle weight on the qth day in the current feed feeding cycle minus the livestock cycle weight on the q-1th day; q∈[1,Q]; Q represents the total number of days in the current feed feeding cycle; the livestock cycle weight is equal to the real-time average weight of the livestock in the monitored breeding pen; S303: Analyze the adaptation value AV of the real-time ratio sequence of the current feed feeding cycle according to the real-time monitoring data set: AV = [1÷(2πα 2 ) 1 / 2 ]×exp[-(M Q -M 均 ) 2 ÷(2×α 2 )]; Among them, M 均 represents the mean of the real-time monitoring data set, α 2 Represents the variance of the mean of the real-time monitoring data set; If the adaptation value AV is greater than or equal to the adaptation value threshold, the real-time ratio sequence of the current feed feeding cycle continues to be used for feed configuration and feeding; if the adaptation value AV is less than the adaptation value threshold, the current feed feeding cycle is terminated.

[0010] In the same current feed feeding cycle, livestock have a certain adaptability to feed, and the feed conversion rate will gradually decrease. Analyzing the adaptation value of the real-time ratio sequence of the current feed feeding cycle can effectively show the abnormal situation of low feed conversion rate, and update the feed feeding cycle for the abnormal situation of low feed conversion rate, providing data support for subsequent analysis; According to the above scheme, S4 includes the following contents: S401: extract the updated feed feeding cycle and the initial weight of the livestock cycle corresponding to the feed feeding cycle after the adaptation value analysis, and generate an updated cycle matching set (MG p , T p ), where MG prepresents the initial weight of the livestock cycle in the pth updated feed feeding cycle, T p represents the total number of days in the pth updated feed feeding cycle; S402: Construct an update cycle matching model based on the update cycle matching set: y=β1×x+β2; wherein β1 and β2 represent fitting coefficients, x represents the independent variable of the initial weight of the livestock cycle, and y represents the dependent variable of the total number of days of the updated feed feeding cycle; calculate and solve β1 and β2 in the update cycle matching model using the least squares method; on the first day of the feed feeding cycle, analyze the total number of days of the feed feeding cycle according to the update cycle matching model.

[0011] Analyzing the total number of days of the feed feeding cycle according to the updated cycle matching model can effectively reduce the abnormal situation of low feed conversion rate, improve the rationality of feed feeding, and realize intelligent and reasonable feeding.

[0012] Another aspect of the present application provides a feed feeding control system based on data analysis, the system is applied to the above-mentioned feed feeding control method based on data analysis, the system includes a feeding data extraction module, a feed ratio prediction module, a feed ratio adaptation analysis module and a feed feeding cycle analysis module; The feeding data extraction module is used to obtain feed feeding data and corresponding livestock growth data according to historical feed feeding cycles; analyze the feed feeding data and extract the ratio sequence of historical feed feeding data; The feed ratio prediction module forms a related data group based on the ratio sequence of historical feed feeding data and the corresponding livestock growth data; uses the Gaussian kernel function in the Kriging interpolation method as the kernel function to perform high-dimensional space linearization processing on the related data group; and constructs a feed ratio prediction model based on the support vector regression method; The feed ratio adaptation analysis module is used to analyze the real-time ratio sequence corresponding to the current feed feeding cycle, and perform feed configuration and feeding according to the real-time ratio sequence; analyze the adaptation value of the real-time ratio sequence of the current feed feeding cycle according to the real-time monitoring data set, and judge the duration of the current feed feeding cycle according to the adaptation value; The feed feeding cycle analysis module is used to generate an update cycle matching set according to the updated feed feeding cycle and the corresponding initial weight of the livestock cycle; construct an update cycle matching model based on the update cycle matching set, and analyze the total number of days of the subsequent feed feeding cycle according to the update cycle matching model.

[0013] According to the above scheme, in the feeding data extraction module, one historical feed feeding cycle corresponds to a group of feed feeding data; the duration of the historical feed feeding cycle is preset by the system; the feed feeding data includes the feed ingredient name and feed weight data; the livestock growth data includes the total weight of livestock in the breeding pen; and the feed weight data is extracted according to the feed feeding data to form a ratio sequence.

[0014] According to the above scheme, the feed ratio prediction module includes a ratio sequence body weight association unit and a feed ratio prediction model construction unit; The ratio sequence weight association unit is used to associate the ratio sequence of the historical feed feeding cycle with the corresponding initial weight of the livestock cycle to form an associated data group; The feed ratio prediction model building unit adopts the Gaussian kernel function in the Kriging interpolation method as the kernel function to perform high-dimensional space linearization processing on the associated data group; and builds the feed ratio prediction model based on the support vector regression method.

[0015] According to the above scheme, the feed ratio adaptation analysis module includes an adaptation value analysis unit and a feed feeding cycle update unit; The adaptation value analysis unit is used to obtain the livestock growth data of the livestock in the breeding pen to be monitored, calculate the initial weight of the livestock cycle of the livestock in the breeding pen to be monitored, bring the initial weight of the livestock cycle into the feed ratio prediction model to obtain the real-time ratio sequence, and perform feed configuration and feeding according to the real-time ratio sequence; obtain the livestock growth data of the livestock in the breeding pen to be monitored in real time, and calculate the corresponding livestock cycle weight; extract the livestock cycle weight at the same time of the day to calculate the livestock cycle weight, form a real-time monitoring data set, and analyze the adaptation value of the real-time ratio sequence of the current feed feeding cycle according to the real-time monitoring data set; The feed feeding cycle updating unit is used to determine the duration of the current feed feeding cycle according to the comparison between the adaptation value and the adaptation value threshold.

[0016] According to the above scheme, the feed feeding cycle analysis module includes an update cycle matching unit and an update cycle matching model building unit; The update cycle matching unit is used to extract the updated feed feeding cycle and the initial weight of the livestock cycle corresponding to the feed feeding cycle after the adaptation value analysis, and generate an update cycle matching set; The update cycle matching model construction unit constructs an update cycle matching model based on the update cycle matching set and calculates and solves the fitting coefficient in the update cycle matching model using the least squares method; on the first day of the feed feeding cycle, the total number of days of the feed feeding cycle is analyzed according to the update cycle matching model.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: by analyzing the feed feeding data and extracting the corresponding ratio sequence, the numerical relationship in the feed ratio can be effectively displayed, and effective data support is provided for subsequent analysis; the present application adopts the Gaussian kernel function in the Kriging interpolation method as the kernel function to perform high-dimensional space linearization processing on the associated data group; effectively solves complex regression problems and improves the accuracy of the overall prediction; constructs a feed ratio prediction model based on the support vector regression method, which can perform regression fitting on the data, has strong generalization ability, effectively handles nonlinear and high-dimensional problems, and further improves the accuracy of the prediction model; in the same current feed feeding cycle, livestock has a certain adaptability to feed, and the feed conversion rate will gradually decrease. By analyzing the adaptation value of the real-time ratio sequence of the current feed feeding cycle, the abnormal situation of low feed conversion rate can be effectively displayed, and the feed feeding cycle can be updated for the abnormal situation of low feed conversion rate, which provides data support for subsequent analysis; analyzing the total number of days of the feed feeding cycle according to the updated cycle matching model can effectively reduce the abnormal situation of low feed conversion rate, improve the rationality of feed feeding, and realize intelligent and reasonable feeding. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A schematic diagram of a feed feeding control system and method based on data analysis of the present invention; Figure 2 The present invention is a structural schematic diagram of a feed feeding control system and method based on data analysis. DETAILED DESCRIPTION

[0019] 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.

[0020] See also Figure 1 The present invention provides a technical solution: a feed feeding control method based on data analysis, the method comprising the following steps: S1: Obtain feed feeding data and corresponding livestock growth data according to historical feed feeding cycles; analyze the feed feeding data and extract the ratio sequence of historical feed feeding data; In S1, a historical feed feeding cycle corresponds to a set of feed feeding data; the duration of the historical feed feeding cycle is preset by the system; the feed feeding data includes the feed ingredient name and feed weight data; the livestock growth data includes the total weight of the livestock in the breeding pen; According to the feed feeding data, the feed weight data is extracted to form a ratio sequence, and the ratio sequence of the i-th historical feed feeding cycle is recorded as PS i ={PS （i，1） , P.S. （i，2） , …, PS （i，j） , …, PS （i，J）}; where PS （i，j） Represents the weight of the jth ingredient in the i-th historical feed feeding cycle; i∈[1,I]; j∈[1,J]; where I represents the total number of historical feed feeding cycles; J represents the total number of ingredients.

[0021] S2: Based on the ratio sequence of historical feed feeding data, the corresponding livestock growth data are combined to form a related data group; the Gaussian kernel function in the Kriging interpolation method is used as the kernel function to perform high-dimensional space linear processing on the related data group; and a feed ratio prediction model is constructed based on the support vector regression method; In S2, the following are included: S201: Associating the ratio sequence of the i-th historical feed feeding cycle with the initial weight of the corresponding livestock cycle to form an associated data set denoted as (PS i ,MG i MG i represents the initial weight of livestock cycle corresponding to the ratio sequence of the i-th historical feed feeding cycle; the initial weight of livestock cycle represents the average weight of livestock in the monitored breeding pen at the beginning of the historical feed feeding cycle; S202: using the Gaussian kernel function in the Kriging interpolation method as the kernel function to perform high-dimensional spatial linear processing on the associated data set; and constructing a feed ratio prediction model based on the support vector regression method.

[0022] S3: Analyze the real-time ratio sequence corresponding to the current feed feeding cycle, and perform feed configuration and feeding according to the real-time ratio sequence; analyze the adaptation value of the real-time ratio sequence of the current feed feeding cycle according to the real-time monitoring data set, and judge the duration of the current feed feeding cycle according to the adaptation value; In S3, include the following: S301: On the first day of the current feeding cycle, livestock growth data of livestock in the monitored breeding pen are obtained, the initial weight of the livestock cycle in the monitored breeding pen is calculated, the initial weight of the livestock cycle is brought into the feed ratio prediction model to obtain a real-time ratio sequence, and feed is configured and fed according to the real-time ratio sequence; S302: In the current feeding cycle, the livestock growth data of the livestock in the monitored breeding pen are acquired in real time, and the corresponding livestock cycle weight is calculated; the livestock cycle weight at the same time of the day is extracted to calculate the livestock cycle weight, and a real-time monitoring data set is formed, which is recorded as M={M1, M2, ..., M q , …, M Q}; where M q It is equal to the livestock cycle weight on the qth day of the current feed feeding cycle minus the livestock cycle weight on the q-1th day; q∈[1,Q]; Q represents the total number of days in the current feed feeding cycle; the livestock cycle weight is equal to the real-time average weight of the livestock in the monitored breeding pen; S303: Analyze the adaptation value AV of the real-time ratio sequence of the current feed feeding cycle according to the real-time monitoring data set: AV = [1÷(2πα 2 ) 1 / 2 ]×exp[-(M Q -M 均 ) 2 ÷(2×α 2 )]; Among them, M 均 represents the mean of the real-time monitoring data set, α 2 Represents the variance of the mean of the real-time monitoring data set; Example 1: In this example, the real-time monitoring data set M={10, 9, 10, 11, 9, 5}; Therefore, M 均 = (10+9+10+11+9+5)÷6=9;α 2 ≈3.67; Therefore, the adaptation value on the 6th day is AV = [1÷(2×π×3.67) 1 / 2 ]×exp[-(5-9) 2 ÷(2×3.67)]≈0.02; In this embodiment, the adaptation value threshold is 0.1, so the adaptation value AV on the 6th day is less than the adaptation value threshold, and the current feed feeding cycle ends, that is, the next day is the first day of a new feed feeding cycle.

[0023] S4: Generate an update cycle matching set according to the updated feed feeding cycle and the corresponding initial weight of the livestock cycle; construct an update cycle matching model based on the update cycle matching set, and analyze the total number of days of the subsequent feed feeding cycle according to the update cycle matching model.

[0024] In S4, the following are included: S401: extract the updated feed feeding cycle and the initial weight of the livestock cycle corresponding to the feed feeding cycle after the adaptation value analysis, and generate an updated cycle matching set (MG p , Tp ), where MG p represents the initial weight of the livestock cycle in the pth updated feed feeding cycle, T p represents the total number of days in the pth updated feed feeding cycle; S402: Construct an update cycle matching model based on the update cycle matching set: y=β1×x+β2; wherein β1 and β2 represent fitting coefficients, x represents the independent variable of the initial weight of the livestock cycle, and y represents the dependent variable of the total number of days of the updated feed feeding cycle; calculate and solve β1 and β2 in the update cycle matching model using the least squares method; on the first day of the feed feeding cycle, analyze the total number of days of the feed feeding cycle according to the update cycle matching model.

[0025] See also Figure 2 , the present invention provides a technical solution: a feed feeding control system based on data analysis, the system includes a feeding data extraction module, a feed ratio prediction module, a feed ratio adaptation analysis module and a feed feeding cycle analysis module; The feeding data extraction module is used to obtain feed feeding data and corresponding livestock growth data according to the historical feed feeding cycle; analyze the feed feeding data and extract the ratio sequence of the historical feed feeding data; The feed ratio prediction module is based on the ratio sequence of historical feed feeding data and combines it with the corresponding livestock growth data to form a related data group; the Gaussian kernel function in the Kriging interpolation method is used as the kernel function to perform high-dimensional space linearization processing on the related data group; and the feed ratio prediction model is constructed based on the support vector regression method; The feed ratio adaptation analysis module is used to analyze the real-time ratio sequence corresponding to the current feed feeding cycle, and perform feed configuration and feeding according to the real-time ratio sequence; analyze the adaptation value of the real-time ratio sequence of the current feed feeding cycle according to the real-time monitoring data set, and judge the duration of the current feed feeding cycle according to the adaptation value; The feed feeding cycle analysis module is used to generate an update cycle matching set according to the updated feed feeding cycle and the corresponding initial weight of the livestock cycle; construct an update cycle matching model based on the update cycle matching set, and analyze the total number of days of the subsequent feed feeding cycle according to the update cycle matching model.

[0026] In the feeding data extraction module, a historical feed feeding cycle corresponds to a set of feed feeding data; the duration of the historical feed feeding cycle is preset by the system; the feed feeding data includes the feed ingredient name and feed weight data; the livestock growth data includes the total weight of livestock in the breeding pen; the feed weight data is extracted according to the feed feeding data to form a ratio sequence.

[0027] The feed ratio prediction module includes a ratio sequence body weight association unit and a feed ratio prediction model construction unit; The ratio sequence weight association unit is used to associate the ratio sequence of the historical feed feeding cycle with the corresponding livestock cycle initial weight to form an associated data group; The feed ratio prediction model building unit uses the Gaussian kernel function in the Kriging interpolation method as the kernel function to perform high-dimensional space linearization processing on the associated data group; and builds the feed ratio prediction model based on the support vector regression method.

[0028] The feed ratio adaptation analysis module includes an adaptation value analysis unit and a feed feeding cycle update unit; The adaptation value analysis unit is used to obtain the livestock growth data of the livestock in the monitored breeding pen, calculate the initial weight of the livestock cycle of the livestock in the monitored breeding pen, bring the initial weight of the livestock cycle into the feed ratio prediction model to obtain the real-time ratio sequence, and perform feed configuration and feeding according to the real-time ratio sequence; obtain the livestock growth data of the livestock in the monitored breeding pen in real time, and calculate the corresponding livestock cycle weight; extract the livestock cycle weight at the same time of the day to calculate the livestock cycle weight, form a real-time monitoring data set, and analyze the adaptation value of the real-time ratio sequence of the current feed feeding cycle according to the real-time monitoring data set; The feed feeding cycle updating unit is used to determine the duration of the current feed feeding cycle according to the comparison between the adaptation value and the adaptation value threshold.

[0029] The feed feeding cycle analysis module includes an update cycle matching unit and an update cycle matching model building unit; The update cycle matching unit is used to extract the updated feed feeding cycle and the initial weight of the livestock cycle corresponding to the feed feeding cycle after the adaptation value analysis, and generate an update cycle matching set; The update cycle matching model construction unit constructs an update cycle matching model based on the update cycle matching set and calculates and solves the fitting coefficients in the update cycle matching model using the least squares method; on the first day of the feed feeding cycle, the total number of days of the feed feeding cycle is analyzed according to the update cycle matching model.

[0030] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0031] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A feed feeding control method based on data analysis, characterized in that: The method comprises the following steps: S1: Obtain feed feeding data and corresponding livestock growth data according to historical feed feeding cycles; analyze the feed feeding data and extract the ratio sequence of historical feed feeding data; S2: Based on the ratio sequence of historical feed feeding data, the corresponding livestock growth data are combined to form a related data group; the Gaussian kernel function in the Kriging interpolation method is used as the kernel function to perform high-dimensional space linear processing on the related data group; and a feed ratio prediction model is constructed based on the support vector regression method; S3: Analyze the real-time ratio sequence corresponding to the current feed feeding cycle, and perform feed configuration and feeding according to the real-time ratio sequence; analyze the adaptation value of the real-time ratio sequence of the current feed feeding cycle according to the real-time monitoring data set, and judge the duration of the current feed feeding cycle according to the adaptation value; S4: Generate an update cycle matching set according to the updated feed feeding cycle and the corresponding initial weight of the livestock cycle; construct an update cycle matching model based on the update cycle matching set, and analyze the total number of days of the subsequent feed feeding cycle according to the update cycle matching model.

2. A feed feeding control method based on data analysis according to claim 1, characterized in that: In S1, A historical feed feeding cycle corresponds to a set of feed feeding data; the duration of the historical feed feeding cycle is preset by the system; the feed feeding data includes feed ingredient names and feed weight data; the livestock growth data includes the total weight of livestock in the breeding pen; According to the feed feeding data, the feed weight data is extracted to form a ratio sequence, and the ratio sequence of the i-th historical feed feeding cycle is recorded as PS i ={PS （i，1） , P.S. （i，2） , …, PS （i，j） , …, PS （i，J） }; where PS （i，j） Represents the weight of the jth ingredient in the i-th historical feed feeding cycle; i∈[1,I]; j∈[1,J]; where I represents the total number of historical feed feeding cycles; J represents the total number of ingredients.

3. A feed feeding control method based on data analysis according to claim 2, characterized in that: In S2, the following are included: S201: Associating the ratio sequence of the i-th historical feed feeding cycle with the initial weight of the corresponding livestock cycle to form an associated data set denoted as (PS i ,MG i MG i represents the initial weight of livestock cycle corresponding to the ratio sequence of the i-th historical feed feeding cycle; the initial weight of livestock cycle represents the average weight of livestock in the monitored breeding pen at the beginning of the historical feed feeding cycle; S202: using the Gaussian kernel function in the Kriging interpolation method as the kernel function to perform high-dimensional spatial linear processing on the associated data set; and constructing a feed ratio prediction model based on the support vector regression method.

4. A feed feeding control method based on data analysis according to claim 3, characterized in that: In S3, include the following: S301: On the first day of the current feeding cycle, livestock growth data of livestock in the monitored breeding pen are obtained, the initial weight of the livestock cycle in the monitored breeding pen is calculated, the initial weight of the livestock cycle is brought into the feed ratio prediction model to obtain a real-time ratio sequence, and feed is configured and fed according to the real-time ratio sequence; S302: In the current feeding cycle, the livestock growth data of the livestock in the monitored breeding pen are acquired in real time, and the corresponding livestock cycle weight is calculated; the livestock cycle weight at the same time of the day is extracted to calculate the livestock cycle weight, and a real-time monitoring data set is formed, which is recorded as M={M1, M2, ..., M q , …, M Q }; where M q It is equal to the livestock cycle weight on the qth day in the current feed feeding cycle minus the livestock cycle weight on the q-1th day; q∈[1,Q]; Q represents the total number of days in the current feed feeding cycle; the livestock cycle weight is equal to the real-time average weight of the livestock in the monitored breeding pen; S303: Analyze the adaptation value AV of the real-time ratio sequence of the current feed feeding cycle according to the real-time monitoring data set: AV=[1÷(2πα 2 ) 1 / 2 ]×exp[-(M Q -M 均 ) 2 ÷(2×α) 2 )]; Among them, M 均 represents the mean of the real-time monitoring data set, α 2 Represents the variance of the mean of the real-time monitoring data set; If the adaptation value AV is greater than or equal to the adaptation value threshold, the real-time ratio sequence of the current feed feeding cycle continues to be used for feed configuration and feeding; if the adaptation value AV is less than the adaptation value threshold, the current feed feeding cycle is terminated.

5. A feed feeding control method based on data analysis according to claim 4, characterized in that: In S4, the following are included: S401: extract the updated feed feeding cycle and the initial weight of the livestock cycle corresponding to the feed feeding cycle after the adaptation value analysis, and generate an updated cycle matching set (MG p , T p ), where MG p represents the initial weight of the livestock cycle in the pth updated feed feeding cycle, T p represents the total number of days in the pth updated feed feeding cycle; S402: Construct an update cycle matching model based on the update cycle matching set: y=β1×x+β2; wherein β1 and β2 represent fitting coefficients, x represents the independent variable of the initial weight of the livestock cycle, and y represents the dependent variable of the total number of days of the updated feed feeding cycle; calculate and solve β1 and β2 in the update cycle matching model using the least squares method; on the first day of the feed feeding cycle, analyze the total number of days of the feed feeding cycle according to the update cycle matching model.

6. A feed feeding control system based on data analysis, the system is applied to the feed feeding control method based on data analysis according to any one of claims 1 to 5, characterized in that: The system includes a feeding data extraction module, a feed ratio prediction module, a feed ratio adaptation analysis module and a feed feeding cycle analysis module; The feeding data extraction module is used to obtain feed feeding data and corresponding livestock growth data according to historical feed feeding cycles; analyze the feed feeding data and extract the ratio sequence of historical feed feeding data; The feed ratio prediction module forms a related data group based on the ratio sequence of historical feed feeding data and the corresponding livestock growth data; uses the Gaussian kernel function in the Kriging interpolation method as the kernel function to perform high-dimensional space linearization processing on the related data group; and constructs a feed ratio prediction model based on the support vector regression method; The feed ratio adaptation analysis module is used to analyze the real-time ratio sequence corresponding to the current feed feeding cycle, and perform feed configuration and feeding according to the real-time ratio sequence; analyze the adaptation value of the real-time ratio sequence of the current feed feeding cycle according to the real-time monitoring data set, and judge the duration of the current feed feeding cycle according to the adaptation value; The feed feeding cycle analysis module is used to generate an update cycle matching set according to the updated feed feeding cycle and the corresponding initial weight of the livestock cycle; construct an update cycle matching model based on the update cycle matching set, and analyze the total number of days of the subsequent feed feeding cycle according to the update cycle matching model.

7. A feed feeding control system based on data analysis according to claim 6, characterized in that: In the feeding data extraction module, one historical feed feeding cycle corresponds to a group of feed feeding data; the duration of the historical feed feeding cycle is preset by the system; the feed feeding data includes feed ingredient names and feed weight data; the livestock growth data includes the total weight of livestock in the breeding pen; and feed weight data is extracted according to the feed feeding data to form a ratio sequence.

8. A feed feeding control system based on data analysis according to claim 6, characterized in that: The feed ratio prediction module includes a ratio sequence body weight association unit and a feed ratio prediction model construction unit; The ratio sequence weight association unit is used to associate the ratio sequence of the historical feed feeding cycle with the corresponding initial weight of the livestock cycle to form an associated data group; The feed ratio prediction model building unit adopts the Gaussian kernel function in the Kriging interpolation method as the kernel function to perform high-dimensional space linearization processing on the associated data group; and builds the feed ratio prediction model based on the support vector regression method.

9. A feed feeding control system based on data analysis according to claim 6, characterized in that: The feed ratio adaptation analysis module includes an adaptation value analysis unit and a feed feeding cycle update unit; The adaptation value analysis unit is used to obtain the livestock growth data of the livestock in the breeding pen to be monitored, calculate the initial weight of the livestock cycle of the livestock in the breeding pen to be monitored, bring the initial weight of the livestock cycle into the feed ratio prediction model to obtain the real-time ratio sequence, and perform feed configuration and feeding according to the real-time ratio sequence; obtain the livestock growth data of the livestock in the breeding pen to be monitored in real time, and calculate the corresponding livestock cycle weight; extract the livestock cycle weight at the same time of the day to calculate the livestock cycle weight, form a real-time monitoring data set, and analyze the adaptation value of the real-time ratio sequence of the current feed feeding cycle according to the real-time monitoring data set; The feed feeding cycle updating unit is used to determine the duration of the current feed feeding cycle according to the comparison between the adaptation value and the adaptation value threshold.

10. A feed feeding control system based on data analysis according to claim 6, characterized in that: The feed feeding cycle analysis module includes an update cycle matching unit and an update cycle matching model building unit; The update cycle matching unit is used to extract the updated feed feeding cycle and the initial weight of the livestock cycle corresponding to the feed feeding cycle after the adaptation value analysis, and generate an update cycle matching set; The update cycle matching model construction unit constructs an update cycle matching model based on the update cycle matching set and calculates and solves the fitting coefficient in the update cycle matching model using the least squares method; on the first day of the feed feeding cycle, the total number of days of the feed feeding cycle is analyzed according to the update cycle matching model.

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