Sheep breeding growth monitoring data transmission method and system

Abstract

The invention relates to the technical field of data transmission, in particular to a sheep breeding growth monitoring data transmission method and system, and the method comprises the steps: collecting growth data monitored in a sheep breeding growth process; according to the figure data and the feed intake data, obtaining a trend fluctuation difference factor and a corrected trend fluctuation abnormal factor of each kind of figure data every day; obtaining the trend characteristics of each kind of figure data every day according to the change condition of the figure data; according to the trend characteristics of each type of figure data every day and the correction trend fluctuation abnormal factor of each type of figure data every day, obtaining an adjustment coefficient of a compression window corresponding to each type of figure data every day; adjusting the compression window according to the adjustment coefficient of the compression window corresponding to each type of figure data every day; and according to the adjusted compression window, compressing the figure data through a revolving door compression algorithm, and transmitting the compressed figure data. The compression rate and the compression efficiency of the figure data can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a method and system for data transmission for sheep breeding growth monitoring. Background Art

[0002] Sheep breeding growth monitoring data transmission is to use modern technology to monitor the growth status of sheep in real time and transmit data for breeding management, decision support and health monitoring. The sheep breeding growth monitoring data transmission system uses advanced technology to achieve comprehensive monitoring and management of the sheep's growth process. This not only improves breeding efficiency and scientific decision-making, but also promotes the breeding industry to move towards intelligent and sustainable development.

[0003] During the growth process of sheep farming, monitoring will generate a lot of data. Currently, these monitoring data will be compressed using a revolving door compression algorithm and then the compressed data will be transmitted.

[0004] However, when using the revolving door compression algorithm to compress sheep breeding growth data, the size of the compression window is set manually. If the compression window is set larger, although the compression rate can be improved, it requires a longer matching search time, resulting in a decrease in the algorithm running speed and lower compression efficiency. If the window is set smaller, the compression rate will be lower. Summary of the invention

[0005] In order to solve the technical problem that the compression rate and compression efficiency of the monitored body shape data are low, the purpose of the present invention is to provide a sheep breeding growth monitoring data transmission method and system, and the technical scheme adopted is as follows: In a first aspect of the present disclosure, a method for transmitting data for sheep breeding growth monitoring is provided, the method comprising: Collecting the growth data monitored during the sheep breeding and growth process, the growth data includes: body shape data and feed intake data; Obtaining a trend fluctuation difference factor for each type of body shape data every day according to the fluctuation range of the body shape data; The trend fluctuation difference factor is corrected by the correlation between the body shape data and the feed intake data to obtain the corrected trend fluctuation abnormality factor for each body shape data of each day; Obtaining trend characteristics of each type of body shape data every day through changes in the body shape data; According to the trend characteristics and the modified trend fluctuation abnormality factor, an adjustment coefficient of the compression window corresponding to each body shape data of each day is obtained; Adjusting the compression window corresponding to each type of body shape data every day according to the adjustment coefficient; According to the adjusted compression window, each body shape data of each day is compressed and transmitted by a revolving door compression algorithm.

[0006] In one embodiment, obtaining the trend fluctuation difference factor of each body shape data every day according to the fluctuation range of the body shape data includes: The following steps are performed for each of the body shape data: Obtain the slope of the preceding adjacent data and the slope of the following adjacent data of the current body shape data; The difference between the slope of the preceding adjacent data and the slope of the succeeding adjacent data is obtained as a trend fluctuation difference factor of the current body shape data.

[0007] In one embodiment, the trend fluctuation difference factor is corrected by the correlation between the body shape data and the feed intake data to obtain the corrected trend fluctuation abnormality factor of each body shape data per day, including: Perform the following steps for the body shape data of each body shape: Acquire current body shape data corresponding to the current body shape in a preset time period from the body shape data; acquiring target feed intake data for the preset time period from the feed intake data; Obtaining a correlation coefficient between the current body shape data and the target feed intake data; Acquire an abnormal factor corresponding to the current body shape data according to a correlation coefficient between the current body shape data and the target feed intake data; The trend fluctuation difference factor corresponding to the current body shape data is corrected according to the abnormal factor corresponding to the current body shape data to obtain the corrected trend fluctuation abnormal factor corresponding to the current body shape data.

[0008] In one embodiment, the obtaining of the correlation coefficient between the current body shape data and the target feed intake data includes: Acquire a body shape data sequence corresponding to the current body shape data according to a preset order; Acquire a feed intake data sequence corresponding to each feed in the target feed intake data according to the preset order; The correlation coefficient between the current body shape data and the target feed intake data is obtained according to the body shape data sequence corresponding to the current body shape data and the feed intake data sequence corresponding to all types of the feed.

[0009] In one embodiment, the correlation coefficient includes: a Pearson correlation coefficient.

[0010] In one embodiment, obtaining the trend characteristics of each body shape data of each day through the change of the body shape data includes: Perform the following steps for the body shape data of each body shape: Acquire all current body shape data corresponding to the current body shape in the preset time period from the body shape data; Acquire the slope of the adjacent current body shape data corresponding to each current body shape data, and the trend fluctuation difference factor of each current body shape data; Obtaining information entropy of slopes of all adjacent current body shape data; Obtaining the standard deviation corresponding to the trend fluctuation difference factor of all the current body shape data; The trend feature of the current body shape data is obtained according to the information entropy of the slopes of all adjacent current body shape data and the standard deviation.

[0011] In one embodiment, obtaining the adjustment coefficient of the compression window corresponding to each body shape data of each day according to the trend feature and the modified trend fluctuation abnormality factor includes: The trend feature and the corresponding modified trend fluctuation abnormal factor are linearly normalized to obtain the adjustment coefficient of the compression window corresponding to each body shape data of each day.

[0012] In a second aspect of the present disclosure, a sheep breeding growth monitoring data transmission system is provided, the system comprising: A collection module is used to collect growth data monitored during the growth of sheep breeding, wherein the growth data includes: body shape data and feed intake data; A first acquisition module is used to acquire a trend fluctuation difference factor of each body shape data every day according to the fluctuation range of the body shape data; A second acquisition module is used to correct the trend fluctuation difference factor according to the correlation between the body shape data and the feed intake data, so as to obtain the corrected trend fluctuation abnormal factor of each body shape data of each day; A third acquisition module is used to acquire the trend characteristics of each type of body shape data every day according to the change of the body shape data; A fourth acquisition module, for obtaining an adjustment coefficient of the compression window corresponding to each body shape data of each day according to the trend feature and the modified trend fluctuation abnormality factor; An adjustment module, used for adjusting the compression window corresponding to each body shape data of each day according to the adjustment coefficient of the compression window; The processing module is used to compress and transmit each body shape data of each day through a revolving door compression algorithm according to the adjusted compression window.

[0013] In one embodiment, the first acquisition module is specifically configured to perform the following steps on each of the body shape data: Obtain the slope of the preceding adjacent data and the slope of the following adjacent data of the current body shape data; The difference between the slope of the preceding adjacent data and the slope of the succeeding adjacent data is obtained as a trend fluctuation difference factor of the current body shape data.

[0014] In one embodiment, the second acquisition module is specifically configured to perform the following steps on the body shape data of each body shape: Acquire current body shape data corresponding to the current body shape in a preset time period from the body shape data; acquiring target feed intake data for the preset time period from the feed intake data; Obtaining a correlation coefficient between the current body shape data and the target feed intake data; Acquire an abnormal factor corresponding to the current body shape data according to a correlation coefficient between the current body shape data and the target feed intake data; The trend fluctuation difference factor corresponding to the current body shape data is corrected according to the abnormal factor corresponding to the current body shape data to obtain the corrected trend fluctuation abnormal factor corresponding to the current body shape data.

[0015] In one embodiment, in terms of obtaining the correlation coefficient between the current body shape data and the target feed intake data, the second obtaining module is specifically used to: Acquire a body shape data sequence corresponding to the current body shape data according to a preset order; Acquire a feed intake data sequence corresponding to each feed in the target feed intake data according to the preset order; The correlation coefficient between the current body shape data and the target feed intake data is obtained according to the body shape data sequence corresponding to the current body shape data and the feed intake data sequence corresponding to all types of the feed.

[0016] In one embodiment, the correlation coefficient includes: a Pearson correlation coefficient.

[0017] In one embodiment, the third acquisition module is specifically used to perform the following steps on the body shape data of each body shape: Acquire all current body shape data corresponding to the current body shape in the preset time period from the body shape data; Acquire the slope of the adjacent current body shape data corresponding to each current body shape data, and the trend fluctuation difference factor of each current body shape data; Obtaining information entropy of slopes of all adjacent current body shape data; Obtaining the standard deviation corresponding to the trend fluctuation difference factor of all the current body shape data; The trend feature of the current body shape data is obtained according to the information entropy of the slopes of all adjacent current body shape data and the standard deviation.

[0018] In one embodiment, the fourth acquisition module is specifically used for: The trend feature and the corresponding modified trend fluctuation abnormal factor are linearly normalized to obtain the adjustment coefficient of the compression window corresponding to each body shape data of each day.

[0019] The present invention has the following beneficial effects: in the present application, the trend fluctuation difference factor of each body shape data is obtained according to the fluctuation range of the body shape data, and the trend fluctuation difference factor is corrected through the correlation between the body shape data and the feed intake data to obtain the corrected trend fluctuation abnormal factor, thereby improving the accuracy of abnormal data fluctuation analysis; the adjustment coefficient of the compression window of each body shape data is obtained through the trend characteristics of each body shape data every day during the sheep breeding and growth process and the corrected trend fluctuation abnormal factor of the body shape data every day, thereby improving the accuracy of data trend analysis; and the compression window is adjusted through the adjustment coefficient, and each body shape data is compressed and transmitted through the revolving door compression algorithm according to the adjusted compression window. Since the size of the compression window of the revolving door compression algorithm can be adjusted based on the abnormality of the body shape data, a smaller window is used to compress the abnormal body shape data, and a larger window is used to compress the normal data, thereby improving the compression rate and compression efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A flowchart of a sheep breeding growth monitoring data transmission method provided by an embodiment of the present invention; Figure 2 An embodiment of the present invention provides Figure 1 Flow chart of step S102; Figure 3 An embodiment of the present invention provides Figure 1 Flow chart of step S103; Figure 4 An embodiment of the present invention provides Figure 3 Flow chart of step S1033; Figure 5 An embodiment of the present invention provides Figure 1 Flow chart of step S104; Figure 6 A schematic diagram of a sheep breeding growth monitoring data transmission system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a sheep breeding growth monitoring data transmission method and system proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0023] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0024] During the growth process of sheep farming, monitoring will generate a lot of data. Currently, these monitoring data will be compressed using a revolving door compression algorithm and then the compressed data will be transmitted.

[0025] However, when using the revolving door compression algorithm to compress sheep breeding growth data, the size of the window is set manually. Although a larger window can improve the compression rate, a larger window requires a longer matching search time, resulting in a decrease in the algorithm running speed. The matching efficiency and compression rate of a smaller window are lower. Therefore, the revolving door compression algorithm in the related art cannot reliably compress the monitoring data.

[0026] The specific scheme of a sheep breeding growth monitoring data transmission method and system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0027] See also Figure 1 , which shows a flow chart of a sheep breeding growth monitoring data transmission method provided by an embodiment of the present invention, the method comprises the following steps S101-S107: S101. Collecting growth data monitored during the sheep breeding and growth process, the growth data including: body shape data and feed intake data.

[0028] This step is the data preparation stage. Since the condition of sheep is analyzed through data from the sheep breeding and growth process, it is necessary to collect various types of data from sheep, and transmit the various types of data monitored from sheep to the background through compression, and complete the analysis through the data transmitted to the background.

[0029] In the present application, the body shape data and feed intake data of the sheep can be continuously collected every day. These data can be collected manually or obtained through corresponding sensors. The present disclosure does not limit the method of obtaining data.

[0030] Among them, body shape data include: weight, body length and body height; feed intake data include: feed intake, feed type, feed formula, and the content of sea buckthorn feed additives.

[0031] When a sheep is just born, its weight, length and height are more important during its growth process. And the weight, length and height of the sheep after birth show an upward trend in the first year. Therefore, a larger window can be used to reflect its trend characteristics and improve its compression rate. However, when the data is abnormal, the data will not be able to reflect the trend characteristics. Therefore, using a larger window will not only fail to improve the compression rate, but also lose important details of the data. However, at this time, using a smaller window can retain important details. Therefore, the trend fluctuation difference factor of a section of data can be determined first by the abnormal fluctuation of the data. Because the feed intake, additive content, and number of feed types are positively correlated with weight, length and height during the growth of sheep, the positive correlation between the data can be used to determine which data is abnormal. For abnormal data, a lower window may be used for compression to ensure the quality of a data and facilitate reference analysis after subsequent decompression. Therefore, the trend fluctuation difference factor of each section of data is determined by the correlation between the sheep body shape data and the feed intake data. During the growth process of sheep, the initial stage of the growth curve of its body shape usually shows a steeper upward trend. As the individual approaches maturity, the growth rate gradually slows down and eventually stabilizes to form an S-shaped curve. Therefore, the trend fluctuation difference factor of each segment of data is corrected by corresponding to whether the sheep's body shape data belongs to the S-shaped curve (quantified by the change in the slope) to obtain the corrected trend fluctuation abnormal factor of each segment of data. The initial window size is adjusted according to the corrected trend fluctuation abnormal factor and repetition degree of each segment of data to obtain the adjusted compressed window of each segment.

[0032] S102. Obtain a trend fluctuation difference factor for each type of body shape data every day according to the fluctuation range of the body shape data.

[0033] When obtaining the trend fluctuation difference factor of each body shape data every day according to the fluctuation range of body shape data, such as Figure 2 As shown, the following steps S1021-S1022 may be performed for each body shape data: S1021. Obtain the slope of the preceding adjacent data and the slope of the following adjacent data of the current body shape data.

[0034] S1022. Obtain the difference between the slope of the preceding adjacent data and the slope of the succeeding adjacent data as a trend fluctuation difference factor of the current body shape data.

[0035] When a sheep is just born, its weight, length and height are more important during its growth process, and its weight, length and height show an upward trend in the first year after birth. Therefore, using a larger compression window can reflect its trend characteristics and improve its compression rate. However, when the body shape data is abnormal, its body shape data not only has no trend characteristics, so using a larger compression window not only cannot improve the compression rate, but also loses important detailed information of the body shape data. However, using a smaller compression window can retain important detailed information. Therefore, the trend fluctuation difference factor of each body shape data can be determined first through the abnormal fluctuation trend around the body shape data.

[0036] Specific operation: The trend fluctuation abnormal factor of each body shape data is obtained by the slope difference between each body shape data and the connected body shape data, which is expressed as: ; In the formula, Indicates The difference between the slope of the individual shape data and the adjacent shape data before and after. The greater the difference in the slope, the greater the abnormal fluctuation of the trend of the shape data. Indicates Abnormal factor of trend fluctuation of individual body shape data. (Analyzed based on adjacent body shape data of each day). The greater the difference in slope between adjacent body shape data, the greater the possibility of abnormal fluctuation of the body shape data.

[0037] S103. Correct the trend fluctuation difference factor based on the correlation between the body shape data and the feed intake data to obtain a corrected trend fluctuation abnormal factor for each body shape data every day.

[0038] Because the feed intake, additive content, and number of feed types are positively correlated with the body weight, body length, and body height of sheep during their growth; therefore, the positive correlation between the data is used to determine which body shape data is abnormal. Abnormal body shape data may need to be compressed with a lower window to ensure the quality of the body shape data and facilitate reference analysis after subsequent decompression. Therefore, the initial abnormal factor of each piece of data is determined through the correlation between the sheep body shape data and the feed intake data.

[0039] Since there is a positive correlation between the overall trend of sheep's body shape and feed intake data, when the sheep are abnormal, that is, within a short local range, there is no positive correlation between the growth body shape and feed intake data of the sheep, so the correlation between the sheep's body shape data and feed intake data in the local area is used to analyze whether there is any abnormality in the sheep's body shape data.

[0040] Specifically, when the trend fluctuation difference factor is corrected through the correlation between the body shape data and the feed intake data to obtain the corrected trend fluctuation abnormal factor for each body shape data every day, such as Figure 3 As shown, the following steps S1031-S1035 may be performed on the body shape data of each body shape: S1031. Acquire current body shape data corresponding to the current body shape in a preset time period from the body shape data.

[0041] The analysis can be performed based on local data of each day, and the body shape data corresponding to the current body shape in a preset time period of each day can be selected, for example: the current body shape data corresponding to the current body shape between 11:00-16:00 every day can be selected.

[0042] S1032. Obtain target feed intake data for a preset time period from the feed intake data.

[0043] Since the correlation between body shape data and feed intake data is to be obtained later, it is also necessary to select the feed intake in the same time period as the body shape data acquisition time period. Continuing with the above example: obtain the feed intake data between 11:00-16:00 every day as the target feed intake data.

[0044] S1033. Obtain the correlation coefficient between the current body shape data and the target feed intake data.

[0045] In one embodiment, Figure 4 As shown, obtaining the correlation coefficient between the current body shape data and the target feed intake data includes the following sub-steps S10331-S10333: S10331. Acquire a body shape data sequence corresponding to the current body shape data according to a preset order.

[0046] Before obtaining the correlation coefficient, the data needs to be divided into sequences. In the present disclosure, each current body shape data can be used as the center of a local window, and 25 can be used as the local window size, including the center data, that is, one body shape data sequence includes 25 current body shape data.

[0047] S10332. Obtain a feed intake data sequence corresponding to each feed in the target feed intake data according to a preset order.

[0048] Correspondingly, the feed intake data corresponding to each feed is also formed into a feed intake data sequence corresponding to each feed in the same manner as the current body shape data, that is, the feed intake data sequence corresponding to each feed includes 25 target feed intake data.

[0049] S10333. Obtain a correlation coefficient between the current body shape data and the target feed intake data according to the body shape data sequence corresponding to the current body shape data and the feed intake data sequence corresponding to all types of feed.

[0050] Among them, the correlation coefficient includes: Pearson correlation coefficient.

[0051] S1034. Obtain an abnormal factor corresponding to the current body shape data according to a correlation coefficient between the current body shape data and the target feed intake data.

[0052] According to the correlation coefficient between each body shape data series and all feed intake data series, the abnormal factor of each body shape data per day is obtained, which is specifically expressed by the formula: ; In the formula, Indicates the daily The body shape data sequence and the corresponding The Pearson correlation coefficient between the feed intake data sequences is calculated. The larger the correlation coefficient is and the closer it is to 1, the more normal it is. Otherwise, the more abnormal it is. The Pearson correlation coefficient is a well-known technology. Indicates the daily Anomaly factor of body shape data.

[0053] S1035. Correct the trend fluctuation difference factor corresponding to the current body shape data according to the abnormal factor corresponding to the current body shape data, and obtain the corrected trend fluctuation abnormal factor corresponding to the current body shape data.

[0054] Since there are errors when analyzing the overall trend of data through adjacent data, a smaller window is used to compress the data in order to retain detailed information. However, the actual body shape data may be within a normal fluctuation range, so a larger window is needed to compress the data to improve the data compression rate.

[0055] Therefore, it is also necessary to correct the trend fluctuation difference factor of each body shape data every day through the abnormal factor of each body shape data every day, so as to obtain the corrected trend fluctuation abnormal factor of each body shape data every day; the formula is: ; In the formula, Indicates Tiandi The abnormal fluctuation factor of the trend of body shape data correction, It represents an exponential function with a natural constant as the base. The greater the positive correlation between the body shape data and the feed intake data, the less likely the body shape data will fluctuate abnormally. Therefore, the value after inverse proportional mapping of the positive correlation coefficient is corrected.

[0056] S104, obtaining trend characteristics of each type of body shape data every day through changes in the body shape data.

[0057] During the growth process of sheep, the trend of body shape and feed intake in different stages is different, that is, it gradually increases at the beginning, and after reaching a certain level, the trend of its body shape data increase will gradually slow down until it stabilizes after a certain level. Therefore, for data with a certain trend, using a larger compression window can not only improve the compression rate but also the overall compression efficiency; at the nodes where the body shape data changes at different periods, the trend characteristics of the data are not obvious, so a smaller compression window is needed for compression.

[0058] For data with obvious trend characteristics, that is, within a local range, the smaller the difference in slopes between all adjacent body shape data, the more obvious the trend of the body shape data within the local range.

[0059] When obtaining the trend characteristics of each body shape data every day through the changes in body shape data, such as Figure 5 As shown, the following steps S1041-S1045 may be performed on the body shape data of each body shape: S1041. Acquire all current body shape data corresponding to the current body shape in a preset time period from the body shape data.

[0060] S1042: Obtain the slope of the adjacent current body shape data corresponding to each current body shape data, and the trend fluctuation difference factor of each current body shape data.

[0061] S1043. Obtain information entropy of slopes of all adjacent current body shape data.

[0062] S1044. Obtain the standard deviation corresponding to the trend fluctuation difference factor of all current body shape data.

[0063] S1045. Obtain trend characteristics of the current body shape data according to the information entropy and standard deviation of the slopes of all adjacent current body shape data.

[0064] Obtain the data in the local range of each body shape data every day, and obtain the trend characteristics of each body shape data every day through the chaos degree of the slopes of all adjacent body shape data in the local range, which can be expressed as follows: ; In the formula, The information entropy of the slopes of all adjacent body shape data within the local range of each body shape data every day. The larger the information entropy, the more chaotic the body shape data within the local range, that is, the trend of the body shape data within the local range is not obvious. On the contrary, the smaller the information entropy, the more obvious the trend of the body shape data within the local range. It represents the standard deviation of all adjacent slope differences within a local range. The smaller the standard deviation is, the smaller the fluctuation of the slope of the body shape data within the local range is, that is, the more obvious the trend characteristics are; conversely, the less obvious the trend characteristics of the body shape data within the local range are. Indicates that every day Trend characteristics of body shape data.

[0065] S105. Obtain an adjustment coefficient of a compression window corresponding to each body shape data every day according to the trend characteristics and the modified trend fluctuation abnormality factor.

[0066] As the trend characteristics are more obvious and the trend fluctuation anomaly factor of the body shape data in the local range is smaller, a larger compression window can better reflect the trend of the data and improve the compression rate of the body shape data. The trend characteristics of each body shape data every day and the corresponding corrected trend fluctuation anomaly factor of each body shape data every day can be linearly normalized to obtain the adjustment coefficient of the compression window corresponding to each body shape data every day.

[0067] Specifically, according to the modified trend fluctuation abnormal factor of each body shape data every day and the trend characteristics of each body shape data every day, the adjustment coefficient of the compression window of each body shape data every day is obtained, which is expressed by the formula: ; In the formula, Indicates Tiandi The adjustment coefficient of the compression window of the body shape data, represents the linear normalization function.

[0068] S106: Adjust the compression window corresponding to each type of body shape data every day according to the adjustment coefficient.

[0069] Through the Tiandi The adjustment coefficient of the compression window of the body shape data is proportional to the parameters of the initial compression window Adjustment is made, and the formula is expressed as: ; In the formula, Indicates the adjusted Tiandi The corresponding parameters of the compression window of the body shape data are as follows, wherein when the adjustment coefficient of the compression window is greater than 0.5, the compression window is increased, and when it is less than 0.5, the compression window is decreased. There is no specific limitation and it can be adjusted according to the actual scene requirements. In the process of using the revolving door for compression, the corresponding parameters of the compression window of each body shape data after adjustment are used at the starting point to adjust.

[0070] The corresponding parameters of the compression window include: the size of the compression window.

[0071] S107, compressing and transmitting each type of body shape data every day through a revolving door compression algorithm according to the adjusted compression window.

[0072] The compression window is adjusted by adjusting the corresponding parameters of the compression window of each body shape data every day through a revolving door compression algorithm, and the body shape data of the sheep during the growth process of breeding is compressed according to the adjusted compression window, and the compressed data is transmitted. The revolving door compression algorithm is a well-known technology.

[0073] In the present application, the trend fluctuation difference factor of each body shape data is obtained every day according to the fluctuation range of the body shape data, and the trend fluctuation difference factor is corrected through the correlation between the body shape data and the feed intake data to obtain the corrected trend fluctuation abnormal factor, thereby improving the accuracy of the abnormal data fluctuation analysis; the adjustment coefficient of the compression window of each body shape data every day is obtained through the trend characteristics of each body shape data every day during the sheep breeding and growth process and the corrected trend fluctuation abnormal factor of the body shape data every day, thereby improving the accuracy of the data trend analysis; and the compression window is adjusted through the adjustment coefficient, and each body shape data is compressed and transmitted through the revolving door compression algorithm according to the adjusted compression window. Since the size of the compression window of the revolving door compression algorithm can be adjusted based on the abnormality of the body shape data, a smaller window is used to compress the abnormal body shape data, and a larger window is used to compress the normal data, thereby improving the compression rate and compression efficiency.

[0074] The present disclosure also provides a sheep breeding growth monitoring data transmission system, such as Figure 6 As shown, the system comprises: The collection module 11 is used to collect the growth data monitored during the sheep breeding and growth process, and the growth data includes: body shape data and feed intake data; A first acquisition module 12 is used to acquire a trend fluctuation difference factor of each body shape data every day according to the fluctuation range of the body shape data; The second acquisition module 13 is used to correct the trend fluctuation difference factor according to the correlation between the body shape data and the feed intake data, so as to obtain the corrected trend fluctuation abnormal factor of each body shape data of each day; A third acquisition module 14 is used to acquire the trend characteristics of each type of body shape data every day according to the change of the body shape data; A fourth acquisition module 15 is used to obtain the adjustment coefficient of the compression window corresponding to each body shape data of each day according to the trend characteristics and the modified trend fluctuation abnormality factor; An adjustment module 16, configured to adjust the compression window corresponding to each body shape data of each day according to the adjustment coefficient of the compression window; The processing module 17 is used to compress and transmit each body shape data of each day through a revolving door compression algorithm according to the adjusted compression window.

[0075] In one embodiment, the first acquisition module 12 is specifically configured to perform the following steps on each of the body shape data: Obtain the slope of the preceding adjacent data and the slope of the following adjacent data of the current body shape data; The difference between the slope of the preceding adjacent data and the slope of the succeeding adjacent data is obtained as a trend fluctuation difference factor of the current body shape data.

[0076] In one embodiment, the second acquisition module 13 is specifically configured to perform the following steps on the body shape data of each body shape: Acquire current body shape data corresponding to the current body shape in a preset time period from the body shape data; acquiring target feed intake data for the preset time period from the feed intake data; Obtaining a correlation coefficient between the current body shape data and the target feed intake data; Acquire an abnormal factor corresponding to the current body shape data according to a correlation coefficient between the current body shape data and the target feed intake data; The trend fluctuation difference factor corresponding to the current body shape data is corrected according to the abnormal factor corresponding to the current body shape data to obtain the corrected trend fluctuation abnormal factor corresponding to the current body shape data.

[0077] In one embodiment, in terms of obtaining the correlation coefficient between the current body shape data and the target feed intake data, the second obtaining module 13 is specifically used to: Acquire a body shape data sequence corresponding to the current body shape data according to a preset order; Acquire a feed intake data sequence corresponding to each feed in the target feed intake data according to the preset order; The correlation coefficient between the current body shape data and the target feed intake data is obtained according to the body shape data sequence corresponding to the current body shape data and the feed intake data sequence corresponding to all types of the feed.

[0078] In one embodiment, the correlation coefficient includes: a Pearson correlation coefficient.

[0079] In one embodiment, the third acquisition module is specifically used to perform the following steps on the body shape data of each body shape: Acquire all current body shape data corresponding to the current body shape in the preset time period from the body shape data; Acquire the slope of the adjacent current body shape data corresponding to each current body shape data, and the trend fluctuation difference factor of each current body shape data; Obtaining information entropy of slopes of all adjacent current body shape data; Obtaining the standard deviation corresponding to the trend fluctuation difference factor of all the current body shape data; The trend feature of the current body shape data is obtained according to the information entropy of the slopes of all adjacent current body shape data and the standard deviation.

[0080] In one embodiment, the fourth acquisition module 15 is specifically used for: The trend characteristics of each body shape data of each day and the corresponding modified trend fluctuation abnormal factors of each body shape data of each day are linearly normalized to obtain the adjustment coefficient of the compression window corresponding to each body shape data of each day.

[0081] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0082] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A sheep breeding growth monitoring data transmission method, characterized in that: The method comprises: Collecting the growth data monitored during the sheep breeding and growth process, the growth data includes: body shape data and feed intake data; Obtaining a trend fluctuation difference factor for each type of body shape data every day according to the fluctuation range of the body shape data; The trend fluctuation difference factor is corrected by the correlation between the body shape data and the feed intake data to obtain the corrected trend fluctuation abnormality factor for each body shape data of each day; Obtaining trend characteristics of each type of body shape data every day through changes in the body shape data; According to the trend characteristics and the modified trend fluctuation abnormality factor, an adjustment coefficient of the compression window corresponding to each body shape data of each day is obtained; Adjusting the compression window corresponding to each type of body shape data every day according to the adjustment coefficient; According to the adjusted compression window, each body shape data of each day is compressed and transmitted by a revolving door compression algorithm.

2. A sheep breeding growth monitoring data transmission method according to claim 1, characterized in that: The step of obtaining the trend fluctuation difference factor of each body shape data every day according to the fluctuation range of the body shape data includes: The following steps are performed for each of the body shape data: Obtain the slope of the preceding adjacent data and the slope of the following adjacent data of the current body shape data; The difference between the slope of the preceding adjacent data and the slope of the succeeding adjacent data is obtained as a trend fluctuation difference factor of the current body shape data.

3. A sheep breeding growth monitoring data transmission method according to claim 2, characterized in that: The method of correcting the trend fluctuation difference factor by the correlation between the body shape data and the feed intake data to obtain the corrected trend fluctuation abnormality factor for each body shape data of each day includes: Perform the following steps for the body shape data of each body shape: Acquire current body shape data corresponding to the current body shape in a preset time period from the body shape data; acquiring target feed intake data for the preset time period from the feed intake data; Obtaining a correlation coefficient between the current body shape data and the target feed intake data; Acquire an abnormal factor corresponding to the current body shape data according to a correlation coefficient between the current body shape data and the target feed intake data; The trend fluctuation difference factor corresponding to the current body shape data is corrected according to the abnormal factor corresponding to the current body shape data to obtain the corrected trend fluctuation abnormal factor corresponding to the current body shape data.

4. A sheep breeding growth monitoring data transmission method according to claim 3, characterized in that: The obtaining of the correlation coefficient between the current body shape data and the target feed intake data comprises: Acquire a body shape data sequence corresponding to the current body shape data according to a preset order; Acquire a feed intake data sequence corresponding to each feed in the target feed intake data according to the preset order; The correlation coefficient between the current body shape data and the target feed intake data is obtained according to the body shape data sequence corresponding to the current body shape data and the feed intake data sequence corresponding to all types of the feed.

5. A sheep breeding growth monitoring data transmission method according to claim 4, characterized in that: The correlation coefficient includes: Pearson correlation coefficient.

6. A sheep breeding growth monitoring data transmission method according to claim 5, characterized in that: The step of obtaining the trend characteristics of each type of body shape data every day through the change of the body shape data includes: Perform the following steps for the body shape data of each body shape: Acquire all current body shape data corresponding to the current body shape in the preset time period from the body shape data; Acquire the slope of the adjacent current body shape data corresponding to each current body shape data, and the trend fluctuation difference factor of each current body shape data; Obtaining information entropy of slopes of all adjacent current body shape data; Obtaining the standard deviation corresponding to the trend fluctuation difference factor of all the current body shape data; The trend feature of the current body shape data is obtained according to the information entropy of the slopes of all adjacent current body shape data and the standard deviation.

7. The sheep breeding growth monitoring data transmission method according to claim 1 is characterized in that: The step of obtaining the adjustment coefficient of the compression window corresponding to each body shape data of each day according to the trend feature and the modified trend fluctuation abnormality factor includes: The trend feature and the corresponding modified trend fluctuation abnormal factor are linearly normalized to obtain the adjustment coefficient of the compression window corresponding to each body shape data of each day.

8. A sheep breeding growth monitoring data transmission system, characterized in that: The system comprises: A collection module is used to collect growth data monitored during the growth of sheep breeding, wherein the growth data includes: body shape data and feed intake data; A first acquisition module is used to acquire a trend fluctuation difference factor of each body shape data every day according to the fluctuation range of the body shape data; A second acquisition module is used to correct the trend fluctuation difference factor according to the correlation between the body shape data and the feed intake data, so as to obtain the corrected trend fluctuation abnormal factor of each body shape data of each day; A third acquisition module is used to acquire the trend characteristics of each type of body shape data every day according to the change of the body shape data; A fourth acquisition module, for obtaining an adjustment coefficient of the compression window corresponding to each body shape data of each day according to the trend feature and the modified trend fluctuation abnormality factor; An adjustment module, used for adjusting the compression window corresponding to each body shape data of each day according to the adjustment coefficient of the compression window; The processing module is used to compress and transmit each body shape data of each day through a revolving door compression algorithm according to the adjusted compression window.

9. A sheep breeding growth monitoring data transmission system according to claim 8, characterized in that: The first acquisition module is specifically configured to perform the following steps on each of the body shape data: Obtain the slope of the preceding adjacent data and the slope of the following adjacent data of the current body shape data; The difference between the slope of the preceding adjacent data and the slope of the succeeding adjacent data is obtained as a trend fluctuation difference factor of the current body shape data.

10. A sheep breeding growth monitoring data transmission system according to claim 9, characterized in that: The second acquisition module is specifically configured to perform the following steps on the body shape data of each body shape: Acquire current body shape data corresponding to the current body shape in a preset time period from the body shape data; acquiring target feed intake data for the preset time period from the feed intake data; Obtaining a correlation coefficient between the current body shape data and the target feed intake data; Acquire an abnormal factor corresponding to the current body shape data according to a correlation coefficient between the current body shape data and the target feed intake data; The trend fluctuation difference factor corresponding to the current body shape data is corrected according to the abnormal factor corresponding to the current body shape data to obtain the corrected trend fluctuation abnormal factor corresponding to the current body shape data.

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