Intelligent livestock and poultry feeding method, system, terminal and storage medium

Abstract

The present application discloses a method, system, terminal and storage medium for intelligent feeding of livestock and poultry, and relates to the field of intelligent animal feeding technology. The method includes: collecting weight characteristic parameters of pigs in a piggery; obtaining the estimated weight of the pigs in the piggery based on the breed, weight characteristic parameters and weight estimation model of the pigs; obtaining the nutritional needs of the pigs based on the estimated weight, health status and nutritional needs calculation model of the pigs; obtaining the mixing ratio of different types of feed based on the nutritional needs of the pigs and the feed intake information of the pigs; obtaining the demand for different types of feed based on the total feed intake of the pigs and the mixing ratio of different types of feed; controlling the operation of the motor based on the mixing ratio and demand of the different types of feed, mixing different types of feed, and transporting the mixed feed to the feed trough of the piggery. The present application solves the problem of low matching between feed nutrition and pig needs.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent animal feeding, and in particular to an intelligent livestock and poultry feeding method, system, terminal and storage medium. Background Art

[0002] During the growth of fattening pigs, the optimal concentration of nutrients in the diet decreases as the pigs age, as their appetite increases faster than their nutritional needs. Currently, pigs on the market typically have three different feeding stages, with feeds of different nutrient concentrations being fed at different stages. Specifically, pigs weighing 30kg to 60kg require feeds with higher nutrient concentrations; pigs weighing 60kg to 90kg require feeds with moderate nutrient concentrations; and pigs weighing 90kg to 120kg and at the market stage require feeds with lower nutrient concentrations.

[0003] Since the daily nutritional needs of pigs are constantly changing, feed mills and pig farms cannot adjust the feed formula every day to feed pigs; if the pigs in the pig house are only roughly fed according to the three stages, and the feeding days in a single stage are very long, the nutrition of the feed will not be able to accurately match the pigs' daily needs, and the nutrition of the feed may be excessive or insufficient compared to the pigs' daily needs. Summary of the Invention

[0004] In order to solve the problem of low matching between feed nutrition and pig needs in existing pig feeding methods, the present application provides an intelligent livestock and poultry feeding method, system, terminal and storage medium.

[0005] In a first aspect, the present application provides a method for intelligent feeding of livestock and poultry, which adopts the following technical solution: the method comprises:

[0006] Based on the acquisition trigger signal, the weight characteristic parameters of the pigs in the pig house are collected; the weight characteristic parameters include one or more of body length, body width, body height, chest circumference, and hip circumference;

[0007] Obtaining an estimated weight of the pigs in the pig house based on the breed and weight characteristic parameters of the pigs in the pig house and a pre-built weight estimation model;

[0008] Obtaining the nutritional requirements of the pigs in the piggery based on the estimated weight and health status of the pigs in the piggery and a pre-established pig nutritional requirement calculation model;

[0009] Based on the nutritional needs of the pigs in the pig house and the feed intake information of the pigs, the mixing ratio of different types of feed is obtained; and based on the total feed intake of the pigs in the pig house and the mixing ratio of the different types of feed, the demand for different types of feed is obtained; based on the mixing ratio and demand for different types of feed, the motor is controlled to mix the different types of feed, and the mixed feed is transported to the feed trough in the pig house; the motor is used to control the output amount and output speed of the feed.

[0010] By adopting the above technical solution, the optimal feed formula and feed requirement can be calculated based on the actual weight, health status and feed intake of the pigs on that day, so that the nutritional concentration of the daily diet just meets the needs of the pigs on that day, and the pigs are provided with feed with the optimal nutritional concentration, so as to achieve the purpose of intelligent and precise feeding, improve the matching degree between feed nutrition and pig needs, and at the same time, reduce feed costs, avoid feed waste, reduce carbon emissions, improve the growth performance of pigs, and realize the intelligent, automated and efficient feeding of livestock and poultry.

[0011] In a specific embodiment, before collecting the weight characteristic parameters of the pigs in the pig house, the method includes:

[0012] Dividing the piggery into a plurality of collection areas;

[0013] Based on the distribution of pigs in each collection area and the pre-built density assessment model, the density assessment results of each collection area are obtained;

[0014] Based on the density assessment results of each collection area, a comprehensive density assessment result of the pig house is obtained, and based on the comprehensive density assessment result, it is judged whether the current density of the pig distribution in the pig house meets the density requirements; if it does, the current time is determined as the collection time node, and a collection trigger signal is generated.

[0015] By adopting the above technical solution, by dividing the pig house into areas and using different density judgment standards for pigs in different areas, the density assessment results of the pig house are more accurate and more in line with the actual situation of the pig house.

[0016] In a specific implementation plan, based on the distribution of pigs in each collection area and a pre-built density assessment model, a density assessment result of each collection area is obtained, specifically including:

[0017] Obtaining the number of pigs in the neighborhood of the pig in the collection area, and obtaining a density assessment result of the pigs based on the number of pigs in the neighborhood of the pig;

[0018] Based on the density evaluation results of each pig in the collection area, a density evaluation result of the collection area is obtained.

[0019] By adopting the above technical solution, for the pigs in the collection area, the density assessment result of the pigs is obtained according to the number of pigs in the pig's neighborhood range, and then the density assessment result corresponding to the collection area is obtained according to the density assessment results of all pigs in the collection area, so that the density assessment result of the collection area is more accurate.

[0020] In a specific embodiment, the determination of the pig neighborhood range specifically includes:

[0021] If the collection area to which the pig belongs is the first target collection area, a first circle is drawn with the pig's position as the center and a first preset length a as the radius, and the area covered by the first circle is used as the neighborhood range of the pig;

[0022] If the collection area to which the pig belongs is the second target collection area, a second circle is made with the position of the pig as the center and the second preset length b as the radius, and the area covered by the second circle is used as the neighborhood range of the pig.

[0023] By adopting the above technical solution, the neighborhood range of the pigs is set according to the different collection areas to which the pigs belong, so that the result of judging the density of the pig houses is more accurate, and the timing of generating the collection signal is more accurate, thereby improving the accuracy of collecting the characteristic parameters of the pig weight.

[0024] In a specific embodiment, after transporting the mixed feed to the trough of the pig house, the method further includes: recording the growth status of the pigs in the pig house during a first preset period of time, and generating a pig actual growth status model based on the growth status of the pigs in the pig house during the first preset period of time;

[0025] Based on the actual growth condition model of the pigs and the pre-built target growth condition model of the pigs, it is determined whether the growth progress of the pigs meets the expected requirements; if not, the mixing ratio of the different types of feed is adjusted.

[0026] By adopting the above technical solution, after feeding according to the feeding method in this embodiment, the growth status of the pigs in the pig house is recorded and compared with the target growth status to determine whether the mixing ratio of the feed is reasonable, and the mixing ratio of the feed is adjusted accordingly to further improve the matching degree between the feed nutrition and the pigs' needs, and provide pigs with a more accurate feed ratio.

[0027] In a specific embodiment, based on the breeds, weight characteristic parameters and pre-built weight estimation model of the pigs in the pig house, the estimated weight of the pigs in the pig house is obtained, which specifically includes:

[0028] Based on the weight characteristic parameters of the pig, determining whether the weight characteristic parameters of the pig are complete;

[0029] If the weight characteristic parameters are complete, the weight characteristic parameters of the pig are added to the weight characteristic data set; if the weight characteristic parameters are incomplete, the pig is marked as a target pig, and the weight characteristic parameters of the target pig are continuously tracked and collected within a second preset time period until the weight characteristic parameters of the target pig are completely collected, and the weight characteristic parameters of the target pig are added to the weight characteristic data set; based on the breed of the pigs in the pig house, the weight characteristic data set and a pre-built weight estimation model, the estimated weight corresponding to each group of weight characteristic parameters in the weight characteristic data set is obtained;

[0030] Based on the estimated weights corresponding to each set of weight feature parameters in the weight feature data set, a mean of the estimated weight set is obtained, and the mean of the estimated weight set is used as the estimated weight of the pigs in the piggery.

[0031] By adopting the above technical solution, we can initially obtain the complete weight characteristic parameters of most pigs, and then track the pigs with incomplete collected data, so as to increase the amount of complete data in the weight characteristic data set and improve the estimation accuracy of the pig weight in the pig house.

[0032] In a specific embodiment, obtaining the mean of the estimated weight set based on the estimated weight corresponding to each set of weight feature parameters in the weight feature data set specifically includes:

[0033] Obtaining an estimated weight set based on the estimated weight corresponding to each group of weight feature parameters in the weight feature data set;

[0034] sorting the data in the estimated weight set, and removing data within a preset range in the estimated weight set based on the sorted estimated weight set;

[0035] Based on the remaining data in the estimated weight set, an average value method is used to obtain the mean of the estimated weight set.

[0036] By adopting the above technical solution, the data in the weight feature data set is sorted and eliminated, and finally a more accurate value is obtained by using the average method. This value is used as the estimated weight of the pigs in the pig house. Through a series of data processing, the accuracy of the estimated weight of the pigs is maximized.

[0037] In a second aspect, the present application provides an intelligent livestock and poultry feeding system, which applies the intelligent livestock and poultry feeding method described in the first aspect or any one of the embodiments of the first aspect, and includes a first module, a second module, a third module, a fourth module, and a fifth module;

[0038] The first module is used to collect weight characteristic parameters of pigs in the piggery based on the collection trigger signal; the weight characteristic parameters include one or more of body length, body width, body height, chest circumference, and hip circumference;

[0039] The second module is used to obtain the estimated weight of the pigs in the pig house based on the breed, weight characteristic parameters of the pigs in the pig house and a pre-built weight estimation model;

[0040] A third module is configured to obtain the nutritional requirements of the pigs in the pig house based on the estimated weight and health status of the pigs in the pig house and a pre-built pig nutritional requirement calculation model;

[0041] The fourth module is used to obtain the mixing ratio of different types of feed based on the nutritional needs of the pigs in the pig house and the feed intake information of the pigs; and to obtain the required amount of different types of feed based on the total feed intake of the pigs in the pig house and the mixing ratio of the different types of feed;

[0042] The fifth module is used to control the operation of the motor to mix different types of feed based on the mixing ratio and required amount of the different types of feed, and transport the mixed feed to the feed trough in the pig house; the motor is used to control the output amount and output speed of the feed.

[0043] In a third aspect, the present application provides a terminal comprising: a processor, a memory, and a communication bus; the communication bus is used to realize connection and communication between the processor and the memory, and the processor is used to execute one or more programs stored in the memory to realize the intelligent livestock and poultry feeding method as described in the first aspect or any feasible embodiment of the first aspect.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed, the intelligent livestock and poultry feeding method as described in the first aspect or any one of the embodiments of the first aspect is executed.

[0045] In summary, the technical solution of this application includes at least the following beneficial technical effects:

[0046] 1. The technical solution of the present application can calculate the optimal feed formula and feed requirement based on the actual weight, health status and feed intake of the pigs on that day, so that the nutritional concentration of the daily diet just meets the needs of the pigs on that day, and provide pigs with feed with the optimal nutritional concentration, thereby achieving the purpose of intelligent and precise feeding, improving the matching degree between feed nutrition and pig needs, and at the same time, it can also reduce feed costs, avoid feed waste, reduce carbon emissions, improve pig growth performance, and realize intelligent, automated and efficient livestock and poultry feeding;

[0047] 2. After feeding according to the feeding method of this application, the growth status of the pigs in the pig house is recorded and compared with the target growth status to determine whether the feed mixing ratio is reasonable and adjust the feed mixing ratio accordingly to further improve the matching degree between the feed nutrition and the pigs' needs and provide a more accurate feed ratio for the pigs;

[0048] 3. By dividing the pig house into areas and using different density evaluation criteria for pigs in different areas, the density assessment results of the pig house are made more accurate, which makes the timing of collecting the weight characteristic parameters of the pigs in the pig house more accurate, and maximizes the accuracy of pig weight estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a first flow chart of the intelligent livestock and poultry feeding method according to an embodiment of the present application;

[0050] Figure 2 This is a second flow chart of the intelligent livestock and poultry feeding method in an embodiment of the present application;

[0051] Figure 3 This is a third flow chart of the intelligent livestock and poultry feeding method in an embodiment of the present application;

[0052] Figure 4 It is a schematic diagram of the regional division of the pig house and the pig neighborhood range division in the embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0054] The present application provides an intelligent livestock and poultry feeding method. Figure 1 As shown, the method includes steps S4-S8.

[0055] S4, based on the acquisition trigger signal, collecting weight characteristic parameters of the pigs in the pig house; the weight characteristic parameters include one or more of body length, body width, body height, chest circumference, and hip circumference.

[0056] S5, based on the breeds and weight characteristic parameters of the pigs in the pig house and a pre-built weight estimation model, obtaining an estimated weight of the pigs in the pig house. Specifically, the estimated weight of the pigs in the pig house is an average estimated weight of the pigs in the pig house.

[0057] In this embodiment, the breed of the pigs may be pre-set.

[0058] In this embodiment, step S5 can use an AI weight estimation camera to collect the weight characteristic parameters of the pigs in real time through the AI ​​weight estimation camera, and through the set pig breed and the pre-built weight estimation model, combined with the collected pig weight characteristic parameters, the estimated weight of the pigs in the pig house is obtained. The estimated weight of the pigs can also be uploaded to the cloud server in real time for real-time viewing on the mobile phone. In order to make the estimated weight of the pigs more accurate, the weight estimation model can also be obtained by model training, specifically based on the pig breed, weight characteristic parameters and corresponding pig weight sample data, and obtain the weight estimation model through model training. Of course, the weight estimation model in the prior art can also be used, and this application is not limited by comparison.

[0059] In a possible implementation, step S5 specifically includes steps S51-S54.

[0060] S51, based on the weight characteristic parameters of the pig, determining whether the weight characteristic parameters of the pig are complete.

[0061] Specifically, the complete weight characteristic parameters of the pigs specifically refer to: all data in the weight characteristic parameters of the pigs are complete; the incomplete weight characteristic parameters of the pigs specifically refer to: one or more data in the weight characteristic parameters of the pigs are missing. The incomplete weight characteristic parameters of the pigs indicate that when collecting the weight characteristic parameters of the pigs, one or more data in the weight characteristic parameters of the pigs were not collected, or a certain data collected was removed due to obvious errors, such as chest circumference data not being collected, body width data not being collected, chest circumference and hip circumference data not being collected, etc.

[0062] S52: If the weight characteristic parameters of the pig are complete, the weight characteristic parameters of the pig are added to the weight characteristic dataset; if the weight characteristic parameters of the pig are incomplete, the pig is marked as a target pig, and the weight characteristic parameters of the target pig are continuously tracked and collected within a second preset time period until the weight characteristic parameters of the target pig are completely collected, and the weight characteristic parameters of the target pig are added to the weight characteristic dataset. For example, referring to Table 1, multiple sets of complete weight characteristic parameters of pigs constitute the weight characteristic dataset.

[0063] Table 1 Weight feature dataset

[0064]

[0065] Specifically, the second preset period can be 5 minutes, 10 minutes or 30 minutes, etc. Preferably, the second preset period should not be too long. If it is too long, errors may occur in tracking and collecting weight characteristic parameters of the pig due to random movement and aggregation of the pigs.

[0066] S53, based on the breeds of the pigs in the pig house, the weight feature data set and a pre-built weight estimation model, obtaining an estimated weight corresponding to each group of weight feature parameters in the weight feature data set.

[0067] S54, obtaining a mean of the estimated weight set based on the estimated weight corresponding to each group of weight feature parameters in the weight feature data set, and using the mean of the estimated weight set as the estimated weight of the pigs in the pig house.

[0068] In a possible implementation, step S54 specifically includes steps S541 - S543 .

[0069] S541, based on the estimated weight corresponding to each set of weight characteristic parameters in the weight characteristic data set, obtain an estimated weight set. Referring to Table 2, there are multiple sets of weight characteristic parameters and the estimated weight corresponding to each set of weight characteristic parameters.

[0070] Table 2 Multiple groups of weight characteristic parameters and corresponding estimated weights

[0071]

[0072]

[0073] Therefore, the estimated weight set is [Weight-1, Weight-2, Weight-3...Weight-n].

[0074] S542, sorting the data in the estimated weight set, and based on the sorted estimated weight set, removing data within a preset range in the estimated weight set.

[0075] Specifically, the preset range may be the data in the first a% and the data in the last b% of the estimated weight set after sorting. For example, after sorting the data in the estimated weight set according to the rule of small to large, the sorted estimated weight set is as follows:

[0076] [Weight-2, Weight-5, Weight-9, Weight-1...Weight-19, Weight-10, Weight-2, Weight-3].

[0077] According to the above sorted estimated weight set, after removing the first 3% of the data and the last 3% of the data, the estimated weight set obtained is as follows:

[0078] [Weight-9, Weight-1...Weight-19, Weight-10].

[0079] S543: Based on the remaining data in the estimated weight set, obtain the mean of the estimated weight set using the average method.

[0080] Specifically, continuing to refer to the above example, based on the data in the estimated weight set [Weight-9, Weight-1...Weight-19, Weight-10], the average value method is used to calculate the mean of the data in the estimated weight set to obtain the estimated weight of the pigs in the pig house.

[0081] In the above steps S541-S543, the data in the weight feature data set are sorted and eliminated, and finally a more accurate value is obtained by using the average method as the estimated weight of the pigs in the pig house. Through a series of data processing, the accuracy of the estimated weight of the pigs is maximized.

[0082] In the above steps S51-S54, by preliminarily obtaining complete weight characteristic parameters of most pigs in the pig house, and then tracking the pigs with incomplete collected data, the amount of complete data in the weight characteristic data set is increased, thereby improving the estimation accuracy of the weight of the pigs in the pig house.

[0083] S6, based on the estimated weight and health status of the pigs in the piggery and a pre-established pig nutrition requirement calculation model, obtaining the nutritional requirements of the pigs in the piggery. Specifically, the health status of the pigs is pre-set.

[0084] In this embodiment, the pre-constructed pig nutritional requirement calculation model can be based on China's pig nutritional requirements, NRC nutritional standards and Anyou Group feed database, and obtained through model training. Specifically, the pig nutritional requirement calculation model is obtained through model training based on sample data of pig weight, health status and corresponding pig nutritional requirements.

[0085] S7. Based on the nutritional requirements of the pigs in the pig house and the feed intake information of the pigs, a mixing ratio of different types of feed is obtained; and based on the total feed intake of the pigs in the pig house and the mixing ratio of the different types of feed, the required amount of the different types of feed is obtained. The feed intake information of the pigs is pre-stored and includes information on the different types of feed and the nutrient concentration of each type of feed. Specifically, the total feed intake of the pigs in the pig house is pre-set.

[0086] In this embodiment, after obtaining the mixing ratio of different types of feed, the mixing ratio of different types of feed can also be uploaded to the cloud server in real time, so that it can be viewed in real time on the mobile phone.

[0087] For example, the nutritional requirements of the pigs in the piggery are specifically: the pigs' requirements for the nutrient concentration in the feed. Since pigs of different weights correspond to different growth stages, their requirements for the nutrient concentration in the feed are also different. The following example illustrates the above process:

[0088] Suppose the pre-stored feed information includes high-nutrient-density pig feed A and low-nutrient-density pig feed B. Feed A is high in protein and lysine, while feed B is low in protein and lysine. The higher the proportion of feed A in the pig's feed, the higher the nutrient concentration; the higher the proportion of feed B, the lower the nutrient concentration. Mixing feed A and feed B in different proportions can meet the nutritional needs of pigs throughout their entire growth cycle, from 30 kg to market.

[0089] Therefore, the ratio of feed A to feed B can be adjusted step by step from 100 parts:0 parts to 0 parts:100 parts, and the weight ratio is as follows:

[0090] Weight of feed A: weight of feed B = 100:0

[0091] Weight of feed A: weight of feed B = 99:1

[0092] Weight of feed A: weight of feed B = 97:3

[0093] Weight of feed A: weight of feed B = 96:4

[0094] Weight of feed A: weight of feed B = 95:5

[0095] …

[0096] Weight of feed A: weight of feed B = 2:98

[0097] Weight of feed A: weight of feed B = 1:99

[0098] Weight of feed A: weight of feed B = 0:100

[0099] S8. Based on the mixing ratio and required quantity of the different types of feed, control the operation of a motor to mix the different types of feed and transport the mixed feed to a feed trough in the piggery. The motor is used to control the output amount and output speed of the feed, with one motor corresponding to each type of feed. Specifically, the feed output speed is controlled by controlling the rotation speed of the motor, and the feed output amount is controlled by controlling the rotation time of the motor. The mixed feed can be transported to the feed trough in the piggery via a feed line.

[0100] In this embodiment, an equidistant spiral mixing device can be used to mix different types of feed to complete the batching operation, and the startup of the equidistant spiral mixing device can be controlled by the pig farm staff. Specifically, the equidistant spiral mixing device includes a motor, which is used to control the output per unit time of the corresponding type of feed. In combination with the above example, the motor includes a first motor and a second motor; the first motor and the second motor can adopt equidistant spiral motors. The first motor is used to control the output speed and output of high-nutrient concentration pig feed A, and the second motor is used to control the output speed and output of low-nutrient concentration pig feed B. In order to make the two feeds more evenly mixed, the rotation speed of the corresponding motor is controlled according to the mixing ratio of different types of feed; the rotation time of the corresponding motor is controlled according to the demand for different types of feed. When the feed output is completed, the motor is controlled to stop rotating, thereby realizing the control of the feed output and ensuring that the feed output meets the feed demand.

[0101] In one practicable manner, after the different types of feed are mixed and the batching operation is completed in step S8, the following steps are further included:

[0102] The mixed feed is randomly inspected to determine whether the mixing uniformity of the feed meets the mixing requirements of the complete feed; if so, the mixed feed is transported to the feed trough in the pig house; if not, the mixed feed is mixed again until the mixing uniformity of the feed meets the mixing requirements of the complete feed.

[0103] The above steps S4-S8 collect the weight characteristic parameters of the pigs every day, and obtain the estimated weight of the pigs based on the weight estimation model, and then combine the health status of the pigs and the pig nutritional requirement calculation model to judge the nutritional needs of the pigs on that day, so as to mix the feed in the corresponding proportion and transport it to the feed trough for the pigs to eat. The technical solution of the present application can calculate the optimal feed formula and feed requirement according to the actual weight, health status and feed intake of the pigs on that day, so that the nutritional concentration of the daily diet accurately matches the nutritional needs of the pigs on that day, and provide pigs with feed with the optimal nutritional concentration, so as to achieve the purpose of intelligent and precise feeding, improve the matching degree of feed nutrition with pig needs, and at the same time, reduce feed costs, avoid feed waste, improve feed nutrient utilization, save soybean meal and feed raw materials, and reduce carbon emissions; in addition, it is also possible to formulate a growth curve for pigs, improve the growth performance of pigs, and realize the intelligence, automation and efficiency of livestock and poultry feeding.

[0104] In one practicable manner, Figure 3 As shown, after step S8, steps S9-S10 are also included.

[0105] S9, recording the growth status of the pigs in the pig house during a first preset period of time, and generating a pig actual growth status model based on the growth status of the pigs in the pig house during the first preset period of time.

[0106] In this embodiment, the growth status of the pig can be obtained based on the weight characteristic parameters of the pig, and the growth status of the pig represents the growth progress of the pig.

[0107] Specifically, by recording the pig's body length, body width, body height, chest circumference, hip circumference and other data every day during the first preset time period, the growth status of the pig during the first preset time period is obtained; the first preset time period can be one week, two weeks, one month, etc., and technical personnel in this field can flexibly adjust it according to the pig's current growth stage and growth characteristics.

[0108] S10, based on the actual growth condition model of the pig and the pre-built target growth condition model of the pig, judging whether the growth progress of the pig meets the expected requirements; if not, adjusting the mixing ratio of the different types of feed.

[0109] In this embodiment, the pre-constructed pig target growth condition model represents the growth condition of the pig under ideal conditions.

[0110] In the above steps S9-S10, after feeding according to the feeding method in this embodiment, the growth status of the pigs in the pig house is recorded and compared with the target growth status to determine whether the mixing ratio of the feed is reasonable, and the mixing ratio of the feed is adjusted accordingly to further improve the matching degree between the feed nutrition and the pigs' needs, and provide pigs with a more accurate feed ratio.

[0111] In one possible implementation, Figure 2 As shown, before step S4, the method further includes steps S1-S3.

[0112] S1, divide the pig house into multiple collection areas. Exemplarily, the collection areas divided in the pig house include a first target collection area and a second target collection area.

[0113] S2, based on the distribution status of pigs in each collection area and the pre-built density assessment model, obtain the density assessment results of each collection area.

[0114] In a possible implementation, the process of obtaining the density evaluation results of each acquisition area in step S2 specifically includes steps S21-S22.

[0115] S21, obtaining the number of pigs in the neighborhood of the pig in the collection area, and obtaining a density assessment result of the pigs based on the number of pigs in the neighborhood of the pig.

[0116] In a possible implementation, in step S21, the determination of the pig neighborhood range may be performed in the following manner:

[0117] If the collection area to which the pig belongs is the first target collection area, a first circle is drawn with the pig's position as the center and a first preset length a as the radius, and the area covered by the first circle is used as the neighborhood range of the pig;

[0118] If the collection area to which the pig belongs is the second target collection area, a second circle is made with the position of the pig as the center and the second preset length b as the radius, and the area covered by the second circle is used as the neighborhood range of the pig.

[0119] Among them, the first target area represents the central area of ​​the pig house, and the second target area represents the surrounding area of ​​the pig house; the specific division method of the first target area and the second target area can be flexibly divided by technical personnel in this field according to factors such as the shape of the pig house and the area of ​​the pig house.

[0120] For example, referring to Figure 4 For a rectangular pig house, the first target collection area, the neighborhood range of the pigs in the first target collection area, the second target collection area, and the neighborhood range of the pigs in the second target collection area are divided as follows: Figure 4 shown.

[0121] Preferably, the first preset length a is greater than the second preset length b. Because collecting weight characteristic parameters of pigs in the surrounding area may be difficult due to blind spots or long distances from the weight characteristic parameter collection equipment, reducing the second preset length b can prevent the collection of pig weight characteristic parameters when the pigs are densely distributed within the second target collection area.

[0122] Therefore, according to the different collection areas to which the target pigs belong, the neighborhood range of the target pigs is set accordingly, so that the result of the pig house density judgment is more accurate, and the timing of generating the collection signal is more accurate, thereby improving the accuracy of the collection of pig weight characteristic parameters.

[0123] In a possible implementation, in step S21, the density assessment result of the pigs is obtained based on the number of pigs in the neighborhood corresponding to the pig, which can be obtained in the following manner:

[0124] Based on the neighborhood range of the pig, obtaining the neighborhood range area of ​​the pig;

[0125] Obtaining a density assessment result of the pigs based on the area of ​​the neighborhood range of the pigs and the number of pigs in the neighborhood range;

[0126] The pig density assessment result = the number of pigs in the neighborhood / the area of ​​the neighborhood.

[0127] S22, obtaining a density assessment result of the collection area based on the density assessment results of each pig in the collection area.

[0128] In a possible implementation, step S22 may be performed in the following manner:

[0129] The density assessment results of each pig in the collection area are accumulated to obtain the density assessment result of the collection area.

[0130] In the above steps S21-S22, for the pigs in the collection area, the number of pigs in the neighborhood of the pigs is determined to obtain the density assessment result of the pigs, and then the density assessment result of the collection area is obtained based on the density assessment results of all pigs in the collection area, so that the density assessment result of the collection area is more accurate.

[0131] S3, based on the density assessment results of each collection area, obtain the comprehensive density assessment result of the pig house, and based on the comprehensive density assessment result, judge whether the current density of the pig distribution in the pig house meets the density requirements; if it does, determine the current time as the collection time node, and generate a collection trigger signal.

[0132] In a possible embodiment, step S3 specifically includes: if the density assessment results of one or more collection areas in the pig house exceed the corresponding preset density threshold, it is determined that the current density of the pig distribution in the pig house does not meet the density requirements, and no collection trigger signal is generated at this time; if the density of the various collection areas in the pig house does not exceed the corresponding preset density threshold, it is determined that the current density of the pig distribution in the pig house meets the density requirements, and a collection trigger signal is generated at this time to start collecting the weight characteristic parameters of the pigs in the pig house.

[0133] Among them, each collection area of ​​the pig house does not exceed the corresponding preset density threshold, which means that there are no pigs gathered together in the pig house at this time. At this time, collecting the weight characteristic parameters of each pig in the pig house can collect more data to the greatest extent, thereby making the estimated weight of the pigs more accurate.

[0134] The above steps S1-S3 divide the pig house into areas and adopt different density judgment standards for pigs in different areas, so that the density assessment results of the pig house are more accurate, thereby making the timing of collecting the weight characteristic parameters of the pigs in the pig house more accurate, and ensuring the estimation accuracy of the pig weight to the greatest extent.

[0135] An embodiment of the present application provides an intelligent livestock and poultry feeding system, which applies the above-mentioned intelligent livestock and poultry feeding method. The system includes a first module, a second module, a third module, a fourth module and a fifth module.

[0136] The first module is used to collect weight characteristic parameters of pigs in the piggery based on the collection trigger signal; the weight characteristic parameters include one or more of body length, body width, body height, chest circumference, and hip circumference;

[0137] The second module is used to obtain the estimated weight of the pigs in the pig house based on the breed, weight characteristic parameters of the pigs in the pig house and a pre-built weight estimation model;

[0138] A third module is configured to obtain the nutritional requirements of the pigs in the pig house based on the estimated weight and health status of the pigs in the pig house and a pre-built pig nutritional requirement calculation model;

[0139] The fourth module is used to obtain the mixing ratio of different types of feed based on the nutritional needs of the pigs in the pig house and the feed intake information of the pigs; and to obtain the required amount of different types of feed based on the total feed intake of the pigs in the pig house and the mixing ratio of the different types of feed;

[0140] The fifth module is used to control the operation of the motor to mix different types of feed based on the mixing ratio and required amount of the different types of feed, and transport the mixed feed to the feed trough in the pig house; the motor is used to control the output amount and output speed of the feed.

[0141] An embodiment of the present application provides a terminal, including a processor, a memory, and a communication bus; the communication bus is used to realize connection and communication between the processor and the memory, and the processor is used to execute one or more programs stored in the memory to implement the intelligent livestock and poultry feeding method described in the above embodiment.

[0142] An embodiment of the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed, the intelligent livestock and poultry feeding method described in the above embodiment is executed.

[0143] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for intelligent feeding of livestock and poultry, characterized in that: include: Based on the acquisition trigger signal, the weight characteristic parameters of the pigs in the pig house are collected; the weight characteristic parameters include one or more of body length, body width, body height, chest circumference, and hip circumference; Obtaining an estimated weight of the pigs in the pig house based on the breed and weight characteristic parameters of the pigs in the pig house and a pre-built weight estimation model; Obtaining the nutritional requirements of the pigs in the piggery based on the estimated weight and health status of the pigs in the piggery and a pre-established pig nutritional requirement calculation model; Based on the nutritional needs of the pigs in the pig house and the feed intake information of the pigs, the mixing ratio of different types of feed is obtained; and based on the total feed intake of the pigs in the pig house and the mixing ratio of the different types of feed, the required amount of different types of feed is obtained; Based on the mixing ratio and required amount of the different types of feed, the motor is controlled to mix the different types of feed and transport the mixed feed to the feed trough in the pig house; The motor is used to control the output amount and output speed of the feed; Before collecting the weight characteristic parameters of the pigs in the piggery, the method includes: Dividing the piggery into a plurality of collection areas; Based on the distribution of pigs in each collection area and the pre-built density assessment model, the density assessment results of each collection area are obtained; Based on the density assessment results of each collection area, a comprehensive density assessment result of the pig house is obtained, and based on the comprehensive density assessment result, whether the density of the current pig distribution in the pig house meets the density requirement is determined; if it does, the current time is determined as the collection time node, and a collection trigger signal is generated; After the mixed feed is transported to the trough of the pig house, the process further includes: Recording the growth status of the pigs in the pig house during a first preset period of time, and generating a pig actual growth status model based on the growth status of the pigs in the pig house during the first preset period of time; Based on the actual growth condition model of the pigs and the pre-built target growth condition model of the pigs, determining whether the growth progress of the pigs meets the expected requirements; if not, adjusting the mixing ratio of the different types of feed; Based on the breed and weight characteristic parameters of the pigs in the pig house and a pre-built weight estimation model, the estimated weight of the pigs in the pig house is obtained, specifically including: Based on the weight characteristic parameters of the pig, determining whether the weight characteristic parameters of the pig are complete; If the weight characteristic parameters are complete, the weight characteristic parameters of the pig are added to the weight characteristic data set; if the weight characteristic parameters are incomplete, the pig is marked as a target pig, and the weight characteristic parameters of the target pig are continuously tracked and collected within a second preset time period until the weight characteristic parameters of the target pig are completely collected, and the weight characteristic parameters of the target pig are added to the weight characteristic data set; Based on the breed of the pigs in the pig house, the weight feature data set and a pre-built weight estimation model, obtaining an estimated weight corresponding to each group of weight feature parameters in the weight feature data set; Based on the estimated weight corresponding to each group of weight feature parameters in the weight feature data set, a mean of the estimated weight set is obtained; and the mean of the estimated weight set is used as the estimated weight of the pigs in the pig house.

2. The intelligent livestock and poultry feeding method according to claim 1, characterized in that: Based on the distribution of pigs in each collection area and the pre-built density assessment model, the density assessment results of each collection area were obtained, including: Obtaining the number of pigs in the neighborhood of the pig in the collection area, and obtaining a density assessment result of the pigs based on the number of pigs in the neighborhood of the pig; Based on the density evaluation results of each pig in the collection area, a density evaluation result of the collection area is obtained.

3. The intelligent livestock and poultry feeding method according to claim 2, characterized in that: The determination of the pig neighborhood range specifically includes: If the collection area to which the pig belongs is the first target collection area, a first circle is drawn with the pig's position as the center and a first preset length a as the radius, and the area covered by the first circle is used as the neighborhood range of the pig; If the collection area to which the pig belongs is the second target collection area, a second circle is made with the position of the pig as the center and the second preset length b as the radius, and the area covered by the second circle is used as the neighborhood range of the pig.

4. The intelligent livestock and poultry feeding method according to claim 1, characterized in that: Based on the estimated weights corresponding to the weight feature parameters of each group in the weight feature data set, a mean of the estimated weight set is obtained, specifically including: Obtaining an estimated weight set based on the estimated weight corresponding to each group of weight feature parameters in the weight feature data set; sorting the data in the estimated weight set, and removing data within a preset range in the estimated weight set based on the sorted estimated weight set; Based on the remaining data in the estimated weight set, an average value method is used to obtain the mean of the estimated weight set.

5. An intelligent livestock and poultry feeding system, used to implement the intelligent livestock and poultry feeding method according to any one of claims 1 to 4, characterized in that: including a first module, a second module, a third module, a fourth module and a fifth module; The first module is used to collect weight characteristic parameters of pigs in the piggery based on the collection trigger signal; the weight characteristic parameters include one or more of body length, body width, body height, chest circumference, and hip circumference; The second module is used to obtain the estimated weight of the pigs in the pig house based on the breed, weight characteristic parameters of the pigs in the pig house and a pre-built weight estimation model; A third module is configured to obtain the nutritional requirements of the pigs in the pig house based on the estimated weight of the pigs in the pig house, the pre-stored health status, and the pre-built pig nutritional requirement calculation model; The fourth module is used to obtain the mixing ratio of different types of feed based on the nutritional requirements of the pigs in the pig house and the pre-stored feed information; and to obtain the required amount of different types of feed based on the total feed intake of the pigs in the pig house and the mixing ratio of the different types of feed; The fifth module is used to control the operation of the motor to mix different types of feed based on the mixing ratio and required amount of the different types of feed, and transport the mixed feed to the feed trough in the pig house; the motor is used to control the output amount and output speed of the feed.

6. A terminal, characterized in that: include: A processor, a memory and a communication bus; the communication bus is used to realize connection and communication between the processor and the memory, and the processor is used to execute one or more programs stored in the memory to implement the intelligent livestock and poultry feeding method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the intelligent livestock and poultry feeding method according to any one of claims 1 to 4 is executed.

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