A method and system for intelligent pasture concentrate control management based on the Internet of Things

Abstract

The present invention relates to the field of automatic control technology, and in particular to an intelligent ranch concentrate control management method and system based on the Internet of Things. The method comprises the following steps: obtaining livestock growth status data; using sensors to collect environmental parameters of a ranch management module to obtain ranch environmental parameter information; collecting feed demand data of the ranch management module in real time based on the livestock growth status data and the ranch environmental parameter information to obtain ranch feed demand data; extracting feed components from the ranch feed demand data to generate ranch feed component data. The present invention realizes centralized batching and unified production of feed through an automatic control system based on livestock growth status data and ranch environmental parameter information through the Internet of Things technology, data processing technology, automatic control technology, and intelligent management technology; realizes real-time monitoring of the remaining inventory of feed, and automatically issues inventory warnings to improve the accuracy of ranch feed control.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to an intelligent pasture concentrate control management method and system based on the Internet of Things. Background Art

[0002] Traditional artificial ranches mainly rely on manual experience to mix and distribute feed, which is labor-intensive, inefficient, and difficult to achieve precise management. With the advancement of technology, ranches have begun to use some basic mechanical equipment, such as manual cart-type feed mixers and tractor-type feed mixers, to assist in the mixing and distribution of feed, but these devices still rely on manual operation and have limited accuracy. With the application of Internet of Things technology, high-definition cameras, sensors and other equipment are installed in ranches to monitor the health status and behavior patterns of livestock in real time, thus achieving real-time monitoring of livestock, environment, feed, equipment, etc. and optimizing ranch feed management. However, the existing ranch feed control and management methods fail to centrally mix and uniformly produce feed, resulting in unbalanced feed nutrition and unstable quality. In addition, it is difficult to monitor the remaining inventory feed in real time, and inventory warnings cannot be automatically issued in a timely manner, resulting in inaccurate ranch feed control. Summary of the invention

[0003] Based on this, it is necessary to provide an intelligent pasture concentrate control management method and system based on the Internet of Things to solve at least one of the above technical problems.

[0004] To achieve the above purpose, an intelligent pasture concentrate control and management method based on the Internet of Things is provided, the method comprising the following steps:

[0005] Step S1: Acquire livestock growth status data; use sensors to collect environmental parameters of a pasture management module to obtain pasture environmental parameter information; based on the livestock growth status data and pasture environmental parameter information, collect feed demand data of the pasture management module in real time to obtain pasture feed demand data; extract feed components from the pasture feed demand data to generate pasture feed component data;

[0006] Step S2: Determine the composition ratio of the pasture feed ingredient data according to the livestock growth status data and the pasture environmental parameter information to obtain the pasture feed formula data; based on the pasture feed formula data, execute the feed production and preparation task through the central pasture management module to generate feed preparation task data; prepare livestock feed for the feed premixing module according to the feed preparation task data to generate pasture feed preparation data; perform feed quality analysis on the pasture feed preparation data through the precise weighing module to obtain feed preparation quality data; perform intelligent and precise ratio adjustment on the pasture feed formula data according to the feed preparation quality data to generate feed formula adjustment measures;

[0007] Step S3: extracting the pasture feed consumption from the pasture feed demand data to generate pasture feed consumption; calculating the feed consumption quantity from the pasture feed consumption to obtain feed consumption quantity data; executing the feed inventory output task through the central pasture management module based on the feed consumption quantity data to generate inventory output task data; outputting the feed inventory to the feed storage cylinder module according to the inventory output task data to generate feed inventory output data;

[0008] Step S4: Based on the feed inventory output data, the feed storage cylinder module is used to monitor the inventory feed in real time to obtain the inventory feed data; the inventory weight of the inventory feed data is measured by the precise weighing module to obtain the inventory feed weight data; the inventory feed weight data is compared with the minimum inventory feed threshold to generate inventory feed status data; the central forage management module is used to adaptively manage the inventory according to the inventory feed status data to generate adaptive inventory management measures; the pasture management module is used to control the concentrate feed based on the feed formula adjustment measures and the adaptive inventory management measures to perform intelligent pasture concentrate feed control management operations.

[0009] The present invention collects environmental parameters of a pasture management module through sensors, and can accurately obtain environmental parameter information of the pasture, including temperature, humidity, light, etc., which is helpful for subsequent evaluation of the growth environment of livestock; combines livestock growth status data and pasture environmental parameter information to collect feed demand data in real time to ensure that the feed supply matches the actual needs of livestock; extracts feed components from pasture feed demand data to provide accurate raw material information for subsequent feed formula design; determines the composition ratio of feed components according to livestock growth status data and pasture environmental parameter information, which can ensure the nutritional balance of the feed and meet the nutritional needs of livestock at different growth stages. Based on the pasture feed formula data, the central forage management module can perform feed production and preparation tasks, guide the actual feed production process, and ensure the efficiency and accuracy of feed production; livestock feed is prepared through the feed premixing module, and the detailed process of feed preparation is clearly recorded, providing a basis for quality control; the precise weighing module performs feed quality analysis on the pasture feed preparation data, which can ensure that the feed quality meets the standards and protect the health of livestock; the pasture feed formula data is adjusted intelligently and accurately according to the feed preparation quality data, thereby improving the nutritional value and palatability of the feed and optimizing the feed formula. Pasture feed demand data is used to extract pasture feed consumption to provide accurate consumption information for feed management; feed consumption quantity is calculated based on pasture feed consumption, which can provide an accurate digital basis for feed stock management; based on feed consumption quantity data, the central forage management module performs feed stock output tasks to ensure the rationality of feed stock and avoid feed waste or shortage; feed stock is output through the feed storage cylinder module, and the storage status of feed is monitored in real time to ensure the freshness and availability of feed. Based on the feed inventory output data, the feed storage cylinder module is monitored in real time for inventory feed, and the obtained inventory feed data can provide real-time information for inventory management; the inventory weight is measured by the accurate weighing module to clarify the weight information of the inventory; the inventory feed weight data is compared with the minimum inventory feed threshold, and the situation of insufficient inventory can be warned in time to ensure the continuity of feed supply; the central pasture management module is adaptively managed according to the inventory feed status data, and the feed procurement and storage strategy is automatically adjusted to optimize the inventory level; the pasture management module is controlled and managed based on the feed formula adjustment measures and the adaptive inventory management measures, and the intelligent pasture concentrate control management operation executed can ensure the accurate delivery of feed, improve the feed conversion rate, and enhance the concentrate control management of the pasture. Therefore, the present invention realizes the centralized batching and unified production of feed through the automatic control system and according to the livestock growth status data and pasture environmental parameter information through the Internet of Things technology, data processing technology, automatic control technology, and intelligent management technology; it realizes the real-time monitoring of the remaining inventory feed, and automatically issues an inventory warning to improve the accuracy of pasture feed control.

[0010] Preferably, step S2 comprises the following steps:

[0011] Step S21: identifying the livestock weight status of the livestock growth status data to obtain livestock weight data; determining the growth period of the livestock growth status data according to the livestock weight data to generate livestock growth period data; determining the required feed ingredients for the livestock growth period data to obtain feed data required for growth;

[0012] Step S22: extracting temperature characteristics of the pasture environment parameter information to obtain pasture environment temperature data; measuring the humidity of the pasture environment parameter information through the pasture environment temperature data to generate pasture environment humidity data; determining the composition ratio of pasture feed component data based on the feed data required for growth and the pasture environment humidity data to obtain pasture feed formula data;

[0013] Step S23: inputting the pasture feed formula data into the instruction control module to generate a pasture feed formula instruction signal; executing feed production and preparation operations on the central pasture feed management module according to the pasture feed formula instruction signal to obtain feed preparation task data; determining the feed raw material delivery sequence of the feed preparation task data to generate raw material delivery sequence data; calculating the delivery time of the raw material delivery sequence data to obtain raw material delivery time data; preparing livestock feed on the feed premixing module according to the raw material delivery sequence data and the raw material delivery time data to generate pasture feed preparation data;

[0014] Step S24: Perform feed quality analysis on the pasture feed preparation data through the precise weighing module to obtain feed preparation quality data; perform intelligent and precise ratio adjustment on the pasture feed formula data according to the feed preparation quality data to generate feed formula adjustment measures.

[0015] The present invention accurately obtains the weight data of each livestock by identifying the weight conditions in the livestock growth status data; using these weight data, the growth period of the livestock can be determined, so as to determine the nutritional needs of the livestock at different growth stages; further, according to the livestock growth period data, the required feed ingredients are determined to ensure that the livestock obtain appropriate nutritional support; the analysis of the pasture environmental parameter information can extract the temperature characteristics and measure the humidity of the pasture environment; using the feed data required for growth and the pasture environmental humidity data, the feed composition ratio is determined; it is ensured that the feed formula can adapt to environmental changes and meet the nutritional needs of livestock in a specific environment; the pasture feed formula data is input into the instruction The control module guides the central forage management module to perform feed production and preparation operations; determines the order of feed raw materials and calculates the time of delivery, ensuring the accuracy and efficiency of the feed preparation process; based on the raw material delivery sequence data and raw material delivery time data, the feed premixing module can accurately prepare livestock feed; through the precise weighing module, the pasture feed preparation data is analyzed to obtain feed preparation quality data, clarify the direct assessment of feed quality, and ensure that the feed meets the predetermined standards; it enables intelligent and precise ratio adjustment of pasture feed formula data, which helps to optimize feed formula, improve the nutritional value of feed and livestock feeding effect, and also improve feed utilization efficiency.

[0016] Preferably, step S24 includes the following steps:

[0017] Step S241: Perform feed weight detection on the pasture feed preparation data by using a precise weighing module to obtain feed preparation weight data; perform feed component weight statistics on the feed preparation weight data to generate feed component weight data; perform component average value calculation on the feed component weight data to obtain feed component average value;

[0018] Step S242: comparing the standard deviation of the average value of the feed ingredients with the preset feed formula to generate feed quality deviation data; determining the quality deviation degree of the feed preparation weight data according to the feed quality deviation data to obtain quality deviation degree data;

[0019] Step S243: performing feed component accuracy measurement on the feed component weight data according to the quality deviation degree data to generate feed component accuracy data; performing feed preparation quality determination on the pasture feed preparation data according to the feed component accuracy data to obtain feed preparation quality data;

[0020] Step S244: intelligently and accurately adjust the ranch feed formula data according to the feed preparation quality data to generate feed formula adjustment measures.

[0021] The present invention detects the weight of pasture feed preparation data through a precise weighing module, and can obtain accurate feed preparation weight data; statistics on feed preparation weight data can help managers clarify the specific usage of various feed ingredients; further, the average value of feed ingredient weight data is calculated, which can provide a scientific basis for the evaluation and optimization of feed formulas, thereby ensuring that the feed formula can meet the nutritional needs of livestock; the average value of feed ingredients is compared with the standard deviation of the preset feed formula, which clarifies the degree of deviation between the actual feed formula and the ideal formula, and provides important information for feed quality control; the feed preparation weight data is compared according to the feed quality deviation data Determining the degree of quality deviation can help managers adjust the feed preparation process in a timely manner, ensure that feed quality meets expected standards, and avoid affecting livestock health and production performance due to feed quality problems; accurately calculate the feed ingredients based on the feed ingredient weight data according to the quality deviation degree data to ensure accurate delivery of feed ingredients; determine the feed preparation quality based on the pasture feed preparation data to comprehensively reflect the quality level of the feed preparation process and provide a basis for optimizing and improving the feed preparation process; intelligently and accurately adjust the pasture feed formula data based on the feed preparation quality data to ensure that the feed formula is always in the best state to adapt to the growth needs of livestock and environmental changes.

[0022] Preferably, step S244 includes the following steps:

[0023] Step S2441: checking the raw material delivery sequence data according to the feed preparation quality data to obtain delivery sequence verification data; performing time verification on the raw material delivery time data according to the delivery sequence verification data to generate delivery time node data; performing feed component supply abnormality identification on the feed component accuracy data to obtain component supply abnormality data;

[0024] Step S2442: matching the order characteristics of the delivery sequence check data with the raw material delivery sequence data. If the order characteristics of the delivery sequence check data and the raw material delivery sequence data do not completely match, generating delivery process disordered data; measuring the delivery time node data and the raw material delivery time data. If the delivery time node data and the raw material delivery time data are inconsistent in time length, generating delivery time abnormal data; integrating the delivery process disordered data and the delivery time abnormal data to obtain the raw material delivery process abnormal data;

[0025] Step S2443: performing formula component correlation identification on the feed preparation quality data to obtain formula component correlation data; performing component mutual cancellation feature extraction on the formula component correlation data to generate feed mutual cancellation features; performing feed component mutual cancellation detection on the feed mutual cancellation features according to the abnormal data of the raw material delivery process to obtain component mutual cancellation detection data;

[0026] Step S2444: performing feed component cancellation mapping on the pasture feed formula data according to the component cancellation detection data to obtain cancellation component mapping data; determining the abnormal data of the raw material delivery process according to the cancellation component mapping data; if the abnormal delivery process is disordered delivery process data, performing raw material delivery sequence adjustment on the central forage management module to generate raw material delivery sequence adjustment data; if the abnormal delivery process is abnormal delivery time data, performing a delivery time reduction operation on the premixing module to generate raw material delivery time adjustment data;

[0027] Step S2445: identifying the component supply abnormality type of the component supply abnormality data to obtain component supply abnormality type data; inputting the component supply abnormality type data into the instruction control module to obtain a component type supply instruction signal; executing specific feed component supply to the central forage management module according to the component type supply instruction signal to obtain component supply adjustment data;

[0028] Step S2446: Integrate the raw material delivery sequence adjustment data, raw material delivery time adjustment data, and ingredient supply adjustment data to generate feed formula adjustment measures.

[0029] The present invention analyzes the feed preparation quality data to ensure that the raw material delivery at each step is carried out in accordance with the established formula requirements; uses the delivery sequence verification data to perform time verification on the raw material delivery time data, and can accurately control the time point of each raw material delivery, thereby ensuring the timeliness and synchronization of the feed preparation process; analyzes the feed component accuracy data to identify abnormal conditions in the feed component supply; performs feature matching between the delivery sequence verification data and the raw material delivery sequence data, thereby correcting delivery sequence errors in a timely manner; compares the delivery time node data with the raw material delivery time data, so as to make necessary adjustments; conducts in-depth analysis of the feed preparation quality data, identifies the correlation between the formula components, and improves the nutrition of the feed. The system can adjust the feed formula data of the pasture according to the component cancellation detection data to ensure that the components in the feed formula can complement each other and avoid nutrient waste; analyze the abnormal data of the raw material delivery process to determine the specific process of delivery abnormality, so as to ensure the accuracy and efficiency of the feed preparation process; analyze the abnormal data of the component supply to identify the specific abnormal type, which helps to ensure the timely replenishment and quality control of feed ingredients and improve the response speed and flexibility of feed preparation; integrate the raw material delivery sequence adjustment data, raw material delivery time adjustment data and component supply adjustment data to help realize the automation and intelligence of the feed preparation process, improve the nutritional value and breeding efficiency of the feed, and reduce costs and waste. Through this refined management, livestock can be ensured to obtain balanced nutrition.

[0030] Preferably, step S3 comprises the following steps:

[0031] Step S31: identifying demand scenarios for pasture feed demand data according to livestock growth status data and pasture environmental parameter information to obtain pasture feed demand scenario data; determining feed types for pasture feed demand scenario data to generate feed demand type data; extracting feed type consumption for pasture feed demand data according to feed demand type data to generate pasture feed consumption;

[0032] Step S32: extracting the consumption characteristics of the pasture feed consumption to obtain feed consumption characteristic data; according to the feed consumption characteristic data; measuring the feed consumption weight of the feed consumption characteristic data by a precise weighing module to obtain feed consumption weight data; performing numerical measurement conversion on the feed consumption weight data to obtain feed consumption quantity data;

[0033] Step S33: Input feed consumption quantity data into the instruction control module to generate a feed quantity output instruction signal; execute the feed inventory output task on the central forage management module according to the feed quantity output instruction signal to generate inventory output task data; output feed inventory to the feed storage barrel module according to the inventory output task data to generate feed inventory output data.

[0034] The present invention can accurately identify the demand scenario of pasture feed and determine the required feed type by analyzing livestock growth status data and pasture environmental parameter information; based on the feed demand type data, it is helpful for pasture managers to understand the actual use of various feeds; by conducting in-depth analysis of pasture feed consumption, it is possible to extract the characteristics of feed consumption, reflecting the trend and pattern of feed use; by using a precise weighing module to measure the weight of these characteristic data, it is possible to ensure accurate measurement of feed weight and avoid feed waste or shortage due to weighing errors; by inputting feed consumption quantity data into the instruction control module, it is possible to provide clear operation guidance for the central forage management module, ensure the accurate execution of feed inventory output tasks, clearly record the details of feed output, and provide data support for feed traceability and quality management; according to the inventory output task data, it is possible to automatically control the feed storage cylinder module to output the feed inventory, thereby realizing the automated management of feed inventory, while also reducing human errors and ensuring the continuity and stability of feed supply.

[0035] Preferably, step S4 comprises the following steps:

[0036] Step S41: Based on the feed inventory output data, the feed storage cylinder module performs real-time monitoring of the inventory feed to generate real-time monitoring data of the inventory feed; the feed inventory is determined based on the real-time monitoring data of the inventory feed to obtain inventory feed data; the inventory weight of the inventory feed data is measured by the precise weighing module to obtain inventory feed weight data;

[0037] Step S42: comparing the inventory feed weight data with the minimum inventory feed threshold, if the inventory feed weight data is higher than the minimum inventory feed threshold, generating normal inventory status data; if the inventory feed weight data is lower than or equal to the minimum inventory feed threshold, generating abnormal inventory status data; integrating the normal inventory status data and the abnormal inventory status data to obtain the inventory feed status data;

[0038] Step S43: Adaptively manage the inventory of the central forage management module according to the inventory feed status data and generate adaptive inventory management measures; perform concentrate control management on the pasture management module based on the feed formula adjustment measures and the adaptive inventory management measures to execute intelligent pasture concentrate control management operations.

[0039] The present invention ensures the real-time update and accuracy of feed inventory and clarifies the exact quantity of feed inventory by real-time monitoring of the feed storage cylinder module based on the feed inventory output data; measures the weight of the inventory feed data through the precise weighing module, provides an accurate weight basis for the purchase and use of feed, and ensures the accuracy and efficiency of feed management; compares the inventory feed weight data with the minimum inventory feed threshold, which is crucial to timely identify whether the inventory level meets production demand, thereby avoiding production interruptions caused by insufficient inventory; performs adaptive inventory management of the central forage management module based on the inventory feed status data, optimizes the inventory level, and reduces inventory costs; combines feed formula adjustment measures to perform concentrate control management on the pasture management module, executes intelligent pasture concentrate control management operations, improves feed utilization efficiency, and reduces waste.

[0040] Preferably, step S41 includes the following steps:

[0041] Step S411: extracting feed inventory data features from the feed storage cylinder module to generate feed inventory data; deducting feed inventory quantity from the feed inventory data according to the feed inventory output data to obtain inventory quantity deduction data;

[0042] Step S412: Based on the inventory quantity deduction data, the feed storage cylinder module is monitored in real time to generate inventory feed real-time monitoring data; the inventory feed inventory is measured on the inventory feed real-time monitoring data to obtain inventory feed data; the inventory feed quantity characteristics are identified on the inventory feed data to generate inventory feed quantity characteristics;

[0043] Step S413: convert the quantity characteristics of the stock feed into weight values ​​to obtain the weight characteristics of the stock feed; calculate the stock weight of the weight characteristics of the stock feed through a precise weighing module to obtain the weight data of the stock feed.

[0044] The present invention monitors and analyzes the feed storage cylinder module in real time, ensuring an accurate grasp of the feed storage status; deducts the feed inventory quantity from the feed inventory data according to the feed inventory output data, reducing errors and time consumption in manual operations, and improving the efficiency and accuracy of inventory management; based on the inventory quantity deduction data, it is possible to timely understand inventory changes, respond quickly to potential inventory risks, and accurately measure the inventory of inventory feed; converts the inventory feed quantity characteristics into weight values, allowing managers to evaluate the inventory status from a weight perspective, providing more intuitive data for the purchase and use of feed; and measures the inventory feed weight characteristics through a precise weighing module, improving the accuracy of inventory management.

[0045] Preferably, step S43 includes the following steps:

[0046] Step S431: extracting insufficient feed data from the inventory feed status data to obtain insufficient feed data; inputting the insufficient feed data into the instruction control module to generate an insufficient feed instruction signal; inputting the insufficient feed instruction signal into the feed storage cylinder module to perform automatic inventory warning, and obtaining feed inventory warning information;

[0047] Step S432: Identify the feed inventory shortage type for the feed inventory warning information to obtain the inventory shortage type data; perform feed replenishment operation on the central forage management module according to the inventory shortage type data to obtain feed replenishment task data; increase the feed inventory quantity on the feed storage cylinder module based on the feed replenishment task data to obtain feed inventory replenishment information;

[0048] Step S433: Integrate the feed inventory warning information and the feed inventory replenishment information to generate adaptive inventory management measures;

[0049] Step S434: Based on the feed formula adjustment measures and the adaptive inventory management measures, the pasture management module is controlled and managed to perform the intelligent pasture concentrate control management operation.

[0050] The present invention obtains feed shortage data accurately by extracting insufficient feed data from inventory feed status data; inputs insufficient feed data into a command control module to generate an insufficient stock command signal; the insufficient stock command signal provides an automatic inventory warning for a feed storage barrel module, ensures the implementation of a real-time monitoring and warning mechanism for feed inventory, and helps to replenish inventory in a timely manner; analyzes feed inventory warning information, identifies the specific type of insufficient feed inventory, and provides precise guidance for a central forage management module to perform feed replenishment operations; can accurately increase the feed inventory quantity of the feed storage barrel module according to feed replenishment task data, thereby improving the efficiency and accuracy of feed replenishment and ensuring timely replenishment of feed inventory; integrates feed inventory warning information and feed inventory replenishment information, can automatically adjust inventory levels according to actual conditions, and reduce the risk of excess or shortage; based on feed formula adjustment measures and adaptive inventory management measures, performs intelligent pasture concentrate control management operations, ensures the scientific nature and dynamic adjustment capabilities of feed formulas, while improving the accuracy and efficiency of feed use, and precisely controls the delivery of feed.

[0051] Preferably, step S434 includes the following steps:

[0052] Step S4341: performing real-time demand feedback of pasture feed on the pasture management module according to the pasture feed demand data to generate feed demand feedback data; performing feed preparation demand feature identification on the feed demand feedback data to obtain feed preparation demand data; performing feed formula adjustment and determination on the feed preparation demand data to generate feed adjustment demand data;

[0053] Step S4342: inputting the feed adjustment demand data into the instruction control module to generate a feed adjustment demand instruction signal; executing feed formula adjustment measures on the central forage management module according to the feed adjustment demand instruction signal to obtain feed adjustment task data;

[0054] Step S4343: performing feed inventory replenishment feature recognition on the feed demand feedback data to obtain inventory replenishment demand data; inputting the inventory replenishment demand data into the command control module to generate an inventory replenishment demand command signal; executing adaptive inventory management measures on the central forage management module according to the inventory replenishment demand command signal to obtain inventory management task data;

[0055] Step S4344: Integrate the feed adjustment task data and the inventory management task data to obtain the concentrate control management data; control and manage the ranch management module based on the concentrate control management data to perform the intelligent ranch concentrate control management operation.

[0056] The present invention can timely feedback the feed demand of the ranch through real-time analysis of the feed demand data of the ranch, help to quickly respond to the actual demand of the ranch, and ensure the timeliness of feed supply; identify the feed preparation demand characteristics of the feed demand feedback data to provide accurate guidance for feed production; adjust and determine the feed formula based on the feed preparation demand data, so as to ensure that the feed formula always meets the nutritional needs and growth stage of the livestock; input the feed adjustment demand data into the instruction control module, so as to guide the central forage management module to adjust the feed formula, ensure the accuracy and timeliness of the feed formula adjustment, so that the feed formula can quickly adapt to the changes in livestock needs, and reduce feed waste or malnutrition caused by improper formula; identify the feed inventory replenishment characteristics of the feed demand feedback data, and guide the central forage management module to perform adaptive inventory management measures, so as to realize the intelligent management of feed inventory, ensure that the inventory level is always in the best state, and avoid excess or shortage; by integrating the feed adjustment task data and the inventory management task data, comprehensive control management information is provided, so that the ranch can perform intelligent ranch concentrate control management operations, ensure the accurate delivery of feed, and reduce feed costs; through intelligent concentrate control management, intelligent ranch concentrate control management operations can be realized.

[0057] In this specification, an intelligent pasture concentrate control and management system based on the Internet of Things is provided, which includes a pasture management module, a central pasture management module, a command control module, a feed storage cylinder module, a precision weighing module, and a feed premixing module, which are used to execute the above-mentioned intelligent pasture concentrate control and management method based on the Internet of Things. The intelligent pasture concentrate control and management system based on the Internet of Things includes:

[0058] The pasture feed demand data collection module is used to obtain livestock growth status data; use sensors to collect environmental parameters of the pasture management module to obtain pasture environmental parameter information; based on livestock growth status data and pasture environmental parameter information, the pasture management module collects feed demand data in real time to obtain pasture feed demand data; extracts feed components from pasture feed demand data to generate pasture feed component data;

[0059] The pasture feed formula adjustment measures module is used to determine the composition ratio of pasture feed ingredient data according to livestock growth status data and pasture environmental parameter information to obtain pasture feed formula data; based on pasture feed formula data, the central forage management module executes feed production and preparation tasks to generate feed preparation task data; according to the feed preparation task data, the feed premixing module performs livestock feed preparation to generate pasture feed preparation data; the precise weighing module performs feed quality analysis on pasture feed preparation data to obtain feed preparation quality data; according to the feed preparation quality data, the pasture feed formula data is adjusted intelligently and precisely to generate feed formula adjustment measures;

[0060] The feed inventory output task module is used to extract the pasture feed consumption situation from the pasture feed demand data to generate the pasture feed consumption situation; to calculate the feed consumption quantity from the pasture feed consumption situation to obtain the feed consumption quantity data; based on the feed consumption quantity data, the central forage management module executes the feed inventory output task to generate the inventory output task data; according to the inventory output task data, the feed storage cylinder module outputs the feed inventory to generate the feed inventory output data;

[0061] The pasture concentrate feed control and management module is used to monitor the inventory feed of the feed storage cylinder module in real time based on the feed inventory output data to obtain the inventory feed data; to measure the inventory weight of the inventory feed data through the precise weighing module to obtain the inventory feed weight data; to compare the inventory feed weight data with the minimum inventory feed threshold to generate the inventory feed status data; to perform adaptive inventory management on the central pasture management module according to the inventory feed status data to generate adaptive inventory management measures; to perform concentrate feed control and management on the pasture management module based on the feed formula adjustment measures and the adaptive inventory management measures to perform intelligent pasture concentrate feed control and management operations.

[0062] The present invention uses a pasture feed demand data collection module and a sensor to collect environmental parameters of a pasture management module, so as to accurately obtain environmental parameter information of the pasture, including temperature, humidity, light, etc., which is helpful for subsequent evaluation of the growth environment of livestock; combines livestock growth status data and pasture environmental parameter information to collect feed demand data in real time to ensure that the feed supply matches the actual needs of livestock; extracts feed components from pasture feed demand data to provide accurate raw material information for subsequent feed formula design; determines the composition ratio of feed components according to livestock growth status data and pasture environmental parameter information, so as to ensure that the nutrition of the feed is balanced and meets the nutritional needs of livestock at different growth stages. Through the pasture feed formula adjustment measures module, based on the pasture feed formula data, the central pasture management module can perform feed production and preparation tasks, guide the actual feed production process, and ensure the efficiency and accuracy of feed production; through the feed premixing module, livestock feed is prepared, and the detailed process of feed preparation is clearly recorded, providing a basis for quality control; the precise weighing module performs feed quality analysis on the pasture feed preparation data, which can ensure that the feed quality meets the standards and protect the health of livestock; the pasture feed formula data is adjusted intelligently and accurately according to the feed preparation quality data, thereby improving the nutritional value and palatability of the feed and optimizing the feed formula. Through the feed inventory output task module, the pasture feed demand data is extracted from the pasture feed consumption situation to provide accurate consumption information for feed management; the feed consumption quantity is calculated for the pasture feed consumption situation, which can provide an accurate digital basis for feed inventory management; based on the feed consumption quantity data, the central pasture management module performs the feed inventory output task to ensure the rationality of the feed inventory and avoid feed waste or shortage; the feed inventory is output through the feed storage cylinder module, and the storage status of the feed is monitored in real time to ensure the freshness and availability of the feed. Through the pasture concentrate control and management module, the feed storage cylinder module is monitored in real time based on the feed inventory output data, and the obtained inventory feed data can provide real-time information for inventory management; the inventory weight is calculated through the precise weighing module to clarify the inventory weight information; the inventory feed weight data is compared with the minimum inventory feed threshold to timely warn of insufficient inventory and ensure the continuity of feed supply; the central pasture management module is adaptively managed according to the inventory feed status data, and the feed procurement and storage strategies are automatically adjusted to optimize the inventory level; the pasture management module is controlled and managed based on feed formula adjustment measures and adaptive inventory management measures. The intelligent pasture concentrate control and management operations performed can ensure the accurate delivery of feed, improve feed conversion rate, and enhance the pasture concentrate control and management.Therefore, the present invention realizes centralized batching and unified production of feed through Internet of Things technology, data processing technology, automatic control technology, and intelligent management technology, through an automatic control system, and according to livestock growth status data and pasture environmental parameter information; realizes real-time monitoring of the remaining inventory feed, and automatically issues inventory warnings, thereby improving the accuracy of pasture feed control. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 The figure is a schematic diagram of the steps of a smart pasture concentrate control management method based on the Internet of Things;

[0064] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0065] Figure 3 for Figure 2 Detailed implementation steps of step S24;

[0066] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0067] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0068] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0069] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0070] To achieve this, please refer to Figures 1 to 3 , an intelligent pasture concentrate control and management method based on the Internet of Things, the method comprising the following steps:

[0071] Step S1: Acquire livestock growth status data; use sensors to collect environmental parameters of a pasture management module to obtain pasture environmental parameter information; based on the livestock growth status data and pasture environmental parameter information, collect feed demand data of the pasture management module in real time to obtain pasture feed demand data; extract feed components from the pasture feed demand data to generate pasture feed component data;

[0072] Step S2: Determine the composition ratio of the pasture feed ingredient data according to the livestock growth status data and the pasture environmental parameter information to obtain the pasture feed formula data; based on the pasture feed formula data, execute the feed production and preparation task through the central pasture management module to generate feed preparation task data; prepare livestock feed for the feed premixing module according to the feed preparation task data to generate pasture feed preparation data; perform feed quality analysis on the pasture feed preparation data through the precise weighing module to obtain feed preparation quality data; perform intelligent and precise ratio adjustment on the pasture feed formula data according to the feed preparation quality data to generate feed formula adjustment measures;

[0073] Step S3: extracting the pasture feed consumption from the pasture feed demand data to generate pasture feed consumption; calculating the feed consumption quantity from the pasture feed consumption to obtain feed consumption quantity data; executing the feed inventory output task through the central pasture management module based on the feed consumption quantity data to generate inventory output task data; outputting the feed inventory to the feed storage cylinder module according to the inventory output task data to generate feed inventory output data;

[0074] Step S4: Based on the feed inventory output data, the feed storage cylinder module is used to monitor the inventory feed in real time to obtain the inventory feed data; the inventory weight of the inventory feed data is measured by the precise weighing module to obtain the inventory feed weight data; the inventory feed weight data is compared with the minimum inventory feed threshold to generate inventory feed status data; the central forage management module is used to adaptively manage the inventory according to the inventory feed status data to generate adaptive inventory management measures; the pasture management module is used to control the concentrate feed based on the feed formula adjustment measures and the adaptive inventory management measures to perform intelligent pasture concentrate feed control management operations.

[0075] The present invention collects environmental parameters of a pasture management module through sensors, and can accurately obtain environmental parameter information of the pasture, including temperature, humidity, light, etc., which is helpful for subsequent evaluation of the growth environment of livestock; combines livestock growth status data and pasture environmental parameter information to collect feed demand data in real time to ensure that the feed supply matches the actual needs of livestock; extracts feed components from pasture feed demand data to provide accurate raw material information for subsequent feed formula design; determines the composition ratio of feed components according to livestock growth status data and pasture environmental parameter information, which can ensure the nutritional balance of the feed and meet the nutritional needs of livestock at different growth stages. Based on the pasture feed formula data, the central forage management module can perform feed production and preparation tasks, guide the actual feed production process, and ensure the efficiency and accuracy of feed production; livestock feed is prepared through the feed premixing module, and the detailed process of feed preparation is clearly recorded, providing a basis for quality control; the precise weighing module performs feed quality analysis on the pasture feed preparation data, which can ensure that the feed quality meets the standards and protect the health of livestock; the pasture feed formula data is adjusted intelligently and accurately according to the feed preparation quality data, thereby improving the nutritional value and palatability of the feed and optimizing the feed formula. Pasture feed demand data is used to extract pasture feed consumption to provide accurate consumption information for feed management; feed consumption quantity is calculated based on pasture feed consumption, which can provide an accurate digital basis for feed stock management; based on feed consumption quantity data, the central forage management module performs feed stock output tasks to ensure the rationality of feed stock and avoid feed waste or shortage; feed stock is output through the feed storage cylinder module, and the storage status of feed is monitored in real time to ensure the freshness and availability of feed. Based on the feed inventory output data, the feed storage cylinder module is monitored in real time for inventory feed, and the obtained inventory feed data can provide real-time information for inventory management; the inventory weight is measured by the accurate weighing module to clarify the weight information of the inventory; the inventory feed weight data is compared with the minimum inventory feed threshold, and the situation of insufficient inventory can be warned in time to ensure the continuity of feed supply; the central pasture management module is adaptively managed according to the inventory feed status data, and the feed procurement and storage strategy is automatically adjusted to optimize the inventory level; the pasture management module is controlled and managed based on the feed formula adjustment measures and the adaptive inventory management measures, and the intelligent pasture concentrate control management operation executed can ensure the accurate delivery of feed, improve the feed conversion rate, and enhance the concentrate control management of the pasture. Therefore, the present invention realizes the centralized batching and unified production of feed through the automatic control system and according to the livestock growth status data and pasture environmental parameter information through the Internet of Things technology, data processing technology, automatic control technology, and intelligent management technology; it realizes the real-time monitoring of the remaining inventory feed, and automatically issues an inventory warning to improve the accuracy of pasture feed control.

[0076] In the embodiment of the present invention, reference Figure 1 As shown, it is a schematic diagram of the steps of an intelligent ranch concentrate control and management method based on the Internet of Things of the present invention. In this example, the intelligent ranch concentrate control and management method based on the Internet of Things includes the following steps:

[0077] Step S1: Acquire livestock growth status data; use sensors to collect environmental parameters of a pasture management module to obtain pasture environmental parameter information; based on the livestock growth status data and pasture environmental parameter information, collect feed demand data of the pasture management module in real time to obtain pasture feed demand data; extract feed components from the pasture feed demand data to generate pasture feed component data;

[0078] In an embodiment of the present invention, by installing smart ear tags or collars on livestock, RFID technology is used to collect livestock growth status data in real time, and specifically monitor key indicators such as livestock body temperature, activity level, and eating conditions; a variety of sensors are deployed in the pasture, including temperature and humidity sensors, gas concentration sensors, and light intensity sensors, to monitor the pasture environment of the pasture management module to obtain pasture environmental parameter information; in combination with livestock growth data and environmental parameters, sensors are used to monitor feed consumption, and feed demand data is collected in real time according to the growth stage and nutritional needs of livestock, thereby obtaining pasture feed demand data; near-infrared spectroscopy analysis technology is used to extract feed ingredients, specifically extracting the content of key nutrients such as crude protein, fiber, and energy in the feed; in addition, accelerated solvent extraction (ASE) technology is used to extract drug components in animal feed, thereby generating pasture feed ingredient data.

[0079] Step S2: Determine the composition ratio of the pasture feed ingredient data according to the livestock growth status data and the pasture environmental parameter information to obtain the pasture feed formula data; based on the pasture feed formula data, execute the feed production and preparation task through the central pasture management module to generate feed preparation task data; prepare livestock feed for the feed premixing module according to the feed preparation task data to generate pasture feed preparation data; perform feed quality analysis on the pasture feed preparation data through the precise weighing module to obtain feed preparation quality data; perform intelligent and precise ratio adjustment on the pasture feed formula data according to the feed preparation quality data to generate feed formula adjustment measures;

[0080] In the embodiment of the present invention, the best feed formula suitable for the current growth stage and environmental conditions of the livestock is automatically calculated in combination with the livestock growth status data and the pasture environmental parameter information; specifically, the ratio of components such as energy, protein, fiber, minerals and vitamins in the feed is determined according to the livestock's weight, daily weight gain, milk production, and environmental temperature and humidity parameters; based on the pasture feed formula data, the central forage management module automatically starts the feed production equipment; specifically, the feed mixer and granulator accurately weigh various raw materials according to the formula data to prepare the feed; during the preparation process, the production time, raw material usage, production batch and other data of each batch of feed are recorded to generate feed preparation task data; the feed premixing module automatically adjusts the operating parameters of the mixer, such as mixing time, speed and sequence, according to the feed preparation task data issued by the central forage management module; during the premixing process, Real-time monitoring of mixing quality, such as particle size, moisture and density; after feed preparation is completed, pasture feed preparation data is generated, including preparation quantity, nutrient test results, preparation efficiency, etc.; the quality of the prepared feed is analyzed using a precise weighing module, including nutrient analysis, mycotoxin detection and hygiene index detection; the weighing module is equipped with near-infrared spectroscopy analysis technology to accurately determine key nutrients such as crude protein, crude fiber, energy, etc. in the feed; in addition, accelerated solvent extraction technology is used to extract the drug components in the feed to obtain feed preparation quality data; the pasture feed formula data is intelligently and accurately adjusted according to the feed preparation quality data, and the deviation of nutrients, such as energy level, protein content, etc., is specifically analyzed; and the proportion of each raw material in the formula is intelligently adjusted according to the growth feedback of livestock and environmental changes; the adjusted feed formula will generate feed formula adjustment measures.

[0081] Step S3: extracting the pasture feed consumption from the pasture feed demand data to generate pasture feed consumption; calculating the feed consumption quantity from the pasture feed consumption to obtain feed consumption quantity data; executing the feed inventory output task through the central pasture management module based on the feed consumption quantity data to generate inventory output task data; outputting the feed inventory to the feed storage cylinder module according to the inventory output task data to generate feed inventory output data;

[0082] In the embodiment of the present invention, the eating conditions and health status of animals are monitored in real time by sensors and intelligent devices installed in the breeding farm, and the feed intake of each livestock is specifically recorded; and the feed consumption of the ranch is extracted, including the total consumption and the consumption rate, to generate ranch feed consumption data; based on the ranch feed consumption, the feed consumption quantity is accurately calculated by using data analysis technology to generate feed consumption quantity data; according to the feed consumption quantity data, the central forage management module performs the feed inventory output task; and according to the feed demand and inventory situation of the ranch, the inventory feed is allocated to generate inventory output task data; the instructions from the central forage management module are received through the feed storage cylinder module, and the feed distribution is automatically controlled according to the inventory output task data; specifically, the feed storage cylinder module records the distribution amount and time of the feed, generates feed inventory output data, and realizes the precise delivery of the feed.

[0083] Step S4: Based on the feed inventory output data, the feed storage cylinder module is used to monitor the inventory feed in real time to obtain the inventory feed data; the inventory weight of the inventory feed data is measured by the precise weighing module to obtain the inventory feed weight data; the inventory feed weight data is compared with the minimum inventory feed threshold to generate inventory feed status data; the central forage management module is used to adaptively manage the inventory according to the inventory feed status data to generate adaptive inventory management measures; the pasture management module is used to control the concentrate feed based on the feed formula adjustment measures and the adaptive inventory management measures to perform intelligent pasture concentrate feed control management operations.

[0084] In the embodiment of the present invention, by installing sensors and weighing modules in the feed storage cylinder module, such as the Copre weighing module, the stock changes of the feed are monitored in real time, the weight of the feed is specifically measured, and the inventory feed data is automatically updated according to the inflow and outflow of the feed to obtain the inventory feed data; the weighing sensor technology of the precision weighing module is used to accurately weigh the feed in the feed storage cylinder module, and the specific weighing module automatically calculates the total weight of the feed according to the weighing result and generates the inventory feed weight data; a minimum inventory feed threshold is set, and the inventory feed weight data monitored in real time is compared with the threshold; if the inventory feed weight is lower than the threshold, the inventory feed status data will be automatically generated. The central forage management module automatically starts the adaptive inventory management algorithm, such as the feature-based non-parametric inventory algorithm, after receiving the inventory feed status data, to dynamically adjust the inventory level and generate adaptive inventory management measures; the feed formula adjustment measures and adaptive inventory management measures are combined to automatically adjust the feeding amount and proportion of concentrate feed through the pasture management module, and specifically carry out precise mixing and feeding of feed; the feed formula is intelligently adjusted according to the growth stage, nutritional needs and feed inventory of the animals to ensure that the animals get balanced nutrition, while improving the utilization efficiency of feed and reducing costs, so as to perform intelligent pasture concentrate control management operations.

[0085] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0086] Step S21: identifying the livestock weight status of the livestock growth status data to obtain livestock weight data; determining the growth period of the livestock growth status data according to the livestock weight data to generate livestock growth period data; determining the required feed ingredients for the livestock growth period data to obtain feed data required for growth;

[0087] Step S22: extracting temperature characteristics of the pasture environment parameter information to obtain pasture environment temperature data; measuring the humidity of the pasture environment parameter information through the pasture environment temperature data to generate pasture environment humidity data; determining the composition ratio of pasture feed component data based on the feed data required for growth and the pasture environment humidity data to obtain pasture feed formula data;

[0088] Step S23: inputting the pasture feed formula data into the instruction control module to generate a pasture feed formula instruction signal; executing feed production and preparation operations on the central pasture feed management module according to the pasture feed formula instruction signal to obtain feed preparation task data; determining the feed raw material delivery sequence of the feed preparation task data to generate raw material delivery sequence data; calculating the delivery time of the raw material delivery sequence data to obtain raw material delivery time data; preparing livestock feed on the feed premixing module according to the raw material delivery sequence data and the raw material delivery time data to generate pasture feed preparation data;

[0089] Step S24: Perform feed quality analysis on the pasture feed preparation data through the precise weighing module to obtain feed preparation quality data; perform intelligent and precise ratio adjustment on the pasture feed formula data according to the feed preparation quality data to generate feed formula adjustment measures.

[0090] In the embodiment of the present invention, the livestock weight data is automatically collected by a weight monitor installed in the pasture, such as a weight scale based on RFID technology; specifically, when the livestock passes through the scale, the sensor will record its weight and generate livestock weight data; in combination with the livestock weight data and historical growth data, the growth period of the livestock is measured by using a machine learning algorithm; specifically, the growth stage of the livestock is determined according to the weight data, such as newborn, weaned and rearing, and the livestock growth period data is generated; according to the livestock growth period data, the nutritional demand standards, such as the "Technical Specifications for Standardized Scale Breeding and Production of Dairy Cows", are referred to to determine the feed ingredients required for livestock at different growth stages. The system automatically calculates the content of the required nutrients such as energy, protein, vitamins and minerals, and obtains the feed data required for growth; deploys temperature and humidity sensors in the pasture to monitor the ambient temperature in real time; and extracts the temperature characteristics of the pasture environment according to the data collected by the sensor to generate pasture environment temperature data; uses a humidity sensor matched with the temperature sensor to measure the humidity of the pasture environment and generate pasture environment humidity data. Combined with the feed data required for growth and the pasture environmental humidity data, through intelligent formulation technology, such as automatic feed batching technology, the feed formula that adapts to the current environmental conditions is automatically calculated; the specific proportion of feed ingredients is calculated to generate pasture feed formula data; the pasture feed formula data is input into the command control module, which is responsible for converting the formula data into executable command signals; these command signals guide the feed production equipment to accurately mix raw materials; after receiving the pasture feed formula command signal, the central forage management module automatically executes the feed production preparation operation; this module uses an automated control system, such as a PLC-based control system, to accurately control the feed production equipment according to the formula requirements. The system automatically determines the order of raw material delivery according to the feed preparation task data; calculates the specific delivery time of each raw material according to the raw material delivery sequence data; the feed premixing module automatically prepares livestock feed according to the raw material delivery sequence data and delivery time data; specifically includes the steps of accurate delivery, mixing and granulation of raw materials; uses precise weighing modules, such as dynamic and static weighing modules and weighing sensors, to conduct quality analysis on pasture feed preparation data, specifically and accurately measure the weight and nutritional components of feed and perform quality control; intelligently adjusts feed formula according to feed preparation quality data to compensate for deviations in the production process and generate feed formula adjustment measures.

[0091] As an example of the present invention, refer to Figure 3 As shown, in this example, step S24 includes:

[0092] Step S241: Perform feed weight detection on the pasture feed preparation data by using a precise weighing module to obtain feed preparation weight data; perform feed component weight statistics on the feed preparation weight data to generate feed component weight data; perform component average value calculation on the feed component weight data to obtain feed component average value;

[0093] Step S242: comparing the standard deviation of the average value of the feed ingredients with the preset feed formula to generate feed quality deviation data; determining the quality deviation degree of the feed preparation weight data according to the feed quality deviation data to obtain quality deviation degree data;

[0094] Step S243: performing feed component accuracy measurement on the feed component weight data according to the quality deviation degree data to generate feed component accuracy data; performing feed preparation quality determination on the pasture feed preparation data according to the feed component accuracy data to obtain feed preparation quality data;

[0095] Step S244: intelligently and accurately adjust the ranch feed formula data according to the feed preparation quality data to generate feed formula adjustment measures.

[0096] In the embodiment of the present invention, a precise weighing module, such as dynamic weighing sensor technology, is used to detect the weight of each batch of feed in the feed preparation process; specifically, the weighing sensor measures and records the weight of the feed when it passes through the weighing area; based on the feed preparation weight data, the weight of different feed components is statistically analyzed by quality detection technology, specifically including summarizing the weight of various raw materials such as grains, protein sources, minerals and vitamins; a data analysis method, such as a multivariate linear regression model, is used to process the feed component weight data and calculate the average value of each component; the calculated feed component average value is compared with the preset feed formula standard, and the standard deviation is calculated using a statistical method, specifically by comparing the actual components The deviation between the actual content and the expected content is used to evaluate the accuracy of feed preparation; the feed quality deviation data is used to determine whether the weight of the feed preparation is within the acceptable quality control range, and the feed preparation weight data is specifically analyzed again to determine the degree of quality deviation and generate quality deviation degree data; based on the quality deviation degree data, the weight data of the feed ingredients are accurately measured, and the difference between the actual content and the expected content of the feed ingredients is specifically analyzed; based on the feed ingredient accuracy data, the quality of the entire feed preparation process is evaluated to generate feed preparation quality data; the feed preparation quality data is used to make intelligent and accurate adjustments to the feed formula, and feed formula adjustment measures are generated in combination with the nutritional value and environmental parameters of the raw materials.

[0097] Preferably, step S244 includes the following steps:

[0098] Step S2441: checking the raw material delivery sequence data according to the feed preparation quality data to obtain delivery sequence verification data; performing time verification on the raw material delivery time data according to the delivery sequence verification data to generate delivery time node data; performing feed component supply abnormality identification on the feed component accuracy data to obtain component supply abnormality data;

[0099] Step S2442: matching the order characteristics of the delivery sequence check data with the raw material delivery sequence data. If the order characteristics of the delivery sequence check data and the raw material delivery sequence data do not completely match, generating delivery process disordered data; measuring the delivery time node data and the raw material delivery time data. If the delivery time node data and the raw material delivery time data are inconsistent in time length, generating delivery time abnormal data; integrating the delivery process disordered data and the delivery time abnormal data to obtain the raw material delivery process abnormal data;

[0100] Step S2443: performing formula component correlation identification on the feed preparation quality data to obtain formula component correlation data; performing component mutual cancellation feature extraction on the formula component correlation data to generate feed mutual cancellation features; performing feed component mutual cancellation detection on the feed mutual cancellation features according to the abnormal data of the raw material delivery process to obtain component mutual cancellation detection data;

[0101] Step S2444: performing feed component cancellation mapping on the pasture feed formula data according to the component cancellation detection data to obtain cancellation component mapping data; determining the abnormal data of the raw material delivery process according to the cancellation component mapping data; if the abnormal delivery process is disordered delivery process data, performing raw material delivery sequence adjustment on the central forage management module to generate raw material delivery sequence adjustment data; if the abnormal delivery process is abnormal delivery time data, performing a delivery time reduction operation on the premixing module to generate raw material delivery time adjustment data;

[0102] Step S2445: identifying the component supply abnormality type of the component supply abnormality data to obtain component supply abnormality type data; inputting the component supply abnormality type data into the instruction control module to obtain a component type supply instruction signal; executing specific feed component supply to the central forage management module according to the component type supply instruction signal to obtain component supply adjustment data;

[0103] Step S2446: Integrate the raw material delivery sequence adjustment data, raw material delivery time adjustment data, and ingredient supply adjustment data to generate feed formula adjustment measures.

[0104] In the embodiment of the present invention, the dynamic weighing sensor technology is used to monitor the order of raw material delivery in the feed preparation process in real time; specifically, the raw material delivery order information captured by the sensor is checked according to the preset formula requirements to see whether the actual delivery order complies with the standard operating procedures; the time point of raw material delivery is verified, specifically by comparing the preset time node and the actual delivery time; combined with the feed component accuracy data, the stability and consistency of raw material supply are analyzed; specifically by identifying the supply of components that deviate from the preset standard, thereby obtaining component supply abnormality data; the generated delivery order verification data is compared with the original raw material delivery order data, and specifically analyzing whether the sequence characteristics of the two are completely matched; if there is any inconsistency, the delivery process disorder data will be generated; the delivery time node data and the raw material delivery time data are measured for duration, by comparing the preset delivery duration and the actual duration; if inconsistency is found, the delivery time abnormality data will be generated; the delivery process disorder data and the delivery time abnormality data are integrated to obtain the raw material delivery process abnormality data; the feed preparation quality data is identified The correlation between the components in the formula; by analyzing the formula component correlation data, specifically identify The characteristics of mutual cancellation between the components are found, so as to extract the feed mutual cancellation characteristics; the feed mutual cancellation characteristics are detected in combination with the abnormal data of the raw material delivery process; if it is found that the raw material delivery is abnormal, which leads to the situation that the components cancel each other, the component cancellation detection data will be generated; the component cancellation detection data is used to identify the canceling feed components that need to be adjusted, and map them to obtain the canceling component mapping data; the specific process of delivery abnormality is determined according to the canceling component mapping data; if the analysis result shows that the delivery abnormality is caused by the disorder of the delivery sequence, the central forage management module is used to adjust and optimize the raw material delivery sequence; if the delivery abnormality is caused by improper delivery time, the premixing module is used to adjust the operation to reduce the delivery time of the raw materials; the component supply abnormal data is identified, and the type of abnormality is specifically identified, such as supply shortage and quality fluctuation; the identified abnormal type data is input into the command control module; according to the component type supply command signal, the central forage management module is adjusted to execute the specific feed component supply; the data fusion technology is used to integrate the raw material delivery sequence adjustment data, the raw material delivery time adjustment data, and the component supply adjustment data to generate feed formula adjustment measures.

[0105] Preferably, step S3 comprises the following steps:

[0106] Step S31: identifying demand scenarios for pasture feed demand data according to livestock growth status data and pasture environmental parameter information to obtain pasture feed demand scenario data; determining feed types for pasture feed demand scenario data to generate feed demand type data; extracting feed type consumption for pasture feed demand data according to feed demand type data to generate pasture feed consumption;

[0107] Step S32: extracting the consumption characteristics of the pasture feed consumption to obtain feed consumption characteristic data; according to the feed consumption characteristic data; measuring the feed consumption weight of the feed consumption characteristic data by a precise weighing module to obtain feed consumption weight data; performing numerical measurement conversion on the feed consumption weight data to obtain feed consumption quantity data;

[0108] Step S33: Input feed consumption quantity data into the instruction control module to generate a feed quantity output instruction signal; execute the feed inventory output task on the central forage management module according to the feed quantity output instruction signal to generate inventory output task data; output feed inventory to the feed storage barrel module according to the inventory output task data to generate feed inventory output data.

[0109] In the embodiment of the present invention, livestock growth status data and pasture environmental parameter information are combined, such as livestock positioning data collected by using GPS and RFID technology; physiological information such as livestock body temperature, respiration, heart rate, etc. are collected by sensors to identify the feed demand scenarios of different livestock at different growth stages, thereby determining the required feed type; based on the feed demand type data, the consumption of the corresponding feed type is automatically extracted to generate pasture feed consumption data; the pasture feed consumption is extracted, and consumption characteristics are specifically extracted, such as consumption peak period and consumption fluctuation, to generate feed consumption characteristic data; dynamic weighing is used to Sensor technology is used to accurately measure the weight of feed consumption characteristic data to obtain feed consumption weight data; the feed consumption weight data is converted into quantity data to provide an accurate numerical basis for the purchase and distribution of feed; the feed consumption quantity data is input into the command control module to generate a feed quantity output command signal; the central forage management module is used to automatically execute the feed inventory output task according to the command signal to generate inventory output task data; specific feed delivery and inventory management are carried out; according to the inventory output task data, the feed storage cylinder module is automatically controlled to output the feed inventory and generate feed inventory output data.

[0110] Preferably, step S4 comprises the following steps:

[0111] Step S41: Based on the feed inventory output data, the feed storage cylinder module performs real-time monitoring of the inventory feed to generate real-time monitoring data of the inventory feed; the feed inventory is determined based on the real-time monitoring data of the inventory feed to obtain inventory feed data; the inventory weight of the inventory feed data is measured by the precise weighing module to obtain inventory feed weight data;

[0112] Step S42: comparing the inventory feed weight data with the minimum inventory feed threshold, if the inventory feed weight data is higher than the minimum inventory feed threshold, generating normal inventory status data; if the inventory feed weight data is lower than or equal to the minimum inventory feed threshold, generating abnormal inventory status data; integrating the normal inventory status data and the abnormal inventory status data to obtain the inventory feed status data;

[0113] Step S43: Adaptively manage the inventory of the central forage management module according to the inventory feed status data and generate adaptive inventory management measures; perform concentrate control management on the pasture management module based on the feed formula adjustment measures and the adaptive inventory management measures to execute intelligent pasture concentrate control management operations.

[0114] In the embodiment of the present invention, in combination with the automatic control technology, the feed storage cylinder module is monitored in real time, and the real-time data of the feed inventory is collected by the sensor to generate the real-time monitoring data of the inventory feed; the data are analyzed to determine the inventory of the feed and obtain the inventory feed data; the inventory feed weight data is measured by using the precise weighing module, such as the dynamic weighing sensor technology, to obtain the inventory feed weight data; the inventory feed weight data is compared with the preset minimum inventory feed threshold, if the inventory feed weight data is higher than the threshold, the normal inventory status data will be generated, indicating that the current inventory level is sufficient to meet the production demand; if the inventory feed weight data is lower than or equal to the threshold, the abnormal inventory status data will be generated, indicating that the inventory needs to be replenished to avoid production interruption; the inventory feed status data is formed by using the data fusion technology; according to the inventory feed status data, the operation strategy of the central forage management module will be automatically adjusted to implement adaptive inventory management, including automatically triggering the purchase order, adjusting the feed formula or optimizing the feed distribution strategy, and generating adaptive inventory management measures; combined with the feed formula adjustment measures, the pasture management module is controlled and managed, and the intelligent pasture concentrate control management operation is performed.

[0115] Preferably, step S41 includes the following steps:

[0116] Step S411: extracting feed inventory data features from the feed storage cylinder module to generate feed inventory data; deducting feed inventory quantity from the feed inventory data according to the feed inventory output data to obtain inventory quantity deduction data;

[0117] Step S412: Based on the inventory quantity deduction data, the feed storage cylinder module is monitored in real time to generate inventory feed real-time monitoring data; the inventory feed inventory is measured on the inventory feed real-time monitoring data to obtain inventory feed data; the inventory feed quantity characteristics are identified on the inventory feed data to generate inventory feed quantity characteristics;

[0118] Step S413: convert the quantity characteristics of the stock feed into weight values ​​to obtain the weight characteristics of the stock feed; calculate the stock weight of the weight characteristics of the stock feed through a precise weighing module to obtain the weight data of the stock feed.

[0119] In an embodiment of the present invention, the Internet of Things technology is used to collect feed inventory data in real time through sensors installed in the feed storage cylinder module, such as edge computing sensors, specifically including characteristic information such as the current storage amount and storage time of the feed; a data mining algorithm, such as cluster analysis or pattern recognition, is used to extract key data features of the feed inventory and generate feed inventory data; according to the feed inventory output data, that is, the amount of feed that has been used or allocated, the corresponding quantity is automatically deducted from the feed inventory data; the inventory quantity deduction data is used to implement real-time monitoring of the inventory feed of the feed storage cylinder module; the dynamic changes of the feed inventory are collected through real-time monitoring technology, such as wireless sensor network (WSN); the feed inventory is accurately measured using database technology such as MySQL to obtain inventory feed data; the inventory feed data is feature identified through data analysis methods, such as multivariate linear regression models, and the quantity characteristics of the inventory feed are extracted; the quantity characteristics of the inventory feed are converted into weight values, and unit conversion and numerical calculation are specifically performed; the corresponding weight is calculated according to the density and volume of the feed; and the weight characteristics of the inventory feed are accurately measured using a precise weighing module, such as dynamic weighing sensor technology.

[0120] Preferably, step S43 includes the following steps:

[0121] Step S431: extracting insufficient feed data from the inventory feed status data to obtain insufficient feed data; inputting the insufficient feed data into the instruction control module to generate an insufficient feed instruction signal; inputting the insufficient feed instruction signal into the feed storage cylinder module to perform automatic inventory warning, and obtaining feed inventory warning information;

[0122] Step S432: Identify the feed inventory shortage type for the feed inventory warning information to obtain the inventory shortage type data; perform feed replenishment operation on the central forage management module according to the inventory shortage type data to obtain feed replenishment task data; increase the feed inventory quantity on the feed storage cylinder module based on the feed replenishment task data to obtain feed inventory replenishment information;

[0123] Step S433: Integrate the feed inventory warning information and the feed inventory replenishment information to generate adaptive inventory management measures;

[0124] Step S434: Based on the feed formula adjustment measures and the adaptive inventory management measures, the pasture management module is controlled and managed to perform the intelligent pasture concentrate control management operation.

[0125] In the embodiment of the present invention, wireless sensor network technology is used to monitor feed inventory in real time through sensors deployed in the feed storage cylinder module, and extract feed data with insufficient inventory; specifically analyze feed data with inventory below a preset threshold to generate feed insufficient inventory data; input the feed insufficient inventory data into the command control module to generate an insufficient inventory command signal; transmit the insufficient inventory command signal back to the feed storage cylinder module to trigger an automatic inventory warning mechanism, thereby obtaining feed inventory warning information; identify the feed inventory warning information, identify the specific type of insufficient feed inventory, such as the lack of specific nutrients or the shortage of specific feed types, and generate insufficient inventory type data; based on the insufficient inventory type data, automatically Automatically perform feed replenishment operations on the central forage management module to obtain feed replenishment task data; guide the feed storage cylinder module to replenish feed based on the feed replenishment task data, thereby increasing the inventory quantity and obtaining feed inventory replenishment information; integrate the feed inventory warning information and the feed inventory replenishment information, and analyze the trend of feed consumption and the efficiency of replenishment to generate adaptive inventory management measures; combine the feed formula adjustment measures and the adaptive inventory management measures to perform concentrate control management on the pasture management module, specifically including automatically adjusting the feed formula and optimizing the nutritional content of the feed; and intelligently control the concentrate feed delivery of the feed according to the growth needs of the livestock and the feed inventory status, and perform intelligent pasture concentrate control management operations.

[0126] Preferably, step S434 includes the following steps:

[0127] Step S4341: performing real-time demand feedback of pasture feed on the pasture management module according to the pasture feed demand data to generate feed demand feedback data; performing feed preparation demand feature identification on the feed demand feedback data to obtain feed preparation demand data; performing feed formula adjustment and determination on the feed preparation demand data to generate feed adjustment demand data;

[0128] Step S4342: inputting the feed adjustment demand data into the instruction control module to generate a feed adjustment demand instruction signal; executing feed formula adjustment measures on the central forage management module according to the feed adjustment demand instruction signal to obtain feed adjustment task data;

[0129] Step S4343: performing feed inventory replenishment feature recognition on the feed demand feedback data to obtain inventory replenishment demand data; inputting the inventory replenishment demand data into the command control module to generate an inventory replenishment demand command signal; executing adaptive inventory management measures on the central forage management module according to the inventory replenishment demand command signal to obtain inventory management task data;

[0130] Step S4344: Integrate the feed adjustment task data and the inventory management task data to obtain the concentrate control management data; control and manage the ranch management module based on the concentrate control management data to perform the intelligent ranch concentrate control management operation.

[0131] In the embodiment of the present invention, the Internet of Things technology is used to collect feed consumption data in real time through a wireless sensor network deployed in a pasture; the feed consumption pattern is analyzed through a data mining algorithm, such as an Apriori algorithm, to generate feed demand feedback data; the feed demand feedback data is deeply analyzed to specifically identify the demand characteristics of feed preparation and obtain feed preparation demand data; based on the feed preparation demand data, the feed formula adjustment demand is determined to generate feed adjustment demand data; the generated feed adjustment demand data is input into a command control module to generate a feed adjustment demand command signal; the feed adjustment demand command signal is transmitted to a central forage management module, and real-time feed formula adjustment is performed, and accurate feed formula adjustment measures are executed to obtain feed. Feed adjustment task data; further analysis of feed demand feedback data to specifically identify the replenishment characteristics of feed inventory and obtain inventory replenishment demand data; input the inventory replenishment demand data into the command control module to generate an inventory replenishment demand command signal; according to the inventory replenishment demand command signal, the central forage management module automatically executes adaptive inventory management measures, such as timely replenishment or adjustment of procurement plans, to obtain inventory management task data; integrates the feed adjustment task data and the inventory management task data to obtain concentrate control management data; based on the concentrate control management data, the pasture management module is accurately controlled and managed, and intelligent pasture concentrate control management operations are executed to ensure the accurate delivery and effective use of feed, and realize the automation and intelligence of feed management.

[0132] In this specification, an intelligent pasture concentrate control and management system based on the Internet of Things is provided, which includes a pasture management module, a central pasture management module, a command control module, a feed storage cylinder module, a precision weighing module, and a feed premixing module, which are used to execute the above-mentioned intelligent pasture concentrate control and management method based on the Internet of Things. The intelligent pasture concentrate control and management system based on the Internet of Things includes:

[0133] The pasture feed demand data collection module is used to obtain livestock growth status data; use sensors to collect environmental parameters of the pasture management module to obtain pasture environmental parameter information; based on livestock growth status data and pasture environmental parameter information, the pasture management module collects feed demand data in real time to obtain pasture feed demand data; extracts feed components from pasture feed demand data to generate pasture feed component data;

[0134] The pasture feed formula adjustment measures module is used to determine the composition ratio of pasture feed ingredient data according to livestock growth status data and pasture environmental parameter information to obtain pasture feed formula data; based on pasture feed formula data, the central forage management module executes feed production and preparation tasks to generate feed preparation task data; according to the feed preparation task data, the feed premixing module performs livestock feed preparation to generate pasture feed preparation data; the precise weighing module performs feed quality analysis on pasture feed preparation data to obtain feed preparation quality data; according to the feed preparation quality data, the pasture feed formula data is adjusted intelligently and precisely to generate feed formula adjustment measures;

[0135] The feed inventory output task module is used to extract the pasture feed consumption situation from the pasture feed demand data to generate the pasture feed consumption situation; to calculate the feed consumption quantity from the pasture feed consumption situation to obtain the feed consumption quantity data; based on the feed consumption quantity data, the central forage management module executes the feed inventory output task to generate the inventory output task data; according to the inventory output task data, the feed storage cylinder module outputs the feed inventory to generate the feed inventory output data;

[0136] The pasture concentrate feed control and management module is used to monitor the inventory feed of the feed storage cylinder module in real time based on the feed inventory output data to obtain the inventory feed data; to measure the inventory weight of the inventory feed data through the precise weighing module to obtain the inventory feed weight data; to compare the inventory feed weight data with the minimum inventory feed threshold to generate the inventory feed status data; to perform adaptive inventory management on the central pasture management module according to the inventory feed status data to generate adaptive inventory management measures; to perform concentrate feed control and management on the pasture management module based on the feed formula adjustment measures and the adaptive inventory management measures to perform intelligent pasture concentrate feed control and management operations.

[0137] The present invention uses a pasture feed demand data collection module and a sensor to collect environmental parameters of a pasture management module, so as to accurately obtain environmental parameter information of the pasture, including temperature, humidity, light, etc., which is helpful for subsequent evaluation of the growth environment of livestock; combines livestock growth status data and pasture environmental parameter information to collect feed demand data in real time to ensure that the feed supply matches the actual needs of livestock; extracts feed components from pasture feed demand data to provide accurate raw material information for subsequent feed formula design; determines the composition ratio of feed components according to livestock growth status data and pasture environmental parameter information, so as to ensure that the nutrition of the feed is balanced and meets the nutritional needs of livestock at different growth stages. Through the pasture feed formula adjustment measures module, based on the pasture feed formula data, the central pasture management module can perform feed production and preparation tasks, guide the actual feed production process, and ensure the efficiency and accuracy of feed production; through the feed premixing module, livestock feed is prepared, and the detailed process of feed preparation is clearly recorded, providing a basis for quality control; the precise weighing module performs feed quality analysis on the pasture feed preparation data, which can ensure that the feed quality meets the standards and protect the health of livestock; the pasture feed formula data is adjusted intelligently and accurately according to the feed preparation quality data, thereby improving the nutritional value and palatability of the feed and optimizing the feed formula. Through the feed inventory output task module, the pasture feed demand data is extracted from the pasture feed consumption situation to provide accurate consumption information for feed management; the feed consumption quantity is calculated for the pasture feed consumption situation, which can provide an accurate digital basis for feed inventory management; based on the feed consumption quantity data, the central pasture management module performs the feed inventory output task to ensure the rationality of the feed inventory and avoid feed waste or shortage; the feed inventory is output through the feed storage cylinder module, and the storage status of the feed is monitored in real time to ensure the freshness and availability of the feed. Through the pasture concentrate control and management module, the feed storage cylinder module is monitored in real time based on the feed inventory output data, and the obtained inventory feed data can provide real-time information for inventory management; the inventory weight is calculated through the precise weighing module to clarify the inventory weight information; the inventory feed weight data is compared with the minimum inventory feed threshold to timely warn of insufficient inventory and ensure the continuity of feed supply; the central pasture management module is adaptively managed according to the inventory feed status data, and the feed procurement and storage strategies are automatically adjusted to optimize the inventory level; the pasture management module is controlled and managed based on feed formula adjustment measures and adaptive inventory management measures. The intelligent pasture concentrate control and management operations performed can ensure the accurate delivery of feed, improve feed conversion rate, and enhance the pasture concentrate control and management.Therefore, the present invention realizes centralized batching and unified production of feed through Internet of Things technology, data processing technology, automatic control technology, and intelligent management technology, through an automatic control system, and according to livestock growth status data and pasture environmental parameter information; realizes real-time monitoring of the remaining inventory feed, and automatically issues inventory warnings, thereby improving the accuracy of pasture feed control.

[0138] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0139] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent pasture concentrate control and management method based on the Internet of Things, comprising the following steps: Step S1: Acquire livestock growth status data; use sensors to collect environmental parameters of a pasture management module to obtain pasture environmental parameter information; based on the livestock growth status data and pasture environmental parameter information, collect feed demand data of the pasture management module in real time to obtain pasture feed demand data; extract feed components from the pasture feed demand data to generate pasture feed component data; Step S2: Determine the composition ratio of the pasture feed ingredient data according to the livestock growth status data and the pasture environmental parameter information to obtain the pasture feed formula data; based on the pasture feed formula data, execute the feed production and preparation task through the central pasture management module to generate feed preparation task data; prepare livestock feed for the feed premixing module according to the feed preparation task data to generate pasture feed preparation data; perform feed quality analysis on the pasture feed preparation data through the precise weighing module to obtain feed preparation quality data; perform intelligent and precise ratio adjustment on the pasture feed formula data according to the feed preparation quality data to generate feed formula adjustment measures; Step S3: extracting the pasture feed consumption from the pasture feed demand data to generate pasture feed consumption; calculating the feed consumption quantity from the pasture feed consumption to obtain feed consumption quantity data; executing the feed inventory output task through the central pasture management module based on the feed consumption quantity data to generate inventory output task data; outputting the feed inventory to the feed storage cylinder module according to the inventory output task data to generate feed inventory output data; Step S4: Based on the feed inventory output data, the feed storage cylinder module is monitored in real time to obtain the inventory feed data; The inventory feed weight data is calculated through the precise weighing module to obtain the inventory feed weight data; Compare the inventory feed weight data with the minimum inventory feed threshold to generate inventory feed status data; Conduct adaptive inventory management on the central forage management module based on inventory feed status data and generate adaptive inventory management measures; The farm management module is used to control the concentrate feed based on feed formula adjustment measures and adaptive inventory management measures to perform intelligent farm concentrate feed control management operations; The intelligent and accurate proportioning adjustment of the pasture feed formula data according to the feed preparation quality data to generate the feed formula adjustment measures specifically includes: According to the feed preparation quality data, the raw material delivery sequence data is checked to obtain delivery sequence check data; according to the delivery sequence check data, the raw material delivery time data is time-checked to generate delivery time node data; the feed component accuracy data is used to identify the feed component supply anomaly to obtain component supply anomaly data; The order characteristics of the delivery sequence verification data and the raw material delivery sequence data are matched. If the order characteristics of the delivery sequence verification data and the raw material delivery sequence data do not completely match, the delivery process disordered data is generated; the delivery time node data and the raw material delivery time data are measured for duration. If the time length of the delivery time node data and the raw material delivery time data is inconsistent, the delivery time abnormal data is generated; the delivery process disordered data and the delivery time abnormal data are integrated to obtain the raw material delivery process abnormal data; The formula component correlation of feed preparation quality data is identified to obtain formula component correlation data; the component mutual cancellation feature of the formula component correlation data is extracted to generate feed mutual cancellation feature; the feed component mutual cancellation detection is performed on the feed mutual cancellation feature according to the abnormal data of the raw material delivery process to obtain component mutual cancellation detection data; According to the component cancellation detection data, the pasture feed formula data is mapped with the cancel feed components to obtain the cancel component mapping data; according to the cancel component mapping data, the abnormal data of the raw material delivery process is determined according to the abnormal delivery process. If the abnormal delivery process is the disordered delivery process data, the central forage management module is adjusted to adjust the raw material delivery sequence to generate the raw material delivery sequence adjustment data; if the abnormal delivery process is the abnormal delivery time data, the premixing module is operated to reduce the delivery time to generate the raw material delivery time adjustment data; Identify the abnormal component supply type of the abnormal component supply data to obtain abnormal component type data; input the abnormal component type data into the instruction control module to obtain an ingredient type supply instruction signal; and execute specific feed component supply to the central forage management module according to the ingredient type supply instruction signal to obtain ingredient supply adjustment data; Integrate raw material delivery sequence adjustment data, raw material delivery time adjustment data, and ingredient supply adjustment data to generate feed formula adjustment measures.

2. The intelligent farm concentrate control and management method based on the Internet of Things according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: identifying the livestock weight status of the livestock growth status data to obtain livestock weight data; determining the growth period of the livestock growth status data according to the livestock weight data to generate livestock growth period data; determining the required feed ingredients for the livestock growth period data to obtain feed data required for growth; Step S22: extracting temperature characteristics of the pasture environment parameter information to obtain pasture environment temperature data; measuring the humidity of the pasture environment parameter information through the pasture environment temperature data to generate pasture environment humidity data; determining the composition ratio of pasture feed component data based on the feed data required for growth and the pasture environment humidity data to obtain pasture feed formula data; Step S23: inputting the pasture feed formula data into the instruction control module to generate a pasture feed formula instruction signal; executing feed production and preparation operations on the central pasture feed management module according to the pasture feed formula instruction signal to obtain feed preparation task data; determining the feed raw material delivery sequence of the feed preparation task data to generate raw material delivery sequence data; calculating the delivery time of the raw material delivery sequence data to obtain raw material delivery time data; preparing livestock feed on the feed premixing module according to the raw material delivery sequence data and the raw material delivery time data to generate pasture feed preparation data; Step S24: Perform feed quality analysis on the pasture feed preparation data through the precise weighing module to obtain feed preparation quality data; perform intelligent and precise ratio adjustment on the pasture feed formula data according to the feed preparation quality data to generate feed formula adjustment measures.

3. The intelligent pasture concentrate control and management method based on the Internet of Things according to claim 2 is characterized in that: Step S24 includes the following steps: Step S241: Perform feed weight detection on the pasture feed preparation data by using a precise weighing module to obtain feed preparation weight data; perform feed component weight statistics on the feed preparation weight data to generate feed component weight data; perform component average calculation on the feed component weight data to obtain feed component average value; Step S242: comparing the standard deviation of the average value of the feed ingredients with the preset feed formula to generate feed quality deviation data; determining the quality deviation degree of the feed preparation weight data according to the feed quality deviation data to obtain quality deviation degree data; Step S243: performing feed component accuracy measurement on the feed component weight data according to the quality deviation degree data to generate feed component accuracy data; performing feed preparation quality determination on the pasture feed preparation data according to the feed component accuracy data to obtain feed preparation quality data; Step S244: intelligently and accurately adjust the pasture feed formula data according to the feed preparation quality data to generate feed formula adjustment measures.

4. The intelligent farm concentrate control and management method based on the Internet of Things according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: identifying demand scenarios for pasture feed demand data according to livestock growth status data and pasture environmental parameter information to obtain pasture feed demand scenario data; determining feed types for pasture feed demand scenario data to generate feed demand type data; extracting feed type consumption for pasture feed demand data according to feed demand type data to generate pasture feed consumption; Step S32: extracting the consumption characteristics of the pasture feed consumption to obtain feed consumption characteristic data; according to the feed consumption characteristic data; measuring the feed consumption weight of the feed consumption characteristic data by a precise weighing module to obtain feed consumption weight data; performing numerical measurement conversion on the feed consumption weight data to obtain feed consumption quantity data; Step S33: Input feed consumption quantity data into the instruction control module to generate a feed quantity output instruction signal; execute the feed inventory output task on the central forage management module according to the feed quantity output instruction signal to generate inventory output task data; output feed inventory to the feed storage barrel module according to the inventory output task data to generate feed inventory output data.

5. The intelligent farm concentrate control and management method based on the Internet of Things according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Based on the feed inventory output data, the feed storage cylinder module performs real-time monitoring of the inventory feed to generate real-time monitoring data of the inventory feed; the feed inventory is determined based on the real-time monitoring data of the inventory feed to obtain inventory feed data; the inventory weight of the inventory feed data is measured by the precise weighing module to obtain inventory feed weight data; Step S42: comparing the inventory feed weight data with the minimum inventory feed threshold, if the inventory feed weight data is higher than the minimum inventory feed threshold, generating normal inventory status data; if the inventory feed weight data is lower than or equal to the minimum inventory feed threshold, generating abnormal inventory status data; integrating the normal inventory status data and the abnormal inventory status data to obtain the inventory feed status data; Step S43: Adaptively manage the inventory of the central forage management module according to the inventory feed status data and generate adaptive inventory management measures; perform concentrate control management on the pasture management module based on the feed formula adjustment measures and the adaptive inventory management measures to execute intelligent pasture concentrate control management operations.

6. The method for controlling and managing intelligent pasture concentrate based on the Internet of Things according to claim 5, characterized in that: Step S41 includes the following steps: Step S411: extracting feed inventory data features from the feed storage cylinder module to generate feed inventory data; deducting feed inventory quantity from the feed inventory data according to the feed inventory output data to obtain inventory quantity deduction data; Step S412: Based on the inventory quantity deduction data, the feed storage cylinder module is monitored in real time to generate inventory feed real-time monitoring data; the inventory feed inventory is measured on the inventory feed real-time monitoring data to obtain inventory feed data; the inventory feed quantity characteristics are identified on the inventory feed data to generate inventory feed quantity characteristics; Step S413: convert the quantity characteristics of the stock feed into weight values ​​to obtain the weight characteristics of the stock feed; calculate the stock weight of the weight characteristics of the stock feed through a precise weighing module to obtain the weight data of the stock feed.

7. The method for controlling and managing intelligent pasture concentrate based on the Internet of Things according to claim 5, characterized in that: Step S43 includes the following steps: Step S431: extracting insufficient feed data from the inventory feed status data to obtain insufficient feed data; inputting the insufficient feed data into the instruction control module to generate an insufficient feed instruction signal; inputting the insufficient feed instruction signal into the feed storage cylinder module to perform automatic inventory warning, and obtaining feed inventory warning information; Step S432: Identify the feed inventory shortage type for the feed inventory warning information to obtain the inventory shortage type data; perform feed replenishment operation on the central forage management module according to the inventory shortage type data to obtain feed replenishment task data; increase the feed inventory quantity on the feed storage cylinder module based on the feed replenishment task data to obtain feed inventory replenishment information; Step S433: Integrate the feed inventory warning information and the feed inventory replenishment information to generate adaptive inventory management measures; Step S434: Based on the feed formula adjustment measures and the adaptive inventory management measures, the pasture management module is controlled and managed to perform the intelligent pasture concentrate control management operation.

8. The method for controlling and managing intelligent pasture concentrate based on the Internet of Things according to claim 7, characterized in that: Step S434 includes the following steps: Step S4341: performing real-time demand feedback of pasture feed on the pasture management module according to the pasture feed demand data to generate feed demand feedback data; performing feed preparation demand feature identification on the feed demand feedback data to obtain feed preparation demand data; performing feed formula adjustment and determination on the feed preparation demand data to generate feed adjustment demand data; Step S4342: inputting the feed adjustment demand data into the instruction control module to generate a feed adjustment demand instruction signal; executing feed formula adjustment measures on the central forage management module according to the feed adjustment demand instruction signal to obtain feed adjustment task data; Step S4343: performing feed inventory replenishment feature recognition on the feed demand feedback data to obtain inventory replenishment demand data; inputting the inventory replenishment demand data into the command control module to generate an inventory replenishment demand command signal; executing adaptive inventory management measures on the central forage management module according to the inventory replenishment demand command signal to obtain inventory management task data; Step S4344: Integrate the feed adjustment task data and the inventory management task data to obtain the concentrate control management data; control and manage the ranch management module based on the concentrate control management data to perform the intelligent ranch concentrate control management operation.

9. An intelligent pasture concentrate control and management system based on the Internet of Things, characterized in that: The system comprises a pasture management module, a central forage management module, a command control module, a feed storage cylinder module, a precision weighing module, and a feed premixing module, and is used to execute the intelligent pasture concentrate control and management method based on the Internet of Things as claimed in claim 1. The intelligent pasture concentrate control and management system based on the Internet of Things comprises: The pasture feed demand data collection module is used to obtain livestock growth status data; use sensors to collect environmental parameters of the pasture management module to obtain pasture environmental parameter information; based on livestock growth status data and pasture environmental parameter information, the pasture management module collects feed demand data in real time to obtain pasture feed demand data; extracts feed components from pasture feed demand data to generate pasture feed component data; The pasture feed formula adjustment measures module is used to determine the composition ratio of pasture feed ingredient data according to livestock growth status data and pasture environmental parameter information to obtain pasture feed formula data; based on pasture feed formula data, the central forage management module executes feed production and preparation tasks to generate feed preparation task data; according to the feed preparation task data, the feed premixing module performs livestock feed preparation to generate pasture feed preparation data; the precise weighing module performs feed quality analysis on pasture feed preparation data to obtain feed preparation quality data; according to the feed preparation quality data, the pasture feed formula data is adjusted intelligently and precisely to generate feed formula adjustment measures; The feed inventory output task module is used to extract the pasture feed consumption situation from the pasture feed demand data to generate the pasture feed consumption situation; to calculate the feed consumption quantity from the pasture feed consumption situation to obtain the feed consumption quantity data; based on the feed consumption quantity data, the central forage management module executes the feed inventory output task to generate the inventory output task data; according to the inventory output task data, the feed storage cylinder module outputs the feed inventory to generate the feed inventory output data; The pasture concentrate feed control and management module is used to monitor the inventory feed of the feed storage cylinder module in real time based on the feed inventory output data to obtain the inventory feed data; to measure the inventory weight of the inventory feed data through the precise weighing module to obtain the inventory feed weight data; to compare the inventory feed weight data with the minimum inventory feed threshold to generate the inventory feed status data; to perform adaptive inventory management on the central pasture management module according to the inventory feed status data to generate adaptive inventory management measures; to perform concentrate feed control and management on the pasture management module based on the feed formula adjustment measures and the adaptive inventory management measures to perform intelligent pasture concentrate feed control and management operations.

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