A breeding management system based on ameba management mode

Abstract

The application discloses a breeding management system based on an amoeba management mode, which comprises pig identification devices, data acquisition equipment, a data processing unit and an application terminal. The pig identification devices are radio frequency identification ear tags, the data acquisition equipment comprises a passageway video inventory equipment, the data processing unit is provided with a cost accounting module, a virtual pricing module and an input-output analysis module, and the application terminal is provided with an amoeba management module. The radio frequency identification ear tags store unique ID numbers of pigs, the passageway video inventory equipment tracks the transfer of pigs, the cost accounting module accounts for the daily cost of a single pig, the virtual pricing module calculates the virtual profit of a single unit, and the amoeba management module quantifies the performance of personnel. The system solves the problem of rough cost accounting in traditional breeding, realizes fine accounting, scientific performance evaluation and rapid response to management problems.

Description

Technical Field

[0001] This invention relates to the fields of agricultural informatization, intelligent breeding, and Internet of Things, specifically to a breeding management system based on the amoeba management model. Background Technology

[0002] Traditional pig farming management suffers from significant flaws in cost accounting methods, becoming a key issue hindering the efficient development of the industry. Current management models generally use the entire farm as a unified accounting unit, resulting in excessively high levels of cost data aggregation, making it impossible to track costs for individual pig houses or even each pig. This extensive accounting approach leads to ambiguity in the allocation of various costs incurred during the farming process. Expenses for feed, water, electricity, and medicine cannot be accurately allocated to specific farming stages and animals, making it difficult for farmers to accurately grasp the cost composition at different stages and in different units. Inaccurate cost data further results in a lack of targeted cost control, making it difficult to identify cost waste in a timely manner and to develop scientific and reasonable cost optimization strategies. Anomalies in costs that arise during operations often go undetected until discovered during overall accounting, leading to delayed problem-solving and unnecessary economic losses.

[0003] Based on the above problems, there is an urgent need for a technical solution that can achieve precise cost accounting and accurately track the cost consumption of each pig at different breeding stages and in different pig house units, so as to solve the core problem of crude cost accounting in traditional breeding management. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a livestock management system based on the Amoeba Management model. This system includes a pig identification device, a data acquisition device, a data processing unit, and an application terminal. The pig identification device is a radio frequency identification (RFID) ear tag. The data acquisition device includes an aisle video inventory device. The data processing unit includes a cost accounting module, a virtual pricing module, and an input-output analysis module. The application terminal includes an Amoeba Management module. The RFID ear tag stores a unique ID number for each pig. The aisle video inventory device identifies pig transfer events and records location changes. The cost accounting module collects daily consumption data based on the pig house unit where the pig is located and calculates the daily cost per pig. The virtual pricing module sets the virtual revenue price and virtual cost price for each pig house unit and calculates the virtual profit. The input-output analysis module analyzes the input and output of each pig house unit. The Amoeba Management module uses virtual profit and job value coefficients to quantitatively evaluate personnel performance.

[0005] Preferably, the data acquisition equipment also includes environmental sensors, which are divided into air sensors, wastewater sensors and energy consumption sensors. The air sensors collect data on temperature, humidity, ammonia concentration and oxygen concentration in each pig house unit. The wastewater sensors collect data on wastewater discharge and pollutant concentration. The energy consumption sensors collect data on water, electricity and gas consumption in each unit. All collected data are transmitted to the data processing unit in real time.

[0006] In a further optimized manner, the daily consumption data collected by the cost accounting module includes feed consumption data, water and electricity consumption data, medicine consumption data, and depreciation allocation data. Feed consumption data is obtained through an automatic feeding system, water and electricity consumption data is obtained through smart meters and water meters, medicine consumption data is obtained through an inventory management system, and depreciation allocation data is calculated based on the usage of pig house equipment and buildings.

[0007] In a further optimized version, the virtual revenue price of the virtual pricing module is determined based on the market pig price, pig weight, and health status; the virtual cost price is determined based on the actual consumption data of each pig house unit; the virtual profit is calculated by the difference between virtual revenue and virtual cost; and the virtual revenue of each pig is calculated by combining the virtual revenue price and the pig weight.

[0008] In a further preferred embodiment, the aisle video inventory device consists of a high-definition camera and an image recognition algorithm. The high-definition camera is installed at the entrance of the passage between each pig house unit. When pigs are transferred, the image recognition algorithm counts the number of pigs and forms a transfer event record containing ID information, a unique ID number obtained through radio frequency identification ear tags, quantity information, transfer time, source pig house unit identifier, and target pig house unit identifier. The transfer event record is transmitted to the data processing unit in real time, and the cost accounting module switches the cost accounting attribution of the corresponding pig based on the transfer event record.

[0009] In a further optimized approach, the performance score of the Amoeba Management module is calculated using virtual profit and job value coefficient. The job value coefficient is calculated comprehensively based on three dimensions: responsibility complexity, years of work experience, and historical performance results. Responsibility complexity is categorized according to the difficulty of the pig house unit's breeding stage, years of work experience are calculated cumulatively based on actual years of work experience, and historical performance results are determined based on the average performance over a preset period of time.

[0010] Further preferably, the system also includes a PC terminal, an APP terminal, and a mini-program terminal. The PC terminal is equipped with system management, warehouse management, production data collection management, process management, statistical analysis, and intelligent IoT system. The APP terminal is equipped with basic management, production data collection, and key production report functions. The mini-program terminal is equipped with production reminders, production and operation reports, and collaborative office functions. The PC terminal, APP terminal, and mini-program terminal are all connected to the data processing unit.

[0011] Further optimized, the statistical analysis functions on the PC include business analysis and production analysis. Business analysis displays the virtual profit, cost structure, input-output ratio and year-on-year and month-on-month changes of each pig house unit. Production analysis displays the pig survival rate, weight gain rate, feed conversion rate and average breeding cycle data. All data are generated based on the calculation and analysis results of the data processing unit.

[0012] In a further preferred embodiment, the intelligent IoT system connects the feed tower feeding system, the weighbridge weighing system, the access control system, the video surveillance system, the wastewater treatment system, and the environmental monitoring system. The feed tower feeding system records feed output data, the weighbridge weighing system collects pig weight data and associates it with the unique ID number of the RFID ear tag, the access control system records personnel entry and exit data, the video surveillance system transmits monitoring images and analyzes abnormal pig behavior, the wastewater treatment system records wastewater treatment data, and the environmental monitoring system integrates data from various sensors and generates an environmental quality report.

[0013] In a further preferred embodiment, the data processing unit is also equipped with an early warning module. The early warning module sets virtual profit early warning thresholds, cost consumption early warning thresholds, and health status early warning thresholds. When a unit's relevant indicators are detected to reach the early warning threshold, the early warning module sends early warning information through the APP and mini-program. At the same time, the abnormal unit is marked and an abnormal analysis report is generated in the PC-side statistical analysis module.

[0014] The technical effects achieved by the above embodiments include:

[0015] The core innovative technology of this invention is the integration of the Amoeba Management concept with Internet of Things (IoT) technology. It achieves precise pig tracking through RFID ear tags and aisle video inventory devices, and combines these with cost accounting, virtual pricing, and Amoeba Management modules to construct a unit-level independent accounting system. This technical solution precisely addresses the main problem of imprecise cost accounting in traditional farming, enabling accurate daily cost accounting for individual pigs, quantification of unit input and output, and scientific evaluation of personnel performance, thus facilitating rapid response to operational issues. Attached Figure Description

[0016] Figure 1 This is a connection diagram of the aquaculture management system based on the amoeba management model in this application;

[0017] Figure 2 This is a detailed connection diagram of the aquaculture management system based on the amoeba management model in this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] Traditional farming management suffers from technical problems such as cost accounting being based on the farm as a unit, which cannot be broken down to the pig house unit or the cost data per pig being inaccurate, leading to difficulties in cost control.

[0020] Based on this, please refer to Figure 1-2This embodiment provides a livestock management system based on the Amoeba Management model, including pig identification devices, data acquisition equipment, a data processing unit, and an application terminal. The pig identification devices are radio frequency identification (RFID) ear tags. The data acquisition equipment includes aisle video inventory equipment. The data processing unit includes a cost accounting module, a virtual pricing module, and an input-output analysis module. The application terminal includes an Amoeba Management module. The RFID ear tags store a unique ID number for each pig. The aisle video inventory equipment identifies pig transfer events and records location changes. The cost accounting module collects daily consumption data based on the pig house unit where the pig is located and calculates the daily cost per pig. The virtual pricing module sets the virtual revenue price and virtual cost price for each pig house unit and calculates the virtual profit. The input-output analysis module analyzes the input and output of each pig house unit. The Amoeba Management module uses virtual profit and job value coefficients to quantitatively evaluate personnel performance. This solution solves the problem of crude traditional cost accounting through multi-module collaboration, achieving refined accounting and scientific management. Radio frequency identification (RFID) ear tags are uniformly worn on the ears of each pig. The unique ID number stored on each tag is specifically linked to basic information such as the pig's breed, breeding batch, and initial breeding unit, ensuring traceability of each pig throughout its entire lifecycle from introduction to slaughter, providing fundamental identification support for accurate cost allocation. High-definition cameras in the aisle video inventory system are fixedly installed at the entrances of the passageways between each pigpen unit, with the camera lens facing the direction of passage to ensure that the ear area of ​​each pig is fully captured during pig transfers. When a pig is transferred from one unit to another through the passageway, the camera automatically starts continuous shooting. The image recognition algorithm analyzes the captured image sequence frame by frame, first locating the pig's ear area, then extracting the unique ID number from the RFID ear tag. Simultaneously, the number of pigs passing through the passageway is counted by comparing image frames, ultimately forming a transfer event record containing ID information, quantity information, transfer time, source pigpen unit identifier, and target pigpen unit identifier. The transfer time is accurate to the minute, ensuring the timeliness of location change recording.The cost accounting module receives real-time consumption data from each pig house unit. Feed consumption data comes from the automatic feeding system, which records the type and weight of feed fed to the corresponding unit each time, and calculates feed costs based on the feed purchase price. Water and electricity consumption data come from smart water meters and smart electricity meters, which collect the water and electricity consumption of the corresponding unit in real time, and calculate water and electricity costs based on preset water and electricity prices. Medicine consumption data comes from the inventory management system, which records the type and quantity of medicines and vaccines used by the corresponding unit, and calculates medicine costs based on purchase prices. Depreciation costs cover the depreciation of pig house equipment and buildings. Equipment depreciation is calculated by dividing the total equipment purchase cost by the estimated total number of days of use, and building depreciation is calculated by dividing the total building construction cost by the estimated total number of days of use. The daily depreciation is then allocated to the corresponding unit based on the usage area ratio of each pig house unit. Finally, the cost accounting module determines the pig house unit where each pig is located on that day based on the pig transfer event records, and divides the sum of all consumption costs of that unit by the total number of pigs in the unit on that day to obtain the daily cost per pig. The virtual pricing module first collects publicly available industry hog transaction prices for the day as the market price base. Then, it comprehensively assesses the health status of each pig using environmental data collected by environmental sensors and pig behavior data collected by aisle video monitoring equipment. Based on this, a health score adjustment coefficient is set to determine the virtual revenue price for each pigpen unit. The virtual cost price is directly calculated by the cost accounting module based on the actual total cost consumed by the corresponding unit. Virtual profit is obtained by subtracting the total virtual cost of the unit from the total virtual revenue of all pigs in that unit. The amoeba management module first calculates the job value coefficient based on the complexity of each position's responsibilities, the employee's years of experience, and historical performance results. Then, it multiplies the virtual profit of the corresponding pigpen unit by the job value coefficient to obtain the performance score of the employee responsible for that unit, providing a quantitative basis for bonus allocation and job adjustments. To accurately calculate the daily cost per pig, a cost accounting formula is designed: Feed cost is the total value of feed consumed by a single pig daily, expressed in yuan. It is calculated by multiplying the total weight of feed used by the unit on that day, as recorded by the automatic feeding system, by the corresponding feed purchase price. The feed purchase price is the unit weight price stipulated in the purchase contract, expressed in yuan per kilogram. This price data is synchronized to the cost accounting module in real time to ensure price accuracy. Water and electricity cost is the total cost of water and electricity consumed by a single pig daily, expressed in yuan. Water consumption is collected in real time by a smart water meter, expressed in cubic meters. Electricity consumption is collected in real time by a smart electricity meter, expressed in kilowatt-hours. The water and electricity prices are pre-entered into the system based on local unified charging standards, expressed in yuan per cubic meter and yuan per kilowatt-hour, respectively. Water and electricity costs are calculated by multiplying water consumption by the water price and electricity consumption by the electricity price, and then summing the results. Drug cost is the total value of drugs and vaccines consumed per pig per day, expressed in yuan. It is calculated by multiplying the total quantity of drugs issued to the unit on that day by the corresponding purchase price, as recorded in the inventory management system. Issuance records must specify the exact drug name and specifications, and the purchase price is based on the price recorded upon receipt. Depreciation cost is the daily depreciation expense of pigsty equipment and buildings, expressed in yuan. The estimated total service life of equipment is calculated based on the lifespan stated in the equipment's instruction manual, and the estimated total service life of buildings is calculated based on industry-standard building service life. Allocation is based on the actual usable area of ​​each pigsty unit, calculated according to the actual measured building area, ensuring a fair and reasonable allocation ratio. The number of pigs is the total number of pigs in the unit on the accounting day, expressed in heads. It is based on the last count from the aisle video inventory equipment on that day. If no pigs are transferred on that day, the previous day's count is used, adjusted in conjunction with the day's mortality and culling records. The logical derivation of this formula is based on the core need of cost allocation in the livestock industry. Feed, water, electricity, and medicine are direct costs in the livestock process, while equipment and construction are indirect costs. By summing these two types of costs and allocating them equally among the pigs in a unit, the actual resource cost per pig can be accurately reflected. This aligns with the principles of objectivity and fairness in cost accounting and addresses the shortcomings of traditional overall accounting, which cannot distinguish individual costs. The technical effects achieved by the above embodiment include: accurate daily cost accounting for a single pig, clearly presenting the cost composition; quantifying the input and output of each pigsty unit, promoting positive competition between units; scientifically evaluating personnel performance, fully motivating employees; and timely identifying cost anomalies and other issues during operation, providing data support for optimal resource allocation.

[0021] Traditional aquaculture data collection suffers from technical problems such as collecting only partial aquaculture data, lacking key data such as environmental energy consumption, and incomplete data dimensions, which affects the accuracy of analysis.

[0022] Based on this, the data acquisition equipment also includes environmental sensors, which are divided into air sensors, wastewater sensors, and energy consumption sensors. Air sensors collect data on temperature, humidity, ammonia concentration, and oxygen concentration within each pigsty unit; wastewater sensors collect data on wastewater discharge and pollutant concentration; and energy consumption sensors collect data on water, electricity, and gas consumption in each unit. All collected data is transmitted to the data processing unit in real time. This solution addresses the problem of incomplete data by enriching the data acquisition dimensions, providing comprehensive data support for subsequent accounting and analysis. Air sensors are evenly distributed within each pigsty unit at a density of one sensor per 20 square meters, with the sensors installed 1.5 meters above the ground. This avoids the influence of ground dust while accurately collecting environmental data from the pigs' activity areas. The temperature data collection range covers -10 to 50 degrees Celsius, the humidity data collection range covers 0 to 100%, the ammonia concentration collection range covers 0 to 100 milligrams per cubic meter, and the oxygen concentration collection range covers 15 to 25%, ensuring that the actual needs of livestock environment monitoring are met. Wastewater sensors are fixedly installed inside the wastewater discharge outlets of each pigsty unit, with the sensor probes completely submerged in the wastewater. Wastewater discharge volume is collected by the sensor's built-in flow metering module, measured in cubic meters. Pollutant concentrations are primarily monitored for chemical oxygen demand (COD) and suspended solids content, measured in milligrams per liter (mg / L), ensuring the accuracy of wastewater discharge data. Energy consumption sensors are connected to the smart water, electricity, and gas meters in each pigsty unit via wiring, directly collecting water consumption, electricity consumption, and gas consumption, measured in cubic meters, kilowatt-hours (kWh), and cubic meters, respectively. The sensor's data sampling frequency is set to once per minute to ensure real-time capture of changes in consumption data. Data collected by all environmental sensors is transmitted wirelessly to the data processing unit in real time, with transmission latency controlled within 5 seconds to ensure data timeliness. This provides comprehensive and timely data support for energy cost calculations and pig health status assessments in cost accounting. The technical effects achieved by the above embodiments include comprehensive coverage of key indicators such as environment, wastewater, and energy consumption in data collection; real-time data transmission to ensure data timeliness; and improved system analysis accuracy by providing data support for cost accounting and health assessments.

[0023] Traditional cost accounting suffers from technical problems such as fragmented data acquisition channels and a lack of unified collection and integration, impacting cost accounting efficiency. To address this, the cost accounting module collects daily consumption data including feed consumption, water and electricity consumption, medicine consumption, and depreciation allocation data. Feed consumption data is obtained through an automatic feeding system, water and electricity consumption data through smart meters, medicine consumption data through an inventory management system, and depreciation allocation data is calculated based on the usage of pigsty equipment and buildings. This solution improves cost accounting efficiency by clearly defining data acquisition channels to solve the data fragmentation problem. The automatic feeding system establishes a dedicated data transmission channel with the cost accounting module. After each feed delivery to a pigsty unit, the system automatically records the type of feed, its weight, the feeding time, and the corresponding pigsty unit identifier. This data is synchronized to the cost accounting module in real time, eliminating the need for manual entry and ensuring the timeliness and accuracy of feed consumption data. Smart meters and water meters are directly linked to the cost accounting module. Electricity meters collect hourly electricity consumption data in real time, and water meters collect hourly water consumption data in real time. The collected data is automatically uploaded to the data processing unit hourly. The cost accounting module summarizes the electricity and water consumption data for each unit daily, preventing data backlog. The inventory management system specifically records the medication requisition information for each pigsty unit, including requisition time, medication name, specifications, quantity, requisitioning personnel, and the corresponding pigsty unit identifier. After the requisition operation is completed, the data is immediately synchronized to the cost accounting module, ensuring traceability of medication consumption data. The calculation of depreciation allocation data is updated monthly. Equipment usage is assessed based on runtime and maintenance records from the equipment management system, while building usage is assessed based on actual usable area and maintenance records. Equipment depreciation is calculated daily by dividing the total equipment purchase cost by the estimated total number of days of use, and building depreciation is calculated daily by dividing the total building construction cost by the estimated total number of days of use. The allocation ratio is then determined based on the proportion of usable area and usage time of each pigsty unit, and the daily depreciation is allocated to the corresponding unit. The allocation ratio is dynamically adjusted monthly based on usage to ensure the rationality of depreciation cost allocation. The technical effects achieved by the above embodiments include: unified data acquisition channels for efficient collection of consumption data; accurate data collection to ensure the accuracy of cost accounting; and reduced manual intervention to improve accounting efficiency.

[0024] Traditional pig farming unit assessments suffer from a lack of scientific methods for calculating unit revenue and profit, hindering accurate evaluation of unit operational status. To address this, the virtual pricing module determines virtual revenue based on market pig prices, pig weight, and health status; virtual cost prices are determined by actual consumption data for each pig unit; and virtual profit is calculated as the difference between virtual revenue and virtual cost. Virtual revenue for each pig is calculated by combining the virtual revenue price and pig weight. This solution addresses the unscientific nature of unit assessments through a virtual pricing mechanism, achieving accurate evaluation of unit operational status. Market pig prices are obtained three times daily from an authoritative industry data platform: at 9:00 AM, 3:00 PM, and 8:00 PM. The average of these three data points is used as the daily market price benchmark, and a price is quoted in yuan per kilogram to ensure the objectivity of market prices. Pig weight is collected using a weighbridge system at three key stages: pig introduction, transfer to new pens, and slaughter. The weighing data at introduction and transfer is used for phased cost allocation calculations. The health status assessment of pigs employs a multi-dimensional scoring mechanism. Environmental data includes temperature, humidity, ammonia concentration, and oxygen concentration. Each data point is assigned a score based on its appropriate range; full marks are awarded if the data falls within the appropriate range, and points are deducted for deviations beyond the range. Behavioral data includes activity frequency, feeding duration, and watering frequency, derived through image sequence analysis using image recognition algorithms. Scores are also assigned based on normal ranges. The final health score is obtained by weighting the environmental and behavioral data scores in a 6:4 ratio, with a range of 0 to 100. The virtual income price is calculated by combining the market pig price with a health score adjustment coefficient. To accurately calculate the virtual income price, the following formula is designed: The market pig price is the average of three authoritative industry data collections on the calculation day, expressed in yuan per kilogram. The data source has been systematically verified to ensure its authenticity and timeliness. The health score adjustment coefficient is strictly determined based on the pig's health score. A health score below 60 indicates poor pig health and a market value below normal levels; the adjustment coefficient is set at -5% to -0.5%, with a larger absolute value for the lower the health score. A health score between 60 and 80 indicates normal pig health; the adjustment coefficient is set to 0, and the virtual income price is consistent with the market pig price. A health score above 80 indicates excellent pig health and a market value above normal levels; the adjustment coefficient is set at 0.5% to 5%, with a larger adjustment coefficient for higher health scores. This coefficient has no unit and is only used to adjust the ratio between the virtual income price and the market pig price. The logical derivation of this formula is based on market value principles. In the hog trading market, pigs in good health tend to fetch higher prices, while those in poor health command lower prices. By introducing a health score adjustment coefficient, the virtual income price can simulate price differences in the real market, making the virtual income of each pig unit more closely reflect actual operating scenarios and providing a scientific and accurate basis for unit profit calculation. The virtual cost price is directly calculated using the corresponding unit's actual total cost consumed on that day, expressed in yuan, ensuring the matching of cost and income calculations. The virtual income of each pig is calculated by multiplying the virtual income price by the pig's weight on that day. The unit's virtual income is the sum of the virtual income of all pigs within that unit, expressed in yuan. The virtual profit is the unit's virtual income minus the unit's virtual cost, expressed in yuan, directly reflecting the unit's operating efficiency. The technical effects achieved by the above embodiments include: virtual income prices that align with market realities; virtual cost prices that reflect actual consumption; scientific virtual profit calculation that accurately assesses unit operating conditions; and providing a basis for inter-unit competition and performance evaluation.

[0025] Traditional pig transfer tracking suffers from technical problems such as low efficiency and error-proneness due to reliance on manual transfer records, leading to inaccurate cost accounting. To address this, the aisle video inventory system comprises high-definition cameras and image recognition algorithms. The high-definition cameras are installed at the entrances of the passageways between each pigpen unit. During pig transfers, the image recognition algorithm extracts the unique ID number of the RFID ear tag and counts the number of pigs, generating a transfer event record containing ID information, quantity information, transfer time, source pigpen unit identifier, and target pigpen unit identifier. This transfer event record is transmitted in real-time to the data processing unit, and the cost accounting module switches the cost accounting allocation for the corresponding pig based on the transfer event record. This solution solves the problem of manual recording by automatically identifying and recording pig transfers, ensuring accurate cost accounting. The high-definition cameras in the aisle video inventory system are high-definition infrared cameras with a resolution of at least 20 megapixels and night-time infrared shooting capabilities. This ensures clear imaging of the RFID ear tags on the pigs' ears even in dimly lit or unlit conditions at night, avoiding recognition failures due to lighting issues. The image recognition algorithm has undergone specialized training, with training samples covering ear images of pigs of different breeds and growth stages, as well as ear tag images under different lighting conditions and shooting angles. This ensures that the algorithm can quickly and accurately extract the unique ID number from the ear tag, with an extraction response time of no more than 0.5 seconds and an ID number recognition accuracy of no less than 99.5%. Quantity counting employs a combination of inter-frame difference and contour detection methods. First, inter-frame difference is used to detect moving targets, and then contour detection is used to determine the individual pig contours, avoiding double counting or undercounting. The quantity counting accuracy is no less than 99%. All information recorded in the transfer event log is complete and formatted uniformly. The ID information is the unique code of the RFID ear tag, the quantity information is the total number of pigs transferred, the transfer time is accurate to the minute, and the source pig house unit identifier and target pig house unit identifier are unique codes preset by the system, ensuring that each unit is distinguishable. After a transfer event record is generated, it is transmitted in real time to the data processing unit via a dedicated data channel, with a transmission delay of no more than 1 second. Upon receiving the transfer event record, the cost accounting module immediately updates the corresponding pig's unit affiliation information. Starting from the transfer time, the cost accounting affiliation of these pigs is switched to the target pig house unit, ensuring that cost allocation is consistent with the actual unit where the pigs are located and avoiding cost affiliation confusion. The technical effects achieved by the above embodiments include: automatic recording of pig transfers improves recording efficiency; accurate and complete recorded information reduces human error; and timely switching of cost accounting affiliation ensures the accuracy of cost accounting.

[0026] Traditional performance appraisal methods suffer from limitations due to their reliance on single evaluation indicators, failing to reflect employees' comprehensive contributions and resulting in ineffective incentives. To address this, the performance score of the Amoeba Management module is calculated using virtual profit and a job value coefficient. The job value coefficient is calculated comprehensively across three dimensions: responsibility complexity, years of work experience, and historical performance results. Responsibility complexity is categorized according to the difficulty of each pig farm unit's breeding stage; years of work experience are calculated cumulatively based on actual years of service; and historical performance results are determined by the average performance over a pre-defined period. This multi-dimensional assessment method solves the problem of single-dimensional evaluation and achieves scientific performance evaluation of employees. The complexity of responsibilities is determined based on three sub-dimensions: technical requirements, labor intensity, and risk level at each stage of the breeding process. The mating and farrowing pens involve key technical aspects such as pig breeding and farrowing, resulting in high labor intensity and risk; their responsibility complexity coefficient is set at 1.2 to 1.5. The nursery and fattening pens mainly involve pig feeding and daily management, with moderate technical requirements and labor intensity; their responsibility complexity coefficient is set at 1.0 to 1.2. The isolation pens, replacement pens, and boar stations have relatively simple breeding and management processes and lower labor intensity; their responsibility complexity coefficient is set at 0.8 to 1.0. The weights for each sub-dimension are 40% for technical requirements, 30% for labor intensity, and 30% for risk level, respectively. The final responsibility complexity coefficient is calculated by weighting these factors. Years of work experience are calculated based on the employee's actual full-time work experience in the breeding industry, with an increase of 0.1 for each full year, up to a maximum increase of 0.5. This means that for employees with more than 5 years of work experience, the work experience coefficient is uniformly calculated as 1.5, ensuring that the advantage of experience is reasonably reflected without excessive bias. Historical performance results are determined based on the average performance score of the past 12 months. Monthly performance scores are calculated based on the virtual profit of the responsible unit and the job value coefficient for that month. The average of the 12 months' scores is taken. The historical performance coefficient is 1.1 to 1.3 when the average score is above 80, 1.0 when it is between 60 and 80, and 0.8 to 0.9 when it is below 60. For every 10-point increase in the average score, the historical performance coefficient increases by 0.1, ensuring that historical performance is linked to current performance evaluation. The job value coefficient is the product of the responsibility complexity coefficient, the years of work experience coefficient, and the historical performance coefficient, and its value is strictly controlled between 0.8 and 2.0. If the product exceeds this range, the nearest boundary value is used. A formula is designed to scientifically calculate personnel performance scores: The virtual profit represents the total virtual profit of the pigsty unit managed by the employee within a one-month accounting period, expressed in yuan. Virtual profit data is directly derived from the virtual pricing module to ensure data accuracy. The job value coefficient is a comprehensive coefficient calculated based on the employee's job complexity, years of experience, and historical performance results. It has no unit and its range of 0.8 to 2.0 is based on common coefficient ranges in industry performance evaluations, ensuring that performance differences between employees are reflected while avoiding inaccurate performance scores due to excessively large or small coefficients. The formula's logical derivation is based on the contribution matching principle. In the Amoeba Management philosophy, an employee's performance should be directly related to the operating results of their small group. Virtual profit directly reflects the employee's contribution to the unit they manage, while the job value coefficient reflects individual employee ability differences and the importance of their responsibilities. Multiplying the two comprehensively reflects the employee's overall contribution, linking performance scores to operating results while also considering individual abilities and responsibilities, thus meeting the requirements of fairness and effectiveness in incentive mechanisms. The Amoeba Management module automatically generates monthly performance reports, clearly presenting key data such as virtual profit, job value coefficient, and performance score, providing a direct basis for bonus allocation and job adjustments. The technical effects achieved by the above embodiments include: evaluation indicators comprehensively reflecting employees' overall contributions; scientifically sound performance scores providing excellent incentives; and encouraging employees to focus on cost control and unit efficiency, thereby improving management efficiency.

[0027] Traditional aquaculture management terminals suffer from limitations due to their limited functionality, making them unsuitable for diverse management scenarios and inconvenient to use. To address this, the system also includes a PC, an app, and a mini-program. The PC terminal provides system management, warehouse management, production data collection and management, process management, statistical analysis, and smart IoT functionality. The app offers basic management, production data collection, and key production reports. The mini-program provides production reminders, production and operation reports, and collaborative office functions. All three terminals communicate with the data processing unit. This solution overcomes the limitation of limited functionality through multi-terminal collaboration, meeting the management needs of different scenarios. The PC-based system is primarily deployed in the farm's office. Addressing the comprehensive management needs of administrators, its functions include access control, user management, log management, organizational management, and security management. This allows for the allocation of permissions to different personnel, user account maintenance, operation log queries, organizational structure management, and system security settings. The warehouse management function specifically handles operations such as procurement planning, inbound registration, outbound issuance, and inventory checks for medicines, vaccines, tools, bulk feed, and miscellaneous materials. The production data acquisition and management function supports detailed registration and data entry for production operations in each pig house unit, including data on key events such as pig introduction, transfer, slaughter, and mortality. The process management function enables collaborative office-related processes such as initiation, approval, transfer, and archiving. The statistical analysis function provides multi-dimensional data analysis, including operational analysis, production analysis, indicator analysis, and performance analysis. The intelligent IoT system is responsible for the connection, configuration, status monitoring, and data reception of IoT devices. The APP is primarily for on-site management personnel. Basic management functions include account management and cache management, supporting operations such as account login, password modification, and data cache clearing. The production data collection function allows direct input of on-site breeding data via mobile devices and also supports electronic ear tag recognition and deep learning recognition to assist data collection. The key production report function provides viewing and exporting of pig batch reports, cost consumption reports, and performance score reports, facilitating quick on-site data access. The mini-program primarily meets the mobile operation needs of management personnel. Production reminder functions include production and financial alerts, promptly reminding relevant personnel to handle issues via push notifications. The production and operation report function provides a concise display of key operational data. The collaborative office function supports online operations for material requisition applications, process approvals, and work handover records, allowing basic collaborative work to be completed without logging into a PC. The PC, APP, and mini-program establish a stable communication connection with the data processing unit through an encrypted data channel. Data transmission uses an encryption protocol to ensure security, and data is synchronized in real time across all terminals with a synchronization delay of no more than 3 seconds, ensuring that the information obtained by management personnel remains consistent across different scenarios. The technical effects achieved by the above embodiments include complementary multi-terminal functions to meet the needs of different scenarios; convenient operation to improve management efficiency; and real-time data synchronization to ensure information consistency.

[0028] Traditional livestock farming data analysis suffers from a lack of systematic data analysis capabilities, failing to provide a clear and intuitive display of operational and production status. To address this, the PC-based statistical analysis function includes operational and production analysis. Operational analysis displays virtual profits, cost structure, input-output ratios, and year-on-year and month-on-month changes for each pig unit. Production analysis displays pig survival rate, weight gain rate, feed conversion ratio, and average breeding cycle data. All data are generated based on the calculation and analysis results of the data processing unit. This solution addresses the shortcomings in analysis by enriching data analysis functions, providing a clear and intuitive display of operational and production status. The virtual profit data in the business analysis function is displayed in three time dimensions: daily, weekly, and monthly. It supports viewing the historical data trends of a single pig house unit and comparing the data of multiple pig house units in the same period. The cost composition data is categorized into four types: feed cost, water and electricity cost, medicine cost, and depreciation cost. The proportion of each type of cost is presented intuitively in the form of pie charts. The input-output ratio is the ratio of virtual profit to total input cost. The total input cost is the cumulative value of the four types of costs. The input-output ratio is also displayed in different time dimensions to facilitate the analysis of unit profitability. The year-on-year and month-on-month change data are used to compare the data changes of the current period with the same period of the previous year and the previous period, clearly reflecting the business trend. The pig survival rate in the production analysis function is calculated by batch, i.e., (number of pigs slaughtered + number of pigs currently in stock) divided by the number of introduced pigs, then multiplied by 100%, showing the survival status of pigs in each batch; the weight gain rate is (current weight - initial weight) divided by the number of days of rearing, reflecting the growth rate of the pigs; the feed conversion rate is the total weight of feed consumed divided by the total weight gain of the pigs, reflecting feed utilization efficiency; the average rearing cycle is the average number of days from introduction to slaughter, reflecting rearing efficiency. All data analysis results are presented in both chart and table formats. Charts include line charts, bar charts, pie charts, etc., and chart export and data export to Excel format are supported. The production analysis data is also compared with industry average standard data, allowing managers to quickly identify gaps between their farm and industry standards and clarify areas for improvement. The technical effects achieved by the above embodiments include comprehensive coverage of key operational and production indicators by the data analysis function; intuitive data display for easy management to quickly grasp the situation; and support for data export and comparative analysis to aid decision-making.

[0029] Traditional IoT devices suffer from the technical problem of independent operation, resulting in a lack of data sharing and synergy. To address this, a smart IoT system connects a feed tower system, a weighbridge system, an access control system, a video surveillance system, a wastewater treatment system, and an environmental monitoring system. The feed tower system records feed output data; the weighbridge system collects pig weight data and associates it with the unique ID of RFID ear tags; the access control system records personnel entry and exit data; the video surveillance system transmits monitoring footage and analyzes abnormal pig behavior; the wastewater treatment system records wastewater treatment data; and the environmental monitoring system integrates data from various sensors and generates environmental quality reports. This solution solves the data silo problem and leverages synergy through device interconnection. The smart IoT system, as the core connection hub, uses a unified communication protocol to establish connections with each IoT device, enabling real-time monitoring of device status and centralized data collection. The feed tower system records the time, type, weight, and corresponding pigpen unit for each feed output. This data is synchronized to the smart IoT system in real time and then forwarded to the warehouse management and cost accounting modules, supporting inventory management and cost calculation. The weighbridge system is installed at the entrance and exit of each pig unit. When pigs are weighed, the system automatically collects weight data and extracts the pig's unique ID number via an RFID ear tag identification module. The weight data is then bound to the ID number and stored. This data is synchronized to the production data acquisition and management module and the input-output analysis module, providing data for production analysis and profit calculation. The access control system is installed at the entrance of each pig unit and at the farm's entrance and exit, recording the time of personnel entry and exit, personnel identification information, and operational content. Identification information is confirmed via card swiping or facial recognition. Data is synchronized to the security management module and the process management module, ensuring the safety of the breeding area and the traceability of operations. The video monitoring system consists of high-definition cameras distributed inside and around each pig unit. It transmits monitoring images in real time to the PC video monitoring module and the APP video viewing function. Simultaneously, image recognition algorithms analyze the monitoring images in real time. When abnormal pig behavior is detected, such as falling to the ground or prolonged non-feeding, an automatic alert is triggered and relevant information is recorded, eliminating the need for manual real-time monitoring. The wastewater treatment system records daily wastewater treatment volume, energy consumption, and pollutant removal rate data. This data is synchronized to the environmental monitoring and cost accounting modules, providing support for environmental assessment and cost calculation. The environmental monitoring system integrates data from various sensors, including air sensors, wastewater sensors, and energy consumption sensors, generating daily environmental quality reports for each pigsty unit. These reports include average, maximum, and minimum values ​​for key indicators such as temperature, humidity, ammonia concentration, oxygen concentration, wastewater discharge, pollutant concentration, and energy consumption, along with a determination of whether standards are met, providing a basis for adjusting the breeding environment. The technical effects achieved by the above embodiments include: equipment working collaboratively to maximize overall efficiency; data sharing and integration improving data utilization; automatic analysis of abnormal situations reducing manual intervention; and providing comprehensive data support for management decisions.

[0030] Traditional pig farming management suffers from technical problems, such as the inability to detect operational anomalies in a timely manner, leading to the escalation of problems and economic losses. To address this, the data processing unit includes an early warning module. This module sets warning thresholds for virtual profit, cost consumption, and health status. When relevant indicators for a unit reach these thresholds, the module sends warning information via the app and mini-program, while simultaneously marking the abnormal unit and generating an anomaly analysis report in the PC-based statistical analysis module. This solution addresses the problem of delayed anomaly detection and addresses operational anomalies promptly. The various warning thresholds are set based on a comprehensive analysis of historical data from each pig farm unit over the past 12 months and industry standard data. The virtual profit warning threshold is set at 50% of the historical average virtual profit for each unit, the cost consumption warning threshold is set at 130% of the historical average cost consumption for the same period, and the health status warning threshold is set at a pig health score of 60. These thresholds are dynamically adjustable; managers can manually modify them based on changes in the farming stage or market environment, or the system can be set to automatically update the thresholds quarterly. The early warning module monitors relevant indicators of each pig house unit in real time. Virtual profit is monitored daily; if it falls below the virtual profit warning threshold for seven consecutive days, a virtual profit warning is triggered. Cost consumption is monitored daily; if daily cost consumption exceeds the cost consumption warning threshold, a cost consumption warning is triggered. Health status is monitored daily; if the health score of pigs in a unit falls below the health status warning threshold for three consecutive days, a health status warning is triggered. Once an early warning is triggered, it immediately sends a warning message to the mobile devices of the corresponding managers via the APP and mini-program. The warning message includes the abnormal unit number, abnormal indicator name, abnormal value, abnormal duration, and suggested handling measures, ensuring that managers are aware of the abnormal situation immediately. Simultaneously, the abnormal unit is highlighted in red in the PC-side statistical analysis module, automatically generating an abnormality analysis report. The report includes a preliminary analysis of the cause of the abnormality, a correlation data trend chart, and improvement measures suggestions. The preliminary analysis of the cause of the abnormality is based on historical data and real-time collected environmental and consumption data. The correlation data trend chart shows the changing trend of the abnormal indicator over the past 30 days. The improvement measures suggestions are based on industry best practices and the farm's historical handling experience. The abnormality analysis report can be exported to Word format for easy archiving and subsequent analysis. The technical effects achieved by the above embodiments include timely detection of operational anomalies to prevent problems from escalating; detailed early warning information for quick processing; and generation of analysis reports to aid in root cause investigation and improvement.

[0031] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A livestock farming management system based on the Amoeba Management model, comprising a pig identification device, a data acquisition device, a data processing unit, and an application terminal, wherein the pig identification device is a radio frequency identification ear tag, the data acquisition device includes an aisle video inventory device, the data processing unit is equipped with a cost accounting module, a virtual pricing module, and an input-output analysis module, and the application terminal is equipped with an Amoeba Management module, characterized in that, Radio frequency identification ear tags store the unique ID number of each pig; aisle video inventory equipment identifies pig transfer events and records location changes; the cost accounting module collects daily consumption data based on the pig house unit where the pig is located and calculates the daily cost of each pig; the virtual pricing module sets the virtual revenue price and virtual cost price for each pig house unit and calculates the virtual profit; the input-output analysis module analyzes the input-output of each pig house unit; and the amoeba management module realizes quantitative evaluation of personnel performance based on virtual profit and job value coefficient.

2. The aquaculture management system based on the amoeba management model according to claim 1, characterized in that, The data acquisition equipment also includes environmental sensors, which are divided into air sensors, wastewater sensors, and energy consumption sensors. The air sensors collect data on temperature, humidity, ammonia concentration, and oxygen concentration in each pigsty unit. The wastewater sensors collect data on wastewater discharge and pollutant concentration. The energy consumption sensors collect data on water, electricity, and gas consumption in each unit. All collected data are transmitted to the data processing unit in real time.

3. The aquaculture management system based on the amoeba management model according to claim 1, characterized in that, The daily consumption data collected by the cost accounting module includes feed consumption data, water and electricity consumption data, medicine consumption data, and depreciation allocation data. Feed consumption data is obtained through the automatic feeding system, water and electricity consumption data is obtained through smart meters and water meters, medicine consumption data is obtained through the inventory management system, and depreciation allocation data is calculated based on the usage of pig house equipment and buildings.

4. The aquaculture management system based on the amoeba management model according to claim 1, characterized in that, The virtual revenue price of the virtual pricing module is determined based on the market price of pigs, the weight of pigs, and their health status. The virtual cost price is determined based on the actual consumption data of each pig house unit. The virtual profit is calculated by the difference between the virtual revenue and the virtual cost. The virtual revenue of each pig is calculated by combining the virtual revenue price and the weight of the pig.

5. The aquaculture management system based on the amoeba management model according to claim 1, characterized in that, The aisle video inventory equipment consists of high-definition cameras and image recognition algorithms. The high-definition cameras are installed at the entrance of the passage between each pig house unit. When pigs are transferred, the image recognition algorithm counts the number of pigs and generates a unique ID number containing ID information and RFID ear tags. It also generates a transfer event record containing ID information, quantity information, transfer time, source pig house unit identifier, and target pig house unit identifier. The transfer event record is transmitted to the data processing unit in real time, and the cost accounting module switches the cost accounting assignment of the corresponding pig based on the transfer event record.

6. The aquaculture management system based on the amoeba management model according to claim 1, characterized in that, The performance score of the Amoeba Management module is calculated using virtual profit and job value coefficient. The job value coefficient is calculated by comprehensively considering three dimensions: responsibility complexity, years of work experience, and historical performance results. Responsibility complexity is divided according to the difficulty of the pig house unit's breeding stage, years of work experience are calculated cumulatively based on actual years of work experience, and historical performance results are determined based on the average performance over a preset period of time.

7. The aquaculture management system based on the amoeba management model according to claim 1, characterized in that, The system also includes a PC terminal, an APP terminal, and a mini-program terminal. The PC terminal is equipped with system management, warehouse management, production data collection management, process management, statistical analysis, and intelligent IoT system. The APP terminal is equipped with basic management, production data collection, and key production report functions. The mini-program terminal is equipped with production reminders, production and operation reports, and collaborative office functions. The PC terminal, APP terminal, and mini-program terminal are all connected to the data processing unit.

8. The aquaculture management system based on the amoeba management model according to claim 7, characterized in that, The PC-based statistical analysis functions include business analysis and production analysis. Business analysis displays the virtual profit, cost structure, input-output ratio, and year-on-year and month-on-month changes of each pig house unit. Production analysis displays data on pig survival rate, weight gain rate, feed conversion rate, and average breeding cycle. All data are generated based on the calculation and analysis results of the data processing unit.

9. The aquaculture management system based on the amoeba management model according to claim 7, characterized in that, The intelligent IoT system connects the feed tower feeding system, the weighbridge weighing system, the access control system, the video surveillance system, the wastewater treatment system, and the environmental monitoring system. The feed tower feeding system records feed output data, the weighbridge weighing system collects pig weight data and associates it with the unique ID number of the RFID ear tag, the access control system records personnel entry and exit data, the video surveillance system transmits monitoring images and analyzes abnormal pig behavior, the wastewater treatment system records wastewater treatment data, and the environmental monitoring system integrates data from various sensors and generates environmental quality reports.

10. The aquaculture management system based on the amoeba management model according to claim 1, characterized in that, The data processing unit is also equipped with an early warning module. The early warning module sets virtual profit early warning thresholds, cost consumption early warning thresholds, and health status early warning thresholds. When a unit's relevant indicators are detected to reach the early warning threshold, the early warning module sends early warning information through the APP and mini-program. At the same time, the abnormal unit is marked and an abnormal analysis report is generated in the statistical analysis module on the PC.

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