Method and system for realizing closed-loop management of pig raising by using online operation analysis system

Abstract

The application provides a method and system for realizing closed-loop management of pig raising by using an online operation analysis system, comprising: setting production mode parameters and pig flow operation mode; determining data sources according to the production mode parameters and the pig flow operation mode; performing forward calculation when the data sources are existing inventory; performing rolling prediction of mating when the data sources are future plans; generating future daily pig population structure according to the forward calculation and the rolling prediction of mating; calculating predicted total cost and estimated cost per head of each month in the future according to calculated feed cost, fixed cost allocation cost and variable cost; calculating predicted total profit according to the estimated cost per head and predicted output; comparing the predicted total profit with the actual total profit; realizing high-precision future deduction based on the current inventory and the production parameters; establishing a dynamic linkage mechanism of production indicators and cost and profit; realizing digital closed loop from target setting, process early warning to operation review.

Description

Technical Field

[0001] This invention relates to the field of pig farming management technology, and in particular to a method and system for achieving closed-loop management of pig farming using an online management analysis system. Background Technology

[0002] In today's context of large-scale pig farming, pig farm operators typically possess some farming experience but lack business acumen. They are unclear about the relationship between production performance, costs, profits, and overall results at each stage, and fail to maximize the use of existing land resources and infrastructure. Based on the current pig farm's hardware facilities, operators hope to understand the future output, costs, and profits under different performance levels. They aim to use this information to analyze monthly operational results, identify specific discrepancies, and optimize corresponding aspects in subsequent operations to improve performance and achieve profit targets.

[0003] Existing technologies sometimes estimate the corresponding month of calving, month of slaughter, and number of animals slaughtered based on factors such as mating time. However, the calculation granularity is too coarse, and it does not consider sales during the process or the situation of multiple joint operations. It also lacks related cost and profit data, and lacks specific profit quantification for comparing current and historical performance targets.

[0004] The existing technology has the following drawbacks: 1) Coarse calculation granularity and lack of dynamic evolution capability: Existing technologies usually only make simple "point-to-point" calculations (such as estimating November slaughter based on January mating), ignoring multiple variable factors such as mortality rate fluctuations, mid-term transfers, cross-farm allocations, breeding pig culling and sales, and piglet sales, resulting in huge deviations between the predicted slaughter numbers and the actual slaughter numbers.

[0005] 2) Production and finance are disconnected, lacking a business perspective: The existing system's production data (number of pigs / weight) and financial data (cost / profit) are separate. The production management software cannot automatically link costs such as feed consumption, asset depreciation, and water and electricity allocation, resulting in managers only seeing "how many pigs were raised" and not knowing in real time "how much money was made from this batch of pigs".

[0006] 3) Lack of multi-farm joint operation models: Group farming typically involves multi-level circulation of "sow farm - nursery farm - fattening farm". Existing technologies are mostly designed for single farms and cannot simulate the impact of complex pig circulation within a group on overall profits and capacity utilization.

[0007] 4) Lack of quantitative attribution analysis: When profits fail to meet targets, existing technologies cannot accurately quantify whether the loss is greater due to "a 1% decrease in the birth rate" or "a 0.1 yuan increase in feed prices," thus failing to provide clear priority guidance for decision-making. Summary of the Invention

[0008] In view of this, the purpose of this invention is to provide a method and system for realizing closed-loop management of pig farming using an online business analysis system, and to achieve high-precision future projection based on current inventory and production parameters; Establish a dynamic linkage mechanism between production indicators and costs and profits; solve the problems of cost allocation and profit accounting in multi-site collaboration mode, and realize a digital closed loop from target setting, process early warning to business review.

[0009] In a first aspect, embodiments of the present invention provide a method for achieving closed-loop management of pig farming using an online business analysis system, the method comprising: Set production mode parameters and pig flow operation mode; The data source is determined based on the production mode parameters and the pig flow operation mode. When the data source is existing inventory, perform forward extrapolation; When the data source is a future plan, rolling mating forecasts are performed. Based on forward extrapolation and rolling breeding forecasts, the future daily pig herd structure is generated; wherein, the future daily pig herd structure includes the predicted slaughter volume for each month; Calculate feed costs, fixed cost allocations, and variable costs; Based on the feed cost, the fixed cost allocation, and the variable costs, calculate the projected total cost and estimated cost per head for each future month; Calculate the predicted total profit based on the estimated cost per head and the predicted number of animals slaughtered. The predicted total profit is compared with the actual total profit, and the result of the comparison determines whether the target has been met.

[0010] Furthermore, the production mode parameters include the feeding cycle at each stage, the preset mortality rate at each stage, the group transfer rules, and the designed production capacity; wherein, the feeding cycle at each stage includes the gestation period, lactation period, nursery period, and fattening period; and the pig flow operation mode is an association between multiple farms.

[0011] Furthermore, when the data source is existing inventory, forward extrapolation is performed, including: Based on the current real-time inventory of pigs of various ages or gestation ages, combined with the pig herd growth curve and mortality model, the future growth status and inventory changes are projected day by day until slaughter.

[0012] Furthermore, when the data source is future plans, rolling mating forecasts are performed, including: Based on historical breeding data and future breeding plans, the future newborn piglet flow can be predicted by applying the farrowing rate and the average number of healthy piglets per litter. Based on the predicted future piglet flow, the projected slaughter volume for each month is derived.

[0013] Furthermore, the predicted total profit will be compared with the actual total profit, and the result will be used to determine whether the target has been met, including: When the predicted total profit is greater than the actual total profit, it is determined to be unsatisfactory; When the predicted total profit is less than or equal to the actual total profit, it is determined that the target has been met.

[0014] Furthermore, the method also includes: The predicted total profit is then reverse-calculated using an attribution analysis algorithm to obtain the key KPI indicators; The key KPIs were analyzed, and the results were obtained. The key KPIs include reserve utilization rate, number of reserves added to the herd, number of breeding stock, number of farrowings, average number of weaned litters, 21-day weight, survival rate, daily weight gain, feed conversion ratio, and market weight.

[0015] Secondly, embodiments of the present invention provide a system for achieving closed-loop management of pig farming using an online business analysis system, the system comprising: The settings module is used to set production mode parameters and pig flow operation mode; The determination module is used to determine the data source based on the production mode parameters and the pig flow operation mode; The first execution module is used to perform forward calculation when the data source is an existing inventory. The second execution module is used to perform rolling mating prediction when the data source is a future plan; The generation module is used to generate the future daily pig herd structure based on forward extrapolation and rolling breeding prediction; wherein, the future daily pig herd structure includes the predicted slaughter volume for each month; The first calculation module is used to calculate feed costs, fixed cost allocation costs, and variable costs. The second calculation module is used to calculate the predicted total cost and estimated per head cost for each future month based on the feed cost, the fixed cost allocation cost, and the variable cost. The third calculation module calculates the predicted total profit based on the estimated cost per head and the predicted number of animals slaughtered. The comparison module is used to compare the predicted total profit with the actual total profit and determine whether the target is met based on the comparison results.

[0016] Furthermore, the production mode parameters include the feeding cycle at each stage, the preset mortality rate at each stage, the group transfer rules, and the designed production capacity; wherein, the feeding cycle at each stage includes the gestation period, lactation period, nursery period, and fattening period; and the pig flow operation mode is an association between multiple farms.

[0017] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the method described above.

[0018] Fourthly, embodiments of the present invention provide a computer-readable medium having processor-executable non-volatile program code that causes the processor to perform the method described above.

[0019] This invention provides a method and system for achieving closed-loop management of pig farming using an online business analysis system, including: setting production mode parameters and pig flow operation mode; determining the data source based on the production mode parameters and pig flow operation mode; performing forward extrapolation when the data source is the existing stock; performing rolling breeding forecasts when the data source is future plans; generating the future daily pig herd structure based on the forward extrapolation and rolling breeding forecasts; wherein the future daily pig herd structure includes the predicted slaughter volume for each month; calculating feed costs, fixed cost allocation costs, and variable costs; calculating the predicted total cost and estimated cost per head for each future month based on feed costs, fixed cost allocation costs, and variable costs; calculating the predicted total profit based on the estimated cost per head and the predicted slaughter volume; comparing the predicted total profit with the actual total profit, and determining whether the target is met based on the comparison result; achieving high-precision future extrapolation based on the current stock and production parameters; establishing a dynamic linkage mechanism between production indicators and cost and profit; solving the problem of cost allocation and profit calculation under multi-farm collaborative mode, and realizing a digital closed loop from target setting, process early warning to business review.

[0020] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1This is a flowchart of a method for achieving closed-loop management of pig farming using an online business analysis system, as provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of a closed-loop management system for pig farming provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of key indicators for each region provided in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the overall process of the method for achieving closed-loop management of pig farming using an online business analysis system, as provided in Embodiment 1 of the present invention. Figure 5 This is a schematic diagram of a system for implementing closed-loop management of pig farming using an online business analysis system, as provided in Embodiment 2 of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] This application relates to a system that utilizes an online business analysis system to convey the group's business management ideas to the front line, quantifies the impact of production indicators, and achieves efficient analysis across multiple stages, including target setting, early warning, and profit management, to support decision-making and identify key areas of focus.

[0026] The core innovation of this application lies in building an intelligent platform for business template management and financial operating results analysis. First, by analyzing historical performance, pig herd life cycle, and farm pig flow patterns, the number of pigs in each farm is calculated. Simultaneously, based on the production model of each pig farm, the daily number of matings is predicted. Based on historical and future mating numbers, as well as the sales plan for breeding pigs, the monthly output is accurately calculated. On the other hand, feed costs, fixed costs (pig house depreciation, breeding pig depreciation, rental and maintenance), vehicles, labor, materials, water, electricity, and fuel are categorized and summarized to calculate the cost per head. Finally, by combining market conditions and selling price forecasts, the final monthly total profit and profit per head are derived.

[0027] The following explains the key indicators (key targets affecting final profits in pig production) in this application: Reserve utilization rate Number of reserve mates / Number of reserve mates added to the herd at the same time Production mode How long is a cycle in the delivery room? Number of pregnant women conceived This refers to the number of sows that complete mating within a certain period of time (such as monthly, quarterly, or annually), reflecting the intensity of reproductive activity in a sow population.

[0028] First-time mother breeding ratio This refers to the proportion of sows that are mated for the first time (primiparous sows) out of all mated sows, and is used to assess the efficiency of replenishing replacement sows and the rationality of the population structure.

[0029] Maternal delivery rate The percentage of sows that successfully give birth out of the total number of sows bred reflects the pregnancy success rate after mating.

[0030] Formula: Farrowing rate = (Number of farrowing sows / Number of mated sows) × 100%.

[0031] Average number of live piglets per nursing mother The average number of healthy piglets surviving each litter reflects the sow's reproductive capacity and piglet survival rate.

[0032] Average number of weanings per nursing mother The average number of piglets successfully weaned per litter reflects the sow's feeding and management level during lactation and the survival rate of piglets.

[0033] Wet nurse's weight at the end of 21 days The average weight of piglets at 21 days after birth (at weaning) measures the growth rate of piglets during lactation and the sow's milk production capacity.

[0034] Piglet survival rate Survival rate of piglets from birth to weaning (or a specific stage).

[0035] Formula: Survival rate = (Number of surviving piglets / Total number of piglets born) × 100%.

[0036] Daily weight gain of piglets The average daily weight gain of piglets during a certain stage (such as the lactation or nursery period) reflects growth efficiency.

[0037] Formula: Daily weight gain = (weight at the end of the stage - weight at the beginning of the stage) / number of feeding days.

[0038] Piglet feed conversion ratio Feed conversion efficiency is the ratio of the total amount of feed consumed by piglets at a certain stage to their weight gain.

[0039] Formula: Material ratio = Total material consumption (kg) / Total weight gain (kg).

[0040] Pig survival rate The survival rate of fattening pigs from weaning to slaughter reflects the management level and disease control effectiveness during the fattening period.

[0041] Daily weight gain of fat pigs The average daily weight gain of fattening pigs during the fattening period is directly related to fattening efficiency and time to market.

[0042] Pig feed conversion ratio The ratio of total feed consumed by fattening pigs during the fattening period to their total weight gain is a core indicator for cost control.

[0043] Formula: Material ratio = Total material consumption (kg) / Total weight gain (kg).

[0044] Average weight of fattened pigs at market The average weight of pigs at slaughter affects the selling price per pig and the degree of matching with market demand.

[0045] The ratio of first and second grade fattened pigs The percentage of fattened pigs that meet the first or second grade carcass quality standards reflects the meat quality grade and the ability to command a premium in the market.

[0046] Pig slaughter volume The total number of fattened pigs sold or slaughtered within a certain period (such as monthly or annually) is a core production capacity indicator.

[0047] Target cost The company's expected cost per unit of live pig (or unit weight gain) is usually expressed in "yuan / kg" or "yuan / head" and is used for cost control assessment.

[0048] Corrected total profit (in ten thousand) Actual profit adjusted for factors such as market fluctuations and non-recurring gains and losses is used for more objective performance evaluation.

[0049] Total prize money (ten thousand) The total bonus for a team or employee is calculated based on the achievement of production targets (such as cost, profit, survival rate, etc.).

[0050] The following explains the input indicators in this application: 1. Total cost per fattened pig: refers to all costs incurred by each fattened pig during the production process, including feed, labor, fixed asset depreciation, and other expenses.

[0051] 2. Cost per kilogram of fattened pig - monthly: The production cost per kilogram of fattened pig, usually calculated monthly, to facilitate monitoring of production efficiency and cost control.

[0052] 3. Feed cost (RMB / head): The feed cost consumed per pig, calculated in RMB.

[0053] 4. Feed weight for pregnant sows (allocated to farrowing pigs in kg): refers to the weight of feed allocated to each farrowing pig during the sow's pregnancy.

[0054] 5. Sow feed weight (allocated to fattening pigs kg): refers to the weight of sow feed allocated to each fattening pig during the lactation period.

[0055] 6. Piglet feed conversion ratio: This refers to the ratio of feed required by piglets to their body weight during their growth process, reflecting the feed conversion rate.

[0056] 7. Feed conversion ratio for fattening pigs: This refers to the ratio of feed required by fattening pigs to their body weight during the growth process, and measures feed utilization efficiency.

[0057] 8. Average feed weight per head: The total weight of feed consumed by each pig throughout the entire breeding cycle.

[0058] 9. Total feed price: refers to the average price of feed throughout the entire process from piglet to market-ready pig.

[0059] 10. Employee Compensation: Wages and related welfare expenses paid by the company to its employees.

[0060] 11. Pregnancy: The period from fertilization to farrowing in sows.

[0061] 12. Lactation: The stage during which the sow feeds the piglets after farrowing.

[0062] 13. Nursery: refers to the management and feeding process of piglets from weaning until they reach the fattening stage.

[0063] 14. Fattening: refers to the stage of raising pigs to the slaughter weight.

[0064] 15. Logistics Package · X Yuan / Head - Corrected 120kg: Logistics costs allocated per pig, adjusted based on a weight of 120 kg.

[0065] 16. Logistics Package (in ten thousand yuan): Total expenditure of the enterprise in logistics support, in ten thousand yuan.

[0066] 17. Management base salary: The basic salary of management or executives in the company.

[0067] 18. Number of Registered Employees - Modified Based on Site Requirements: The number of employees currently employed, adjusted according to actual site requirements.

[0068] 19. Social insurance: refers to the social insurance premiums paid by the company for its employees, including pension insurance, medical insurance, etc.

[0069] 20. Average health cost per head: The average cost per pig in terms of health management and disease prevention.

[0070] 21. Cost per head of semen: Cost of semen used per pig for artificial insemination.

[0071] 22. Average material consumption per pig per production section (benchmark): The standard material consumption per pig in a certain production section.

[0072] 23. Average water, electricity, and fuel consumption per pig in a production section (benchmark): The standard for water, electricity, and fuel consumption per pig in a certain production section.

[0073] 24. Average water and electricity consumption per pig in a production section: The average water and electricity consumption per pig in a certain production section.

[0074] 25. Fuel consumption per pig in a production section: The amount of fuel consumed per pig in a certain production section.

[0075] 26. Fixed Costs - Section: Fixed expenses within a specific section, including equipment depreciation, rentals, etc.

[0076] 1) Section - Depreciation and Leasing: Costs of equipment depreciation and equipment rental within the section.

[0077] 2) Depreciation of breeding pigs in the production section: Depreciation expenses of fixed assets such as breeding pigs used for breeding.

[0078] 3) Section - Vehicles, Inspection, Maintenance and other: Fixed costs related to vehicles, inspection and maintenance within the section.

[0079] 4) Section-vehicle usage fee: Costs incurred for vehicles used for transportation within the section.

[0080] 5) Section-Maintenance Costs: Maintenance costs for equipment and facilities within a section.

[0081] 6) Section - Inspection and Others: Inspection and other related costs within the section.

[0082] 27. Fixed Costs - Production Line: Fixed expenses within a specific production line.

[0083] 1) Field Line - On-site Logistics: Fixed costs for on-site logistics management.

[0084] 2) Environmental protection operation within the plant: Fixed costs related to environmental protection within the plant.

[0085] 3) Other auxiliary production costs such as factory office, catering, water and electricity: Fixed costs related to other auxiliary production within the factory.

[0086] 28. Fixed Costs - Area: Fixed expenses within a specific area.

[0087] 1) Operations: Salaries and expenses for core functional departments, including office, external cooperation entertainment, venue rental, etc.: fixed costs of core functional departments.

[0088] 2) Salaries, equipment and site depreciation and rental and office expenses of auxiliary production departments: fixed expenses of auxiliary production departments.

[0089] 29. Fixed Costs - Regional Compensation: Fixed compensation costs for employees within a designated region.

[0090] 30. Fixed Costs - Headquarters: Fixed expenses incurred in the operation of the headquarters.

[0091] 31. Fixed Costs per Kilogram (Fixed Cost Amount / Total Weight of Pigs Sold in the Month): The fixed cost per kilogram of pig, calculated by dividing the total fixed costs by the total weight of pigs sold in the month.

[0092] 32. Other (feed freight, pig freight): Other costs related to the transportation of feed and pigs.

[0093] 33. Feed transportation costs: Costs of transporting feed to the farm.

[0094] 34. Live pig transportation costs: Costs for transporting live pigs to markets or slaughterhouses.

[0095] 35. Total cost of introducing piglets - RMB 220 per weaned piglet: The total cost of introducing piglets, on average, is approximately RMB 220 per piglet.

[0096] 36. Cost of introducing breeding stock: Cost calculation per kilogram of introduced breeding piglets.

[0097] 37. Selling weight of fattened pigs (10,000 kg): The total weight of fattened pigs sold, in 10,000 kg units.

[0098] 38. Average weight of replacement hogs: The average weight of hogs intended to be used as replacement breeding hogs.

[0099] 39. Sales weight of replacement breeding pigs (10,000 kg): The total sales weight of replacement breeding pigs, in 10,000 kg.

[0100] 40. Piglet sales weight: The total weight of piglets sold.

[0101] 41. Sales weight of retired sows (10,000 kg): The total sales weight of retired sows is expressed in 10,000 kg.

[0102] Total weight sold (10,000 kg): The total weight of all pigs sold, expressed in 10,000 kg.

[0103] This application aims to address the current management pain point of large-scale pig farming enterprises that "understand production but not operations, understand finance but not business." By constructing an intelligent business-finance integration model, it solves the following specific problems: This application enables high-precision future projection (stock size, number of animals sold, and full production rate) based on current inventory and production parameters.

[0104] This application establishes a dynamic linkage mechanism between production indicators and costs and profits, allowing frontline farm managers to see in real time the impact of fluctuations in technical indicators on the final profit amount.

[0105] This application addresses the challenges of cost allocation and profit calculation in multi-event collaborative models, achieving a digital closed loop from goal setting and process early warning to business review.

[0106] To facilitate understanding of this embodiment, the embodiments of the present invention will be described in detail below.

[0107] Example 1: Figure 1 The flowchart illustrates the method for achieving closed-loop management of pig farming using an online business analysis system, as provided in Embodiment 1 of the present invention.

[0108] Reference Figure 1 The method includes the following steps: Step S101: Set production mode parameters and pig flow operation mode; Step S102: Determine the data source based on the production mode parameters and pig flow operation mode; Step S103: When the data source is existing inventory, perform forward extrapolation; Step S104: When the data source is a future plan, perform rolling prediction of mating; Step S105: Based on forward extrapolation and rolling breeding forecasts, generate the future daily pig herd structure; wherein, the future daily pig herd structure includes the predicted slaughter volume for each month. Step S106: Calculate feed costs, fixed cost allocation costs, and variable costs; Step S107: Calculate the projected total cost and estimated cost per head for each future month based on feed costs, fixed cost allocation, and variable costs. Step S108: Calculate the predicted total profit based on the estimated cost per head and the predicted output. Step S109: Compare the predicted total profit with the actual total profit, and determine whether the target is met based on the comparison results.

[0109] Furthermore, the production mode parameters include the feeding cycle at each stage (gestation, lactation, nursery, and fattening), the preset mortality rate at each stage, the transfer rules, and the designed capacity (maximum number of pens); among which, the feeding cycle at each stage includes gestation, lactation, nursery, and fattening; the pig flow operation mode is the linkage between multiple farms.

[0110] Specifically, a multi-dimensional business template (production mode definition) is constructed. The system sets production mode parameters according to the actual situation of the pig farm; defines the "pig flow operation mode": sets the association between multiple farms (e.g., weaning piglets from farm A). B Site Conservation C-farm fattening) supports a combination of various models such as self-breeding and self-raising, and purchasing piglets from outside.

[0111] Furthermore, step S103 includes: Based on the current real-time inventory of pigs of various ages or gestation ages, combined with the pig herd growth curve and mortality model, the future growth status and inventory changes are projected day by day until slaughter.

[0112] Furthermore, step S104 includes: Step S201: Based on historical breeding data and future breeding plans, the future newborn piglet flow is deduced by applying the farrowing rate and the average number of healthy piglets per litter. Step S202: Based on the future newborn piglet flow, deduce the predicted slaughter volume for each month.

[0113] Specifically, accurate prediction (quantity) of pig herd turnover and slaughter: Forward extrapolation (based on inventory): Based on the real-time inventory of pigs of each age / gestation age, combined with the growth curve of a thousand-head pig herd and the mortality and culling model, the future growth status and inventory changes are extrapolated day by day until slaughter.

[0114] Rolling forecast (based on mating): Combining historical mating data and future mating plans, the "mating farrowing rate" and "average number of healthy piglets per litter" are used to predict the future newborn piglet flow, and then to deduce the slaughter volume for each subsequent month.

[0115] Process sales are incorporated: The model is embedded with the breeding pig culling plan, the piglet export plan, and the defective pig disposal plan to ensure that the predicted total number of pigs slaughtered = standard fattened pigs + culled breeding pigs + exported piglets + defective pigs.

[0116] Dynamic calculation of total cost per head (price and cost): Feed costs: Establish an age-based "feed formulation cost" system to dynamically calculate daily feed input.

[0117] Fixed cost allocation: Fixed costs such as pigsty depreciation, equipment rental, and breeding pig amortization are allocated to each pig based on the "number of days in stock" or "capacity utilization rate".

[0118] Variable cost aggregation: Integrate vaccine and drug costs, labor wages, water, electricity, fuel, and manufacturing costs to form a standard cost model.

[0119] Final output: Based on the above data, calculate the "forecast total cost" and "cost per head" for each future month.

[0120] Furthermore, step S109 includes: Step S301: When the predicted total profit is greater than the actual total profit, it is determined as failing to meet the target. Step S302: When the predicted total profit is less than or equal to the actual total profit, it is determined that the target has been met.

[0121] Furthermore, the method also includes the following steps: Step S401: The predicted total profit is calculated in reverse using an attribution analysis algorithm to obtain key KPI indicators; Step S402: Analyze the key KPI indicators and obtain the analysis results; Key performance indicators (KPIs) include reserve utilization rate, number of reserves added to the herd, number of breeding stock, number of farrowings, average number of weaned litters, 21-day weight, survival rate, daily weight gain, feed conversion ratio, and market weight.

[0122] Specifically, the closed loop between operating profit and loss and the target (profit): Profit Forecast: Based on the market hog price forecast model (or manually enter the estimated price), calculate (estimated selling price - estimated cost per head) × predicted number of hogs slaughtered = predicted total profit.

[0123] Target-based reverse calculation: Based on the annual profit target, the system calculates the required key performance indicators (KPIs) such as the number of broodstock, survival rate, and feed conversion ratio.

[0124] Retrospective Analysis: At the end of each month, the actual operating results are automatically retrieved and compared with the forecasts / annual targets at the beginning of the month.

[0125] Specifically, refer to Figure 2 The system includes: a basic data modeling module, a pig herd transfer simulation module, a full cost aggregation and calculation module, an operational efficiency simulation module, and a variance attribution analysis module. (Refer to...) Figure 3 The diagram shows the key indicators for each region.

[0126] The technical effects achieved by this application are as follows: 1) Decision Quantification: Transforming the abstract concept of "strengthening management" into concrete numbers. For example, the system can directly calculate that "increasing the mating and delivery rate by 1% can increase profits by 300,000 yuan by the end of the year," thus helping farm managers clarify their work priorities.

[0127] 2) Maximize resource utilization: By accurately predicting future inventory levels, identify "pen gaps" or "pen vacancies" in advance, and guide the advance planning of pig sales or the purchase of piglets to fill the gaps, thereby reducing fixed costs.

[0128] 3) Lowering the level of business awareness: This tool encapsulates complex financial logic in the back-end, allowing front-line managers to focus on production indicators and see the corresponding cost and profit changes on the front end of the system, effectively improving managers' business awareness.

[0129] 4) Multi-dimensional accurate forecasting: Unlike traditional extensive calculations, this solution covers process sales, multiple circulations and all cost elements, and the forecast results have a very high degree of fit with financial statements (the error can usually be controlled within 5%).

[0130] Reference Figure 4Initialize model parameters; determine data source; when the data source is the existing pig population, perform forward extrapolation; when the data source is future plans, perform rolling breeding forecasts; generate a future pig herd dynamic structure table, which includes daily inventory, age, and estimated weight; calculate feed costs, fixed cost allocation, and variable costs; based on feed costs, fixed cost allocation, and variable costs, calculate the predicted total cost and estimated cost per head for each future month; based on the estimated cost per head and predicted slaughter volume, calculate the predicted total profit.

[0131] Closed-loop comparative analysis is performed. If the target is not met, an automatic attribution algorithm is activated; if the target is met, positive incentives are generated; and finally, an improvement report is generated.

[0132] The following analysis uses the joint operation of a certain group's A sow farm and B fattening farm as an example to illustrate this: Step S501: Initialize model parameters, and input the basic data of field A into the system: The system automatically generates the following data: gestation period 114 days, lactation period 21 days, nursery period 56 days, fattening period 112 days; preset delivery rate 85%. Data entered: The average number of live pups per litter was 11, with a survival rate of 96%.

[0133] Define the pig transfer relationship: all nursery pigs from farm A are transferred to farm B for fattening.

[0134] Step S502: The daily breeding plan and swine flow projection system reads the breeding plan for Farm A and the next 3 months (e.g., breeding 10 pigs per day).

[0135]

[0136] Correction: The system automatically deducts the preset dead number at each stage and adjusts the slaughtering rhythm on specific holidays or sales strategy days.

[0137] Step S503: Cost flow and cash flow forecasting. The system presets the daily feed intake curve for the fattening stage of Farm B (e.g., 1.2kg feed for 30kg body weight and 2.8kg feed for 100kg body weight).

[0138] Based on the current feed price, calculate how much feed these pigs will consume each month in the future.

[0139] In addition, there are monthly fixed depreciation costs for venue B (e.g., 500,000 RMB / month) and labor costs (200,000 RMB / month).

[0140] Result: If the predicted number of pigs slaughtered in a certain month is 1,000, the fixed cost is 700 yuan / head, the feed cost is 1,500 yuan / head, and the medicine and miscellaneous cost is 100 yuan / head, then the predicted total cost for that month is 2,300 yuan / head.

[0141] Step S504: Discrepancy Attribution and Action List Generation. At the end of the month, the system found that the actual total cost of farm B was 2400 yuan / head, 100 yuan higher than the target. The system automatically executes the attribution analysis algorithm: Factor A (Survival Rate): Actual 95% vs. Target 96% The loss amounted to 15 yuan per head.

[0142] Factor B (feed percentage): Actual 2.7 vs. Target 2.6 The loss amounted to 60 yuan per head.

[0143] Factor C (Full Capacity): Actual 80% vs. Target 100% The fixed contribution will increase by 25 yuan per head.

[0144] Output result: The system interface prompts the farm manager: "The main reason for the failure to meet the profit target this month is the high feed conversion ratio and insufficient capacity utilization. The key tasks for next month are: 1) Inspect and repair the feed line to prevent waste; 2) Take in excess piglets from other farms to supplement the pens."

[0145] This application adopts a full-cycle dynamic pig herd extrapolation algorithm: it can dynamically generate a stock structure diagram and a slaughter prediction table for any future time point based on the current stock of pigs at any age / gestation age, combined with growth curves, mortality rates and cross-farm transfer rules.

[0146] This application adopts a quantitative attribution model for operating indicators: protecting a specific analytical logic, namely, automatically calculating the specific monetary impact of changes in a single production indicator (such as delivery rate, feed conversion ratio, mortality rate) on the final profit per head and total profit through the control variable method.

[0147] This application adopts a dynamic cost allocation mechanism based on capacity utilization: in the forecasting system, the fixed cost allocation per pig can be automatically adjusted according to the ratio of the predicted future inventory to the total designed capacity, thereby truly reflecting the contribution of "full production" to cost reduction.

[0148] This application employs a production-finance reverse-engineering target setting method: protecting a calculation model that automatically calculates the required number of breeding stock, number of pigs in stock, and cost control line by inputting target profit and estimated pig price.

[0149] Example 2: Figure 5 This is a schematic diagram of a system for implementing closed-loop management of pig farming using an online business analysis system, as provided in Embodiment 2 of the present invention.

[0150] Reference Figure 5 The system includes: The settings module is used to set production mode parameters and pig flow operation mode; The determination module is used to determine the data source based on production mode parameters and pig flow operation mode; The first execution module is used to perform forward calculations when the data source is existing inventory. The second execution module is used to perform rolling mating forecasts when the data source is a future plan; The generation module is used to generate the future daily pig herd structure based on forward extrapolation and rolling breeding forecasts; the future daily pig herd structure includes the predicted slaughter volume for each month. The first calculation module is used to calculate feed costs, fixed cost allocation costs, and variable costs. The second calculation module is used to calculate the predicted total cost and estimated cost per head for each future month based on feed costs, fixed cost allocation costs, and variable costs. The third calculation module is used to calculate the predicted total profit based on the estimated cost per head and the predicted number of animals slaughtered. The comparison module is used to compare the predicted total profit with the actual total profit and determine whether the target is met based on the comparison results.

[0151] Furthermore, the production mode parameters include the feeding cycle at each stage, the preset mortality rate at each stage, the group transfer rules, and the designed production capacity; among them, the feeding cycle at each stage includes the gestation period, lactation period, nursery period, and fattening period; the pig flow operation mode is the linkage between multiple farms.

[0152] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for achieving closed-loop management of pig farming using an online business analysis system provided in the above embodiments.

[0153] This invention also provides a computer-readable medium having processor-executable non-volatile program code, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the method for implementing closed-loop management of pig farming using an online business analysis system as described in the above embodiments.

[0154] The computer program product provided in this embodiment of the invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0155] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0156] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0157] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0158] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0159] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for achieving closed-loop management of pig farming using an online business analysis system, characterized in that, The method includes: Set production mode parameters and pig flow operation mode; The data source is determined based on the production mode parameters and the pig flow operation mode. When the data source is existing inventory, perform forward extrapolation; When the data source is a future plan, rolling mating forecasts are performed. Based on forward extrapolation and rolling breeding forecasts, the future daily pig herd structure is generated; wherein, the future daily pig herd structure includes the predicted slaughter volume for each month; Calculate feed costs, fixed cost allocations, and variable costs; Based on the feed cost, the fixed cost allocation, and the variable costs, calculate the projected total cost and estimated cost per head for each future month; Calculate the predicted total profit based on the estimated cost per head and the predicted number of animals slaughtered. The predicted total profit is compared with the actual total profit, and the result of the comparison is used to determine whether the target has been met.

2. The method for achieving closed-loop management of pig farming using an online business analysis system according to claim 1, characterized in that, The production mode parameters include the feeding cycle for each stage, the preset mortality rate for each stage, the group transfer rules, and the designed production capacity; wherein, the feeding cycle for each stage includes the gestation period, lactation period, nursery period, and fattening period; the pig flow operation mode is the association between multiple farms.

3. The method for achieving closed-loop management of pig farming using an online business analysis system according to claim 1, characterized in that, When the data source is existing inventory, a forward extrapolation is performed, including: Based on the current real-time inventory of pigs of various ages or gestation ages, combined with the pig herd growth curve and mortality model, the future growth status and inventory changes are projected day by day until slaughter.

4. The method for achieving closed-loop management of pig farming using an online business analysis system according to claim 1, characterized in that, When the data source is a future plan, rolling mating forecasts are performed, including: Based on historical breeding data and future breeding plans, the future newborn piglet flow can be predicted by applying the farrowing rate and the average number of healthy piglets per litter. Based on the predicted future piglet flow, the projected slaughter volume for each month is derived.

5. The method for achieving closed-loop management of pig farming using an online business analysis system according to claim 1, characterized in that, The projected total profit is compared with the actual total profit, and the results are used to determine whether the target has been met, including: When the predicted total profit is greater than the actual total profit, it is determined to be unsatisfactory; When the predicted total profit is less than or equal to the actual total profit, it is determined that the target has been met.

6. The method for achieving closed-loop management of pig farming using an online business analysis system according to claim 1, characterized in that, The method further includes: The predicted total profit is then reverse-calculated using an attribution analysis algorithm to obtain the key KPI indicators; The key KPIs were analyzed, and the results were obtained. The key KPIs include reserve utilization rate, number of reserves added to the herd, number of breeding stock, number of farrowings, average number of weaned litters, 21-day weight, survival rate, daily weight gain, feed conversion ratio, and market weight.

7. A system for achieving closed-loop management of pig farming using an online business analysis system, characterized in that, The system includes: The settings module is used to set production mode parameters and pig flow operation mode; The determination module is used to determine the data source based on the production mode parameters and the pig flow operation mode; The first execution module is used to perform forward calculation when the data source is an existing inventory. The second execution module is used to perform rolling mating prediction when the data source is a future plan; The generation module is used to generate the future daily pig herd structure based on forward extrapolation and rolling breeding prediction; wherein, the future daily pig herd structure includes the predicted slaughter volume for each month; The first calculation module is used to calculate feed costs, fixed cost allocation costs, and variable costs. The second calculation module is used to calculate the predicted total cost and estimated per head cost for each future month based on the feed cost, the fixed cost allocation cost, and the variable cost. The third calculation module is used to calculate the predicted total profit based on the estimated cost per head and the predicted number of animals slaughtered. The comparison module is used to compare the predicted total profit with the actual total profit and determine whether the target is met based on the comparison result.

8. The system for realizing closed-loop management of pig farming using an online business analysis system according to claim 7, characterized in that, The production mode parameters include the feeding cycle for each stage, the preset mortality rate for each stage, the group transfer rules, and the designed production capacity; wherein, the feeding cycle for each stage includes the gestation period, lactation period, nursery period, and fattening period; the pig flow operation mode is the association between multiple farms.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the method described in any one of claims 1 to 6.

10. A computer-readable medium having processor-executable non-volatile program code, characterized in that, The program code causes the processor to execute the method described in any one of claims 1 to 6.

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