Ecological breeding management method and system for animal husbandry

Abstract

The invention discloses an ecological breeding management method and system for animal husbandry, and relates to the technical field of animal husbandry intelligent management, and the method comprises the steps: dividing a breeding region into a plurality of functional blocks, and collecting environment monitoring data and livestock behavior data; calculating an environmental suitability index based on the environmental monitoring data; establishing an epidemic disease risk assessment model in combination with the environmental monitoring data and the livestock behavior data, and calculating an epidemic disease risk index; determining an alternating priority according to the environmental suitability index and the epidemic disease risk index, and carrying out the optimal distribution and migration scheduling of the livestock through a global optimization model; performing three-stage treatment on the livestock manure to obtain an organic fertilizer, determining a fertilization sequence and a fertilization amount of each pasturing functional block by establishing a soil improvement demand evaluation model, and performing soil improvement; and monitoring an ecological restoration state and realizing rotation grazing management. According to the invention, collaborative optimization management is realized, a complete circulating chain is established, and the management efficiency and sustainable development capability of ecological breeding of animal husbandry are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent animal husbandry management, and in particular to a method and system for ecological animal husbandry breeding management. Background Art

[0002] Modern livestock management technologies primarily utilize sensor networks for environmental monitoring, GPS systems for tracking livestock locations, and RFID tags for recording individual information. For grassland management, remote sensing imagery is used to analyze vegetation cover and meteorological data is combined to estimate livestock carrying capacity. For disease prevention and control, regular quarantine measures are combined with individual health monitoring. For manure and sewage treatment, biological treatment technologies such as anaerobic fermentation and aerobic composting are widely used. These technologies have played an important role in their respective fields.

[0003] However, existing technologies suffer from the following shortcomings: a lack of data integration and collaborative optimization across subsystems, resulting in relatively independent modules such as grassland assessment, disease monitoring, and environmental governance; environmental suitability assessments often rely on single factors, lacking a multidimensional, comprehensive evaluation; livestock allocation and scheduling often rely on static rules, lacking dynamic optimization based on real-time data; and a lack of standardized quantitative indicators for evaluating ecological restoration effectiveness. These technical limitations lead to inefficient resource allocation, delayed disease prevention and control responses, and unstable environmental governance outcomes. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for animal husbandry ecological breeding management, which is used to solve the technical problems of low resource allocation efficiency, weak risk prevention and control capabilities, and imperfect ecological cycle in animal husbandry management.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for ecological animal husbandry management, comprising:

[0008] The breeding area is divided into multiple functional blocks, and environmental monitoring data and livestock behavior data are collected and processed in each functional block; the functional blocks include grazing functional blocks and rest grazing functional blocks;

[0009] Based on environmental monitoring data, calculate the environmental suitability index of each grazing functional area;

[0010] Combine environmental monitoring data and livestock behavior data to establish a disease risk assessment model and calculate the disease risk index for each grazing functional area;

[0011] Determine rotation priorities based on environmental suitability index and disease risk index, and optimize livestock allocation and migration scheduling through a global optimization model;

[0012] Livestock manure is treated tertiary to obtain organic fertilizer. A soil improvement demand assessment model is established to determine the fertilization sequence and application amount for each grazing functional area. Organic fertilizer is applied in batches to improve the soil.

[0013] Monitor the ecological recovery status of the rest-grazing functional areas, and when the ecological recovery status reaches the preset standard, the rest-grazing functional areas will be re-incorporated into rotational grazing management.

[0014] As a preferred solution of the present invention for the animal husbandry ecological breeding management method, the breeding area is divided into multiple functional blocks including:

[0015] Obtain geographic information data of the breeding area;

[0016] Divide the breeding area into multiple functional blocks based on geographic information data, and adjust the block boundaries according to the minimum management area requirements and livestock carrying capacity;

[0017] Assess the grassland status, livestock suitability and ecological fragility of each functional block, and determine the initial usage status of each functional block.

[0018] As a preferred solution of the animal husbandry ecological breeding management method of the present invention, the calculation of the environmental suitability index of each grazing functional block includes the following steps:

[0019] Establish an environmental suitability evaluation model and determine the weight coefficient according to the impact of each environmental monitoring parameter on livestock suitability;

[0020] Determine the appropriate range of each environmental monitoring parameter based on the physiological characteristics and seasonal characteristics of the livestock species, and use the deviation evaluation method to calculate the suitability index of each environmental monitoring parameter;

[0021] Based on the synergistic effect among environmental monitoring parameters, the suitability index of each environmental monitoring parameter is revised;

[0022] The modified suitability index and the corresponding weight coefficient are weighted and summed to obtain the environmental suitability index of each grazing functional block.

[0023] As a preferred embodiment of the present invention for the animal husbandry ecological breeding management method, the process of constructing the disease risk assessment model includes:

[0024] Determine the environmental risk weight based on the impact of each environmental monitoring parameter on the disease risk, and calculate the environmental risk index based on the environmental monitoring data;

[0025] Establish a baseline of normal livestock behavior, compare livestock behavior data with the baseline of normal livestock behavior, identify abnormal behavior characteristics and calculate the abnormal behavior index;

[0026] Analyze the correlation and time lag effect between environmental monitoring data and livestock behavior data, and determine the fusion weight coefficient of the environmental risk index and behavioral abnormality index;

[0027] The environmental risk index and behavioral abnormality index are weighted and calculated according to the fusion weight coefficient to obtain the disease risk index of each grazing functional area.

[0028] As a preferred solution of the present invention for the animal husbandry ecological breeding management method, wherein: performing optimal livestock allocation and migration scheduling through a global optimization model includes:

[0029] Establish a two-dimensional coordinate system with the environmental suitability index as the horizontal axis and the disease safety index as the vertical axis, map each grazing functional block to the corresponding coordinate point in the coordinate system, determine the grazing functional blocks that need to be rotated and generate a rotation priority list;

[0030] Determine the recovery status of rest grazing functional blocks and calculate the carrying capacity of available functional blocks and the number of livestock to be allocated;

[0031] A global optimization model was established with minimizing livestock migration costs as the objective function and the carrying capacity of available functional blocks and the number of livestock to be allocated as constraints.

[0032] Using intelligent optimization algorithms to solve the global optimization model and obtain the optimal livestock allocation plan;

[0033] Livestock migration scheduling is carried out according to the optimal livestock allocation plan, and the grazing functional blocks that have completed livestock transfer are adjusted to a grazing rest state.

[0034] As a preferred embodiment of the present invention for the animal husbandry ecological breeding management method, the process of constructing the soil improvement demand assessment model includes:

[0035] Obtain soil state parameters and vegetation requirement parameters for each rest-grazing functional area;

[0036] Calculate the soil nutrient deficiency and pH deviation of each rest-grazing functional area, and determine the improvement urgency level of each rest-grazing functional area based on the soil nutrient deficiency and pH deviation;

[0037] Combining the improvement urgency level, rest grazing duration and block area, a hierarchical decision-making algorithm is used to determine the fertilization order of each rest grazing functional block.

[0038] The basic application amount is determined based on the soil nutrient deficiency and block area, and is adjusted according to the soil type and organic fertilizer characteristics to determine the final application amount for each grazing functional block.

[0039] As a preferred solution of the method for ecological breeding management of livestock industry, the environmental monitoring data comprises temperature data, humidity data, soil pH data, ammonia nitrogen concentration data and noise data; and the livestock behavior data comprises activity trajectory, exercise intensity, aggregation degree and foraging duration.

[0040] In a second aspect, the present application provides a system for ecological breeding management of livestock industry, comprising:

[0041] a data acquisition and processing module, configured to divide the breeding area into a plurality of functional blocks, acquire environmental monitoring data and livestock behavior data in each functional block, and process the data; the functional blocks comprise grazing functional blocks and resting functional blocks;

[0042] an environmental assessment module, configured to calculate an environmental suitability index of each grazing functional block based on the environmental monitoring data;

[0043] a disease risk analysis module, configured to establish a disease risk assessment model in combination with the environmental monitoring data and the livestock behavior data, and calculate a disease risk index of each grazing functional block;

[0044] a rotation scheduling module, configured to determine rotation priorities according to the environmental suitability index and the disease risk index, and perform optimal allocation and migration scheduling of livestock through a global optimization model;

[0045] a soil improvement module, configured to perform three-stage treatment on livestock manure to obtain organic fertilizer, determine a fertilization sequence and application amount of each resting functional block through a soil improvement demand assessment model, and apply the organic fertilizer in batches for soil improvement;

[0046] a resting recovery module, configured to monitor an ecological recovery state of the resting functional block, and when the ecological recovery state reaches a preset standard, the resting functional block is re-included in the rotation management.

[0047] In a third aspect, the present application provides a computer device comprising a memory and a processor, and the memory stores a computer program, wherein the computer program is executed by the processor to implement any step of the method for ecological breeding management of livestock industry according to the first aspect of the present application.

[0048] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement any step of the method for ecological breeding management of livestock industry according to the first aspect of the present application.

[0049] The beneficial effects of the present invention are as follows: by functionally dividing the breeding area and establishing a multi-dimensional data fusion system, the present invention realizes the coordinated optimization of modules such as grassland assessment, disease monitoring, and environmental governance, overcoming the limitations of the relative independence of each subsystem in the existing technology; the multi-dimensional comprehensive evaluation system based on the environmental suitability index and the disease risk index replaces the traditional evaluation method based on a single factor, and uses a global optimization model and an intelligent optimization algorithm to achieve optimal livestock allocation, solving the problems of static rule scheduling and response lag; through the three-level manure treatment process combined with the soil improvement demand assessment model, a complete ecological cycle chain from harmless treatment of manure to precise application of organic fertilizers is realized, and the stability of the environmental governance effect is improved; combined with the quantitative evaluation system of ecological restoration status monitoring, a standardized management process is established, and intelligent management of animal husbandry ecological breeding is realized, effectively improving resource allocation efficiency, risk prevention and control capabilities and the degree of ecological cycle perfection. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 Flowchart of ecological breeding management method for animal husbandry.

[0052] Figure 2 Flowchart for calculating disease risk index for ecological livestock breeding management methods.

[0053] Figure 3 Flowchart for optimal livestock allocation and migration scheduling for ecological livestock farming management methods.

[0054] Figure 4 This is a module connection diagram for the livestock ecological breeding management system. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0058] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides a method for animal husbandry ecological breeding management, the flow chart is as follows Figure 1 As shown, the following steps are included:

[0059] S1: Divide the breeding area into multiple functional blocks, collect environmental monitoring data and livestock behavior data in each functional block, and process the data.

[0060] Specifically, the geographic information data of the breeding area is obtained, which includes terrain data, slope data, vegetation coverage data, etc.; the breeding area is divided into multiple functional blocks based on the geographic information data, and the block boundaries are adjusted according to the minimum management area requirements and livestock carrying capacity, including: using terrain data and slope data to perform spatial clustering analysis, merging areas with similar terrain characteristics into the same functional block to ensure the consistency of terrain conditions within the block; based on vegetation coverage data, the area is divided into different levels according to grassland quality, and adjacent areas of the same level are merged into functional blocks; by setting a minimum management area threshold, preliminary blocks with insufficient area are merged or reallocated; the theoretical livestock carrying capacity of each block is calculated, and the differences in livestock density between adjacent blocks are controlled within a reasonable range through boundary fine-tuning.

[0061] It should be noted that for complex terrain or large-scale aquaculture areas, supplementary measures such as determining the water source service radius, optimizing management convenience, and verifying spatial connectivity need to be considered.

[0062] Furthermore, the grassland status, animal husbandry suitability and ecological fragility of each functional block were evaluated, and the initial use status of each functional block was determined. The steps were as follows: the grassland status index was calculated based on the vegetation coverage data, the animal husbandry suitability index was calculated in combination with the terrain data, slope data and water source distribution data, and the ecological fragility index was calculated based on the slope data and vegetation coverage data; an initial state decision matrix was established with the grassland status index as the horizontal axis and the ecological fragility index as the vertical axis, and the decision boundary was determined in combination with the animal husbandry suitability index; each functional block was mapped to the decision matrix, and blocks with good grassland status and low ecological fragility were set to grazing status, and blocks with poor grassland status or high ecological fragility were set to rest grazing status.

[0063] Furthermore, a monitoring sensor network is deployed within each functional block according to the principle of a grid layout to ensure that the monitoring coverage meets the preset standards. Monitoring sensors include environmental monitoring sensors and motion sensors. Environmental monitoring sensors include temperature and humidity sensors, soil pH sensors, ammonia nitrogen concentration sensors, and noise sensors. The preset standard is that the spatial coverage of each functional block should be no less than 95%. The grid layout includes core monitoring points, boundary monitoring points, and supplementary monitoring points. Core monitoring points are located in high-quality areas within each functional block with flat terrain, good vegetation coverage, and convenient water sources. Boundary monitoring points are used to monitor environmental changes at the boundaries of functional blocks and the edges of breeding areas. Supplementary monitoring points are flexibly configured according to terrain characteristics and environmentally sensitive areas.

[0064] After sensor deployment is complete, environmental monitoring sensors collect real-time environmental data, while motion sensors collect livestock behavior data. Environmental monitoring data includes temperature, humidity, soil pH, ammonia nitrogen concentration, and noise. The frequency of collection is determined by the type of data and the rate of environmental change. For functional blocks in grazing mode, motion sensors record livestock behavior data, including movement trajectory, movement intensity, aggregation, and foraging duration. For functional blocks in rest mode, motion sensors are primarily used to monitor whether livestock have strayed into the area. Motion sensors can utilize GPS locators, accelerometers, and other devices for 24 / 7 monitoring.

[0065] Then, the collected raw data is preprocessed and a data index is established for subsequent analysis and call. The preprocessing includes data cleaning, outlier identification, missing value supplementation and standardization.

[0066] S2: Based on environmental monitoring data, calculate the environmental suitability index of each grazing functional area.

[0067] Specifically, the flow chart for calculating the disease risk index is as follows: Figure 2 Shown, including:

[0068] S2.1: Establish an environmental suitability evaluation model and determine the weight coefficient based on the degree of influence of each environmental monitoring parameter on livestock suitability.

[0069] Alternatively, the implementation process of establishing an environmental suitability assessment model includes three steps: constructing a judgment matrix using the Analytic Hierarchy Process (AHP), performing a consistency check, and calculating weight coefficients. Expert scoring determines the relative importance of each environmental monitoring parameter. After verifying the consistency of the judgment matrix, the eigenvectors are calculated to obtain weight coefficients. Ultimately, a parameter weighting system is established with high weights for temperature and humidity, medium weights for soil pH and ammonia nitrogen concentration, and low weights for noise.

[0070] S2.2: Determine the appropriate range of each environmental monitoring parameter based on the physiological characteristics and seasonal characteristics of the livestock species, and use the deviation evaluation method to calculate the suitability index of each environmental monitoring parameter.

[0071] Specifically, a database of environmental parameter suitability by livestock species and season is established, and the optimal range, acceptable range, and extreme threshold of each environmental monitoring parameter are determined based on the physiological tolerance of different livestock species. A dynamic suitability range adjustment mechanism is established to correct the optimal range and acceptable range of each environmental monitoring parameter using the seasonal adjustment coefficient. The suitability index of each environmental monitoring parameter is calculated using the deviation evaluation method. When the monitoring value is within the optimal range, the suitability index is 1. When the monitoring value deviates from the optimal range and enters the acceptable range, the suitability index decreases according to the preset attenuation function based on the degree of deviation. When the monitoring value exceeds the extreme threshold, the suitability index drops to 0.

[0072] Optionally, an exponential decay function can be used for continuously changing parameters such as temperature and humidity, while a piecewise linear decay function can be used for parameters with clear optimal ranges, such as soil pH. The upper temperature limit can be relaxed by 10-15% in spring and summer, and by 5-10% in autumn and winter. The humidity range can be adjusted accordingly based on seasonal precipitation characteristics.

[0073] S2.3: Based on the synergistic effect among environmental monitoring parameters, the suitability index of each environmental monitoring parameter is revised.

[0074] Furthermore, the interactions between environmental monitoring parameters were identified, and a synergistic effect matrix of environmental monitoring parameters was established to quantify the enhancing or weakening effects of different environmental monitoring parameter combinations on livestock suitability. The synergistic effect matrix uses a numerical range of -1 to 1, where positive values ​​indicate positive synergistic effects, negative values ​​indicate negative synergistic effects, and 0 indicates no significant interaction.

[0075] Furthermore, the correction coefficient is determined according to the synergistic effect between the environmental monitoring parameters. For the positive synergistic effect, an enhancement factor greater than 1 is adopted, and for the negative synergistic effect, a weakening factor less than 1 is adopted. Based on the coupling relationship between the environmental monitoring parameters, a nonlinear correction function is used to synergistically correct the suitability index of each environmental monitoring parameter to obtain a corrected suitability index that takes into account the interaction between the environmental monitoring parameters.

[0076] S2.4: Perform weighted summation on the modified suitability index and the corresponding weight coefficient to obtain the environmental suitability index of each grazing functional area.

[0077] Preferably, through the synergistic effect correction mechanism, the interaction relationship between environmental parameters can be identified and quantified, avoiding the limitations of single parameter evaluation, so that the environmental suitability index can reflect the real environmental status under the comprehensive effect of multiple parameters, and improve the accuracy and reliability of environmental suitability evaluation.

[0078] S3: Combine environmental monitoring data and livestock behavior data to establish a disease risk assessment model and calculate the disease risk index of each grazing functional area.

[0079] Specifically, step S3 includes:

[0080] S3.1: Determine the environmental risk weight based on the impact of each environmental monitoring parameter on the disease risk and calculate the environmental risk index.

[0081] In one embodiment, a historical correlation database of environmental monitoring parameters and disease occurrence probability is established, and the correlation between environmental monitoring parameters and disease occurrence is analyzed; a logistic regression analysis method is used to calculate the influence coefficient of each environmental monitoring parameter on the disease occurrence probability, and the risk contribution of each environmental monitoring parameter is determined based on the influence coefficient, and the environmental risk weight is determined through normalization processing; an epidemic risk threshold interval is established for each environmental monitoring parameter, and the environmental monitoring data is compared with the epidemic risk threshold interval to calculate a single risk score; the single risk score of each environmental monitoring parameter is weighted and summed with the corresponding environmental risk weight to obtain the environmental risk index of each grazing functional block.

[0082] S3.2: Establish a baseline of normal livestock behavior based on historical livestock behavior data, compare livestock behavior data with the baseline of normal livestock behavior, identify abnormal behavior characteristics and calculate the abnormal behavior index.

[0083] It should be noted that historical herd behavior data refers to the historical behavior records of livestock herds in a healthy state. The process of establishing a normal livestock behavior baseline includes: statistical analysis of the herd's historical behavior data, calculation of the mean and standard deviation of each behavioral characteristic parameter, determination of the numerical range of normal behavior, and establishment of a multi-dimensional normal livestock behavior baseline model; comparative analysis of real-time monitored livestock behavior data with the normal livestock behavior baseline model, identification of behavioral characteristic parameters that exceed the normal value boundary and marking them as abnormal behavioral characteristics; and calculation of the abnormal behavior index of livestock herds within each grazing functional block using a weighted scoring method based on the degree of deviation and duration of each abnormal behavioral characteristic.

[0084] S3.3: Analyze the correlation and time lag effect between environmental monitoring data and livestock behavior data, and determine the fusion weight coefficient of the environmental risk index and behavioral abnormality index.

[0085] Furthermore, environmental monitoring data and livestock behavior data were collected within the same time period, and the correlation strength between environmental changes and behavioral changes was calculated through correlation analysis; a time lag effect analysis model was established to identify the delay time of the impact of environmental factors on livestock behavior and determine the optimal time window; based on the analysis results of correlation strength and time lag effect, the principal component analysis method was used to evaluate the contribution of the environmental risk index and behavioral abnormality index in disease prediction; according to the contribution of each index, the fusion weight coefficient of the environmental risk index and behavioral abnormality index was determined.

[0086] S3.4: The environmental risk index and behavioral abnormality index are weighted according to the fusion weight coefficient to obtain the disease risk index of each grazing functional block.

[0087] Preferably, by fusion calculation of environmental risk index and behavioral abnormality index, combined with time lag effect analysis to determine the optimal fusion weight, it is possible to comprehensively reflect the combined impact of environmental factors and behavioral factors on the occurrence of epidemics, achieve a comprehensive quantitative assessment of epidemic risks, and improve the accuracy of risk identification.

[0088] S4: Determine the rotation priority based on the environmental suitability index and disease risk index, and perform optimal livestock allocation and migration scheduling through a global optimization model.

[0089] Specifically, the flowchart of optimal livestock allocation and migration scheduling is as follows: Figure 3 Shown, including:

[0090] S4.1: Establish a two-dimensional coordinate system with the environmental suitability index as the horizontal axis and the disease safety index as the vertical axis, map each grazing functional block to the corresponding coordinate point in the coordinate system, determine the grazing functional blocks that need to be rotated and generate a rotation priority list.

[0091] In one embodiment, the environmental suitability index is normalized to a scale of 0-100, the disease risk index is reversely converted to a disease safety index and normalized to a scale of 0-100, and a two-dimensional coordinate system is established. The ideal grazing state coordinate point is defined as the coordinate point with the best environmental suitability and the highest disease safety, i.e., (100, 100).

[0092] Furthermore, a distance threshold is set, which is determined based on historical rotation effect data and currently available rotation resources; the weighted Euclidean distance is used to calculate the distance from each coordinate point to the ideal grazing state coordinate point, where corresponding weight coefficients are set for environmental suitability and disease safety respectively; grazing functional blocks with a distance greater than the distance threshold are screened out, and the blocks are sorted from large to small according to distance to form a preliminary rotation priority list.

[0093] S4.2: Determine the recovery status of the rest grazing functional blocks and calculate the carrying capacity of the available functional blocks and the number of livestock to be allocated.

[0094] Among them, the available functional blocks include grazing functional blocks that are not included in the rotation priority list and grazing functional blocks with good recovery status.

[0095] S4.3: Establish a global optimization model with minimizing livestock migration costs as the objective function and the carrying capacity of available functional blocks and the number of livestock to be allocated as constraints.

[0096] It should be noted that migration costs include both distance and time costs. Distance costs are calculated based on the geographic distance between functional blocks and the unit-distance migration fee. Time costs are calculated based on the time required for livestock migration and the value of that time. Furthermore, constraints include carrying capacity constraints and allocation integrity constraints. The carrying capacity constraint limits the number of livestock received by each functional block to no more than its carrying capacity limit, while the allocation integrity constraint requires that all livestock to be allocated must be fully allocated.

[0097] S4.4: Use intelligent optimization algorithms to solve the global optimization model and obtain the optimal livestock allocation plan.

[0098] Furthermore, the intelligent optimization algorithm includes one or more of a genetic algorithm, a particle swarm optimization algorithm or a simulated annealing algorithm; the optimal livestock allocation plan includes the specific livestock allocation quantity and the corresponding migration path arrangement for each functional block.

[0099] S4.5: Implement livestock migration scheduling according to the optimal livestock allocation plan, and adjust the grazing functional blocks that have completed livestock transfer to a grazing rest state.

[0100] Preferably, by establishing a two-dimensional coordinate system to map the comprehensive status of each grazing functional block and combining it with a global optimization model to comprehensively consider multiple constraints, the optimal livestock allocation plan can be automatically generated, solving the technical problem of uneven resource allocation in traditional rotational grazing management that relies on experience-based judgment, and improving the accuracy and efficiency of rotational grazing scheduling.

[0101] S5: Carry out tertiary treatment of livestock manure to obtain organic fertilizer, determine the fertilization order and application amount of each grazing functional area by establishing a soil improvement demand assessment model, and apply organic fertilizer in batches to improve the soil.

[0102] Among them, the tertiary treatment includes solid-liquid separation, anaerobic fermentation and aerobic composting. Solid-liquid separation refers to the separation of the solid and liquid phases in manure through mechanical equipment to provide suitable materials for subsequent treatment; anaerobic fermentation refers to the use of microorganisms to decompose organic matter in an anaerobic environment, produce biogas and degrade pollutants; aerobic composting refers to the use of microorganisms to mature and stabilize materials under aerobic conditions to form organic fertilizers.

[0103] Specifically, the construction process of the soil improvement demand assessment model includes: obtaining the soil state parameters and vegetation demand parameters of each grazing functional block. The soil state parameters include soil pH value, nitrogen, phosphorus and potassium content, etc., and the vegetation demand parameters include nitrogen, phosphorus and potassium requirements of the target vegetation, the optimal pH range, etc.; calculating the soil nutrient deficiency and pH deviation of each grazing functional block. The soil nutrient deficiency is calculated by the ratio of the deficiency to the requirement of each nutrient (nitrogen, phosphorus and potassium), and the weighted average of the three items is taken. The pH deviation is calculated by the absolute difference between the actual soil pH value and the median of the optimal pH range of the target vegetation; based on the soil nutrient deficiency and pH deviation, the improvement urgency level of each grazing functional block is determined, and the urgency index is calculated using the weighted comprehensive scoring method. The improvement priority levels are divided into high, medium and low according to the index level.

[0104] Furthermore, combined with the improvement urgency level, grazing rest duration and block area, a hierarchical decision-making algorithm is used to determine the fertilization order of each grazing rest function block, specifically including: establishing a three-level evaluation index system, with the improvement urgency level as the main indicator, and the grazing rest duration and block area as auxiliary indicators; setting weight coefficients for each indicator, with the improvement urgency level taking a dominant position, giving priority to blocks with longer grazing rest durations, and giving priority to blocks with moderate areas; calculating the fertilization priority of each grazing rest function block through a weighted comprehensive score, and determining the fertilization order by ranking them according to priority, to ensure that blocks with urgent improvement needs and suitable conditions get priority in obtaining organic fertilizers.

[0105] Furthermore, a base application rate is determined based on soil nutrient deficiency and plot area, and corrections are made based on soil type and organic fertilizer properties to determine the final application rate for each rest grazing functional area. The base application rate is calculated by multiplying the nutrient deficiency by the plot area. Correction factors include soil type correction factor and organic fertilizer property correction factor. Sandy soils require an increased application rate due to their weak nutrient retention capacity, while clay soils require an appropriate reduction due to their slow nutrient release. Organic fertilizers with high nutrient content require a corresponding reduction in application rate, while those with low nutrient content require an increase. The final application rate is the result of adjusting the base application rate with these correction factors.

[0106] It should be noted that the application in batches is carried out in the order of fertilization, and the number of fertilization blocks in each batch is determined according to the output of organic fertilizer.

[0107] Preferably, a three-stage treatment process is used to achieve full harmlessness and efficient resource utilization of manure and sewage, solid-liquid separation improves the efficiency of subsequent treatment, anaerobic fermentation produces clean energy and degrades pollutants, and aerobic composting ensures the stability of organic fertilizer quality; the soil improvement demand assessment model realizes precise fertilization, avoids environmental pollution and resource waste caused by excessive application, and ensures the maximization of improvement effect; the batch application strategy ensures the rational distribution and timely utilization of organic fertilizers, forming a complete ecological cycle system from manure and sewage treatment to soil improvement.

[0108] S6: Monitor the ecological recovery status of the rest-grazing functional area. When the ecological recovery status reaches the preset standard, the rest-grazing functional area will be re-incorporated into the rotational grazing management.

[0109] Ecological restoration status includes soil physical and chemical indicators, vegetation recovery indicators, and environmental quality indicators. Pre-set standards are determined based on regional environmental characteristics and livestock species needs. Soil physical and chemical indicators must be restored to levels that support normal forage growth, vegetation recovery indicators must meet target livestock carrying capacity requirements, and environmental quality indicators must meet animal health and safety standards.

[0110] This embodiment also provides an ecological breeding management system for animal husbandry, the module connection diagram is as follows Figure 4 As shown, the system includes:

[0111] The data collection and processing module is used to divide the breeding area into multiple functional blocks, collect environmental monitoring data and livestock behavior data in each functional block, and perform data processing; the functional blocks include grazing functional blocks and rest grazing functional blocks;

[0112] Environmental assessment module, used to calculate the environmental suitability index of each grazing functional block based on environmental monitoring data;

[0113] The disease risk analysis module is used to combine environmental monitoring data and livestock behavior data to establish a disease risk assessment model and calculate the disease risk index of each grazing functional block;

[0114] The rotational grazing scheduling module is used to determine the rotation priority based on the environmental suitability index and disease risk index, and to optimize livestock allocation and migration scheduling through a global optimization model;

[0115] The soil improvement module is used to obtain organic fertilizer through tertiary treatment of livestock manure. By establishing a soil improvement demand assessment model, the fertilization sequence and application amount for each grazing functional area are determined, and organic fertilizer is applied in batches to improve the soil.

[0116] The grazing restoration module is used to monitor the ecological restoration status of the grazing functional area. When the ecological restoration status reaches the preset standard, the grazing functional area will be re-incorporated into the rotational grazing management.

[0117] This embodiment also provides a computer device suitable for the livestock ecological breeding management method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the livestock ecological breeding management method proposed in the above embodiment.

[0118] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0119] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for ecological livestock breeding management proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0120] To sum up, the present application realizes the collaborative optimization of grassland evaluation, disease monitoring, environmental governance and other modules by functionally dividing the breeding area and establishing a multi-dimensional data fusion system, overcoming the relative independence of each subsystem in the prior art; the multi-dimensional comprehensive evaluation system based on environmental suitability index and disease risk index replaces the traditional evaluation method based on a single factor, uses a global optimization model and an intelligent optimization algorithm to realize the optimal allocation of livestock, and solves the problems of static rule scheduling and response lag; through the three-stage fecal treatment process combined with the soil improvement demand evaluation model, the complete ecological circulation chain from harmless treatment of fecal pollution to precise application of organic fertilizer is realized, and the stability of the environmental governance effect is improved; combined with the quantitative evaluation system of ecological restoration state monitoring, a standardized management process is established, realizing the intelligent management of livestock ecological breeding, effectively improving the resource allocation efficiency, risk prevention and control ability and ecological circulation perfection degree.

[0121] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for ecological animal husbandry management, characterized by: include: Divide the breeding area into multiple functional blocks, collect environmental monitoring data and livestock behavior data in each functional block, and process the data; The functional blocks include grazing functional blocks and rest grazing functional blocks; Based on environmental monitoring data, calculate the environmental suitability index of each grazing functional area; Establishing a disease risk assessment model by combining the environmental monitoring data and livestock behavior data, and calculating the disease risk index of each grazing functional area; Determine rotation priorities based on environmental suitability index and disease risk index, and optimize livestock allocation and migration scheduling through a global optimization model; Livestock manure is treated tertiary to obtain organic fertilizer. A soil improvement demand assessment model is established to determine the fertilization sequence and application amount for each grazing functional area. Organic fertilizer is applied in batches to improve the soil. Monitor the ecological recovery status of the rest-grazing functional areas, and when the ecological recovery status reaches the preset standard, the rest-grazing functional areas will be re-incorporated into rotational grazing management.

2. The method for ecological animal husbandry management according to claim 1, characterized in that: The division of the breeding area into multiple functional blocks includes: Obtain geographic information data of the breeding area; Dividing the breeding area into a plurality of functional blocks based on the geographic information data, and adjusting the block boundaries according to minimum management area requirements and livestock carrying capacity; Assess the grassland status, livestock suitability and ecological fragility of each functional block, and determine the initial usage status of each functional block.

3. The method for ecological animal husbandry management according to claim 1, wherein: The calculation of the environmental suitability index of each grazing functional block includes the following steps: Establish an environmental suitability evaluation model and determine the weight coefficient according to the impact of each environmental monitoring parameter on livestock suitability; Determine the appropriate range of each environmental monitoring parameter based on the physiological characteristics and seasonal characteristics of the livestock species, and use the deviation evaluation method to calculate the suitability index of each environmental monitoring parameter; Based on the synergistic effect among environmental monitoring parameters, the suitability index of each environmental monitoring parameter is revised; The modified suitability index and the corresponding weight coefficient are weighted and summed to obtain the environmental suitability index of each grazing functional block.

4. The method for ecological animal husbandry management according to claim 1, wherein: The construction process of the disease risk assessment model includes: Determine the environmental risk weight based on the impact of each environmental monitoring parameter on the disease risk, and calculate the environmental risk index based on the environmental monitoring data; Establishing a normal livestock behavior baseline, comparing livestock behavior data with the normal livestock behavior baseline, identifying abnormal behavior characteristics and calculating a behavioral abnormality index; Analyze the correlation and time lag effect between environmental monitoring data and livestock behavior data, and determine the fusion weight coefficient of the environmental risk index and behavioral abnormality index; The environmental risk index and the behavioral abnormality index are weighted and calculated according to the fusion weight coefficient to obtain the disease risk index of each grazing functional block.

5. The method for ecological animal husbandry management according to claim 1, characterized in that: The optimal allocation and migration scheduling of livestock by using a global optimization model includes: Establish a two-dimensional coordinate system with the environmental suitability index as the horizontal axis and the disease safety index as the vertical axis, map each grazing functional block to the corresponding coordinate point in the coordinate system, determine the grazing functional blocks that need to be rotated and generate a rotation priority list; Determine the recovery status of rest grazing functional blocks and calculate the carrying capacity of available functional blocks and the number of livestock to be allocated; A global optimization model was established with minimizing livestock migration costs as the objective function and the carrying capacity of available functional blocks and the number of livestock to be allocated as constraints. Solving the global optimization model using an intelligent optimization algorithm to obtain an optimal livestock allocation plan; Livestock migration scheduling is performed according to the optimal livestock allocation plan, and the grazing functional blocks where livestock transfer has been completed are adjusted to a grazing rest state.

6. The method for ecological animal husbandry management according to claim 1, characterized in that: The construction process of the soil improvement needs assessment model includes: Obtain soil state parameters and vegetation requirement parameters for each rest-grazing functional area; Calculate the soil nutrient deficiency and pH deviation of each rest-grazing functional area, and determine the improvement urgency level of each rest-grazing functional area based on the soil nutrient deficiency and pH deviation; Combining the improvement urgency level, rest grazing duration and block area, a hierarchical decision-making algorithm is used to determine the fertilization order of each rest grazing functional block. The basic application amount is determined based on the soil nutrient deficiency and block area, and is adjusted according to the soil type and organic fertilizer characteristics to determine the final application amount for each grazing functional block.

7. The method for ecological animal husbandry management according to claim 1, characterized in that: The environmental monitoring data includes temperature data, humidity data, soil pH data, ammonia nitrogen concentration data and noise data; the livestock behavior data includes activity trajectory, exercise intensity, aggregation degree and foraging time.

8. A livestock ecological breeding management system, based on the livestock ecological breeding management method according to any one of claims 1 to 7, characterized in that: include: A data collection and processing module is used to divide the breeding area into multiple functional blocks, collect environmental monitoring data and livestock behavior data in each functional block, and perform data processing; the functional blocks include grazing functional blocks and rest grazing functional blocks; Environmental assessment module, used to calculate the environmental suitability index of each grazing functional area based on environmental monitoring data; An epidemic risk analysis module is used to establish an epidemic risk assessment model by combining the environmental monitoring data and livestock behavior data, and calculate the epidemic risk index of each grazing functional block; The rotational grazing scheduling module is used to determine the rotation priority based on the environmental suitability index and disease risk index, and to optimize livestock allocation and migration scheduling through a global optimization model; The soil improvement module is used to obtain organic fertilizer through tertiary treatment of livestock manure. By establishing a soil improvement demand assessment model, the fertilization sequence and application amount for each grazing functional area are determined, and organic fertilizer is applied in batches to improve the soil. The grazing restoration module is used to monitor the ecological restoration status of the grazing functional area. When the ecological restoration status reaches the preset standard, the grazing functional area will be re-incorporated into the rotational grazing management.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the animal husbandry ecological breeding management method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for ecological livestock breeding management according to any one of claims 1 to 7 are implemented.

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