Intelligent decision-making management methods and systems for resource output under integrated crop-livestock farming model

Abstract

This invention relates to the fields of smart agriculture and agricultural ecological cycle engineering technology, specifically to an intelligent decision-making management method and system for resource output under a crop-livestock integrated model. The method includes: initializing system parameters; calculating and generating an environmental resistance index, which characterizes the resistance to resources entering the soil medium; calculating the system's total effective carrying capacity in terms of nitrogen; comparing the environmental resistance index with a critical environmental resistance threshold to determine whether resource return to the field is permitted, and using the total effective carrying capacity as an upper limit constraint to extrapolate future predicted inventory levels; calculating the cumulative value of excess, and based on a preset nutrient excretion and conversion sensitivity coefficient, calculating and outputting the adjustment value for crude protein concentration in the feed formula. This invention solves the spatiotemporal mismatch problem between rigid supply and flexible consumption, realizing a shift from passive response to proactive early warning.

Description

Technical Field

[0001] This invention relates to the field of smart agriculture and agricultural ecological cycle engineering technology, specifically to an intelligent decision-making management method and system for resource output under the integrated crop-livestock model. Background Technology

[0002] Currently, the integrated farming model mainly relies on physical storage facilities to temporarily store livestock waste and then transport it to farmland for disposal according to agricultural plans, so as to realize the resource utilization of agricultural waste. In this process, it is usually assumed that the environmental carrying capacity is relatively stable, and the fixed volume of the storage facilities is used to buffer the flow difference between production and disposal.

[0003] However, with the intensive development of large-scale farming and the improvement of environmental standards, resource management based on static planning faces severe challenges in related technologies. The generation of livestock waste exhibits rigid and continuous characteristics, while the absorption capacity of farmland is limited by dynamic environmental impedance factors such as rainfall, soil moisture, and crop growth stages, exhibiting strong elasticity and discrete characteristics. Existing management methods lack quantitative perception of environmental impedance indices and dynamic definition of ecological carrying capacity, resulting in a serious spatiotemporal mismatch between resource supply flow and farmland demand window. In addition, when physical inventory faces the risk of overflow, there is a lack of reverse feedback adjustment mechanism from end-of-pipe storage to source feed formulation, which can easily lead to secondary environmental disasters such as surface runoff and soil leaching. Therefore, a solution is urgently needed to address the problems existing in the current technology. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent decision-making management method and system for resource output under an integrated crop-livestock farming model. By quantifying the environmental resistance index and dynamically defining the total effective carrying capacity, it solves the spatiotemporal mismatch between the rigid and continuous supply of livestock waste and the flexible and discrete demand for farmland disposal. Furthermore, it can establish a reverse feedback adjustment mechanism from end-of-pipe storage risk to source feed formulation based on inventory evolution, effectively avoiding secondary environmental disasters such as surface runoff and soil leaching. Specifically, the technical solution of this invention is as follows:

[0005] Intelligent decision-making and management methods for resource output under the integrated crop-livestock farming model include:

[0006] Initialize system parameters, and set the critical threshold for environmental impedance, the leaching safety factor, and the natural attenuation factor;

[0007] According to a preset time step, execute the intelligent decision-making process for resource output. The process includes:

[0008] Step 1: Obtain the current rainfall probability data, soil relative humidity data, and normalized nutrient uptake rate of crops. Generate the environmental resistance index through weighted calculation. The environmental resistance index is used to characterize the degree of resistance to resources entering the soil medium.

[0009] Step 2: Obtain the remaining effective volume of the physical storage pool, the average total nitrogen concentration of the waste, and the soil physicochemical parameters of the farmland to be disposed of, and calculate the total effective carrying capacity of the system in terms of nitrogen element.

[0010] Step 3: Compare the environmental impedance index with the environmental impedance critical threshold to determine whether resource return to the field is allowed, and combine the total effective carrying capacity as the upper limit constraint to extrapolate the predicted inventory at future times.

[0011] Step 4: In response to the predicted inventory exceeding the total effective carrying capacity, calculate the cumulative value of the excess, and based on the preset nutrient excretion and conversion sensitivity coefficient, calculate and output the adjustment value of crude protein concentration in the feed formula.

[0012] Preferably, in step 1, the specific steps for generating the environmental impedance index include:

[0013] The preset weighting coefficients correspond to the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient, respectively, for rainfall probability, soil relative humidity, and crop absorption status.

[0014] The first component is obtained by multiplying the rainfall probability by the first weighting coefficient;

[0015] The second component is obtained by multiplying the soil relative humidity by the second weighting coefficient.

[0016] Subtract the normalized nutrient uptake rate of the crop from the value 1, and multiply the difference by the third weighting coefficient to obtain the third component;

[0017] The environmental impedance index is obtained by adding the first, second, and third components.

[0018] Preferably, in step 2, the specific steps for calculating the total effective carrying capacity of the system in terms of nitrogen element include:

[0019] The physical storage capacity is obtained by multiplying the remaining effective volume of the physical storage pool by the average total nitrogen concentration of the waste.

[0020] For each plot of farmland to absorb nitrogen, obtain the farmland area, the effective adsorption depth of the root zone, the soil bulk density, the theoretical maximum nitrogen adsorption capacity of the soil, and the current background nitrogen content of the soil.

[0021] The difference between the theoretical maximum nitrogen adsorption capacity of the soil and the current background nitrogen content of the soil is calculated to obtain the remaining adsorption capacity per unit of soil.

[0022] The ecological adsorption capacity of the plot is obtained by multiplying the farmland area, the effective adsorption depth of the root zone, the soil bulk density, the remaining adsorption capacity per unit soil, and the leaching prevention safety factor.

[0023] The total effective carrying capacity is obtained by summing the physical storage capacity with the ecological absorption capacity of all farmland plots.

[0024] Preferably, in step 3, the specific steps for extrapolating the predicted inventory levels at future times include:

[0025] An inventory evolution logic, including a natural decay term and a net flow accumulation term, is constructed based on the quality conservation logic.

[0026] Calculate the decay value of the current initial inventory over time using the natural decay coefficient;

[0027] Obtain the preset resource output rate and planned return-to-field rate;

[0028] The planned return-to-field rate is gated and adjusted based on the environmental impedance index to obtain the actual outflow rate.

[0029] Calculate the difference between the resource output rate and the actual outflow rate, and integrate and accumulate this difference over the prediction period to obtain the net accumulated amount.

[0030] The predicted inventory level at future time is determined by adding the inventory decay value to the net accumulation.

[0031] Preferably, the specific logic for gating the planned return-to-field rate based on the environmental impedance index is as follows:

[0032] If the environmental impedance index is less than the environmental impedance critical threshold, it is determined that the current environmental conditions allow for operation, and the actual outflow rate is equal to the planned return-to-field rate.

[0033] If the environmental impedance index is greater than or equal to the environmental impedance critical threshold, it is determined that the current environmental conditions prohibit operation, and the actual outflow rate is forcibly set to zero.

[0034] Preferably, in step 4, the specific steps for reverse calculation and outputting the adjusted value of crude protein concentration in the feed formulation include:

[0035] Calculate the difference between the predicted inventory level and the total effective carrying capacity;

[0036] The total accumulated risk is obtained by integrating the difference over the future control period.

[0037] Obtain the number of livestock in stock during the future control period and calculate the total number of head-days of livestock fed during that period;

[0038] Divide the total cumulative risk by the total number of feeding days, and then divide by the nutrient excretion conversion sensitivity coefficient to obtain the percentage reduction in crude protein concentration in the feed formula.

[0039] The nutrient excretion conversion sensitivity coefficient is defined as the reduction in average daily nitrogen excretion per animal corresponding to a 1% decrease in feed protein concentration.

[0040] Preferably, the method further includes:

[0041] The calculated percentage reduction in crude protein concentration is converted into specific feed formulation control instructions;

[0042] The feed ingredient control command is sent to the feed processing control terminal to implement formula adjustments in the next batch of feed production.

[0043] An intelligent decision-making management system for resource output under an integrated crop-livestock farming model includes:

[0044] The environmental impedance calculation module is used to obtain rainfall probability, soil relative humidity and crop normalized absorption rate, and calculate and generate environmental impedance index by combining preset weighting coefficients.

[0045] The capacity definition module is used to calculate the total effective carrying capacity of the system in terms of nitrogen element based on physical storage pool parameters and farmland soil physicochemical parameters, combined with the leaching prevention safety factor.

[0046] The evolution and deduction module is used to execute the inventory evolution logic, control the on / off of the planned return rate based on the environmental resistance index, and combine natural decay and net flow accumulation to predict the inventory level.

[0047] The feedback control module is used to calculate the adjustment value of feed crude protein concentration based on the cumulative amount of excess and the nutrient excretion and conversion sensitivity coefficient when the inventory is predicted to exceed the standard.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. This invention generates an environmental resistance index by weighting the acquisition of rainfall probability, soil relative humidity, and crop normalized absorption rate, transforming multidimensional environmental factors into a quantified single decision variable. This mechanism can accurately identify the degree of environmental resistance to resource return to the field, solve the problem of neglecting the dynamic environmental resistance in traditional models, effectively avoid surface runoff and environmental pollution caused by operations under high-risk conditions such as rainfall or soil saturation, and improve the scientificity and safety of operational decisions.

[0050] 2. This invention breaks through the limitations of traditional methods that rely solely on the volume of physical storage facilities. It innovatively constructs a total effective carrying capacity calculation model that includes both physical storage capacity and the capacity of farmland ecological absorption. By coupling the explicit capacity of physical storage space with the implicit capacity of the soil ecosystem in real time, the total safety boundary of the system is clarified, greatly expanding the system's ability to regulate and buffer livestock waste, and maximizing the utilization of resource carrying potential under the integrated crop-livestock farming model.

[0051] 3. This invention establishes an inventory evolution prediction mechanism based on the principle of quality conservation, using the natural decay coefficient and net flow accumulation term to predict future inventory levels. This mechanism introduces the environmental impedance index as a gating logic to accurately simulate the impact of operational interruptions caused by environmental factors on inventory backlog, thereby enabling early prediction of overflow risks caused by environmental impedance, solving the spatiotemporal mismatch between rigid supply and flexible absorption, and realizing the transformation from passive response to proactive early warning.

[0052] 4. This invention constructs a reverse feedback closed-loop control system from end-of-life storage risk perception to source feed formulation adjustment; when the risk of inventory exceeding the standard is predicted, the adjustment value of feed crude protein concentration is calculated in reverse based on the cumulative amount of excess and the sensitivity coefficient of nutrient excretion and conversion, and control instructions are generated; this source reduction mechanism can dynamically reduce nitrogen input without affecting the needs of breeding, effectively alleviate the pressure of end-of-life disposal, and completely eliminate the environmental hazards of inventory overflow. Attached Figure Description

[0053] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0054] Figure 1 This is a flowchart of the method of the present invention;

[0055] Figure 2 This is a structural diagram of the system of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0057] Example 1:

[0058] Please see Figure 1 Intelligent decision-making and management methods for resource output under the integrated farming and animal husbandry model include:

[0059] Initialize system parameters, and set the critical threshold for environmental impedance, the leaching safety factor, and the natural attenuation factor;

[0060] According to a preset time step, execute the intelligent decision-making process for resource output. The process includes:

[0061] Step 1: Obtain the current rainfall probability data, soil relative humidity data, and normalized nutrient uptake rate of crops. Generate the environmental resistance index through weighted calculation. The environmental resistance index is used to characterize the degree of resistance to resources entering the soil medium.

[0062] Step 2: Obtain the remaining effective volume of the physical storage pool, the average total nitrogen concentration of the waste, and the soil physicochemical parameters of the farmland to be disposed of, and calculate the total effective carrying capacity of the system in terms of nitrogen element.

[0063] Step 3: Compare the environmental impedance index with the environmental impedance critical threshold to determine whether resource return to the field is allowed, and combine the total effective carrying capacity as the upper limit constraint to extrapolate the predicted inventory at future times.

[0064] Step 4: In response to the predicted inventory exceeding the total effective carrying capacity, calculate the cumulative value of the excess, and based on the preset nutrient excretion and conversion sensitivity coefficient, calculate and output the adjustment value of crude protein concentration in the feed formula.

[0065] This embodiment provides an intelligent decision-making and management method for resource output under the integrated crop-livestock model. The method aims to solve the spatiotemporal mismatch problem between the rigid and continuous supply flow of livestock waste and the flexible and discrete demand window for farmland disposal. To achieve this goal, this embodiment uses digital means to quantify environmental impedance and ecological carrying capacity, and establishes a dynamic feedback mechanism accordingly.

[0066] Perform system initialization; this step involves reading global control parameters pre-stored in the database; specifically, it includes setting environmental impedance critical thresholds. This threshold is a safety red line determined based on statistical data of historical extreme weather events and runoff risks, used to determine whether environmental conditions permit resource return to the field. The specific logic for its value is as follows: Environmental impedance index samples corresponding to periods in historical monitoring data where no surface runoff or groundwater pollution events have occurred are selected, and their 90th percentile value or the upper limit of the confidence interval is calculated as... At the same time, a leaching prevention safety factor is set. Its value is usually less than or equal to 1, designed to reserve a redundancy in soil adsorption capacity to prevent groundwater pollution; in addition, a natural attenuation coefficient also needs to be set. It is used to characterize the mass loss characteristics of waste during storage due to biochemical reactions such as anaerobic fermentation;

[0067] The system enters a cyclical process of intelligent decision-making for resource output according to a preset time step; this time step can be set to the hour or day level according to the management precision requirements; in this process, the system executes the following steps in sequence:

[0068] Step 1: Environmental impedance sensing; the system acquires current rainfall probability data, soil relative humidity data, and normalized nutrient uptake rate of crops; these multi-source heterogeneous data are weighted and calculated to generate an environmental impedance index. This index is a dimensionless value used to quantitatively characterize the degree of resistance to resources entering the soil medium. The higher the value, the greater the environmental risk and the higher the difficulty of the operation.

[0069] Step 2: Determining carrying capacity; The system obtains the remaining effective volume of the physical storage pool, the average total nitrogen concentration of waste, and the soil physicochemical parameters of the farmland to which the waste is disposed of; Based on these parameters, the total effective carrying capacity of the system, expressed in terms of nitrogen, is calculated. The core of this step lies in quantifying the physical storage space and the ecological adsorption space of farmland soil in a unified manner, thereby clarifying the overall safety boundary of the system.

[0070] Step 3: Evolutionary Deduction; Calculate the environmental impedance index... Critical threshold of environmental impedance A comparison is performed; the result of this comparison determines whether resource return to the field is permitted; this serves as a logical gating mechanism, combined with the total effective carrying capacity obtained in step 2. As an upper limit constraint, the principle of mass conservation is used to extrapolate the predicted inventory levels at future moments. ;

[0071] Step 4: Backward feedback control; responding to the deduced predicted inventory levels. Greater than the total effective carrying capacity In such cases, the system determines that there is a risk of overflow; at this time, it calculates the cumulative value of the excess amount and, based on the preset nutrient excretion and conversion sensitivity coefficient,... It calculates and outputs the adjustment value of crude protein concentration in the feed formula in reverse. By reducing waste at the source, we can reduce the pressure on end-of-life treatment while ensuring the needs of aquaculture.

[0072] Example 2:

[0073] In step 1, the specific steps for generating the environmental impedance index include:

[0074] The preset weighting coefficients correspond to the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient, respectively, for rainfall probability, soil relative humidity, and crop absorption status.

[0075] The first component is obtained by multiplying the rainfall probability by the first weighting coefficient;

[0076] The second component is obtained by multiplying the soil relative humidity by the second weighting coefficient.

[0077] Subtract the normalized nutrient uptake rate of the crop from the value 1, and multiply the difference by the third weighting coefficient to obtain the third component;

[0078] The environmental impedance index is obtained by adding the first, second, and third components.

[0079] This embodiment details the specific technical implementation of generating the environmental impedance index in step 1; environmental impedance index The construction of the model introduces the concept of resistance from fluid mechanics, treating rainfall, soil moisture, and crop growth stage as frictional factors that hinder resources from entering the soil medium. The specific calculation logic is shown in the following formula:

[0080] ;

[0081] in: Indicates the current time The probability of rainfall is 0 to 1; this data comes from the API interface provided by the meteorological service provider or the real-time monitoring data of the local meteorological station; the higher the probability of rainfall, the greater the risk of surface runoff and the stronger the resistance to resource return to the field.

[0082] Indicates the current time The relative humidity of the soil ranges from 0 to 1; this data is collected in real time by FDR frequency domain reflectance sensors or TDR time domain reflectance sensors deployed in farmland areas; the higher the soil humidity, the weaker its permeability and the greater its resistance to the absorption of new liquid resources.

[0083] Indicates the current time The normalized average absorption rate (NABAR) of nutrients by crops ranges from 0 to 1; this data is determined based on a pre-defined crop growth cycle database; for example, during the vigorous growth stage of crops, such as the jointing stage of corn, A value close to 1 indicates that the crop has a strong absorption capacity. A value close to 0 means that crops have minimal resistance to resource return to the field; conversely, during the fallow period, When the value is 0, the impedance term reaches its maximum value;

[0084] , and These are the first, second, and third weighting coefficients corresponding to rainfall probability, soil relative humidity, and crop absorption status, respectively; these three coefficients satisfy the normalization condition. The specific numerical calibration method is as follows: The surface runoff coefficient or groundwater nitrate concentration increment within the historical monitoring period is selected as the dependent variable, and the rainfall probability, soil relative humidity, and crop normalized uptake rate during the same period are used as independent variables. A multiple linear regression model is established to obtain the respective regression coefficients; and the obtained coefficients are normalized to meet the following conditions. Constraints; for example, in shallow groundwater areas, to enhance sensitivity to soil saturation, fitting calculations can significantly improve... The value of ;

[0085] The above calculation results It can be directly used as a dimensionless probability indicator in the [0,1] interval without the need for additional reference value scaling, thus avoiding the influence of... Signal distortion caused by improper selection;

[0086] Through the weighted calculations described above, this embodiment can transform multi-dimensional environmental factors into a single decision variable through linear combination, thereby accurately quantifying the current environment's acceptance of resource return operations.

[0087] Example 3:

[0088] In step 2, the specific steps for calculating the total effective carrying capacity of the system in terms of nitrogen element include:

[0089] The physical storage capacity is obtained by multiplying the remaining effective volume of the physical storage pool by the average total nitrogen concentration of the waste.

[0090] For each plot of farmland to absorb nitrogen, obtain the farmland area, the effective adsorption depth of the root zone, the soil bulk density, the theoretical maximum nitrogen adsorption capacity of the soil, and the current background nitrogen content of the soil.

[0091] The difference between the theoretical maximum nitrogen adsorption capacity of the soil and the current background nitrogen content of the soil is calculated to obtain the remaining adsorption capacity per unit of soil.

[0092] The ecological adsorption capacity of the plot is obtained by multiplying the farmland area, the effective adsorption depth of the root zone, the soil bulk density, the remaining adsorption capacity per unit soil, and the leaching prevention safety factor.

[0093] The total effective carrying capacity is obtained by summing the physical storage capacity with the ecological absorption capacity of all farmland plots.

[0094] This embodiment details the specific method for calculating the total effective carrying capacity of the system in nitrogen element in step 2. This method innovatively introduces the amount of matter as a unified unit of measurement, coupling the explicit capacity of physical storage facilities with the implicit capacity of the farmland soil ecosystem to calculate the total effective carrying capacity. The calculation model is as follows:

[0095] ;

[0096] in: This refers to the current remaining effective volume of the physical storage pool, in cubic meters (m³). 3 The value is calculated by combining real-time data from the liquid level sensor with the geometric dimensions of the pool. The average total nitrogen concentration in waste, expressed in kilograms of nitrogen per cubic meter. This parameter is determined by an online water quality monitor or set as a constant based on periodic sampling and testing results;

[0097] Calculate the ecological adsorption capacity, i.e., the summation term in the formula; for each plot of farmland to absorb the adsorption. common For each plot of land, the system obtains the following parameters:

[0098] The farmland area of ​​this plot of land, in square meters. It originates from the GIS geographic information system;

[0099] : The effective adsorption depth of the root system in soil, in meters. This data originates from soil profile survey data.

[0100] Soil bulk density, measured in kilograms of soil per cubic meter. This information comes from a soil physical property test report.

[0101] Theoretical maximum nitrogen adsorption capacity of soil, expressed in kilograms of nitrogen per kilogram of soil. This value is calculated based on the soil cation exchange capacity (CEC) and represents the theoretical upper limit of soil colloid adsorption of ammonium nitrogen.

[0102] Current background nitrogen content in soil, expressed in kilograms of nitrogen per kilogram of soil. Data are collected in real time by a rapid soil nutrient analyzer;

[0103] By calculating the difference The remaining adsorption capacity per unit of soil was obtained; the farmland area, effective soil depth, soil bulk density, remaining adsorption capacity per unit of soil, and preset leaching prevention safety factor were then considered. Multiplying these values ​​together yields the ecological absorption capacity of the land parcel at the safety threshold; summing the physical inventory capacity with the ecological absorption capacity of all farmland parcels that have been absorbed gives the total effective carrying capacity of the system at the current moment. This calculation method effectively transforms farmland soil into a virtual buffer pool with specific capacity values, greatly expanding the system's regulatory capacity.

[0104] Example 4:

[0105] In step 3, the specific steps for extrapolating future inventory levels include:

[0106] An inventory evolution logic, including a natural decay term and a net flow accumulation term, is constructed based on the quality conservation logic.

[0107] Calculate the decay value of the current initial inventory over time using the natural decay coefficient;

[0108] Obtain the preset resource output rate and planned return-to-field rate;

[0109] The planned return-to-field rate is gated and adjusted based on the environmental impedance index to obtain the actual outflow rate.

[0110] Calculate the difference between the resource output rate and the actual outflow rate, and integrate and accumulate this difference over the prediction period to obtain the net accumulated amount.

[0111] Add the stock decay value to the net accumulation to determine the projected inventory level at future time.

[0112] The specific logic for gating the planned return-to-field rate based on the environmental impedance index is as follows:

[0113] If the environmental impedance index is less than the environmental impedance critical threshold, it is determined that the current environmental conditions allow for operation, and the actual outflow rate is equal to the planned return-to-field rate.

[0114] If the environmental impedance index is greater than or equal to the environmental impedance critical threshold, it is determined that the current environmental conditions prohibit operation, and the actual outflow rate is forcibly set to zero.

[0115] This embodiment details the specific logic of predicting future inventory levels in step 3. Based on the law of conservation of mass, this process constructs a differential equation containing natural decay terms and net flow accumulation terms, and then integrates and solves it to predict inventory levels. The evolutionary logic is shown in the following equation:

[0116] ;

[0117] in: For the current moment Initial inventory, To predict the time period, Indicating that after experiencing Over a longer period of time, the predicted inventory levels at future moments are obtained through real-time monitoring.

[0118] This is the natural decay term, using the natural decay coefficient. The calculations show that the inventory of materials decreases over time.

[0119] The integral term primarily calculates the net cumulative amount over the forecast period; where:

[0120] for The resource production rate at any given time is determined by the product of the current number of livestock and the excretion coefficient at each growth stage; the specific calculation formula is as follows:

[0121] ;

[0122] in, express The number of livestock in stock at any given time, in heads, is derived from the real-time ledger of the farm's ERP system. This represents the nitrogen excretion coefficient per unit time for a single animal at this growth stage, expressed in units of... This coefficient is retrieved from a pre-set database of planting and breeding standards;

[0123] for The planned rate of returning crops to the field at any given time is derived from the pre-set work schedule;

[0124] To reflect the constraints of environmental factors on the operation, this embodiment introduces a gating adjustment function. The planned return-to-field rate is adjusted to obtain the actual outflow rate; based on the environmental impedance index... The specific logic for gating the planned return-to-field rate is as follows:

[0125] when When, function A value of 1 indicates that the current environmental conditions permit the operation; at this point, the actual outflow rate equals the planned return-to-field rate. ;

[0126] when When, function If the value is 0, it indicates that the current environmental conditions prohibit operation, such as heavy rain or soil saturation. In this case, the actual outflow rate is forcibly set to zero. In the above formula and logic, Explicitly defined as a Heaviside unit step function: when the independent variable hour, When the independent variable hour, The independent variable here is Thus, a precise description of physical on / off control is achieved mathematically;

[0127] The system calculates the difference between the resource production rate and the actual outflow rate, as the value at time step (i.e., the difference between the actual resource production rate and the actual outflow rate). The net flow input; considering that this net flow occurs from time [time] By the end of the prediction Natural decay will also occur within the same time period, therefore it needs to be multiplied by a decay factor. The net flow rate after attenuation correction is within the predicted time period. The effective net increment is obtained by integrating and accumulating the initial stock over the forecast period; finally, the decay value of the initial stock is added to the effective net increment to determine the future time. Forecasted inventory This inference method based on convolution integrals can accurately predict the risk of inventory backlog caused by operational interruptions due to environmental impedance.

[0128] Example 5:

[0129] In step 4, the specific steps for reverse calculation and outputting the adjusted value of crude protein concentration in the feed formulation include:

[0130] Calculate the difference between the predicted inventory level and the total effective carrying capacity;

[0131] The total accumulated risk is obtained by integrating the difference over the future control period.

[0132] Obtain the number of livestock in stock during the future control period and calculate the total number of head-days of livestock fed during that period;

[0133] Divide the total cumulative risk by the total number of feeding days, and then divide by the nutrient excretion conversion sensitivity coefficient to obtain the percentage reduction in crude protein concentration in the feed formula.

[0134] The nutrient excretion conversion sensitivity coefficient is defined as the reduction in average daily nitrogen excretion per animal for every 1% decrease in feed protein concentration.

[0135] The method also includes:

[0136] The calculated percentage reduction in crude protein concentration is converted into specific feed formulation control instructions;

[0137] The feed ingredient control command is sent to the feed processing control terminal to implement formula adjustments in the next batch of feed production.

[0138] This embodiment details the specific steps of reverse calculation of feed formula adjustment values ​​in step 4; this step constitutes the key to closed-loop control, that is, when the risk of excessive inventory is predicted, peak reduction is achieved through source adjustment;

[0139] Before performing the comparison, the system needs to simultaneously simulate future moments. Total effective carrying capacity Given that soil nitrogen content undergoes natural degradation and leaching over time, the following presupposes a soil nitrogen absorption kinetic equation:

[0140] ;

[0141] in, The natural decay rate of soil nitrogen characteristics; based on the updated Substituting into the formula of Example 3, recalculate the future To ensure the spatiotemporal consistency of the comparison benchmark;

[0142] Calculate the difference between the inventory level and the total effective carrying capacity at the predicted time; if If no adjustment is needed; if If the difference is found to be a risk of inventory overflow, then the risk is determined to be present. This refers to the target reduction quality that needs to be eliminated through source reduction within the forecast period;

[0143] Obtain the number of livestock in stock during the future management period The total number of heads fed per day within the cycle is calculated by integrating the time. ;

[0144] Using the above difference and integration results, combined with the nutrient excretion and transformation sensitivity coefficient Calculate the percentage decrease in crude protein concentration in the feed formulation. The calculation formula is as follows:

[0145] ;

[0146] Among them, the sensitivity coefficient of nutrient excretion and transformation Defined as the reduction in average daily nitrogen excretion per animal corresponding to a 1% decrease in feed protein concentration (unit:). This coefficient is derived from metabolic experimental data of a specific livestock species.

[0147] The system will calculate the percentage decrease in crude protein concentration. This is converted into specific feed ingredient control instructions; these instructions are sent to the feed processing control terminal, such as the PLC of the ingredient control system, to perform formula adjustments in the next batch of feed production, thereby reducing nitrogen input at the source and ensuring that the system returns to a safe capacity range in the future.

[0148] Example 6:

[0149] Please see Figure 2 An intelligent decision-making management system for resource output under the integrated farming and animal husbandry model includes:

[0150] The environmental impedance calculation module is used to obtain rainfall probability, soil relative humidity and crop normalized absorption rate, and calculate and generate environmental impedance index by combining preset weighting coefficients.

[0151] The capacity definition module is used to calculate the total effective carrying capacity of the system in terms of nitrogen element based on physical storage pool parameters and farmland soil physicochemical parameters, combined with the leaching prevention safety factor.

[0152] The evolution and deduction module is used to execute the inventory evolution logic, control the on / off of the planned return rate based on the environmental resistance index, and combine natural decay and net flow accumulation to predict the inventory level.

[0153] The feedback control module is used to calculate the adjustment value of feed crude protein concentration based on the cumulative amount of excess and the nutrient excretion and conversion sensitivity coefficient when the inventory is predicted to exceed the standard.

[0154] This embodiment provides an intelligent decision-making and management system for resource output under an integrated crop-livestock farming model. This system is applied to the methods described in the above embodiments. The system includes the following core modules:

[0155] Environmental impedance calculation module: This module is equipped with a data interface for real-time acquisition of external meteorological data, specifically the probability of rainfall. and soil relative humidity data acquired through sensor networks And access the internal database to obtain crop parameters. This module has built-in first, second, and third weighting coefficients, which generate the environmental impedance index by executing a weighted algorithm. This provides a quantitative basis for the system's environmental operability;

[0156] Capacity Delineation Module: This module stores the geometric parameters of the physical storage pool, as well as GIS information and soil physicochemical parameters for each farmland plot. This module is responsible for real-time calculation of physical storage capacity and ecological adsorption capacity, combined with leaching prevention safety factors. The total effective carrying capacity of the dynamic output system, measured in nitrogen element. Establish the system's security boundaries;

[0157] Evolutionary projection module: This module is the predictive core of the system; it receives impedance index from the environmental impedance calculation module and capacity data from the capacity definition module, and executes inventory evolution logic; this module is specially configured with a gating logic unit to control the on / off state of the planned return-to-field rate based on the environmental impedance index. The system switches states and combines natural decay and net flow accumulation algorithms to extrapolate future predicted inventory levels.

[0158] Feedback Control Module: This module is used for closed-loop control; it is triggered when the predicted inventory level output by the evolutionary simulation module exceeds the total effective carrying capacity; it is based on the accumulated excess and a preset nutrient excretion and conversion sensitivity coefficient. It reverse-calculates the adjustment value of crude protein concentration in feed and generates corresponding control commands to send to the feed production subsystem, realizing automated management from end-point risk perception to source production adjustment.

[0159] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent decision-making and management method for resource output under an integrated crop-livestock farming model, characterized in that, include: Initialize system parameters, and set the critical threshold of environmental impedance, the leaching safety factor, and the natural attenuation factor; The environmental impedance critical threshold is a safety red line determined based on statistical data of historical extreme weather events and runoff risks. It is used to determine whether environmental conditions permit resource return to the field. The specific value selection logic is as follows: select environmental impedance index samples corresponding to the period when no surface runoff or groundwater pollution events have occurred in historical monitoring data, and calculate its 90th percentile value or the upper limit of the confidence interval as the environmental impedance critical threshold. The leaching safety factor is less than or equal to 1, which aims to reserve redundant space for soil adsorption capacity to prevent groundwater pollution. The natural decay coefficient is used to characterize the mass loss characteristics of waste caused by biochemical reactions such as anaerobic fermentation during storage. According to a preset time step, execute the intelligent decision-making process for resource output. The process includes: Step 1: Obtain the current rainfall probability data, soil relative humidity data, and normalized nutrient uptake rate of crops. Generate the environmental resistance index through weighted calculation. The environmental resistance index is used to characterize the degree of resistance to resources entering the soil medium. Step 2: Obtain the remaining effective volume of the physical storage pool, the average total nitrogen concentration of the waste, and the soil physicochemical parameters of the farmland to be disposed of, and calculate the total effective carrying capacity of the system in terms of nitrogen element. Step 3: Compare the environmental impedance index with the environmental impedance critical threshold to determine whether resource return to the field is allowed, and combine the total effective carrying capacity as the upper limit constraint to extrapolate the predicted inventory at future times. Step 4: In response to the predicted inventory exceeding the total effective carrying capacity, calculate the cumulative excess value, and based on the preset nutrient excretion and conversion sensitivity coefficient, calculate and output the adjustment value of crude protein concentration in the feed formula. Specific steps include: Calculate the difference between the predicted inventory level and the total effective carrying capacity; The total accumulated risk is obtained by integrating the difference over the future control period. Obtain the number of livestock in stock during the future control period and calculate the total number of head-days of livestock fed during that period; Divide the total cumulative risk by the total number of feeding days, and then divide by the nutrient excretion conversion sensitivity coefficient to obtain the percentage reduction in crude protein concentration in the feed formula. The nutrient excretion conversion sensitivity coefficient is defined as the reduction in average daily nitrogen excretion per animal corresponding to a 1% decrease in feed protein concentration.

2. The intelligent decision-making management method for resource output under the integrated crop-livestock farming model according to claim 1, characterized in that, In step 1, the specific steps for generating the environmental impedance index include: The preset weighting coefficients correspond to the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient, respectively, for rainfall probability, soil relative humidity, and crop absorption status. The first component is obtained by multiplying the rainfall probability by the first weighting coefficient; The second component is obtained by multiplying the soil relative humidity by the second weighting coefficient. Subtract the normalized nutrient uptake rate of the crop from the value 1, and multiply the difference by the third weighting coefficient to obtain the third component; The environmental impedance index is obtained by adding the first, second, and third components.

3. The intelligent decision-making and management method for resource output under the integrated crop-livestock farming model according to claim 1, characterized in that, In step 2, the specific steps for calculating the total effective carrying capacity of the system in terms of nitrogen element include: The physical storage capacity is obtained by multiplying the remaining effective volume of the physical storage pool by the average total nitrogen concentration of the waste. For each plot of farmland to absorb nitrogen, obtain the farmland area, the effective adsorption depth of the root zone, the soil bulk density, the theoretical maximum nitrogen adsorption capacity of the soil, and the current background nitrogen content of the soil. The difference between the theoretical maximum nitrogen adsorption capacity of the soil and the current background nitrogen content of the soil is calculated to obtain the remaining adsorption capacity per unit of soil. The ecological adsorption capacity of the plot is obtained by multiplying the farmland area, the effective adsorption depth of the root zone, the soil bulk density, the remaining adsorption capacity per unit soil, and the leaching prevention safety factor. The total effective carrying capacity is obtained by summing the physical storage capacity with the ecological absorption capacity of all farmland plots.

4. The intelligent decision-making and management method for resource output under the integrated crop-livestock farming model according to claim 1, characterized in that, In step 3, the specific steps for extrapolating future inventory levels include: An inventory evolution logic, including a natural decay term and a net flow accumulation term, is constructed based on the quality conservation logic. Calculate the decay value of the current initial inventory over time using the natural decay coefficient; Obtain the preset resource output rate and planned return-to-field rate; The planned return-to-field rate is gated and adjusted based on the environmental impedance index to obtain the actual outflow rate; Calculate the difference between the resource output rate and the actual outflow rate, and integrate and accumulate this difference over the prediction period to obtain the net accumulated amount. The predicted inventory level at future time is determined by adding the inventory decay value to the net accumulation.

5. The intelligent decision-making and management method for resource output under the integrated crop-livestock farming model according to claim 4, characterized in that, The specific logic for gating the planned return-to-field rate based on the environmental impedance index is as follows: If the environmental impedance index is less than the environmental impedance critical threshold, it is determined that the current environmental conditions allow for operation, and the actual outflow rate is equal to the planned return-to-field rate. If the environmental impedance index is greater than or equal to the environmental impedance critical threshold, it is determined that the current environmental conditions prohibit operation, and the actual outflow rate is forcibly set to zero.

6. The intelligent decision-making management method for resource output under the integrated crop-livestock farming model according to claim 1, characterized in that, The method also includes: The calculated percentage reduction in crude protein concentration is converted into specific feed formulation control instructions; The feed ingredient control command is sent to the feed processing control terminal to implement formula adjustments in the next batch of feed production.

7. An intelligent decision-making management system for resource output under an integrated crop-livestock farming model, applied to the intelligent decision-making management method for resource output under any one of claims 1-6, characterized in that, include: The environmental impedance calculation module is used to obtain rainfall probability, soil relative humidity and crop normalized absorption rate, and calculate and generate environmental impedance index by combining preset weighting coefficients. The capacity definition module is used to calculate the total effective carrying capacity of the system in terms of nitrogen element based on physical storage pool parameters and farmland soil physicochemical parameters, combined with the leaching prevention safety factor. The evolution and deduction module is used to execute the inventory evolution logic, control the on / off of the planned return rate based on the environmental resistance index, and combine natural decay and net flow accumulation to predict the inventory level. The feedback control module is used to calculate the adjustment value of feed crude protein concentration based on the cumulative amount of excess and the nutrient excretion and conversion sensitivity coefficient when the inventory is predicted to exceed the standard.

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