Full-process management system for research and development and demonstration and popularization of selenium-rich bio-organic fertilizer

Abstract

The present application relates to the field of agricultural informatization and intelligent management, in particular to a full-process management system for selenium-rich bio-organic fertilizer research and development and demonstration and popularization, comprising: a raw material digital filing module for executing a sliding average filtering algorithm to clean data and generating a digital raw material fingerprint feature vector; a market demand reverse analysis module for extracting and calculating an environmental demand constraint matrix representing differentiated market demand based on preset element flow rules; a research and production intelligent decision-making module for starting an evolutionary solution model, mapping the raw material fingerprint to the environmental constraint matrix, and outputting a dynamic production management instruction sheet; a production execution supervision module for distributing instructions to terminals and real-time monitoring of process execution data; and a full-life-cycle closed-loop optimization module for obtaining feedback data to calculate deviations and generating a correction factor to update model parameters; the present application solves the contradiction between the heterogeneity of non-standard biomass raw materials and the precise nutritional requirements of functional agricultural products.

Description

Technical Field

[0001] This invention relates to the field of agricultural informatization and intelligent management technology, specifically to a full-process management system for the research, development, demonstration and promotion of selenium-enriched bio-organic fertilizer. Background Technology

[0002] In the field of functional agricultural product cultivation, the research and promotion of selenium-enriched bio-organic fertilizer is a key link in realizing the high-value utilization of agricultural waste and precise nutrient supply to crops. At present, the production and management of this type of fertilizer generally rely on bio-fermentation technology, which converts organic waste into fertilizer through microbial degradation, and usually adopts general process standards for production scheduling and quality control.

[0003] However, existing production management systems mostly adopt a forward production logic, that is, to produce standardized products based on fixed formulas and then seek market demand. This model has exposed obvious defects when facing the precise demand for selenium enrichment. Due to the high heterogeneity of organic raw materials and the significant differences in the physical and chemical properties of soil in the application area, the existing technology lacks a reverse analysis mechanism based on environmental constraints, which leads to a mismatch between the fertilizer release rate and the soil degradation capacity, or functional strains being unable to colonize due to acid and alkalinity stress, affecting the final selenium enrichment effect.

[0004] In the decision-making process of production technology, the coupling relationship between multidimensional parameters is complex. Existing methods often simply superimpose parameters or rely on human experience for adjustment, lacking strict dimensionless processing and physical consistency constraints. This makes it difficult for the model to accurately decouple the specific contributions of process parameters such as turning frequency and aeration rate to selenium activation efficiency and energy consumption cost, making it difficult to achieve multi-objective optimization of quality and energy consumption. Furthermore, traditional systems usually lack the ability to optimize the entire life cycle in a closed loop, and cannot automatically correct the weighted parameters of the model based on the feedback data of the actual selenium content of the crop after product application. This makes the system unable to adapt to the dynamic changes in the agricultural production environment, resulting in low long-term prediction accuracy. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a comprehensive management system for the research, development, demonstration, and promotion of selenium-enriched bio-organic fertilizer. Specifically, the technical solution of this invention includes:

[0006] The raw material digital filing module is used to receive the detection information of organic waste and generate a digital raw material fingerprint feature vector containing material attributes through data cleaning and feature extraction. The material attributes include at least the carbon-nitrogen ratio attribute value, lignin content attribute value and original selenium content attribute value.

[0007] The market demand reverse analysis module is used to access the soil physicochemical property database and the target crop selenium enrichment model library of the target application area. Based on the preset element transfer rules, it calculates and generates an environmental demand constraint matrix representing the differentiated market demand. The calculation and generation of the environmental demand constraint matrix representing the differentiated market demand includes: performing inversion calculation based on the preset element transfer rules, converting the preset selenium content standard of the target crop into the required total soil available selenium concentration through enrichment coefficient, and combining the soil background selenium migration coefficient with the share naturally contributed by the soil background to obtain the required soil available selenium concentration increment as a constraint indicator.

[0008] The intelligent decision-making module for R&D and production scheduling is used to perform resource optimization and allocation calculations. It maps the raw material fingerprint feature vectors to the environmental demand constraint matrix and uses an evolutionary solution model to output dynamic production management instruction sheets containing process work order data and formula ratio data. The evolutionary solution model adopts a real-number encoded genetic algorithm, defines chromosomes containing turning frequency, aeration rate and fermentation cycle, and constructs a fitness function. The fitness function uses weight coefficients determined by the analytic hierarchy process to maximize the estimated selenium activation rate while minimizing energy consumption.

[0009] The production execution monitoring module is used to distribute dynamic production management instructions to the production management terminal and collect process execution data in real time for compliance comparison.

[0010] The full lifecycle closed-loop optimization module is used to obtain performance feedback data after product launch, calculate the deviation index between actual and expected values, and generate model correction factors to update the weighted parameters of the evolutionary solution model.

[0011] Preferably, the intelligent decision-making module for R&D and production scheduling is specifically configured to perform the following data processing steps: Based on the lignin content attribute value in the raw material fingerprint feature vector and the soil degradation capacity index in the environmental demand constraint matrix, retrieve and match the corresponding production cycle management parameters; the production cycle management parameter is the fermentation cycle, which is calculated through a two-factor coupling model; the two-factor coupling model is solved by combining the time constant of the standard raw material retardation effect and the soil degradation capacity index calculated by the normalized weighted algorithm based on biological enzyme activity and temperature; based on the soil pH attribute value in the environmental demand constraint matrix and the carbon-nitrogen ratio attribute value in the raw material fingerprint feature vector, calculate the functional strain compounding ratio data in the resource allocation scheme; associate and encapsulate the production cycle management parameters and the functional strain compounding ratio data to generate a dynamic production management instruction sheet.

[0012] Preferably, when matching production cycle management parameters, the R&D and production scheduling intelligent decision-making module executes the following logical judgment rules: if the lignin content attribute value is not lower than the preset first threshold, the production cycle management parameter is marked as an extended management mode code; if the lignin content attribute value is lower than the first threshold but not lower than the preset second threshold, the production cycle management parameter is marked as a standard management mode code; if the lignin content attribute value is lower than the second threshold, the production cycle management parameter is marked as a fast turnover mode code.

[0013] Preferably, it also includes: a digital traceability management module, which responds to the generation of dynamic production management instruction sheets, calls the coding algorithm to generate a unique digital traceability identity ID for the corresponding management batch, and writes the process work order data and formula ratio data into the traceability database.

[0014] Preferably, the management fields associated with the digital traceability identity ID in the database include: raw material source batch index, process temperature monitoring record, functional strain input record, and target promotion area code.

[0015] Preferably, when calculating the functional strain compounding ratio data, the R&D and production scheduling intelligent decision-making module executes the following resource allocation logic: when the soil pH value is identified as acidic and the carbon-nitrogen ratio value is higher than the preset high-carbon index value, an instruction pointing to the first type of resource allocation strategy is generated; when the soil pH value is identified as neutral, or the carbon-nitrogen ratio value is within the preset range, an instruction pointing to the balanced resource allocation strategy is generated; when the soil pH value is identified as alkaline and the carbon-nitrogen ratio value is lower than the preset low-carbon index value, an instruction pointing to the second type of resource allocation strategy is generated.

[0016] Preferably, the process of updating the model in the closed-loop optimization module throughout the entire lifecycle includes: calculating the numerical difference between the effect feedback data and the preset target value, and constructing a prediction error data package; calling the current version of the model weight configuration, combining the prediction error data package and the preset learning rate parameters, generating the next version of the model weight configuration through algorithm iteration, and storing it in the system knowledge base.

[0017] Preferably, the initial data state of the evolutionary solution model is determined through offline data mining and machine learning training based on waste treatment records, soil migration history records, and crop response history records in the historical business database.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. This invention effectively solves the matching problem between the heterogeneity of non-standard biomass raw materials and the precise nutritional needs of functional agricultural products by constructing a collaborative mechanism between a raw material digital filing module and a market demand reverse analysis module. The system uses a moving average filtering algorithm to clean the original sensor data, eliminating high-frequency random noise and generating accurate digital raw material fingerprint feature vectors. At the same time, it breaks the traditional forward production logic and generates a composite data structure containing the target selenium increment demand and environmental state constraint set based on geographic information system inversion calculation. By introducing the physical dimension consistency calculation of soil background selenium migration coefficient and crop enrichment coefficient, it ensures that the subtraction operation is carried out under the same soil available concentration dimension, thereby enabling precise customization of fertilizer selenium increment index according to the soil physicochemical properties of the target area, realizing a paradigm shift from resource processing to precision nutrition manufacturing.

[0020] 2. This invention employs a decoupled design at the physicochemical level in its intelligent decision-making module for production scheduling, achieving intelligent dynamic optimization of production process parameters. Utilizing an evolutionary solution model and a real-number encoded genetic algorithm, it maximizes the predicted selenium activation rate while minimizing energy consumption. The weighting coefficients determined through the analytic hierarchy process balance efficiency and cost. - Standardization eliminates dimensional differences in parameters such as turning frequency and aeration rate; the system performs cycle matching based on physical degradation characteristics and strain blending based on chemical environment characteristics, comprehensively considers the effects of enzyme activity and temperature through a normalized weighted algorithm, calculates the fermentation cycle using a two-factor coupling model, and uses a constrained quadratic programming algorithm to solve for the optimal blending ratio of functional strains; this design ensures that the fermentation process conforms to the mechanism constraints of biodegradation and achieves local optimization of process energy consumption, significantly improving the scientific and economical nature of production scheduling decisions;

[0021] 3. This invention implements a resource allocation strategy based on microbial ecological niches, significantly enhancing the colonization ability and activity of functional strains in specific soil habitats. The system intelligently generates differentiated resource allocation instructions based on the logical judgment of soil pH and the carbon-nitrogen ratio of the raw materials: in acidic and high-carbon environments, acid-resistant fungi are quantitatively added to accelerate degradation by adjusting the carbon-nitrogen ratio and acidity gain coefficients; in alkaline and low-carbon environments, alkali-resistant actinomycetes are added and exogenous carbon sources are introduced by using low-carbon and alkaline environment compensation coefficients; a balanced strategy is executed under normal conditions; the compensation coefficients determined by response surface methodology precisely offset the activity loss caused by environmental stress, ensuring that after the selenium-enriched bio-organic fertilizer is applied to the soil, functional strains can quickly become the dominant microbial community, efficiently driving the conversion of organic selenium and plant absorption, thus solving the problem of traditional general-purpose formulas failing in specific soils.

[0022] 4. This invention establishes a closed-loop optimization and digital traceability management system throughout the entire lifecycle, endowing the system with the ability to continuously evolve and accurately trace; through the digital traceability management module, a unique digital traceability identity is generated using an encoding algorithm. By strongly linking raw material sources, process temperatures, strain input amounts, and target promotion area codes, an evidence loop from waste source to field application is constructed, reducing the complexity of post-sales attribution analysis. More importantly, the full life-cycle closed-loop optimization module collects field measurement feedback data, constructs a prediction error data package, and uses the gradient descent algorithm to iteratively update the weight configuration of the selenium efficacy prediction function. Combined with the initial model state determined by offline mining based on historical business databases, the system cold start problem is solved, and it can automatically correct systematic errors caused by climate or soil evolution as production batches accumulate, ensuring the long-term stability of the selenium enrichment technology solution. Attached Figure Description

[0023] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0024] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0025] 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.

[0026] Example 1:

[0027] Please see Figure 1 A comprehensive management system for the research, development, demonstration, and promotion of selenium-enriched bio-organic fertilizer, including:

[0028] The raw material digital filing module is used to receive the detection information of organic waste and generate a digital raw material fingerprint feature vector containing material attributes through data cleaning and feature extraction. The material attributes include at least the carbon-nitrogen ratio attribute value, lignin content attribute value and original selenium content attribute value.

[0029] The market demand reverse analysis module is used to access the soil physicochemical property database and the target crop selenium enrichment model library of the target application area. Based on the preset element transfer rules, it calculates and generates an environmental demand constraint matrix representing the differentiated market demand. The calculation and generation of the environmental demand constraint matrix representing the differentiated market demand includes: performing inversion calculation based on the preset element transfer rules, converting the preset selenium content standard of the target crop into the required total soil available selenium concentration through enrichment coefficient, and combining the soil background selenium migration coefficient with the share naturally contributed by the soil background to obtain the required soil available selenium concentration increment as a constraint indicator.

[0030] The intelligent decision-making module for R&D and production scheduling is used to perform resource optimization and allocation calculations. It maps the raw material fingerprint feature vectors to the environmental demand constraint matrix and uses an evolutionary solution model to output dynamic production management instruction sheets containing process work order data and formula ratio data. The evolutionary solution model adopts a real-number encoded genetic algorithm, defines chromosomes containing turning frequency, aeration rate and fermentation cycle, and constructs a fitness function. The fitness function uses weight coefficients determined by the analytic hierarchy process to maximize the estimated selenium activation rate while minimizing energy consumption.

[0031] The production execution monitoring module is used to distribute dynamic production management instructions to the production management terminal and collect process execution data in real time for compliance comparison.

[0032] The full lifecycle closed-loop optimization module is used to obtain performance feedback data after product launch, calculate the deviation index between actual and expected values, and generate model correction factors to update the weighted parameters of the evolutionary solution model.

[0033] This embodiment details a smart agricultural manufacturing architecture based on the Internet of Things (IoT) and data inversion technology, aiming to resolve the contradiction between the heterogeneity of non-standard biomass raw materials and the precise nutritional needs of functional agricultural products. The system performs a digital mapping of the physical world through a raw material digitization module, utilizes a sensor array connected to the feed inlet and a LIMS laboratory interface to acquire the physicochemical parameters of organic waste in real time, and performs a moving average filtering algorithm to clean the raw data. The specific algorithm is as follows:

[0034]

[0035] in, The sampling window length is set to 10 in this embodiment. Represents the current time step sequence of data collection. This is a time lag index relative to the current time, with a value range of [value range missing]. arrive ; By using sampled values ​​from historical moments to eliminate high-frequency random noise from the sensor, a digital raw material fingerprint feature vector is generated. ;

[0036]

[0037] in, The value is derived from an elemental analyzer and its physical meaning is the mass ratio of carbon to nitrogen in the raw material, expressed as a dimensionless ratio.

[0038] The value is derived from a fiber analyzer and its physical meaning is the mass percentage of acid-washed lignin in the raw material, expressed in % (%).

[0039] Derived from atomic fluorescence spectrometry, its physical meaning is the selenium content in the background of the raw material, and the unit is... ;

[0040] The market demand reverse analysis module breaks the traditional forward production logic, based on... The geographic information system retrieves data from the target site and generates an environmental demand constraint matrix through inversion calculation. To support multi-dimensional matching of soil degradation capacity and pH in subsequent processes, a matrix... Defined as including target selenium incremental demand And the environmental state constraint set, including the soil degradation capacity index. ,soil value A composite data structure; among which, the core selenium demand indicator The calculation formula is as follows:

[0041]

[0042] in, The preset selenium content standard for the target crop, in units of , The soil background selenium content is expressed in units of... ; Here, represents the soil background selenium migration coefficient; to ensure the computability of the model parameters in actual engineering, this embodiment defines it as a regression function based on a soil physicochemical property database:

[0043]

[0044] in, The soil organic matter content in the database is expressed as a percentage. For soil pH; to strictly adhere to the principle of dimensional consistency in physical quantities, this embodiment clearly defines the dimensions of each coefficient in the formula: preset constants. The reference mobility is a dimensionless standard mobility. Defined as the organic matter binding gain coefficient, its unit is set to . That is, the reciprocal of the percentage, used to offset. Percentage units; Defined as the acid-base lockout loss coefficient, its unit is set to . ,Right now The reciprocal of the value is used to cancel out. Dimensional difference; by introducing a coefficient with dimensions, this ensures... The calculation results are dimensionless coefficients with clear physical meaning; This is the enrichment coefficient of crops for available selenium in the soil. To ensure the clarity and feasibility of this parameter in practical applications, this embodiment sets its value to be derived from the target crop selenium enrichment model library, and the system obtains it by looking up the table according to the target crop type.

[0045] The specific set of reference values ​​is: rice crops , wheat crop corn crop Leafy vegetables The default value is The model library is built on the principle of conservation of element flow in the soil-crop system. It integrates field trial data from multiple locations in the target application area over the past five years with the GB28050 food safety standard and establishes a standardized parameter set using meta-analysis. Its data citation standards ensure that the enrichment coefficients of different crops under standard soil conditions are statistically significant, thus providing sufficient physical basis for table lookup operations.

[0046] Selenium content standards for target crops Through enrichment coefficient Inversion conversion to the required total soil available selenium concentration Subtract the effective concentration share contributed by the natural background soil. The increase in the concentration of available selenium that needs to be supplemented through fertilizer was rigorously determined. The unit is The revised formula strictly adheres to the principle of consistency of physical dimensions, ensuring that subtraction operations are performed under the same soil available state concentration dimension.

[0047] Based on this, the intelligent decision-making module for research and production scheduling initiates an evolutionary solution model. Searching for targets within the target constraint space The optimal processing path; specifically, this model uses a real-number encoded genetic algorithm to define chromosomes. These represent the turning frequency, aeration rate, and fermentation cycle, respectively; a fitness function is constructed. To maximize the estimated selenium activation rate and minimize energy consumption:

[0048]

[0049] in, These are the preset performance weight, with a value of 0.7, and the energy consumption weight, with a value of 0.3. The environmental mismatch penalty coefficient is set to 1000. This embodiment uses the analytic hierarchy process (AHP) to determine the aforementioned weighting coefficients. Specifically, a judgment matrix was constructed to compare the importance of selenium activation efficiency and production energy consumption. Experts determined that the former was significantly more important than the latter. After normalizing the eigenvectors, the aforementioned weight allocation of 0.7 and 0.3 was obtained. Regarding the penalty coefficient... The penalty term is set based on the order-of-magnitude difference in the range of the objective function, aiming to ensure that when environmental mismatch occurs, the penalty term... The magnitude of this can cover the benefits of the first two items, thus forcing the algorithm to escape the infeasible solution space;

[0050] To eliminate the differences in physical dimensions, all terms in the formula are processed by... - The dimensionless value after standardization; the specific normalization calculation formula is as follows:

[0051]

[0052] The boundary values ​​for each variable are set as follows: The range of values Set as Based on the crop's tolerance limit; The range of values ​​is set to Based on the upper limit of the equipment's rated power; and The range of values ​​is set to Based on soil environmental capacity; the above boundary values ​​were not arbitrarily selected, but based on statistical analysis of production data from the past three years; among them, Set as the 95th percentile of historical data. The data is set to the 5th percentile of historical data to remove outliers caused by abnormal fluctuations in the sensor and ensure that the normalized data is distributed within the effective range.

[0053] To solve the problem in the model The definition of variables is crucial, and to ensure the mathematical closure of the raw material fingerprint-environment constraint mapping logic, this embodiment explicitly defines... The predicted environmental response value, which is the predicted increase in available selenium in the soil, is calculated using the following formula:

[0054]

[0055] in, This represents a 1×3 dimension process transformation feature row vector. Pass row vectors for raw material properties in a 1×3 dimension. Based on the fundamental response deviation, this formula ensures that the calculation of the environmental response also depends on process parameters. With raw material properties ;

[0056] Regarding matrices and scalar Acquisition method: The system retrieves soil migration history records from the historical business database, using the process parameters of historical batches. and raw material properties As the independent variable, the corresponding measured increment of available selenium in the soil is used. For the dependent variable, perform multiple linear regression analysis; where the corresponding variable in the regression equation is... The coefficients of the term are extracted as follows ,correspond The coefficients of the term are extracted as follows The intercept term is ;

[0057] The selenium efficacy prediction function is trained based on historical data. To ensure that the subsequent model update mechanism has a clear mathematical basis, this embodiment defines it as a multinomial regression model containing second-order interaction terms:

[0058]

[0059] in, These are the concatenated vectors. Indexes of each feature component in the data. ; For vectors and The concatenated vector, i.e., the total dimension. ; The term represents the regression intercept, which physically represents the basic selenium activation response value under the zero-input assumption. These are first-order linear coefficients, representing the independent contribution weights of each dimension variable to the objective function; These are second-order interaction coefficients, characterizing the nonlinear coupling effect between different variables, such as the synergistic effect of turning frequency and aeration rate; parameters The specific values ​​are initialized through offline data mining and iteratively updated through closed-loop optimization; For the energy consumption function, in order to resolve the dimensional ambiguity problem in traditional calculations, this embodiment defines it as the unit mass energy consumption form based on the daily energy consumption integral:

[0060]

[0061] in, : This refers to the processing volume of raw materials in a single batch; in this embodiment, it is set to 1. That is, unitization processing;

[0062] : This refers to the energy consumption of the turning equipment per operation, which is set to 12 in this embodiment. ;

[0063] Defined as the daily heap turnover frequency, in units of ;

[0064] : This refers to the energy consumption per unit volume of the ventilation equipment, which is set to 0.04 in this embodiment. ³;

[0065] Defined as the total daily ventilation volume, in units of ³ By dividing the total energy consumption by the batch processing volume This ensures that the calculation results are in rigorous units of specific energy consumption. This ensures that the dimensions are consistent with the normalized boundary, eliminating the risk of dimensional confusion.

[0066] Through selection, crossover, and mutation iterations, the model outputs a dynamic production management instruction sheet containing parameters such as turning frequency and aeration rate; the production execution monitoring module then issues the instructions to... The controller monitors execution deviations in real time; the full lifecycle closed-loop optimization module collects field measurement data to form closed-loop feedback and continuously corrects the system's decision parameters.

[0067] Example 2:

[0068] The R&D and production scheduling intelligent decision-making module is specifically configured to perform the following data processing steps:

[0069] Based on the lignin content attribute value in the raw material fingerprint feature vector and the soil degradation capacity index in the environmental demand constraint matrix, the corresponding production cycle management parameters are retrieved and matched. The production cycle management parameter is the fermentation cycle, which is calculated through a two-factor coupling model. The two-factor coupling model combines the time constant of the standard raw material retardation effect with the soil degradation capacity index calculated by the normalized weighted algorithm based on biological enzyme activity and temperature to solve the problem.

[0070] Based on the soil pH attribute value in the environmental demand constraint matrix and the carbon-nitrogen ratio attribute value in the raw material fingerprint feature vector, the functional strain compounding ratio data in the resource allocation scheme is calculated.

[0071] The production cycle management parameters are linked and encapsulated with the functional strain compounding ratio data to generate a dynamic production management instruction sheet.

[0072] This embodiment decouples the core algorithm logic of the intelligent decision-making module at the physicochemical level; the system executes a periodic matching step based on physical degradation characteristics to extract the lignin content attribute value from the raw material fingerprint. With the environment matrix Soil degradation capacity index ,Right now To address the issue of enzyme activity units, the unit is... To address the ambiguity in the physical meaning of direct weighting due to the inconsistency between the temperature unit (°C) and the unit of measurement, this embodiment employs a normalized weighting algorithm for calculation. The calculation formula is as follows:

[0073]

[0074] in, These are the preset weighting coefficients for enzyme activity and temperature influence, respectively. In this embodiment, they are set as follows: The aforementioned weighting coefficients were obtained through multiple regression analysis of historical soil degradation data. The results showed that the contribution of enzyme activity to the degradation rate was approximately 1.5 times that of temperature; therefore, after normalization, the coefficients were set to 0.6 and 0.4. The regression analysis was based on a sample set constructed by our research team, containing 500 sets of field degradation experiments of organic fertilizer under different climatic conditions. The sample confidence level... By partial least squares method The ratio of the standardized regression coefficients of each factor on the degradation rate is calculated.

[0075] : Derived from soil microbial monitoring data, its physical meaning is the cellulase activity per unit volume of soil, and the unit is . ;

[0076] The preset soil enzyme activity benchmark value is set to 5.0 in this embodiment. , used to eliminate dimensional differences;

[0077] Sourced from meteorological databases, the physical meaning is the average ground temperature during the crop growing season in the target area, expressed in degrees Celsius.

[0078] The preset suitable degradation temperature reference value is set to 25.0°C in this embodiment to eliminate dimensional differences;

[0079] This step aims to balance the depth of pretreatment at the factory level with the humification potential at the field level; the system uses the following two-factor coupling model to calculate the fermentation cycle. :

[0080]

[0081] in, The shortest fermentation cycle allowed by the process, such as 15 days. This serves as a baseline reference value for the lignin content of the raw material, such as 10%. The standard raw material retardation constant is taken as 5 days. The upper limit of the ideal soil degradation index is set at 1.76 in this embodiment, based on the optimal enzyme activity of 10. The theoretical extreme value after normalization to the optimum temperature of 35°C; This is the environmental compensation coefficient, with a value for 8 days. and System through Kinetic experiments with corrected equations were conducted, corresponding to the time constants of the standard lignin retardation effect and the metabolic retardation time constant under low-temperature conditions, respectively; the calculated results... In the system, it is not used as an absolute fixed instruction, but rather as the constraint centroid of the fermentation cycle variable in the aforementioned evolutionary solution model;

[0082] Specifically, the system will incorporate genetic algorithms. The search scope for gene loci is limited to In this embodiment This limits the search space of the genetic algorithm, ensuring that the final solution not only conforms to the mechanism constraints of biodegradation (guaranteed by the formula) but also possesses the local optimum characteristic of process energy consumption. The optimization achieves a logical unification between deterministic mechanism models and stochastic optimization models;

[0083] Simultaneously, the system executes a strain compounding step based on chemical environmental characteristics, strictly adhering to Example 2 and comprehensively considering soil pH properties. Carbon-nitrogen ratio of raw materials Calculate the ratio of functional strains in the mixture. To accurately solve this multivariate coupled problem, this embodiment employs a constrained quadratic programming algorithm, constructing the following optimization objective function:

[0084]

[0085] The constraints are and ,in, In this embodiment, the total number of usable functional strains in the system's bacterial culture library is represented as [the number of strains]. ;

[0086] The formula incorporates two key adaptation factors to fully respond to the calculation logic based on soil pH and the carbon-nitrogen ratio of the raw materials: for The fitness factor is defined as a Gaussian distribution:

[0087]

[0088] in, For the first The optimal pH value for the growth of this strain Tolerance bandwidth parameters; in this embodiment, Set to 1.5; for strains, The set of possible values ​​is set as follows:

[0089]

[0090] These correspond to different bacterial species, ranging from acidophilic to basophilic; regarding The selection was based on strains determined in the laboratory. - The growth rate curve, calculated using Gaussian fitting, corresponds to the standard deviation parameter of the full width at half maximum (FWHM). This ensures that... Deviation from optimal point Even at a unit, the fitness factor remains above 60%;

[0091] The carbon-to-nitrogen ratio fitness factor is defined as asymmetric. Type function:

[0092]

[0093] in, For the first The preferred carbon source concentration thresholds for these strains. The sensitivity coefficient; in this embodiment, The set of values ​​is set as , The unified setting is 0.5; regarding and The set was determined through gradient acclimatization experiments on culture media with different carbon-to-nitrogen ratios. This corresponds to the substrate concentration inflection point at which the growth rate of each strain reaches 50% of its maximum value; this factor quantifies the differences in metabolic activity of strains in feedstocks with different carbon-to-nitrogen ratios, for example: high... Compared to the environment, fungi The value was significantly higher than that of bacteria;

[0094] The antagonistic coefficient between strains is calculated using the following formula:

[0095]

[0096] in, The diameter of the inhibition zone measured in the double-layer plate antagonism experiment is shown in mm. For the diameter of the petri dish, this embodiment uses the standard value of 90 mm; by solving this quadratic programming problem, the system obtains the diameter of the petri dish in the current soil. -raw material Under combined conditions, the strain ratio vector that maximizes the total population activity and minimizes internal antagonism. The system will use the time dimension With the material dimension Perform associative encapsulation to generate executable dynamic instructions.

[0097] Example 3:

[0098] When matching production cycle management parameters, the R&D and production scheduling intelligent decision-making module executes the following logical judgment rules:

[0099] If the lignin content attribute value is not lower than the preset first threshold, the production cycle management parameter will be marked as the extended management mode code.

[0100] If the lignin content attribute value is lower than the first threshold and not lower than the preset second threshold, the production cycle management parameter will be marked as the standard management mode code.

[0101] If the lignin content attribute value is lower than the second threshold, the production cycle management parameter will be marked as a fast-flow mode code.

[0102] This embodiment further refines the dynamic decision tree logic of the production cycle, aiming to optimize the balance between factory capacity and product quality; the system presets two critical points based on the degree of raw material degradation, namely the first threshold. Second threshold ;

[0103]

[0104] in, The term "lignin content" is derived from a historical fermentation database and its physical meaning is the boundary line for determining the lignin content of raw materials that is extremely difficult to degrade; the unit is % (%). In the specific parameter configuration of this embodiment, in order to clearly distinguish agricultural waste with a high degree of lignification, such as corn stalks, It was explicitly set at 25%; this threshold was based on laboratory simulated composting experiments: when the lignin content of the raw material exceeded 25%, the half-life of the raw material was significantly extended to more than 45 days under the standard microbial inoculation amount, which could not meet the requirements of the standard production cycle.

[0105] The term "[value]" is derived from industry standards and its physical meaning refers to the boundary line for determining the easily degradable lignin content of raw materials, expressed in % (%). In this embodiment, it is used to define the boundary between conventional livestock and poultry manure and easily perishable fruits and vegetables. It was explicitly set at 15%;

[0106] Response to real-time detection of lignin content The system determines that the raw materials are mainly composed of high-fiber materials such as straw and bark, and automatically triggers the extended management mode code to forcibly add a secondary aging process to ensure that the macromolecules are fully broken down; in response to The system determines that the raw material is conventional livestock and poultry manure, triggering the standard management mode code; in response to The system determines that the raw material is perishable material such as fruit and vegetable residue, triggering the rapid flow mode code to shorten the fermentation cycle;

[0107] This embodiment maximizes the equipment turnover efficiency of the factory by using a dynamic capacity scheduling mechanism based on raw material properties, while ensuring the consistency of product decomposition. Especially in the scenario of processing agricultural waste from complex sources, this logic not only prevents the risk of seedling burning caused by insufficient processing time for recalcitrant raw materials, but also avoids the waste of capacity caused by excessive occupation of fermentation tanks by easily degradable raw materials.

[0108] Example 4:

[0109] This system also includes:

[0110] The digital traceability management module is used to respond to the generation of dynamic production management instruction sheets and call the coding algorithm to generate a unique digital traceability identity for the corresponding management batch. The process order data and formula ratio data are written into the traceability database.

[0111] Digital traceability identity The management fields associated with storage in the database include:

[0112] Raw material source batch index, process temperature monitoring records, functional strain input records, and target promotion area codes.

[0113] This embodiment constructs a data trust chain based on blockchain principles; in response to the generation of each batch of dynamic instruction orders, the digital traceability management module immediately invokes... -256 or Algorithm to generate unique digital traceability identities The system will As the primary key, it is associated with and stores the metadata of the core dimensions in an immutable traceability database. Specifically, the raw material source batch index is used to trace the origin and collection time of waste backward to ensure the compliance of raw materials; the process temperature monitoring record serves as a digital certificate of harmless treatment, proving that the fermentation process meets the high temperature duration requirements for pathogen inactivation; the functional strain input record is used to verify the actual application of core biotechnology; and in particular, the target promotion area code clarifies the customized attributes of this batch of fertilizer.

[0114] This embodiment establishes a closed-loop evidence system covering the entire chain from waste source to field application by strongly linking all production elements with the target promotion area code. In the case of selenium-enriched agricultural product planting, if the selenium content of the final product does not meet the standard, the system can quickly investigate whether it is due to deviation in the execution of the production process or regional mismatch, such as fertilizer specially customized for acidic soil being mistakenly applied to alkaline soil, resulting in application failure. This greatly reduces the complexity and trust cost of post-sales attribution analysis.

[0115] Example 5:

[0116] When calculating the functional strain compounding ratio data, the R&D and production scheduling intelligent decision-making module executes the following resource allocation logic:

[0117] When the soil pH value is identified as acidic and the carbon-nitrogen ratio value is higher than the preset high-carbon index value, an instruction pointing to the first type of resource allocation strategy is generated.

[0118] When the soil pH value is marked as neutral, or the carbon-nitrogen ratio value is within a preset range, an instruction pointing to a balanced resource allocation strategy is generated.

[0119] When the soil pH value is identified as alkaline and the carbon-nitrogen ratio value is lower than the preset low-carbon index value, an instruction pointing to the second type of resource allocation strategy is generated.

[0120] This embodiment details the intelligent matching logic of microbial niches, aiming to solve the problem of colonization of functional strains in specific soil habitats; the system obtains soil pH. Carbon-nitrogen ratio of raw materials and the preset high carbon index value and low carbon index value A comparison is performed; in this embodiment, it is explicitly set that... ,correspond The ratio is 30:1. ,correspond The ratio is 20:1;

[0121] To ensure that the computer program can accurately determine acidity, neutrality, and alkalinity, this embodiment defines the following strict numerical thresholds for determination: If It is determined to be acidic; if If it is determined to be alkaline; The result was determined to be neutral; meanwhile, to ensure that subsequent resource allocation strategies could be accurately applied to specific strains, the system was based on the optimal strain defined in Example 2. value The following index mapping relationship was established: Define index ,correspond A collection of acid-resistant fungi; define an index. ,correspond A collection of alkali-tolerant actinomycetes; define an index. It is a collection of neutral bacteria;

[0122] In response to Labeled as acidic and The system identifies that the environment possesses the dual characteristics of acidity inhibiting bacterial activity and high carbon source reluctance to decompose. It then generates instructions for the first type of resource allocation strategy, significantly increasing the proportion of acid-resistant fungi, such as Aspergillus niger, to accelerate the degradation of high-carbon materials by utilizing the fungi's powerful extracellular enzyme system. Specifically, the first type of strategy executes quantitative supplementation logic, that is, calculating the results... Assign a value to each of the acid-resistant fungi set The calculation formula is as follows:

[0123]

[0124] in, The target dosage of fungicide. As a baseline quantity, for example, 1 kg / ton. The gain coefficient is set to 0.2 to adjust the carbon-to-nitrogen ratio. This is the acidity adjustment gain coefficient, with a value of 0.3. This linearly compensates for the inhibitory effect caused by high carbon and acidity; in response to Labeled as alkaline and The system identifies the risk of ammonia volatilization and alkaline stress, generates instructions for the second type of resource allocation strategy, specifically executes the following compensation formula, and outputs the results. Assign a value to each of the alkali-tolerant actinomycete sets. :

[0125]

[0126] in, The dosage for nitrogen-fixing bacteria and alkali-tolerant actinomycetes. The low-carbon environmental compensation coefficient is set to 0.25. The alkaline environment compensation coefficient is set to 0.35. Simultaneously, an exogenous carbon source, such as molasses, is introduced to correct the situation. Compare;

[0127] The specific values ​​of the aforementioned gain coefficient and compensation coefficient are not arbitrarily set, but determined through response surface methodology. The research team constructed a central composite design experiment with strain activity as the response value and pH deviation and carbon-nitrogen ratio deviation as independent variables. By fitting the experimental results with a second-order polynomial, the linear inhibition slope of each environmental factor on strain activity was extracted, and the normalized value of the inverse of the slope was taken as the aforementioned compensation coefficient, thereby ensuring that the compensation amount can accurately offset the activity loss caused by environmental stress. Those skilled in the art can obtain the specific values ​​of the aforementioned coefficients through conventional laboratory culture experiments based on the above experimental design ideas, and are not limited to the specific values ​​given in this embodiment.

[0128] In response to other conventional conditions, namely, the soil pH is labeled as neutral, or the carbon-to-nitrogen ratio is within a certain range... When the preset range is reached, the system executes a balanced resource allocation strategy. The specific execution logic of this strategy is as follows: set the total amount of bacterial strains added. The ratio of each functional strain is set to a preset proportional weight or an industry standard ratio, such as... Do not execute based on or Dynamic gain calculations are performed to ensure optimal cost-effectiveness in non-extreme environments.

[0129] Furthermore, to ensure the completeness of the logic, for boundary cases not covered by the above three conditions—namely, the dead zones corresponding to acidic soil with a low carbon-nitrogen ratio or alkaline soil with a high carbon-nitrogen ratio—that are not covered by the aforementioned strategies, the system automatically triggers a default broad-spectrum compensation strategy. This strategy does not perform biased supplementation for specific bacterial species, but rather increases the total dosage of all bacterial strains proportionally. The calculation formula is:

[0130]

[0131] in, Preset The stress redundancy coefficient, which is set to 0.15 in this embodiment, is used to compensate for the loss of natural apoptosis of the strain caused by unsuitable pH.

[0132] This move aims to increase [the impact of] [the pandemic]. The redundancy of microbial death caused by stress ensures that the final colonization density meets the process requirements and avoids logical deadlock;

[0133] In order to calculate the absolute injection amount for each of the above strategies Convert to compound ratio data The system performs the normalization process as follows:

[0134]

[0135] For other background strains not specifically specified in the strategy, the dosage... Keep as This step ensures that the final generated instruction data is accurate. satisfy The mathematical constraints enable the automated batching system to directly parse and execute them.

[0136] It should be noted that the compounding ratio data calculated using the above logical strategy... In the operation of the system, it serves as the initial population centroid or constraint boundary of the multi-objective programming algorithm in Example 2, thereby ensuring that the final resource allocation scheme conforms to both the macroscopic ecological niche law and the microscopic biological antagonism restriction.

[0137] This embodiment intelligently balances the microbial ecology through this multi-condition logical judgment; it not only considers the soil The environment plays a screening role for microorganisms, while also taking into account the raw materials. By comparing the impact on microbial metabolic pathways, the efficiency of microorganisms was maximized, ensuring that after the selenium-enriched organic fertilizer was applied to the soil, functional strains could quickly become the dominant microbial community, efficiently driving the conversion of organic selenium and its absorption by plants.

[0138] Example 6:

[0139] The process of updating the model in the full lifecycle closed-loop optimization module includes:

[0140] The numerical difference between the calculated effect feedback data and the preset target value is used to construct a prediction error data package;

[0141] The system calls the current version of the model weight configuration, combines the prediction error data package with the preset learning rate parameters, and generates the next version of the model weight configuration through algorithm iteration and stores it in the system knowledge base.

[0142] This embodiment introduces an online learning mechanism, giving the system the ability to continuously evolve; the module acquires actual crop selenium content data from field feedback, calculates its relative deviation from the preset target value, and constructs a prediction error data package containing multi-dimensional feature deviations. ;

[0143]

[0144] in, Source: from the The measured feedback from each demonstration site is physically represented as the normalized residual between the predicted and actual values, and the unit is dimensionless.

[0145] The system defines the loss function used for optimization. It is in the form of mean squared error, and the gradient is explicitly defined. The calculation logic:

[0146]

[0147]

[0148] The system calls the current version of the model weight configuration. Combined with preset learning rate parameters The gradient descent algorithm is used to iteratively generate the optimized weight configuration. ;

[0149]

[0150] It is important to clarify here that the model weight configuration... Unlike the preset weight coefficients in the aforementioned fitness function, such as It does not directly point to the population parameters of the genetic algorithm, but specifically to the fitness function embedded in the evolutionary solution model. The core component, namely the selenium efficiency prediction function The internal weight parameters; based on the multinomial regression model structure defined in the foregoing embodiments, this weight configuration... Specifically, it consists of the set of regression coefficients. Composition; through updating weight The system can more accurately evaluate each process plan. The expected results; among them, : Derived from system preset, its physical meaning is the step size control factor for model updates, and its unit is dimensionless;

[0151] The updated weights are embedded in the system knowledge base to guide the next round of production scheduling decisions;

[0152] This embodiment constructs a closed-loop data flow of prediction-feedback-correction, enabling the system to gradually approach the theoretical optimal solution of the nonlinear relationship between raw materials, processes, and effects as production batches accumulate. In long-cycle agricultural production scenarios, this self-evolutionary mechanism can automatically correct systematic errors caused by climate change or soil microecological evolution, ensuring the long-term stability and accuracy of the selenium-enriched technology solution.

[0153] Example 7:

[0154] The initial data state of the evolutionary solution model is determined through offline data mining and machine learning training, based on waste treatment records, soil migration history records, and crop response history records in the historical business database.

[0155] This embodiment addresses the cold start problem in the initial stage of intelligent system deployment; before the system has accumulated sufficient real-time feedback data, it utilizes offline data mining techniques to construct the initial data state of the model. The system cleans and structures the historical business database, which covers waste disposal records, soil migration history records, and crop response history records accumulated over the past few years; given that the evolutionary solution model in Example 1 adopts a deterministic polynomial regression structure... This embodiment abandons black-box models such as random forests that cannot be directly mapped to multinomial weights, and instead uses the least squares method for parameter initialization to ensure the compatibility of the model structure; the specific steps are as follows:

[0156] From the history and By splicing and performing polynomial expansion, a design matrix is ​​constructed. ,in, For historical sample size, This is the total number of terms in the second-order polynomial;

[0157] Construct target vector The initial weight vector is calculated using the normal equation. The calculation formula is as follows:

[0158]

[0159] in, The ridge regression regularization coefficient has a value of [value missing]. It should be noted that this embodiment uses subscripts. The sign Specifically referring to regularization parameters in statistics, to be strictly distinguished from the penalty coefficient used to penalize constraint violations in the fitness function of Example 1 above. This coefficient is used to prevent matrix non-invertibility due to eigencollinearity.

[0160] Regarding the target vector Specific definition: To ensure the consistency between the model's predicted target and the actual agricultural value, this embodiment explicitly stipulates... elements in The selenium content was measured from the edible parts of the corresponding historical batches of crops, and the unit is... For example, for rice crops, the selenium content of brown rice is used; for leafy vegetable crops, the selenium content of leaves is used. This data needs to be standardized to match the model output. The dimensional definition ensures that the model can accurately learn the mapping relationship between process and quality.

[0161] Final determined initial weight configuration That is, set It is loaded into the evolutionary solution model as the baseline for the system's first run;

[0162] This embodiment activates accumulated historical data assets to provide high-confidence initial parameters for the evolutionary model. In the early stages of promoting selenium-enriched agriculture, this strategy significantly shortens the break-in period of the intelligent system, reduces production risks caused by model exploratory trial and error, and ensures that the first batch of products can still meet industry benchmarks even in the absence of real-time feedback.

[0163] 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. A full-process management system for the research, development, demonstration, and promotion of selenium-enriched bio-organic fertilizer, characterized in that: include: The raw material digital filing module is used to receive the detection information of organic waste and generate a digital raw material fingerprint feature vector containing material attributes through data cleaning and feature extraction. The material attributes include at least the carbon-nitrogen ratio attribute value, the lignin content attribute value, and the original selenium content attribute value. The market demand reverse analysis module is used to access the pre-stored soil physicochemical property database and the target crop selenium enrichment model library. Based on the preset element flow rules, it extracts and calculates the environmental demand constraint matrix that represents the differentiated market demand. The calculation to generate the environmental demand constraint matrix representing the differentiated market demand includes: performing inversion calculation based on the preset element flow rules, converting the preset selenium content standard of the target crop into the required total soil available selenium concentration through the enrichment coefficient, and combining the soil background selenium migration coefficient with the share naturally contributed by the soil background to obtain the required soil available selenium concentration increment as a constraint indicator. The intelligent decision-making module for R&D and production scheduling is used to perform resource optimization and allocation calculations, mapping the raw material fingerprint feature vector to the environmental demand constraint matrix, and using an evolutionary solution model to output a dynamic production management instruction sheet containing process work order data and formula ratio data. The evolutionary solution model adopts a real-number encoded genetic algorithm, defines chromosomes containing turning frequency, aeration rate, and fermentation cycle, and constructs a fitness function. The fitness function uses weight coefficients determined by the analytic hierarchy process to maximize the estimated selenium activation rate while minimizing energy consumption. The production execution monitoring module is used to distribute the dynamic production management instruction sheet to the production management terminal and collect process execution data in real time during the production process for compliance comparison. The full lifecycle closed-loop optimization module is used to obtain the effect feedback data after the product is launched, calculate the numerical deviation between the actual value and the expected value as the deviation index, and generate the model correction factor to update the weighted parameters of the evolutionary solution model. The intelligent decision-making module for R&D and production scheduling is specifically configured to perform the following data processing steps: Based on the lignin content attribute value in the raw material fingerprint feature vector and the soil degradation capacity index in the environmental demand constraint matrix, the corresponding production cycle management parameters are retrieved and matched; the production cycle management parameter is the fermentation cycle, which is calculated by a two-factor coupling model; the two-factor coupling model is solved by combining the time constant of the standard raw material retardation effect and the soil degradation capacity index calculated by the normalized weighted algorithm based on biological enzyme activity and temperature. Based on the soil pH attribute value in the environmental demand constraint matrix and the carbon-nitrogen ratio attribute value in the raw material fingerprint feature vector, the functional strain compounding ratio data in the resource allocation scheme is calculated. The production cycle management parameters are associated and encapsulated with the functional strain compounding ratio data to generate the dynamic production management instruction sheet; When matching production cycle management parameters, the intelligent decision-making module for R&D and production scheduling determines whether the preset first threshold is numerically greater than the preset second threshold, and then executes the following logical judgment rule: If the lignin content attribute value is greater than or equal to the first threshold, the production cycle management parameter is marked as the extended management mode code; If the lignin content attribute value is less than the first threshold and greater than or equal to the second threshold, then the production cycle management parameter is marked as the standard management mode code; If the lignin content attribute value is less than the second threshold, the production cycle management parameter is marked as a fast turnover mode code.

2. The whole-process management system for the research, development, demonstration and promotion of selenium-enriched bio-organic fertilizer according to claim 1, characterized in that, Also includes: The digital traceability management module is used to respond to the generation of the dynamic production management instruction sheet, call the encoding algorithm to generate a unique digital traceability identity ID for the corresponding management batch, and write the process work order data and formula ratio data into the traceability database.

3. The whole-process management system for the research, development, demonstration and promotion of selenium-enriched bio-organic fertilizer according to claim 2, characterized in that, The management fields associated with the digital traceability identity ID stored in the database include: Raw material source batch index, process temperature monitoring records, functional strain input records, and target promotion area codes.

4. The whole-process management system for the research, development, demonstration and promotion of selenium-enriched bio-organic fertilizer according to claim 1, characterized in that, When calculating the functional strain compounding ratio data, the intelligent decision-making module for R&D and production scheduling sets a higher preset high-carbon index value than a preset low-carbon index value and executes the following resource allocation logic: If the soil pH value is determined to be acidic and the carbon-nitrogen ratio value is higher than the high carbon index value, an instruction pointing to the first type of resource allocation strategy is generated. If the soil pH value is determined to be alkaline and the carbon-nitrogen ratio value is lower than the low-carbon index value, an instruction pointing to the second type of resource allocation strategy is generated. If none of the above conditions are met, an instruction pointing to the balanced resource allocation strategy is generated.

5. The whole-process management system for the research, development, demonstration and promotion of selenium-enriched bio-organic fertilizer according to claim 1, characterized in that, The process of updating the model by the full lifecycle closed-loop optimization module includes: The numerical difference between the calculated effect feedback data and the preset target value is used to construct a prediction error data package; The current version of the model weight configuration is invoked, and combined with the prediction error data package and the preset learning rate parameters, the next version of the model weight configuration is generated through algorithm iteration and stored in the system knowledge base.

6. The whole-process management system for the research, development, demonstration and promotion of selenium-enriched bio-organic fertilizer according to claim 1, characterized in that, The initial data state of the evolutionary solution model is determined through offline data mining and machine learning training, based on waste treatment records, soil migration history records, and crop response history records in the historical business database.

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