Ecological value chain reconstruction method and system for agricultural waste resource utilization and management

Abstract

The invention belongs to the technical field of ecological value chain reconstruction, and discloses an ecological value chain reconstruction method for agricultural waste resource utilization and management, and the method comprises the steps: building an evaluation system of an agricultural waste resource treatment process based on a life cycle evaluation tool, recognizing the environmental influence factors of the process, and carrying out the reconstruction of an ecological value chain. Performing contribution analysis, uncertainty analysis and sensitivity analysis on the result by applying a statistical method, and screening out a process route with a relatively excellent evaluation result; a machine learning theory is combined, an energy value-carbon sink dual-core driving model is developed, energy value benefits and the like of agricultural waste resource utilization are evaluated, and basic theoretical support is provided for subsequent research; an AI ecological value chain platform is established, big data analysis is combined, an economic association and value conversion mechanism among elements in an ecological system is disclosed, and deep development of a management theory and an ecological economics theory in the agricultural field is promoted.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of ecological value chain reconstruction, and particularly relates to an ecological value chain reconstruction method and system for agricultural waste resource utilization and management. BACKGROUND

[0002] Existing researches are mostly focused on single technical links, such as biogas fermentation, straw returning to field or biochar preparation, etc. However, these links are often fragmented from each other, lacking a complete value chain logic. In the whole process of agricultural waste from collection, transportation, treatment to reuse, a systematic utilization mechanism with multi-link interaction has not yet been formed, resulting in low resource utilization efficiency, insufficient energy cascade utilization, and interrupted material circulation path. The lack of systematic integrated design of the whole process of "waste-energy-fertilizer-carbon sink-ecological products" makes the overall benefits of resource utilization not maximized.

[0003] At present, the ecological and environmental benefit evaluation of agricultural waste resource utilization still stays at a local level, and is mostly estimated by single indicators (such as carbon emission reduction or energy substitution). There is a lack of comprehensive accounting framework covering multiple dimensions such as carbon cycle, nutrient cycle and water cycle. Although Life Cycle Assessment (LCA) is widely used, it has not been deeply integrated with the real operating characteristics of agricultural ecosystems, and has not been able to reflect the real gains and external benefits of waste resource utilization in the ecological value chain. The lack of unified standards and quantitative models makes it difficult to include the ecological contribution of resource utilization into the decision-making system.

[0004] The current policy system for agricultural waste resource utilization is mostly one-dimensional incentive or short-term subsidy, lacking long-term stable institutional support and ecological value transformation mechanism. For example, although there is financial support for fertilizer and energy utilization, there is a lack of supporting systems in terms of market promotion, carbon emission reduction benefit accounting and ecological product value transformation. At the same time, there are problems such as departmental division, non-uniform standards and insufficient supervision at the policy implementation level, which restricts the coordinated development and large-scale operation of the waste resource utilization industry chain.

[0005] Most agricultural waste resource utilization projects are still at the experimental demonstration or regional pilot stage, and have not formed a replicable and promotable industrial model. At the technical level, there are bottlenecks such as low treatment efficiency, high energy consumption and poor equipment adaptability; at the management level, there is a lack of data support and dynamic evaluation mechanism, resulting in insufficient technology integration and ecological benefit verification. An ecological closed-loop system with "agricultural waste resource utilization-soil remediation-carbon sink value-added-ecological product value enhancement" as the core has not yet been established, and the whole process from waste treatment to ecological value creation cannot be dynamically optimized.

[0006] The following are two existing technical patents that are relatively close to the technology of the present scheme.

[0007] (I) WO2024026248A1 (“Autonomous climate technology ecosystem for computer-generation of uniform carbon credit certificates”) This patent discloses a method and system for generating carbon credit certificates based on Internet of Things, artificial intelligence and blockchain technology. Its content includes tracking carbon sinks from agricultural products or industrial waste and generating “uniform encrypted carbon credit certificates” for on-chain transactions.

[0008] Technical problems:

[0009] 1. Although it realizes the generation and blockchain transaction of carbon credit, it does not carry out exergy (exergy / energy-value) and carbon sink double accounting for the whole process of agricultural waste resourceization (including collection, transportation, treatment, product substitution, organic fertilizer application, etc.), and lacks the dynamic balance mechanism of exergy input-output in life cycle assessment (LCA).

[0010] 2. This scheme focuses on the generation and transaction mechanism of carbon credit certificates, but does not cover the optimization of agricultural waste treatment process path, multi-objective (exergy, carbon sink, economy) adaptive prediction model and time series exergy feedback mechanism, resulting in a lack of systematization in the aspects of process optimization and ecological value chain reconstruction.

[0011] (II) US20220101430A1 (“Blockchain-based carbon credit database”) This patent proposes a method / system for establishing a carbon credit database using blockchain, which chains the generation, registration and circulation process of carbon credit to enhance transparency and traceability.

[0012] Technical problems:

[0013] 1. Although this patent uses blockchain technology to provide a reliable record mechanism for carbon credit certificates, its application scenarios are more focused on mineral, oil and gas carbon sequestration or emission reduction projects, and it does not design for the typical resourceization processing scenario of agricultural waste, nor does it integrate exergy accounting models or life cycle assessment frameworks.

[0014] 2. This scheme has advantages in carbon credit transaction management, but lacks real-time monitoring and embedded prediction models for energy consumption, treatment output, organic fertilizer substitution benefits, carbon sink increment and other parameters in the process of agricultural waste treatment, especially without multi-objective machine learning models and time series feedback mechanisms to achieve process path optimization.

[0015] Although the two prior arts have the technical basis of blockchain and carbon credit management, there are still deficiencies in the following aspects: unable to cover the emergy-carbon sink coupling accounting in the resource utilization process of agricultural waste, dynamic life cycle assessment (including time sequence feedback), multi-objective adaptive machine learning model and process path full-time domain optimization. The present scheme is a systematic solution to these technical gaps. SUMMARY

[0016] In view of the problems existing in the prior art, the present application provides an ecological value chain reconstruction method for agricultural waste resource utilization and management.

[0017] The present application is implemented as follows: an ecological value chain reconstruction method for agricultural waste resource utilization and management comprises:

[0018] (1) Collecting the collection energy consumption, transportation energy consumption, treatment energy consumption and reagent energy consumption of agricultural waste as emergy input;

[0019] (2) Collecting the resource product emergy, emergy produced by replacing chemical fertilizer with organic fertilizer and emergy produced by carbon emission reduction as emergy output;

[0020] Data collection and emergy inventory construction:

[0021] Input side (emergy input, unit unified as sej or J multiplied by conversion coefficient)

[0022] Collection energy consumption: electric energy / fuel consumption (kWh, L), collected by intelligent electric meter, refueling single + vehicle-mounted OBD;

[0023] Transportation energy consumption: number of trips, mileage, loading rate, fuel consumption curve → converted to MJ;

[0024] Treatment energy consumption: electricity and fuel for crushing, turning, aeration, stirring, pumping and heating, etc.

[0025] Reagent energy consumption: consumption of lime, microbial agent and flocculant and their implicit emergy in the production stage (conversion coefficient given by database);

[0026] Output side (emergy output)

[0027] Resource products: yield x corresponding emergy conversion coefficient of organic fertilizer, biogas / biogas power and solid fuel, etc.

[0028] Chemical fertilizer replacement: equivalent nitrogen / phosphorus / potassium nutrient amount x respective replacement coefficient (sej / kg-N, etc.)

[0029] Carbon emission reduction: emission reduction amount (tCO2e) x carbon emission reduction emergy coefficient (or expressed by equivalent emergy of emission reduction electricity / fuel) within the boundary;

[0030] (3) Establish the energy balance equation, where the energy conversion efficiency is the ratio of the total output energy to the total input energy;

[0031] Unification from inventory to energy value:

[0032] Let qi(t) be the physical quantity of each activity i, and let λi (including "conversion rate") be its energy conversion coefficient.

[0033] Input energy value: ,

[0034] Output energy value: ,

[0035] Conversion efficiency: ;

[0036] (4) Introduce a time-weighted function and a lag feedback parameter into the energy balance equation, and dynamically correct the energy input and output based on the time series;

[0037] Dynamic time-weighted and hysteresis feedback modeling:

[0038] To address the issues of "data non-stationarity, process inertia, and policy lag," a time-weighted function and a lag feedback parameter are introduced.

[0039] 1. Time weighting function

[0040] Using exponential decay weights: Δt is the time difference (in hours / day) from the current time.

[0041] Dynamically adjusted weighted energy values:

[0042] ,

[0043] Lag feedback parameters

[0044] Process variables (such as compost moisture content M, temperature T, C / N ratio, aeration rate A, and residence time HRT) on The effect has a lag, let the lag be... With feedback gain ;

[0045] Using ARX (with exogenous input) structure or discrete transfer function:

[0046] ,

[0047] Where u is the control vector (process parameter) and ε is the disturbance; {ap,bq,T} is estimated online by recursive least squares (RLS);

[0048] (5) Adjust the waste treatment process parameters according to the real-time changes in energy conversion efficiency to achieve optimal energy efficiency control throughout the entire cycle;

[0049] Optimization and full-cycle optimal control:

[0050] 1) Control Objective

[0051] Main objective: Maximize weighted conversion efficiency Or minimize the unit energy input Functional unit;

[0052] Constraints: Product compliance (moisture content, maturity, hygiene indicators), emission limits (NH3, VOC, odor), equipment safety upper and lower limits;

[0053] 2) Optimization problem (rolling time domain, MPC approach)

[0054] ,

[0055] st process model (ARX / simplified mechanism), process and emission constraints, equipment upper and lower limits; γ is a tradeoff coefficient;

[0056] Solution: If it is approximately linear, use quadratic programming (QP); if it is nonlinear, use heuristics (particle swarm optimization / genetic algorithm) or gradient method.

[0057] 3) Online participation strategy

[0058] when continuously below the threshold Minimize emissions and ensure compliance: Prioritize energy reduction (reduce unnecessary aeration / stirring) and adjust formulations (improve efficiency).

[0059] (C / N to target band);

[0060] When product indicators weaken: prioritize ensuring quality (increase the frequency of turning / aeration / inoculation amount), and then optimize energy consumption;

[0061] Lag compensation: The controller uses the manufacturer's predictive compensation to adjust u in advance / delay.

[0062] Furthermore, the time-weighted function calculates the weight based on the reciprocal of the monitoring time interval, and the lag feedback parameter determines the correction coefficient based on the rate of change of energy value in the previous period, thereby enabling the energy value evaluation results to be updated in real time over time.

[0063] Furthermore, the system calculates a comprehensive ecological benefit index by multiplying the energy conversion efficiency by the carbon balance value, where the carbon balance value is the amount of carbon fixation minus the amount of greenhouse gas emissions.

[0064] Another objective of this invention is to provide an ecological value chain reconstruction system for the resource utilization and management of agricultural waste, comprising:

[0065] The module includes a process evaluation module, a data processing module, a model training module, and a results output module.

[0066] The process evaluation module calculates the energy input and energy output of different treatment routes for agricultural waste;

[0067] The data processing module performs data normalization and feature dimensionality reduction;

[0068] The model training module uses an ensemble learning algorithm to train the joint prediction model of emergy and carbon sink, with the root mean square error minimization as the objective function.

[0069] The result output module provides the optimal processing route and prediction accuracy based on the model output results.

[0070] Furthermore, the model training module employs the gradient boosting tree algorithm, and the model performance is evaluated using the coefficient of determination and root mean square error.

[0071] Furthermore, the system includes a model interpretation unit, which calculates the influence weights of each input variable on the energy value and carbon sink results based on the feature contribution decomposition method.

[0072] Furthermore, the data processing module is connected to the Internet of Things (IoT) acquisition unit to receive real-time data on the power, operating time, and emission concentration of agricultural waste treatment equipment, and to automatically update the model input.

[0073] Another objective of this invention is to provide an agricultural waste resource utilization carbon credit management platform based on blockchain and digital twins, characterized in that it includes a physical layer, a virtual layer and a blockchain interaction layer;

[0074] The physical layer collects data on waste collection volume, processing volume, energy consumption, and carbon emissions through sensor networks;

[0075] The virtual layer uses a digital twin model to simulate the resource utilization process of agricultural waste and predict carbon sink trends.

[0076] The blockchain interaction layer uses smart contracts to automate the generation, storage, trading, and offsetting of carbon credits.

[0077] Furthermore, the digital twin model updates the energy flow and carbon sink status in real time by performing multi-dimensional mapping on the data collected from the physical layer;

[0078] The blockchain interaction layer uses a distributed consensus mechanism to record carbon credit data, ensuring the uniqueness and immutability of the records.

[0079] Furthermore, the platform calculates the market value of ecological products based on the cumulative value of carbon credits and outputs a revenue report for the quantitative management of carbon revenue from the resource utilization of agricultural waste.

[0080] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0081] This invention, titled "Reconstruction of the Ecological Value Chain for the Resource Utilization and Management of Agricultural Waste," aims to evaluate low-carbon and green technologies for the resource utilization of agricultural waste through experimental exploration and LCA (Life Cycle Assessment), calculate their ecological value, and design management systems and policies based on this. It constructs and verifies a closed-loop benefit path of "agricultural waste resource utilization - soil remediation - carbon sink value enhancement - ecological product value enhancement," thereby promoting the sustainable development of agricultural waste resource utilization and providing theoretical support and practical guidance for achieving green agricultural development and ecological environmental protection.

[0082] (1) Based on life cycle assessment tools, an evaluation system for the resource utilization and disposal of agricultural waste is constructed, the environmental impact factors of the process are identified, and statistical methods are applied to conduct contribution analysis, uncertainty analysis and sensitivity analysis on the results to select the process routes with relatively excellent evaluation results.

[0083] (2) Combining machine learning theory, we developed a dual-core driven model of "emergence-carbon sink" to evaluate the emergence benefits of agricultural waste resource utilization, and provide basic theoretical support for subsequent research.

[0084] Establish an AI-powered ecological value chain platform, combining big data analysis to reveal the economic connections and value transformation mechanisms among various elements within the ecosystem, and promote the in-depth development of management theory and ecological economics theory in the agricultural field.

[0085] (1) The expected benefits and commercial value of the technical solution of this invention after transformation are as follows:

[0086] In terms of the environment, this plan efficiently converts agricultural waste such as straw and livestock manure into high-value-added products such as organic fertilizer, bioenergy (such as biogas and biofuel), and feed, thereby significantly reducing greenhouse gas emissions and the risk of eutrophication of water bodies. At the same time, this process enhances the soil's carbon sequestration capacity and improves the regional ecological quality.

[0087] Economically, resource-based products such as organic fertilizer and biochar can replace traditional chemical fertilizers and fossil fuels, reducing agricultural production input costs. Simultaneously, by leveraging carbon trading mechanisms and platforms for realizing the value of ecological products based on technologies such as blockchain (e.g., carbon credit systems), new sources of revenue can be opened up. Practice has shown that the straw-organic fertilizer recycling model, when applied in black soil regions, not only improves soil fertility but also creates considerable carbon sink economic value.

[0088] On the commercial front, this technological solution promotes the development of industries related to the agricultural circular economy, covering links in the industrial chain such as biomass energy, organic fertilizer production, and environmental protection technologies. By introducing an AI-powered ecological value chain platform and leveraging big data analysis to optimize process pathways, it improves resource conversion efficiency, reduces system operating costs, and enhances overall commercial viability. It is expected to form a sustainable business model driven by government and market collaboration, helping to attract investment and promote rural economic revitalization.

[0089] (2) The technical solution of this invention fills a technical gap in the industry both domestically and internationally:

[0090] 1. Lack of systematic integration of ecological value chain

[0091] Existing technologies mostly focus on single resource recovery pathways (such as energy recovery or fertilizer recovery), failing to form a closed-loop benefit system of "resource recovery - soil remediation - carbon sequestration value enhancement - ecological product enhancement". This solution is the first to integrate life cycle assessment, machine learning methods, emergy-carbon sequestration coupling models, and management policies to construct a complete theoretical framework and practical path for the ecological value chain, realizing a systematic integration from waste treatment to ecological benefit transformation.

[0092] 2. Lack of data-driven evaluation methods

[0093] Traditional life cycle assessments (LCA) suffer from incomplete data and limited scope in the field of agricultural waste. This solution introduces machine learning techniques, such as artificial neural network models, to effectively fill the data gaps in LCA and innovatively proposes a dual-core driven model of "emergence-carbon sink," enabling dynamic quantitative assessment of the environmental impact of resource recovery processes and overcoming the static and one-sided limitations of traditional methods.

[0094] 3. Lack of multi-entity collaborative mechanisms

[0095] Currently, the field of agricultural waste resource utilization generally suffers from problems such as an incomplete policy system and low levels of industrial collaboration. This solution improves the Shapley value model, incorporates multiple stakeholders such as farmers, enterprises, and governments into a collaborative allocation mechanism, and combines blockchain technology to build a trusted carbon trading platform, effectively solving the difficulties in industrialization caused by the lack of a collaborative mechanism.

[0096] (3) Whether the technical solution of the present invention solves the technical problem that people have long wanted to solve but have never been able to solve successfully:

[0097] 1. Low waste treatment efficiency and pollution control challenges

[0098] Currently, the comprehensive utilization rate of agricultural waste in my country is less than 30%, and the open burning of large amounts of straw and the disorderly discharge of livestock and poultry manure have led to serious environmental pollution. This solution, based on life cycle assessment and machine learning methods, systematically optimizes the process route, selects and integrates efficient and low-carbon technologies such as pyrolysis co-production of carbon and gas, significantly improves resource utilization efficiency, and realizes the transformation of waste into high-value-added products, thereby effectively overcoming the bottlenecks of low efficiency and high operating costs of traditional technologies.

[0099] 2. Challenges in Quantifying Ecological Value and Transforming it into Markets

[0100] The ecological benefits of agricultural ecosystems, such as carbon sequestration and soil improvement, have long lacked scientific quantification methods and trading mechanisms. This solution, by constructing an AI-driven emergy-carbon sequestration accounting engine and a carbon credit trading platform, achieves dynamic monitoring and market-based transformation of ecological benefits for the first time, solving the key problem of difficulty in quantifying and monetizing ecological gains during resource utilization.

[0101] 3. Challenges in Technology Assessment and Policy Coordination

[0102] Existing research often neglects the effective connection between technological systems and management policies. This solution combines empirical analysis and policy simulation in typical regions (such as the black soil region of Northeast China) with a systematic design of supporting incentive mechanisms and management standards to provide institutional guarantees for technology promotion and effectively address the practical difficulties in implementing advanced technologies in real-world applications.

[0103] (4) Does the technical solution of the present invention overcome technical bias?

[0104] 1. Bias regarding a single technology path

[0105] Traditional research often focuses on single resource utilization methods of agricultural waste (such as focusing solely on energy or fertilizer), neglecting the comprehensive benefits of system integration. This approach introduces a life-cycle assessment method to coordinate environmental, economic, and social impacts, and leverages machine learning to compare, analyze, and optimize various technological routes. This overcomes the cognitive limitation of "technological isolation" and achieves synergy and efficiency enhancement of resource utilization pathways.

[0106] 2. Bias regarding the static nature of LCA data

[0107] Traditional life cycle assessments (LCA) rely heavily on static databases, making them ill-suited to the dynamic and complex nature of agricultural waste resource utilization. This approach combines machine learning methods, such as artificial neural networks, to achieve dynamic prediction and data completion of environmental impact indicators, significantly improving the timeliness and adaptability of LCA and overcoming the shortcomings of traditional methods, such as delayed data updates and limited applicability.

[0108] 3. The bias regarding the disconnect between management policies and technological systems.

[0109] Existing agricultural resource utilization policies often lag behind technological development and lack effective integration with innovation pathways. This solution integrates the triple helix synergy mechanism and blockchain technology into management policy design, constructing a systematic framework that links technology implementation, policy guidance, and market mechanisms. This corrects the one-sided thinking of "emphasizing technology while neglecting management" and promotes the rapid transformation and large-scale application of technological achievements. Attached Figure Description

[0110] Figure 1 This is a flowchart of the ecological value chain reconstruction method for the resource utilization and management of agricultural waste provided in this embodiment of the invention.

[0111] Figure 2 This is an overview diagram of the life cycle assessment framework provided in the embodiments of the present invention.

[0112] Figure 3 This is an environmental impact contribution analysis diagram of the laboratory-scale BDD anode, SnO2 anode, and PbO2 anode electrochemical oxidation process provided in the embodiments of the present invention.

[0113] Figure 4 This is a graph showing the environmental impact contribution of the BDD anode, SnO2 anode, and PbO2 anode provided in the embodiments of the present invention during the electrochemical oxidation process.

[0114] Figure 5 This is an environmental impact contribution analysis diagram of the preparation process of BDD anode, SnO2 anode and PbO2 anode provided in the embodiments of the present invention.

[0115] Figure 6 This is a structural block diagram of the ecological value chain reconstruction system for the resource utilization and management of agricultural waste provided in this embodiment of the invention.

[0116] Figure 7 These are four common application diagrams of machine learning technology provided in the embodiments of the present invention.

[0117] Figure 8 This is a visualization of the distribution of descriptor data arranged in descending order according to the feature nAtom, provided by an embodiment of the present invention.

[0118] Figure 9 This is a visualization of the distribution of descriptor data arranged in descending order according to the tag value GWP, provided by an embodiment of the present invention.

[0119] Figure 10 This is a data processing flowchart provided in the embodiments of the present invention.

[0120] Figure 11The graph shows the performance of ANN ten-fold cross-validation models based on five different data dimensionality reduction methods provided in this embodiment of the invention: (a) GWP, (b) HTP, (c) MDP, (d) FETP, (e) PMFP, and (f) TAP.

[0121] Figure 12 This is a prediction performance graph of the GWP ANN model based on five different data dimensionality reduction methods provided in this embodiment of the invention.

[0122] Figure 13 This is a prediction performance graph of the HTP ANN model based on five different data dimensionality reduction methods provided in this embodiment of the invention.

[0123] Figure 14 This is a prediction performance graph of the MDP ANN model based on five different data dimensionality reduction methods provided in the embodiments of the present invention.

[0124] Figure 15 This is a prediction performance graph of the FETP ANN model based on five different data dimensionality reduction methods provided in this embodiment of the invention.

[0125] Figure 16 This is a prediction performance graph of the PMFP ANN model based on five different data dimensionality reduction methods provided in this embodiment of the invention.

[0126] Figure 17 This is a prediction performance graph of the TAP ANN model based on five different data dimensionality reduction methods provided in this embodiment of the invention.

[0127] Figure 18 This refers to the predictive performance of the ANN model provided in this embodiment of the invention for key environmental indicators.

[0128] Figure 19 This is a comparison of the system performance before and after optimization of the AI ​​ecosystem value chain platform provided in this embodiment of the invention.

[0129] Figure 20 This represents the relative contribution of each step in the anode preparation process provided in this embodiment of the invention to GWP.

[0130] Figure 21 This refers to the contribution rate of different anodes to the environmental impact indicators during the electrochemical oxidation process provided in the embodiments of the present invention.

[0131] Figure 22 This is a comparison chart of energy conversion efficiency provided in an embodiment of the present invention. Detailed Implementation

[0132] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0133] like Figure 1 The diagram shown is a flowchart of an ecological value chain reconstruction method for the resource utilization and management of agricultural waste provided by an embodiment of the present invention. In this embodiment, steps S101-S105 are used to describe in detail the ecological value chain reconstruction method for the resource utilization and management of agricultural waste in this application.

[0134] S101 collects the energy consumption for collection, transportation, treatment, and pesticide use of agricultural waste as energy value input;

[0135] S102 collects the energy value of resource-based products, the energy value generated by organic fertilizer replacing chemical fertilizer, and the energy value generated by carbon emission reduction, and outputs it as energy value.

[0136] Data acquisition and emergy inventory construction:

[0137] Input side (energy value input, unit is unified as sej or J and then multiplied by the conversion coefficient)

[0138] Energy consumption data collection: Electricity / fuel consumption (kWh, L), collected by smart meters, fuel receipts, and on-board OBD.

[0139] Transportation energy consumption: number of trips, mileage, load factor, fuel consumption curve → converted to MJ;

[0140] Energy consumption for processing: electricity and fuel used for crushing / turning / aeration / mixing / pumping / heating, etc.

[0141] Chemical energy consumption: The unit consumption of lime, microbial agents, flocculants, etc. and the implicit energy value of their production stages (conversion coefficients are given by the database);

[0142] Output side (energy output)

[0143] Resource-based products: Production of organic fertilizer, biogas / biogas electricity, solid fuel, etc. x corresponding energy conversion coefficient;

[0144] Fertilizer substitution: Equivalent nitrogen / phosphorus / potassium nutrient content x respective substitution coefficient (sej / kg-N, etc.)

[0145] Carbon emission reduction: emission reduction within the boundary (tCO2e) x carbon emission reduction energy value coefficient (or expressed as the equivalent energy value of emission reduction in electricity / fuel);

[0146] S103, Establish the energy balance equation, where the energy conversion efficiency is the ratio of the total output energy to the total input energy;

[0147] Unification from inventory to energy value:

[0148] Let qi(t) be the physical quantity of each activity i, and let λi (including "conversion rate") be its energy conversion coefficient.

[0149] Input energy value: ,

[0150] Output energy value: ,

[0151] Conversion efficiency: ;

[0152] S104 introduces a time-weighted function and a hysteresis feedback parameter into the energy balance equation to dynamically correct the energy input and output based on the time series.

[0153] Dynamic time-weighted and hysteresis feedback modeling:

[0154] To address the issues of "data non-stationarity, process inertia, and policy lag," a time-weighted function and a lag feedback parameter are introduced.

[0155] 1. Time weighting function

[0156] Using exponential decay weights: Δt is the time difference (in hours / day) from the current time.

[0157] Dynamically adjusted weighted energy values:

[0158] ,

[0159] Lag feedback parameters

[0160] Process variables (such as compost moisture content M, temperature T, C / N ratio, aeration rate A, and residence time HRT) on The effect has a lag, let the lag be... With feedback gain ;

[0161] Using ARX (with exogenous input) structure or discrete transfer function:

[0162] ,

[0163] Where u is the control vector (process parameter) and ε is the disturbance; {ap,bq,T} is estimated online by recursive least squares (RLS);

[0164] S105 adjusts the waste treatment process parameters according to the real-time changes in energy conversion efficiency to achieve optimal energy efficiency control throughout the entire cycle;

[0165] Optimization and full-cycle optimal control:

[0166] 1) Control Objective

[0167] Main objective: Maximize weighted conversion efficiency Or minimize the unit energy input Functional unit;

[0168] Constraints: Product compliance (moisture content, maturity, hygiene indicators), emission limits (NH3, VOC, odor), equipment safety upper and lower limits;

[0169] 2) Optimization problem (rolling time domain, MPC approach)

[0170] ,

[0171] st process model (ARX / simplified mechanism), process and emission constraints, equipment upper and lower limits; γ is a tradeoff coefficient;

[0172] Solution: If it is approximately linear, use quadratic programming (QP); if it is nonlinear, use heuristics (particle swarm optimization / genetic algorithm) or gradient method.

[0173] 3) Online participation strategy

[0174] when continuously below the threshold Minimize emissions and ensure compliance: Prioritize energy reduction (reduce unnecessary aeration / stirring) and adjust formulations (improve efficiency).

[0175] (C / N to target band);

[0176] When product indicators weaken: prioritize ensuring quality (increase the frequency of turning / aeration / inoculation amount), and then optimize energy consumption;

[0177] Lag compensation: The controller uses the manufacturer's predictive compensation to adjust u in advance / delay.

[0178] Figures 2-5 The life cycle assessment framework used for process selection in Example 1 of this invention is presented in sequence, along with the results of the environmental impact contribution analysis of three anodes, BDD, SnO2 and PbO2, in the electrochemical oxidation process and their preparation process based on this framework.

[0179] This invention applies the life cycle assessment model and methodology established during electrochemical oxidation to the evaluation and optimization of the process for preparing biochar from agricultural waste via pyrolysis, across technological fields. Specifically, it utilizes techniques for analyzing the environmental impact contribution of different anode materials (such as BDD, SnO2, and PbO2) during the preparation and operation phases of the electrochemical system (see [link to relevant documentation]). Figures 3-5 , Figures 20-21The system is applied to the comparison and screening of pyrolysis process parameters, with pyrolysis conditions serving as a key control variable, analogous to the selection of anode materials in electrochemistry, jointly determining the structural characteristics of the final product and its life-cycle environmental performance. Through the integration and optimization of this cross-disciplinary LCA method, not only are key links with high environmental impact in the pyrolysis process identified, but a closed-loop optimized technical path is also provided for the high-value, low-environmental-impact resource utilization of biochar, thus significantly enhancing the systematicness and innovation of the ecological value chain reconstruction process.

[0180] A typical agricultural area was selected within the agricultural demonstration zone, producing 50,000 tons of straw and 30,000 tons of livestock manure annually. An experimental system for reconstructing the ecological value chain of agricultural waste resource utilization and management was established. A life cycle assessment model was used to conduct emergy analysis and environmental impact assessment of three treatment processes (including anaerobic fermentation of straw, composting of manure, and combined pyrolysis). The input parameters included fuel consumption, transportation distance, equipment lifespan, and pesticide usage. The model output showed that the unit emergy conversion efficiencies were 0.63, 0.71, and 0.84, respectively, indicating that the combined pyrolysis process had the best overall ecological benefits.

[0181] The processed data is imported into an AI ecosystem value chain platform, which monitors energy consumption, carbon emissions, and material flow in real time, and predicts energy value growth trends through machine learning algorithms. Under optimized control, the system's carbon emissions are reduced by 19.7%, and energy value utilization is increased by 22.5%. This embodiment verifies that the method can effectively construct a closed-loop gain model for the entire process of agricultural waste resource utilization, achieving synergistic optimization of carbon emission reduction and resource recycling.

[0182] Figure 6 and Figure 7 The system structure diagram of the AI ​​ecosystem value chain platform in Embodiment 2 of the present invention and the types of machine learning technologies applied in the virtual layer of the platform are shown respectively.

[0183] like Figure 6 As shown, an ecological value chain reconstruction system for the resource utilization and management of agricultural waste includes:

[0184] The module includes a process evaluation module, a data processing module, a model training module, and a results output module.

[0185] The process evaluation module calculates the energy input and energy output of different treatment routes for agricultural waste;

[0186] The data processing module performs data normalization and feature dimensionality reduction;

[0187] The model training module uses an ensemble learning algorithm to train the joint prediction model of emergy and carbon sink, with the root mean square error minimization as the objective function.

[0188] The result output module provides the optimal processing route and prediction accuracy based on the model output results.

[0189] An AI-powered ecosystem value chain platform is constructed, integrating data acquisition, model computation, and carbon credit management functions. The platform comprises a physical layer, a virtual layer, and an interaction layer. The physical layer deploys 45 IoT nodes for real-time collection of agricultural waste collection rates, energy consumption data, and emission indicators. The virtual layer runs an AI energy-carbon sink engine, employing a dynamic lifecycle algorithm to calculate ecological performance. The interaction layer uses a blockchain consensus mechanism to achieve distributed recording of carbon credit vouchers.

[0190] After one year of operation, the platform processed 180,000 tons of agricultural waste, recorded 120 million energy value flow data entries, and achieved carbon credit trading of 45,000 yuan. After automatic optimization by the platform, unit energy consumption decreased by 13.8%, and carbon emissions decreased by 18.4%. This embodiment verifies that the AI ​​platform has real-time decision-making and traceability functions in the ecological management of agricultural waste.

[0191] Figures 8-10 The following is a sequential presentation of the descriptor data distribution characteristics, key environmental indicators (GWP) data label distribution, and complete data processing flow used by the intelligent modeling module in Embodiment 3 of the present invention.

[0192] Design a modular agricultural waste ecological value chain reconstruction system, including a process evaluation module, an intelligent modeling module, and a platform management module. The process evaluation module is used to perform lifecycle energy value accounting; the intelligent modeling module uses machine learning algorithms to optimize prediction results; and the platform management module integrates an AI interface for data visualization and carbon credit display.

[0193] The system was deployed in an agro-pastoral complex in Heilongjiang Province to intelligently analyze the process of straw and manure mixing. The error between the system's real-time decision recommendations and manual assessment results was less than 5%, and the deviation between carbon sink calculation results and measured data was less than 8%. The results indicate that the system can stably and reliably perform end-to-end ecological value assessment and management.

[0194] Figures 11-17 This paper showcases the predictive performance and cross-validation results of the ANN models constructed by the intelligent modeling module in Embodiment 3 of the present invention based on five data dimensionality reduction methods for six key environmental indicators: GWP, HTP, MDP, FETP, PMFP, and TAP.

[0195] The system was tested at an agricultural circular economy demonstration base. The system's process evaluation module calculates the energy efficiency indicators of each process in real time, and automatically triggers an alarm when the energy input-output ratio is lower than 0.85; the intelligent modeling module updates the prediction model parameters based on historical samples and adjusts key processing parameters, such as the temperature and moisture ratio; the platform management module performs comprehensive comparison and rolling correction of data at different stages to form a continuously learning adaptive system.

[0196] After six months of continuous operation, the system automatically optimized and adjusted its parameters 12 times, resulting in an overall energy efficiency increase of 16% and a carbon emission intensity decrease of 21%. This embodiment demonstrates that the system possesses self-correction and continuous optimization capabilities, enabling long-term improvement in ecological performance.

[0197] This invention also constructs a portable energy value-carbon sequestration joint accounting device, including a power acquisition module, a gas sensing module, a data fusion processing unit, and a display terminal. The device can measure the equipment's operating power, methane concentration, and carbon dioxide concentration in real time, and automatically calculate the energy value output and net carbon sequestration value per unit time.

[0198] In an anaerobic digestion experiment of livestock and poultry manure, the device measured a peak methane concentration of 65% and a carbon emission reduction of 0.27 tons of CO2 equivalent per ton of raw material, with a calculation error of less than 3%. This device enables simultaneous on-site detection of energy and carbon sequestration, and possesses convenient and accurate process accounting capabilities.

[0199] A blockchain-based carbon credit module is embedded in the AI ​​ecosystem value chain platform. The system uses carbon sink accounting results as the basis for credit generation, generating one carbon credit unit for every ton of carbon dioxide equivalent absorbed; credit vouchers are automatically recorded to a distributed ledger via smart contracts, and credit deduction and transfer operations are supported.

[0200] In a trial run conducted by an agricultural cooperative in Heilongjiang Province, 1,450 carbon credits were registered, with a total transaction value of 120,000 yuan. The blockchain ledger ensured transparency throughout the entire process of credit generation, verification, and traceability, preventing duplicate calculations and false declarations. This mechanism effectively promoted the market-oriented operation of agricultural waste resource utilization and the realization of carbon value.

[0201] Figure 18 The predictive performance of the ANN model on key environmental indicators (such as GWP, HTP, MDP, FETP, PMFP, and TAP) was demonstrated. The R² values ​​of all indicators were above 0.8, indicating that the model has strong fitting and predictive capabilities and can accurately reflect the relationship between system energy consumption and environmental impact.

[0202] Figure 19The system performance of the AI ​​ecosystem value platform before and after optimization was compared. After optimization, the unit energy consumption decreased from 100 kWh / t to 86.2 kWh / t, the energy utilization rate increased from 60% to 73%, and carbon emissions decreased from 1 tCO2 / t to 0.8 tCO2 / t, indicating that the AI ​​platform has a significant effect on energy saving, efficiency improvement, and emission reduction.

[0203] Figure 20 The relative contributions of different anodes (BDD, SnO2, and PbO2) to GWP (Global Warming Potential) at each stage of the preparation process were analyzed. It can be seen that thermal decomposition and electrodeposition are the main contributing stages, with BDD anodes exhibiting a slightly higher overall environmental load, indicating that their preparation requires greater energy.

[0204] at last, Figure 21 and Figure 22 This study comprehensively demonstrates the environmental contribution rates of different anodes during the electrochemical oxidation process and the energy conversion efficiency of different treatment processes. Overall, PbO2 anodes exhibit relatively low environmental burden in terms of GWP and FETP, while the combined thermal decomposition process has the highest energy conversion efficiency (84%), which is superior to anaerobic fermentation (63%) and composting (71%), showing its comprehensive advantages in energy efficiency and ecological synergy.

[0205] Example 1: Agricultural Waste Energy Optimization System Based on Dynamic Life Cycle Assessment

[0206] This embodiment provides a method for constructing a dynamic life cycle assessment model based on corn stalks and livestock manure treatment objects. The system collects real-time data on energy consumption, chemical dosage, and equipment operating parameters during waste collection, transportation, and treatment via IoT nodes. The emergy balance equation dynamically corrects the daily monitoring data by introducing time-weighted coefficients; simultaneously, the hysteresis feedback parameters automatically adjust their weights based on the emergy change rate, thereby achieving a time-series correlation between emergy input and output. The system can also dynamically adjust the process ratio of composting and anaerobic fermentation based on real-time monitoring results to achieve closed-loop optimization control of dynamic energy efficiency.

[0207] Experimental results show that after adopting this system, the energy conversion efficiency per unit energy value increased from 68% to 82%, and carbon emission reduction increased by approximately 25%. This method significantly improves the accuracy of life cycle assessment through a time-related model, accurately reflecting changes in energy consumption and environmental impact caused by seasonal production fluctuations, thus providing an innovative description of the dynamic LCA and energy value feedback mechanism involved in claims 1 to 3.

[0208] Example 2: An emergy-carbon sink prediction model based on multi-objective adaptive machine learning

[0209] In this embodiment, a multi-objective machine learning model is constructed with the joint optimization objectives of emergy conversion rate, carbon sequestration gain, and economic return rate. The model is trained using the gradient boosting tree algorithm, constructs a sample set based on historical waste treatment data, and selects energy consumption, temperature, moisture content, and carbon content as key input variables through feature reduction. During the model training phase, the root mean square error is minimized as the objective function, and a Pareto front constraint is introduced to achieve the optimal balance among the multiple objectives, ultimately outputting the joint emergy-carbon sequestration prediction results for different process routes.

[0210] Application results show that the model achieves a determination coefficient of 0.93 on the validation set, with both energy value and carbon sink prediction deviations controlled within 5%. The system can automatically optimize the processing path based on real-time collected data, maximizing energy efficiency while maintaining a high level of carbon sink gain. This embodiment provides effective support for the "multi-objective adaptive learning and model interpretation mechanism" described in claims 4 to 6 from both the method construction and prediction performance perspectives, and verifies the model's self-correcting ability and high-precision prediction characteristics in practical applications.

[0211] Example 3: Agricultural Waste Energy Value-Carbon Sequestration Joint Accounting Device

[0212] In this embodiment, a comprehensive monitoring device is provided, comprising an emergy acquisition unit, a carbon emission detection unit, a data fusion unit, and a display unit. The emergy acquisition unit monitors the power and operating time of the composting equipment and fermentation tank; the carbon emission detection unit detects the concentrations of carbon dioxide, methane, and nitrous oxide in the exhaust gas in real time; and the data fusion unit calculates the emergy-carbon sink composite index based on a time-weighted algorithm and a weighted average method. The system uploads the processed data to the main control terminal, and the display unit outputs the ecological gain index and carbon balance factor.

[0213] Actual test results show that the device operates continuously for 24 hours with a data error rate of less than 2%, and the obtained comprehensive ecological benefit index can accurately distinguish the energy efficiency differences between different treatment processes. The hardware design and data fusion algorithm of this embodiment provide specific technical support for the physical implementation of claims 6 and 7, ensuring the accuracy and real-time performance of emergy and carbon sequestration measurements.

[0214] Example 4: Blockchain-based agricultural waste carbon credit management platform

[0215] This embodiment establishes an agricultural waste carbon credit management system based on a distributed blockchain ledger. The system architecture includes a physical layer, a virtual layer, and an interaction layer. The physical layer collects data on waste treatment volume, energy consumption, and carbon emissions through a sensor network; the virtual layer simulates the treatment process based on a digital twin model to predict future carbon sequestration potential; and the interaction layer utilizes smart contracts to realize the generation, recording, and trading of carbon credits. The platform employs a hash algorithm to ensure the uniqueness and immutability of carbon credit records.

[0216] Test results show that the platform has a latency of no more than 0.5 seconds in the carbon credit storage and transfer process, and the consistency of credit data reaches 100%. The system realizes traceable, tradable, and verifiable management of carbon credits, significantly improving data transparency and marketability in the process of agricultural waste resource utilization, and providing experimental evidence for the blockchain-digital twin collaborative architecture in claims 8 to 10.

[0217] Example 5: Verification of Integrated Application Scenarios in the AI ​​Ecosystem Value Chain

[0218] In this embodiment, a dynamic LCA module, a machine learning prediction system, and a blockchain carbon credit platform are integrated to construct a complete AI ecosystem value chain. The system is deployed on a cloud server. The physical layer uploads data on processing volume, power, emissions, and temperature and humidity in real time via sensor terminals; the virtual layer predicts carbon sink potential based on a dynamic emergy balance model; and the blockchain layer automatically converts the prediction and calculation results into tradable carbon credits. The platform interface displays information such as emergy conversion efficiency, carbon emission reductions, and the market value of carbon credits in real time.

[0219] A 30-day continuous operation verification conducted at a centralized agricultural waste treatment center in a certain city showed that the system can automatically complete the entire process of emergy calculation, carbon sink prediction, and points settlement. Energy utilization efficiency is 15% higher than that of manual management methods, and the accuracy error of carbon revenue calculation is less than 3%. This embodiment effectively verifies the functional synergy and engineering feasibility between the three major technical modules, providing complete and implementable practical support for the entire patent claim system.

[0220] The above five embodiments provide engineering verification and experimental support from five dimensions: dynamic life cycle assessment, intelligent prediction system, physical accounting device, blockchain platform and integrated application. They form a full-chain technical implementation path from method to system and from hardware to platform, which can fully support the legality, inventiveness and implementability of claims 1-10.

[0221] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A dynamic life cycle assessment and emergy feedback optimization method for agricultural waste resource utilization, characterized in that, The method comprises the following steps: Step 1: Collect the energy consumption data of agricultural waste during collection, transportation, treatment and pesticide use, and convert it into energy input; Step 2: Collect the energy value formed by the resource products, organic fertilizer replacing chemical fertilizer and carbon emission reduction as energy output; Step 3: Establish an energy balance equation to calculate the energy conversion efficiency as the ratio of total output energy value to total input energy value; Step 4: Introduce a time weighting function and a lag feedback parameter to dynamically correct the energy input and output, and obtain the time-varying energy conversion efficiency; Step 5: According to the trend of the corrected energy conversion efficiency, real-time adjust the waste treatment process parameters to realize the optimal control of energy efficiency in the whole cycle.

2. The method of claim 1, wherein, The time weighting function adopts an exponential decay form, and the weight is determined by the reciprocal of the time interval; the lag feedback parameter determines the correction coefficient according to the energy change rate of the last cycle, so that the energy evaluation result realizes time sequence updating.

3. The method of claim 1, wherein, In the energy balance equation, the energy input is the sum of the energy consumption of each activity multiplied by its energy conversion coefficient, the energy output is the sum of the resource output multiplied by its energy conversion coefficient, and the energy conversion efficiency is equal to the total output energy value divided by the total input energy value.

4. The method of claim 1, wherein, The system calculates the comprehensive ecological benefit index by multiplying the energy conversion efficiency and the carbon balance value, and the carbon balance value is obtained by subtracting the greenhouse gas emission from the carbon fixation.

5. An eco-value chain reconfiguration system for the resource utilization and management of agricultural wastes, which implements the method according to any one of claims 1 to 4, characterized in that, The system comprises a process evaluation module, a data processing module, a model training module and a result output module; The process evaluation module is used to calculate the energy input and energy output of different processing routes; The data processing module is used to normalize and reduce the dimensionality of the collected data; The model training module uses an ensemble learning algorithm to train an energy and carbon sink joint prediction model; The result output module determines the optimal processing route according to the model output result and outputs the prediction accuracy.

6. The system of claim 5, wherein, The model training module uses gradient boosting tree algorithm, and the model performance is evaluated by coefficient of determination and root mean square error.

7. The system of claim 5, wherein, The system comprises a model explanation unit, which calculates the influence weight of each input variable on the energy and carbon sink result based on a feature contribution degree decomposition method.

8. The system of claim 5, wherein, The data processing module is connected with the Internet of Things collection unit, which is used to receive the power, running time and emission concentration data of the agricultural waste treatment equipment in real time, and automatically update the model input.

9. A blockchain and digital twin based agricultural waste resource carbon credit management platform implementing the method of any one of claims 1 to 4, characterized in that, The platform comprises a physical layer, a virtual layer and a blockchain interaction layer; The physical layer is used to collect waste collection, treatment, energy consumption and carbon emission data; The virtual layer simulates the resourceization process through a digital twin model and predicts the change of carbon sink; The blockchain interaction layer realizes the generation, evidence storage, transaction and offset of carbon credits based on smart contracts.

10. The platform of claim 9, wherein, The digital twin model realizes real-time updating of energy flow and carbon sink flow through multi-dimensional mapping of physical layer collected data; The blockchain interaction layer adopts a distributed consensus mechanism to record carbon credit data, calculates the market value of ecological products according to the cumulative value of carbon credits, and outputs the income report to realize quantitative management of carbon income.

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