Organic waste resource operation optimization management system for pollution reduction and carbon reduction

Abstract

The invention discloses an organic waste resource operation optimization management system for pollution reduction and carbon reduction, and relates to the technical field of organic waste treatment, and the system comprises a formula module which carries out the raw material characteristic analysis of organic wastes, and outputs a waste feeding formula; the monitoring module is used for carrying out formula collaborative conversion and state monitoring treatment on the organic wastes to obtain material state monitoring data; the adjusting module is used for carrying out real-time comparison and deviation trend analysis and outputting a dynamic adjusting instruction; the scheme module is used for executing the dynamic adjustment instruction, performing multi-path collaborative scheduling optimization on the material biotransformation data and the energy market information data through a linear programming algorithm, and outputting a multi-path collaborative scheduling scheme; and the execution module is used for quantitatively evaluating the carbon emission reduction benefit of the multi-path cooperative scheduling scheme and outputting a real-time resource allocation instruction and a linkage production plan. According to the invention, through dual-mechanism cooperation of multi-path cooperative scheduling optimization and carbon-energy dynamic tradeoff optimization, pollution reduction and carbon reduction synergistic interaction is realized.

Description

Technical Field

[0001] This invention relates to the field of organic waste treatment technology, and in particular to an optimized management system for the resource utilization of organic waste aimed at reducing pollution and carbon emissions. Background Technology

[0002] In recent years, the management methods for the resource utilization of organic waste have been evolving towards integration and intelligence. In the field of organic waste treatment, this typically encompasses raw material characteristic analysis, anaerobic digestion process monitoring, biogas energy utilization, and the application of biogas residue and slurry as fertilizer. In practice, offline testing guides the compatibility of raw materials, closed-loop control of key parameters such as temperature and pH maintains the stability of the digestion process, and relatively independent strategies are employed to manage the timing of biogas power generation grid connection, the load of purification facilities, and the planning of biogas fertilizer delivery routes based on geographic information. Furthermore, data acquisition and monitoring enable centralized monitoring of various operational indicators, and intervention in abnormal operating conditions is conducted according to preset rules, forming a technical paradigm that ensures basic operational safety and product output.

[0003] However, existing methods have shortcomings in achieving synergistic effects in pollution and carbon reduction. There is a disconnect between raw material matching and subsequent conversion stages; a lack of precise pretreatment and dynamic feeding strategies based on real-time raw material characteristic fingerprints; and a lack of a closed-loop optimization mechanism based on real-time bioconversion data and fluctuating energy prices between fermentation process control, multi-path energy scheduling, and product market distribution. Operational decisions are generally driven by economic benefits, failing to embed carbon reduction benefits as a core optimization objective into the entire process of coordinated scheduling from raw material matching to multi-product allocation. This results in suboptimal overall resource conversion efficiency and carbon reduction potential, leading to resource waste and additional carbon emissions due to dynamic mismatch between supply and demand. Summary of the Invention

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

[0005] Therefore, this invention provides an optimized management system for the operation of organic waste resource utilization aimed at pollution reduction and carbon reduction, which addresses the shortcomings in achieving synergistic efficiency improvement in pollution reduction and carbon reduction.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides an optimized management system for the resource utilization of organic waste aimed at pollution reduction and carbon reduction. It includes: a formulation module for analyzing the raw material characteristics of organic waste, obtaining characteristic fingerprint data, analyzing the anaerobic digestion synergistic methanogenesis potential and acidification inhibition risk of different raw material combinations based on the characteristic fingerprint data, adjusting the formulation ratio, and outputting a waste feed formulation; a monitoring module for performing synergistic conversion and state monitoring of organic waste according to the waste feed formulation, obtaining material state monitoring data; and an adjustment module for comparing the material state monitoring data with preset material state index data in real time and analyzing deviation trends, generating state deviation data, and implementing dynamic strategies based on the state deviation data. The system comprises several modules: a regulation and command control module, which outputs dynamic regulation commands; a scheme module, which executes the dynamic regulation commands, collects material bioconversion data and energy market information data after the execution of the commands, optimizes multi-path collaborative scheduling using a linear programming algorithm, and outputs a multi-path collaborative scheduling scheme; and an execution module, which quantitatively evaluates the carbon emission reduction benefits of the multi-path collaborative scheduling scheme, generates quantitative carbon benefit data, dynamically weighs and optimizes the quantitative carbon benefit data and the multi-path collaborative scheduling scheme, outputs a carbon-energy collaborative scheduling scheme, and issues and executes the carbon-energy collaborative scheduling scheme, outputting real-time resource allocation commands and linked production plans.

[0007] As a preferred embodiment of the organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction described in this invention, the specific steps for analyzing the raw material characteristics of organic waste and obtaining characteristic fingerprint data are as follows: The physical and chemical properties of organic waste are detected by near-infrared spectroscopy, and raw material property detection data are output. The raw material characteristic test data are normalized and structured to form a set of raw material characteristic parameters; Raw material characteristics are extracted from the raw material characteristic parameter set, and the raw material characteristics are digitally encoded to output characteristic fingerprint data.

[0008] As a preferred embodiment of the organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction described in this invention, the specific steps of analyzing the anaerobic digestion synergistic methanogenesis potential and acidification inhibition risk of different raw material combinations based on characteristic fingerprint data, and correcting the compatibility ratio to output the waste feed formula are as follows: The characteristic fingerprint data is dynamically matched and synergistic effect is quantitatively evaluated with a pre-set compatibility-metabolism association knowledge table to output the potential for synergistic methanogenesis in anaerobic digestion and the risk of acidification inhibition. Obtain the carbon intensity factor of the real-time energy market, and use a multi-objective programming algorithm to optimize the carbon-energy trade-off between the potential for co-methan production in anaerobic digestion, the risk of acidification inhibition, and the carbon intensity factor of the real-time energy market, and output a set of co-complementary ratios. The set of synergistic compatibility ratios is mapped to process control parameters, and the process control parameters are integrated to output the waste feed formulation.

[0009] As a preferred embodiment of the organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction described in this invention, the specific steps of performing formula-based synergistic conversion and state monitoring treatment on organic waste according to the waste feed formula, and obtaining material state monitoring data, are as follows: Organic waste is co-anaerobic digested according to the waste feed formula, and the output conversion process materials are produced. Real-time status monitoring of materials during the conversion process is performed to obtain real-time status monitoring data; Material status parameters are selected from real-time status monitoring data and then fused together to form material status monitoring data.

[0010] As a preferred embodiment of the organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction described in this invention, the specific steps for generating state deviation data are as follows: The material status monitoring data is compared with the preset material status index data in real time to generate real-time material difference data. Statistical analysis of the time series deviation trend of real-time material status difference data, generating material deviation trend data; By comprehensively evaluating real-time material status difference data and material status deviation trend data, status deviation data is generated.

[0011] As a preferred embodiment of the organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction described in this invention, the step of comprehensively evaluating real-time material state difference data and material state deviation trend data to generate state deviation data includes the following steps: Real-time difference data and deviation trend data are uniformly packaged and paired into time-series event stream data with timestamps; The event stream data is subjected to pattern recognition according to the preset cross-parameter collaborative deviation rules to identify the composite pattern of process semantics; Assign an importance score to the composite pattern of process semantics, determine the deviation level between the importance score and the composite pattern of process semantics, output the overall deviation level, and identify the deviation parameters. The overall deviation level and the level deviation parameter are integrated according to the timestamp to generate state deviation data.

[0012] As a preferred embodiment of the organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction described in this invention, the step of using a linear programming algorithm to perform multi-path collaborative scheduling optimization on material bioconversion data and energy market information data, and outputting a multi-path collaborative scheduling scheme, specifically includes the following steps: The biotransformation data of materials and the information data of the energy market are standardized into unified quantitative parameters and integrated to generate multi-path scheduling data; The system calculates the operational revenue maximization solution from the multi-path scheduling data and outputs path load allocation data. Multi-path timing orchestration and execution instruction encapsulation are performed on the path load allocation data to output a multi-path collaborative scheduling scheme.

[0013] As a preferred embodiment of the organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction described in this invention, the specific steps for quantitatively evaluating the carbon emission reduction benefits of the multi-path collaborative scheduling scheme and generating quantitative carbon benefit data are as follows: Extract carbon emission parameters from the multi-path collaborative scheduling scheme, accumulate the carbon emission parameters of each path scheme in the multi-path collaborative scheduling scheme, and output the carbon emission reduction of the path. The carbon emission reductions from each pathway are structured and integrated to generate quantitative data on carbon benefits.

[0014] As a preferred embodiment of the organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction described in this invention, the specific steps of dynamically balancing and optimizing carbon benefit quantification data and multi-path collaborative scheduling schemes to output a carbon-energy collaborative scheduling scheme are as follows: The carbon benefit quantification data is compared with the preset carbon emission reduction target threshold in real time, and the real-time comparison results are converted into carbon constraints. The multi-path collaborative scheduling scheme is iteratively modified according to the carbon constraint conditions to generate a set of alternative scheduling schemes. Establish a comprehensive evaluation value system that integrates operational revenue with carbon emission reduction compliance, and use this system to weigh and select alternative scheduling schemes to output a carbon-energy coordinated scheduling scheme.

[0015] As a preferred embodiment of the organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction described in this invention, wherein: the instruction issuance is to convert the carbon-energy coordinated scheduling scheme into control instructions and transmit them to the corresponding organic waste resource production center; The coordinated execution refers to coordinating the synchronous operation of each organic waste resource production center in time and executing the control instructions in accordance with the control instructions.

[0016] The beneficial effects of this invention are as follows: Through the synergistic effect of multi-path collaborative scheduling optimization and carbon-energy dynamic trade-off optimization, synergistic efficiency improvement in pollution reduction and carbon reduction is achieved. By integrating material bioconversion data and energy market information data through linear programming algorithms and performing multi-path collaborative scheduling optimization, global optimization of load allocation and timing arrangement of multiple resource utilization paths such as biogas power generation, biogas purification, and biogas fertilizer distribution is realized. This solves the problems of resource misallocation and low overall energy efficiency caused by independent decision-making of each path. By quantifying the carbon emission reduction benefits of the multi-path collaborative scheduling scheme into carbon benefit quantification data, and using the carbon benefit quantification data as the core constraint for dynamic trade-off and optimization correction of the multi-path collaborative scheduling scheme, the carbon emission reduction target is transformed from an external evaluation indicator into an internal optimization variable, realizing the simultaneous optimization of economic and environmental benefits in operational decision-making. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A module diagram for an optimized management system for the resource utilization and operation of organic waste aimed at pollution reduction and carbon reduction.

[0019] Figure 2 A flowchart for outputting waste feed formulations.

[0020] Figure 3 A flowchart for generating state deviation data.

[0021] Figure 4 This is a flowchart for outputting a carbon-energy coordinated scheduling scheme. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 This is one embodiment of the present invention, which provides an optimized management system for the resource utilization of organic waste for pollution reduction and carbon reduction, including the following steps: The formulation module analyzes the raw material characteristics of organic waste, obtains characteristic fingerprint data, analyzes the anaerobic digestion synergistic methanogenesis potential and acidification inhibition risk of different raw material combinations based on the characteristic fingerprint data, corrects the compatibility ratio, and outputs the waste feed formulation.

[0026] The physical and chemical properties of organic waste are detected by near-infrared spectroscopy, and the raw material property test data are output.

[0027] Specifically, organic waste is placed in the sample chamber of a near-infrared spectrometer. A near-infrared light source is turned on to irradiate the organic waste. The near-infrared spectrometer receives the near-infrared light signal reflected or transmitted by the organic waste and converts it into an electrical signal (i.e., near-infrared light irradiates the photosensitive element of a photodetector, exciting electron-hole pairs and generating a current signal proportional to the light intensity, thus achieving the conversion from near-infrared light signal to electrical signal). The electrical signal is then filtered and digitized. First, a low-pass filter circuit suppresses high-frequency interference, then an analog-to-digital converter samples and quantizes the signal at time intervals, generating a continuous spectral curve arranged in wavelength order. Based on the position, shape, and relative intensity of each absorption peak in the spectral curve, combined with known spectral characteristics of organic components, physicochemical properties related to the carbon-to-nitrogen ratio, total solids content, volatile solids content, and biomethane potential are obtained. This physicochemical property information is then formatted and organized to output raw material characteristic detection data.

[0028] Furthermore, the spectral characteristics of organic components refer to the specific absorption peak positions and combination / overtone vibrational modes of different organic functional groups (such as C–H, O–H, N–H, and C=O) in the near-infrared region. These characteristics are obtained through the accumulation of near-infrared spectral data from a large number of standard organic samples. For organic wastes with known components (such as kitchen waste, livestock manure, and straw), near-infrared spectra are measured under laboratory conditions, and physicochemical parameters such as carbon-nitrogen ratio, total solids content, volatile solids content, and biomethane potential are simultaneously determined using chemical analysis methods. Statistical correlation analysis is used to establish the correspondence between each physicochemical parameter and spectral absorption characteristics (such as absorbance in specific bands, peak area, and peak shape index). In other words, near-infrared spectra are correlated one-to-one with physicochemical parameters such as carbon-nitrogen ratio, total solids content, volatile solids content, and biomethane potential. Thus, the physicochemical properties of unknown samples can be obtained through near-infrared spectroscopy, forming a reusable basis for spectral analysis.

[0029] The raw material characteristic test data are normalized and structured to form a set of raw material characteristic parameters.

[0030] Specifically, the carbon-nitrogen ratio, total solids content, volatile solids content, and biomethane potential in the raw material characteristic detection data are respectively mapped to the interval [0, 1] using the minimum-maximum normalization method. The normalized carbon-nitrogen ratio, total solids content, volatile solids content, and biomethane potential are then arranged in a structured manner, and each item is labeled with a corresponding characteristic name. The structured and labeled data are then collected by organic waste batch to form a raw material characteristic parameter set.

[0031] Raw material characteristics are extracted from the raw material characteristic parameter set, and the raw material characteristics are digitally encoded to output characteristic fingerprint data.

[0032] Specifically, the system reads the values ​​of four raw material parameters from the raw material characteristic parameter set: carbon-nitrogen ratio, total solids content, volatile solids content, and biomethane potential. Each raw material parameter value is then divided into discrete level intervals based on its numerical value; for example, the interval [0, 1] is divided into ten sub-intervals, each sub-interval corresponding to an integer code from 0 to 9. Based on the sub-interval into which each raw material parameter value falls, it is converted into a corresponding single numerical code. The numerical codes corresponding to the carbon-nitrogen ratio, total solids content, volatile solids content, and biomethane potential are sequentially combined to form a raw material characteristic parameter sequence consisting of four numbers. The raw material characteristic parameter sequence is then bit-extended so that each digit is represented in two-digit decimal form, and the characteristic fingerprint data is output.

[0033] The characteristic fingerprint data is dynamically matched with a pre-set compatibility-metabolism association knowledge table and the synergistic effect is quantitatively evaluated to output the potential for synergistic methanogenesis in anaerobic digestion and the risk of acidification inhibition.

[0034] Specifically, the characteristic fingerprint data, as input, includes four raw material parameters—carbon-nitrogen ratio, total solids content, volatile solids content, and biomethane potential—which are digitized into four-digit sequence codes. The pre-defined compatibility-metabolism association knowledge table is a database established through numerous anaerobic digestion experiments. This table uses combinations of characteristic fingerprint data from different organic waste raw materials as index keys, and stores corresponding process response parameters such as anaerobic co-digestion compatibility coefficients, gas production potential weighting factors, inhibition risk correction values, and nutrient complementarity scores. The dynamic matching process involves using the characteristic fingerprint data... The data is compared one by one with the index keys in the knowledge table. When the characteristic fingerprint data is completely consistent with a certain index key, the anaerobic co-digestion adaptation coefficient, gas production potential weighting factor, inhibition risk correction value, and nutrient complementarity score corresponding to that index key are extracted. The synergistic effect quantitative assessment is obtained by multiplying the anaerobic co-digestion adaptation coefficient and the gas production potential weighting factor to obtain the synergistic methanogenesis potential index. The inhibition risk correction value and the nutrient complementarity score are combined to obtain the acidification inhibition risk level. The quantified anaerobic digestion synergistic methanogenesis potential value and acidification inhibition risk value are output as the basis data for subsequent optimization.

[0035] Furthermore, the pre-defined compatibility-metabolism association knowledge table is established through numerous anaerobic digestion experiments. It analyzes characteristic fingerprint data combinations of different types of organic waste (such as kitchen waste, livestock manure, and straw) at various compatibility ratios, measuring methanogenesis efficiency, volatile fatty acid accumulation kinetics, and microbial community activity responses. This database uses characteristic fingerprint data combinations as index keys and associates them with corresponding anaerobic co-digestion adaptation coefficients, gas production potential weighting factors, inhibition risk correction values, and nutrient complementarity scores, among other process response parameters. The core content of the compatibility-metabolism association knowledge table includes the quantitative mapping relationship between raw material compatibility combinations and key metabolic efficiency indicators (such as methanogenesis potential enhancement rate and acidification inhibition critical point). This forms a compatibility-metabolism association rule base based on statistical verification of historical experimental data, providing data support for real-time compatibility optimization.

[0036] Obtain the carbon intensity factor of the real-time energy market, and use a multi-objective programming algorithm to optimize the carbon-energy tradeoff between the anaerobic digestion co-methanation potential, acidification inhibition risk and the real-time energy market carbon intensity factor, and output a set of co-complementary ratios.

[0037] Specifically, the real-time energy market carbon intensity factor is the average carbon emission value per unit of electricity generated by the current power grid, obtained from an external energy trading interface. Anaerobic digestion co-methanation potential, acidification inhibition risk, and the real-time energy market carbon intensity factor are used as input variables for a multi-objective programming algorithm. The anaerobic digestion co-methanation potential is used to maximize economic benefits, while the acidification inhibition risk is used to minimize process stability. The real-time energy market carbon intensity factor serves as an environmental constraint to adjust carbon emission reduction weights. The multi-objective programming algorithm establishes an objective function, multiplying the co-methanation potential by an energy price coefficient as the benefit term, multiplying the acidification inhibition risk by a risk penalty coefficient as the cost term, and using the real-time energy market carbon intensity factor as a dynamic weight adjustment term. The optimization objective is to find the mass fraction of each raw material that maximizes the overall benefit, under the constraints of a total raw material mixing ratio of 1, non-negative raw material proportions, and acidification inhibition risk below a safety threshold. A set of Pareto optimal solutions is obtained through iterative calculation, with each solution corresponding to a specific raw material ratio. All solutions constitute a set of co-mixing ratios.

[0038] Furthermore, in the carbon-energy tradeoff optimization process, acidification inhibition risk is used as a process stability constraint, and synergistic methanogenesis potential is used as a core indicator of carbon emission reduction benefits. Together with the real-time energy market carbon intensity factor, a multi-objective function is constructed. When the real-time energy market carbon intensity factor increases, the weight of synergistic methanogenesis potential in the objective function is increased, and a higher gas production efficiency ratio is prioritized. Carbon reduction is achieved by increasing clean energy output to replace high-carbon grid electricity. At the same time, the safety limit of acidification inhibition risk is used as a rigid constraint to ensure that any optimization result meets the safety requirements for volatile fatty acid concentration, avoiding acidification failures caused by pursuing gas production, and achieving the pollution reduction target. Through the carbon-energy tradeoff optimization, a dynamic balance between pollution control and carbon emission reduction is achieved at the source of the mixture.

[0039] The set of synergistic compatibility ratios is mapped to process control parameters, and the process control parameters are integrated to output the waste feed formulation.

[0040] Specifically, the mass fraction of each organic waste material is extracted from the set of compatible proportions. Based on the physical characteristics and treatment requirements of each organic waste material, the mass fraction is converted into the corresponding operating frequency of the feeding conveyor, the crusher blade speed, the opening degree of the hot water mixing valve, and the mixing time of the premixing tank. That is, according to the preset process mapping table (the preset process mapping table is obtained by statistically testing the equipment operating parameters and treatment effects of different types of raw materials at different mass treatment rates during pilot and engineering-scale organic waste pretreatment processes, and sets the operating frequency of the feeding conveyor, the crusher blade speed, the opening degree of the hot water mixing valve, and the mixing time of the premixing tank to meet the target crushing particle size, conditioning temperature, and mixing uniformity), the mass fraction is converted into the corresponding mass fraction of the feeding conveyor. The crusher blade speed, hot water mixing valve opening, and premixing tank stirring time are recorded and linked to the corresponding raw material type and unit mass processing requirements to form a mapping table. The mass fraction of each organic waste raw material is associated with the corresponding unit mass required feeding time, crushing particle size requirements, conditioning temperature, and mixing uniformity index, which are respectively converted into the operating frequency of the feeding conveyor equipment, the crusher blade speed, the hot water mixing valve opening, and the premixing tank stirring time. The operating frequency of the feeding conveyor equipment, the crusher blade speed, the hot water mixing valve opening, and the premixing tank stirring time are packaged as process control parameters in a unified format to form a waste feeding formula containing all operation instructions.

[0041] Based on a dynamic matching mechanism using characteristic fingerprint data and a compatibility-metabolism association knowledge table, this system achieves accurate and rapid quantitative prediction of the synergistic effects and inhibition risks of anaerobic digestion, replacing traditional time-consuming trial-and-error experiments. It innovatively introduces a real-time energy market carbon intensity factor and performs carbon-energy synergistic optimization through multi-objective programming. This ensures that compatibility decisions not only meet process stability and gas production efficiency but also dynamically respond to external carbon price signals, maximizing environmental and economic benefits. The optimization results are automatically mapped to executable process control parameters, forming a complete closed loop from intelligent decision-making to precise control. This significantly improves the methanogenesis efficiency, process stability, and responsiveness to the carbon market in the co-anaerobic digestion of organic waste, while substantially reducing operational risks and optimization decision-making time, achieving a refined, intelligent, and resource-oriented upgrade of the process.

[0042] The monitoring module performs formula-based co-conversion and state monitoring of organic waste according to the waste feed formula, and obtains material state monitoring data.

[0043] Organic waste is co-anaerobic digested according to the waste feed formula, and the output conversion process material is produced.

[0044] Specifically, based on the mass fraction of each organic waste raw material in the waste feed formula, the corresponding types and quantities of organic waste are fed into the anaerobic digestion reactor. In the anaerobic digestion reactor, through the sequential action of hydrolytic bacteria, acid-producing bacteria, and methanogenic bacteria, the mixed organic waste undergoes hydrolysis, acidification, acetic acid production, and methane production stages. Under the continuous mixing action of the stirring device, the organic matter is gradually degraded, generating a mixture containing biogas, biogas slurry, and biogas residue. The mixture is discharged from the anaerobic digestion reactor as a conversion process material.

[0045] Real-time status monitoring of materials during the conversion process is performed to obtain real-time status monitoring data.

[0046] Specifically, temperature sensors, pH sensors, redox potential sensors, biogas flow meters, and online volatile fatty acid detectors are installed inside the anaerobic digester and at the discharge port to continuously collect data on the temperature, pH value, redox potential, biogas production rate, and volatile fatty acid concentration of the material inside the reactor. The temperature, pH value, redox potential, biogas production rate, and volatile fatty acid concentration are then synchronously packaged according to timestamps to form a time-series data stream containing multi-dimensional physical and chemical parameters, i.e., real-time status monitoring data.

[0047] Material status parameters are selected from real-time status monitoring data and then fused together to form material status monitoring data.

[0048] Specifically, temperature, pH, redox potential, biogas production rate, and volatile fatty acid concentration were selected from real-time monitoring data as key indicators reflecting the stability of the anaerobic digestion process, and these key indicators were defined as material state parameters. For each material state parameter, the following processing was performed sequentially: values ​​exceeding physically reasonable conditions were identified and removed (e.g., pH less than 4 or greater than 9, temperature below 10℃ or above 70℃); the remaining data were then filtered using a sliding window moving average to eliminate random fluctuations; after smoothing, the five parameters—temperature, pH, redox potential, biogas production rate, and volatile fatty acid concentration—were then processed. By combining and attaching corresponding timestamps and reactor identification information, a structured record is formed. The continuously generated structured records are then aggregated in time series to form material state monitoring data. The volatile fatty acid concentration in the material state parameters directly reflects the acidification risk (a core pollution indicator). An increase in volatile fatty acid concentration can lead to the inhibition of microbial activity and a decrease in methane production. The biogas production rate is directly related to carbon emission reduction benefits, and it characterizes the methane production efficiency of the anaerobic digestion process in real time. By simultaneously monitoring the two types of parameters, volatile fatty acid concentration and biogas production rate, an assessment basis that simultaneously covers pollution status and carbon emission reduction efficiency is provided for subsequent adjustment modules.

[0049] The adjustment module compares and analyzes the deviation trend of material status monitoring data with preset material status index data in real time, generates status deviation data, performs dynamic strategy adjustment and command control on the status deviation data, and outputs dynamic adjustment commands.

[0050] The material status monitoring data is compared with the preset material status index data in real time to generate real-time material difference data.

[0051] Specifically, the current sampled values ​​of temperature, pH, redox potential, biogas production rate, and volatile fatty acid concentration are extracted sequentially from the material condition monitoring data; the set target values ​​of the corresponding parameters are read from the preset material condition index data; for each parameter, the algebraic difference between the current sampled value and the set target value is calculated to obtain the temperature deviation, pH deviation, redox potential deviation, biogas production rate deviation, and volatile fatty acid concentration deviation; the five material condition deviation values ​​of temperature deviation, pH deviation, redox potential deviation, biogas production rate deviation, and volatile fatty acid concentration deviation are combined and appended with the same timestamp to form real-time material difference data.

[0052] Furthermore, the preset material state index data are determined based on engineering practice and experimental observation of long-term stable operation of the anaerobic digestion process. The target values ​​include: temperature set at 35℃ (mesotropic) or 55℃ (high-temperature), based on the optimal metabolic temperature for microorganisms; pH value set at 7.0, as methanogens have the highest activity in a neutral environment; oxidation-reduction potential set at -350mV, reflecting the strict requirements of the anaerobic environment; biogas production rate set at 90% of the theoretical biogas production rate calculated based on the feed organic load rate, with a 10% fluctuation margin; volatile fatty acid concentration set at 2000mg / L, as the critical safety upper limit to avoid acidification inhibition. All numerical settings are derived from statistical analysis of hundreds of batches of stable operating conditions and are solidified in combination with industry-standard operating requirements.

[0053] The time series deviation trend of real-time material status difference data is statistically analyzed to generate material deviation trend data.

[0054] Specifically, the material state deviation values ​​of the same parameter (such as temperature deviation, pH deviation, redox potential deviation, biogas production rate deviation, or volatile fatty acid concentration deviation) at different time points are extracted from the continuously generated real-time material difference data to form the time series of the current parameter. For the time series of each parameter, a first-order linear regression method is used to fit the slope of change. That is, for the time series of each parameter, with the time point as the independent variable and the deviation value as the dependent variable, a univariate linear regression equation is fitted using the least squares method, and the slope value obtained by fitting is used as the deviation trend index value of the current parameter. The slopes of temperature deviation, pH deviation, redox potential deviation, biogas production rate deviation, and volatile fatty acid concentration deviation are combined and the current timestamp is added to form the material deviation trend data.

[0055] The formula for calculating the deviation from the trend indicator value is as follows: ;

[0056] in, This indicates the deviation of the current parameter from the trend indicator value. This represents the identifier of a time point in the time series of the current parameter. This indicates the number of time points in the time series of the current parameter. Indicates the first The time value at each sampling time point Indicates all The arithmetic mean, Indicates the first Material state deviation value at each sampling time point Indicates all The arithmetic mean.

[0057] Real-time difference data and deviation trend data are uniformly packaged and paired into time-series event stream data with timestamps.

[0058] Specifically, the temperature deviation, pH deviation, redox potential deviation, biogas production rate deviation, and volatile fatty acid concentration deviation included in the real-time material state difference data are paired one-to-one with the temperature deviation slope, pH deviation slope, redox potential deviation slope, biogas production rate deviation slope, and volatile fatty acid concentration deviation slope included in the material state deviation trend data, according to the same timestamp and the same parameter name. The deviation value of each parameter at the timestamp is combined with the deviation slope of the current parameter to form a basic data unit. All the basic data units of the parameters are arranged in chronological order to form time-series event stream data with timestamps. Each event in the time-series event stream data contains four elements: timestamp, parameter name, real-time difference data value, and deviation trend data value.

[0059] Pattern recognition is performed on event stream data according to preset cross-parameter collaborative deviation rules to identify composite patterns of process semantics.

[0060] Specifically, the preset cross-parameter collaborative deviation rules define the temporal correlation conditions of event data for multiple parameters; the pattern recognition process continuously monitors the time-series event stream data to check whether there are continuous event sequences that meet the conditions of the current cross-parameter collaborative deviation rule; for example, a rule stipulates that within three consecutive sampling periods, the pH deviation value is continuously less than zero and the pH deviation slope is continuously less than zero, while the volatile fatty acid concentration deviation value is continuously greater than zero and the volatile fatty acid concentration deviation slope is continuously greater than zero; when an event sequence that meets the current cross-parameter collaborative deviation rule appears in the time-series event stream data, a process semantic composite pattern is identified, and the current pattern is marked as "potential acidification tendency"; each cross-parameter collaborative deviation rule corresponds to a process semantic label, which is used to describe the collaborative deviation characteristics between parameters.

[0061] Furthermore, the pre-defined cross-parameter collaborative deviation rules are established based on a combination of anaerobic digestion process mechanism and historical operation data mining. Their core components include three elements: rule triggering conditions, duration requirements, and process semantic tags. Through correlation analysis of a large amount of historical operation data, multi-parameter deviation patterns with temporal correlation that frequently occur before typical faults are identified. For example, in addition to the "potential acidification tendency" rule, another rule might be defined as "within two consecutive sampling periods, the temperature deviation value is consistently below the negative threshold and the slope of the biogas production rate deviation is consistently negative," and marked as "gas production inhibition caused by thermal shock." Each rule strictly specifies the parameter combination involved in the collaborative deviation, the deviation direction and magnitude of each parameter, the minimum time window during which the pattern must continue (pre-defined after in-depth analysis of the anaerobic digestion process mechanism and correlation mining and statistical verification of a large amount of historical operation data to ensure that the rule parameter combination, deviation direction and magnitude, duration window, and diagnostic conclusions highly match the actual fault patterns), and the corresponding process state diagnostic conclusions after the rule is triggered. The cross-parameter collaborative deviation rules form the basis for mapping low-order data streams to high-order process semantics.

[0062] Assign an importance score to the composite pattern of process semantics, determine the deviation level between the importance score and the composite pattern of process semantics, output the overall deviation level, and identify the deviation parameters.

[0063] Specifically, each identified process semantic composite pattern is assigned an importance score based on the severity and scope of impact defined by the cross-parameter collaborative deviation rule. The importance score depends on the parameter deviation magnitude requirement, time duration requirement, and number of parameters involved as defined in the cross-parameter collaborative deviation rule. The importance scores of all active process semantic composite patterns are summarized, and the overall deviation level is determined based on the interval in which the total score falls. The overall deviation level is divided into four levels: normal, attention, warning, and severe. The names of key parameters that contribute to the process semantic composite patterns that constitute high importance scores are extracted to form a list of deviation parameters for each level.

[0064] Furthermore, the overall deviation level is set based on statistical analysis of the total importance score of the process semantic composite pattern in a large amount of historical operating data. The specific values ​​are determined through cluster analysis: a total score of 0-1.9 corresponds to the "normal" level, 2.0-4.9 corresponds to the "attention" level, 5.0-7.9 corresponds to the "warning" level, and 8.0 and above corresponds to the "serious" level. The values ​​are based on the distribution statistics of the total importance score calculated under hundreds of stable and abnormal operating conditions, ensuring that the samples in each interval have obvious state characteristic distinctions. The setting is based on the principle that the level threshold can accurately reflect the actual risk level—when the total score reaches the "attention" level, it indicates that there is a slight collaborative deviation that needs to be observed; when it reaches the "warning" level, it indicates that the deviation of multiple parameters has posed a clear threat to the stability of the process; and the "serious" level corresponds to a major failure precursor that may lead to shutdown. The different level divisions make the overall deviation level a key bridge connecting the pattern recognition results and the control decision.

[0065] The overall deviation level and the level deviation parameter are integrated according to the timestamp to generate state deviation data.

[0066] Specifically, the current timestamp, overall deviation level, and list of deviation parameters are combined into a structured data object. This structured data object contains three fixed fields: the timestamp field records the evaluation time point, the overall deviation level field records the overall evaluation result of the current system state, and the deviation parameter field records the set of key parameter names that contribute most to the current deviation state. This structured data object is the final generated state deviation data, providing a clear input basis for subsequent dynamic strategy adjustments. By encapsulating real-time difference data and deviation trend data into a time-series event stream and performing pattern recognition based on preset cross-parameter collaborative deviation rules, a leap from isolated parameter alarms to multi-parameter collaborative semantic diagnosis is achieved. This enables earlier and more accurate identification of complex fault precursors (such as "potential acidification tendency"), and by scoring the importance of the pattern and determining its level, it outputs structured state deviation data that combines overall risk level and key deviation parameters, thus providing accurate and forward-looking decision-making basis for subsequent control and significantly improving the intelligence level of anomaly identification and the timeliness of early warning.

[0067] The data on the deviation from the state is matched with the preset adjustment strategy rules to generate adjustment strategy parameters.

[0068] Specifically, the system reads the comprehensive deviation scores for temperature, pH, redox potential, biogas production rate, and volatile fatty acid concentration from the state deviation data. It then searches the preset adjustment strategy rules for rule entries that match the combination of these five comprehensive deviation scores. Each rule entry defines the adjustment action type and intensity level corresponding to different deviation ranges. When the comprehensive deviation scores of all parameters simultaneously fall within the numerical range specified by the current rule entry, the current rule entry is triggered, and the feed rate adjustment coefficient, stirring frequency correction value, heating power offset, alkali addition command, and reflux ratio setting value associated with the current rule entry are extracted. These parameters are then output as adjustment strategy parameters.

[0069] Furthermore, the preset adjustment strategy rules are a set of rules established through experiments and data collection during pilot-scale and engineering-scale anaerobic digestion operations, examining the response relationships between different material state deviations and corresponding control operations. Each rule uses the numerical range of the comprehensive deviation scores of five parameters—temperature, pH, redox potential, biogas production rate, and volatile fatty acid concentration—as trigger conditions, corresponding to a set of operational parameter adjustments that have been verified through multiple rounds to effectively restore process stability. For example, when the comprehensive temperature deviation score is negative and its absolute value is greater than 0.3, the comprehensive pH deviation score is less than -0.2, and the comprehensive deviation score of volatile fatty acid concentration is greater than 0.4, the associated adjustment actions include setting the feed rate adjustment coefficient to 0.8, increasing the stirring frequency correction value by 10%, setting the heating power offset to +15%, activating the alkali addition command, and increasing the reflux ratio setting value to 1.2. The numerical ranges and adjustment parameter values ​​are all based on causal response data between state variables and control variables in a large number of operating conditions, determined through statistical clustering and effect backtracking, and solidified into structured preset adjustment strategy rules.

[0070] The adjustment strategy parameters are mapped to action commands, and dynamic adjustment commands are output.

[0071] Specifically, the target operating frequency is obtained by multiplying the feed rate adjustment coefficient by the reference operating frequency of the current feed conveying equipment, and a corresponding inverter frequency setting command is generated; the stirring frequency correction value is superimposed on the reference speed of the stirring device motor to obtain the target speed, and a speed control command for the motor driver is formed; the target heating power is obtained by multiplying the heating power offset by the rated heating power, and mapped to the duty cycle control signal of the solid-state relay; the activation state in the alkali addition command is combined with the preset single addition volume to generate the pulse count or running time command of the metering pump; the target reflux flow rate is obtained by multiplying the reflux ratio setting value by the current biogas slurry discharge flow rate, and a frequency setting command for the reflux pump inverter is formed; the above inverter frequency setting command, motor driver speed control command, duty cycle control signal, metering pump running time command, and reflux pump inverter frequency setting command are encoded and encapsulated according to the communication protocol of each actuator (such as Modbus RTU or EtherCAT) to form a dynamic adjustment command.

[0072] Furthermore, the preset single-dosing volume is determined through alkaline dosing experiments during pilot-scale and engineering operation phases. The specific value is usually 5L, 10L, or 20L. The basis is that under typical acidification disturbance conditions, the preset single-dosing volume can effectively bring the pH of the reaction system back to the safe range of 6.8–7.5 without causing local over-alkaliness or reagent waste. The value takes into account the digester volume, mixing efficiency, alkaline concentration (usually 10% NaOH), and historical control response data, and has been solidified as a control parameter after multiple rounds of verification.

[0073] The scheme module executes dynamic adjustment commands, collects material bioconversion data and energy market information data after the execution of dynamic adjustment commands, and optimizes multi-path collaborative scheduling of material bioconversion data and energy market information data through linear programming algorithm, and outputs multi-path collaborative scheduling scheme.

[0074] Execute dynamic adjustment commands and collect material bioconversion data and energy market information data after the execution of dynamic adjustment commands.

[0075] Specifically, dynamic adjustment commands are issued to the feeding and conveying equipment, mixing equipment, heating equipment, metering pumps, and reflux pumps, causing them to adjust their operating status according to the commands. Within the time window following the execution of the dynamic adjustment commands, biogas production, biogas slurry chemical oxygen demand, biogas residue moisture content, and methane content are collected from the anaerobic digester's outlet as material bioconversion data. Simultaneously, the current electricity price, carbon trading price, and green certificate price are obtained from the public energy trading interface or the power grid dispatch center interface as energy market information data. The material bioconversion data and the energy market information data are stamped with the same timestamp and stored.

[0076] Material bioconversion data and energy market information data are standardized into unified quantitative parameters and integrated to generate multipath scheduling data.

[0077] Specifically, the biogas production, biogas slurry chemical oxygen demand, biogas residue moisture content, and methane content in the bioconversion data are mapped to the [0, 1] interval using the minimum-maximum normalization method. Similarly, the electricity price, carbon trading price, and green certificate price in the energy market information data are mapped to the [0, 1] interval using the minimum-maximum normalization method. The normalized biogas production, biogas slurry chemical oxygen demand, biogas residue moisture content, methane content, electricity price, carbon trading price, and green certificate price are then structured to form a standardized record containing seven dimensions. Multiple standardized records generated within a continuous time window are then aggregated to form multi-path scheduling data.

[0078] The system calculates the operational benefits maximization from multi-path scheduling data and outputs path load allocation data.

[0079] Specifically, the normalized biogas production and methane content from the multi-path scheduling data are used as input variables for the biogas purification path, while the moisture content of the biogas residue and the chemical oxygen demand of the biogas slurry are used as input variables for the biogas fertilizer production path. Electricity price, carbon trading price, and green certificate price are used as energy market revenue parameters. An objective function is constructed to sum the revenue from grid-connected power generation after biogas purification, biogas fertilizer sales revenue, carbon trading revenue, and green certificate revenue. The constraints include: the upper limit of the processing capacity of each product path, the limit of the grid connection index for power generation, the limit of the biogas fertilizer distribution capacity, and the material balance relationship (the upper limit of the processing capacity of each product path, the limit of the grid connection index for power generation, the limit of the biogas fertilizer distribution capacity, and the material balance relationship are derived from equipment performance test reports, grid connection method requirements, transportation fleet scheduling records, and material balance results based on mass conservation and measured yield coefficients, and are uniformly collected and solidified into constraint parameters through engineering acceptance data, regulatory documents, and operation ledgers).

[0080] A linear programming algorithm is used to maximize the objective function under linear constraints consisting of upper limits on processing capacity, grid connection limits, delivery capacity limitations, and material balance relationships. Feasible solutions are calculated iteratively using the simplex method, where the load allocation ratio of each path is set to an adjustable non-negative value. The non-negative value is checked to ensure that all constraints are met, including that the processing capacity of each path cannot exceed its limit, the grid-connected electricity does not exceed the grid's allowable value, the biogas fertilizer transportation volume does not exceed the fleet's capacity, and material input and output remain balanced. Among all combinations that satisfy the constraints, starting from an initial feasible solution, the load ratio of only one path is adjusted at a time using the simplex method. If the total revenue increases, the current adjustment is retained, and the iteration continues until no further adjustment can improve the total revenue. The current load ratio of each path is the optimal solution and is output as the path load allocation data.

[0081] Furthermore, in the multi-path collaborative scheduling optimization, the chemical oxygen demand of biogas slurry is used as a pollution load indicator for the biogas fertilizer path. When the concentration exceeds the standard, the linear programming algorithm will reduce the load of the biogas fertilizer production path and prioritize directing organic matter to the biogas power generation or purification path, thereby reducing the pollution load through energy utilization (pollution reduction). At the same time, the linear programming algorithm dynamically allocates the load of the biogas power generation and purification paths according to the real-time electricity price and carbon trading price: when the carbon price is high, the proportion of the purification path is increased to produce high-value biogas and realize carbon asset value-added (carbon reduction).

[0082] Multi-path timing orchestration and execution instruction encapsulation are performed on the path load allocation data to output a multi-path collaborative scheduling scheme.

[0083] Specifically, based on the load ratios of biogas purification, biogas fertilizer production, and energy grid connection paths in the path load allocation data, and combined with the process processing cycles of each path (e.g., biogas purification requires 2 hours, biogas fertilizer dehydration requires 4 hours, and the grid connection scheduling window is once every 15 minutes; the process processing cycle is determined by continuously monitoring and statistically analyzing the time required to complete the load for each path, such as biogas purification, biogas fertilizer dehydration, and grid connection scheduling, during large-scale engineering operation, based on measured time-series data such as material flow rate, equipment response delay, and process connection interval), a multi-path task sequence arranged in chronological order is generated. In the multi-path task sequence, a start execution time, duration, and resource occupancy identifier are assigned to each path. Each task is converted into operation instructions recognizable by the corresponding equipment, including biogas compressor start / stop time, purification membrane module operating pressure setpoint, biogas fertilizer dehydrator feed flow rate, generator grid connection power setpoint, and carbon asset declaration trigger signal. The operation instructions are structured and encapsulated according to path type and time sequence relationship to form a multi-path collaborative scheduling scheme containing timestamps, path types, equipment actions, and parameter values.

[0084] By integrating dynamically adjusted material bioconversion data with real-time energy market information, a multi-path collaborative scheduling mechanism is constructed with the goal of optimizing the entire process of organic waste resource utilization. Based on linear programming algorithms, the biogas, biogas slurry, and biogas residue produced by anaerobic digestion are dynamically linked with the energy market. This enables optimized load allocation and precise timing arrangement of biogas purification, biogas fertilizer production, and energy grid connection paths. Through closed-loop control of multi-path material and energy flows, the mechanism effectively connects the state of organic waste conversion processes with the market demand for resource utilization products. This solves the problem of resource idleness or supply-demand mismatch caused by path fragmentation in traditional treatment processes, significantly improving the energy output efficiency and carbon emission reduction benefits of the entire waste resource utilization process. This strengthens the synergistic effect of solid waste resource utilization treatment and pollution reduction and carbon reduction.

[0085] The execution module quantifies and evaluates the carbon emission reduction benefits of the multi-path collaborative scheduling scheme, generates carbon benefit quantification data, dynamically weighs and optimizes the carbon benefit quantification data and the multi-path collaborative scheduling scheme, outputs the carbon-energy collaborative scheduling scheme, issues instructions and executes the carbon-energy collaborative scheduling scheme, and outputs real-time resource allocation instructions and linked production plans.

[0086] Extract carbon emission parameters from the multi-path collaborative scheduling scheme, accumulate the carbon emission parameters of each path scheme in the multi-path collaborative scheduling scheme, and output the carbon emission reduction of the path.

[0087] Specifically, the execution load of each of the biogas purification path, biogas fertilizer production path, and energy grid connection path is analyzed from the multi-path collaborative scheduling scheme. For each path, based on the pre-established carbon emission parameter mapping table, the unit load carbon emission factor strictly corresponding to the current path type is obtained. The unit load carbon emission factor includes the negative carbon emission value generated by replacing the use of traditional fossil energy and reducing landfill or incineration, as well as the positive carbon emission value generated by the power consumption of equipment operation and the input of auxiliary materials. The execution load of each path is multiplied by the corresponding unit load carbon emission factor to obtain the carbon emission equivalent of the current path in the current scheduling cycle. The carbon emission equivalents of the biogas purification path, biogas fertilizer production path, and energy grid connection path are algebraically summed. The summation result is the path carbon emission reduction. A positive value indicates that the overall net carbon emission reduction is achieved, and a negative value indicates that there is net carbon emission.

[0088] Furthermore, the pre-established carbon emission parameter mapping table is constructed by continuously monitoring the input and output material flows and energy consumption of various pathways, combined with the whole life cycle carbon accounting method. For biogas purification pathways, biogas fertilizer production pathways, and energy grid connection pathways, direct emissions (such as auxiliary fuel combustion), indirect emissions (such as purchased electricity), and substitution emission reduction effects (such as emissions avoided by biogas replacing fossil fuels and biogas fertilizer replacing chemical fertilizers) under unit load are measured respectively. The three types of impacts, direct emissions, indirect emissions, and substitution emission reduction effects, are uniformly converted into carbon dioxide equivalent per unit of treatment or per unit of output, forming a unit load carbon emission factor that can be offset by positive and negative values. All unit load carbon emission factors are classified and archived according to pathway type, process configuration, and regional power grid characteristics, and calibrated through multi-cycle operation data to form a structured carbon emission parameter mapping table.

[0089] The carbon emission reductions from each pathway are structured and integrated to generate quantitative data on carbon benefits.

[0090] Specifically, carbon emission reductions are categorized and labeled according to path type: carbon emission reductions from biogas purification, carbon emission reductions from biogas fertilizer production, and carbon emission reductions from energy grid connection. Each type of carbon emission reduction is assigned a timestamp, scheduling cycle identifier, and resource output center code consistent with the multi-path collaborative scheduling scheme. The labeled carbon emission reductions are organized into a structured record, with fields including path type, carbon emission reduction value, timestamp, scheduling cycle identifier, and resource output center code. The structured record generated within the current scheduling cycle is stored and used as input for subsequent optimization and correction, forming quantitative data on carbon benefits.

[0091] The carbon benefit quantification data is compared with the preset carbon emission reduction target threshold in real time, and the real-time comparison results are converted into carbon constraints. The multi-path collaborative scheduling scheme is iteratively corrected according to the carbon constraints to generate a set of alternative scheduling schemes. Specifically, the path carbon emission reduction values ​​are extracted from the carbon benefit quantification data, and these values ​​are compared with preset carbon emission reduction target thresholds. If the path carbon emission reduction does not reach the target threshold, the difference is converted into carbon constraints, specifically by tightening the load ceiling for high-carbon emission paths and raising the load floor for negative-carbon emission paths. These carbon constraints are added as new linear inequality constraints to the original optimization solution logic of the multi-path collaborative scheduling scheme. While maintaining the original upper limit of processing capacity, power generation grid connection index limit, biogas fertilizer distribution capacity limit, and material balance relationship, the linear programming algorithm is re-executed to solve for feasible solutions that satisfy the new carbon constraints. The feasible solutions that satisfy the new carbon constraints are solved repeatedly, with the carbon constraint intensity adjusted each time, generating multiple feasible scheduling schemes that meet different carbon emission reduction intensity requirements, which are then aggregated to form a set of alternative scheduling schemes. During the dynamic trade-off process, the path carbon emission reduction is compared with the preset carbon emission reduction target threshold in real time, while key pollution parameters (such as volatile fatty acid concentration) in the material status monitoring data are monitored. If the pollution parameters exceed the safe range (the safe range refers to the allowable fluctuation range set for key pollution control parameters in the preset material status index data, specifically including: the safe range of pH value is 6.8-7.2, with 7.0 as the neutral benchmark to avoid inhibiting methanogenic bacteria activity; the safe upper limit of volatile fatty acid concentration is 2000 mg / L, exceeding which can easily cause acidification failure; the values ​​are based on statistical analysis of the stable operation conditions of hundreds of batches of anaerobic digestion and are solidified with reference to industry general operating requirements to ensure process stability and pollution control effectiveness), then pollution control scheduling schemes (such as reducing feed load and increasing reflux dilution) are given priority, even if the carbon emission reduction is temporarily reduced (pollution reduction priority); if the pollution parameters are normal, the scheme with the greatest carbon emission reduction benefit is given priority (carbon reduction priority) to ensure that the carbon-energy coordinated scheduling scheme has both process stability and carbon emission reduction effectiveness.

[0092] Furthermore, the preset carbon emission reduction target threshold is set based on the pollution reduction and carbon reduction performance requirements that the organic waste resource utilization project needs to achieve within the scheduling cycle. The value is determined comprehensively based on the average carbon emission reduction intensity per unit treatment volume in historical operation, the baseline emission level under the current energy structure, and the phased emission reduction plan. An exemplary value can be a net emission reduction of 0.35tCO2e per ton of organic waste treated (0.35tCO2e means that treating 1 ton of organic waste can achieve the equivalent of reducing greenhouse gas emissions of 0.35 tons of carbon dioxide, where t is tons and CO2e is carbon dioxide equivalent, used to uniformly measure the climate impact of different greenhouse gases). The value comes from the statistical analysis of continuous operation data of similar projects, ensuring that it can be achieved in more than 70% of the scheduling cycles under normal operating conditions, and is consistent with the annual average emission reduction rate requirements of the waste treatment industry in the regional carbon peaking action plan. The setting basis includes the gas production potential of materials, the biogas substitution effect, the changing trend of power grid emission factors, and the energy efficiency improvement space of equipment. The feasibility and economic rationality are verified through multi-scenario simulation and then solidified.

[0093] Establish a comprehensive evaluation value system that integrates operational revenue with carbon emission reduction compliance, and use this system to weigh and select alternative scheduling schemes to output a carbon-energy coordinated scheduling scheme.

[0094] Specifically, a comprehensive evaluation system for operational benefits and carbon emission reduction compliance is established. This involves retrieving path load allocation data, multi-path task sequences, and path carbon emission reductions for each candidate scheduling scheme from the candidate scheduling scheme set. A comprehensive evaluation value is calculated for each candidate scheduling scheme, which is composed of the operational benefit score obtained by maximizing operational benefits and the degree of carbon emission reduction compliance. The operational benefit score is derived from maximizing operational benefits on the multi-path scheduling data, while the degree of carbon emission reduction compliance is determined by the closeness of the path carbon emission reduction to the carbon emission reduction target threshold. The candidate scheduling scheme set is then sorted from highest to lowest comprehensive evaluation value. The candidate scheduling scheme with the highest comprehensive evaluation value is selected as the final decision scheme. Finally, the path load allocation data, multi-path task sequences, and operation instructions for the current candidate scheduling scheme are fully extracted to form a carbon-energy coordinated scheduling scheme.

[0095] The carbon-energy coordinated scheduling scheme issues and executes commands in a coordinated manner, outputting real-time resource allocation commands and coordinated production plans.

[0096] Specifically, instruction issuance involves encoding the operational instructions from the carbon-energy coordinated scheduling scheme, including biogas compressor start / stop times, purification membrane module operating pressure setpoints, biogas fertilizer dewatering machine feed flow rates, generator grid connection power setpoints, and carbon asset declaration trigger signals, according to the communication protocol format of each organic waste resource production center, and transmitting them to the corresponding organic waste resource production center via the industrial network. Coordinated execution refers to coordinating the synchronous operation of each organic waste resource production center in time and executing control instructions according to the start time and duration specified in the multi-path task sequence of the carbon-energy coordinated scheduling scheme. During instruction issuance and coordinated execution, the response status and resource allocation execution status of each organic waste resource production center are recorded in real time. The resource allocation execution status is organized chronologically into a structured instruction stream containing equipment actions, allocation quantities, execution times, and production center identifiers, forming real-time resource allocation instructions and a coordinated production plan.

[0097] In summary, this invention achieves synergistic efficiency improvement in pollution and carbon reduction through a dual mechanism of multi-path collaborative scheduling optimization and carbon-energy dynamic trade-off optimization. By integrating material bioconversion data and energy market information data through a linear programming algorithm and performing multi-path collaborative scheduling optimization, global optimization of load allocation and timing arrangement of multiple resource utilization paths, such as biogas power generation, biogas purification, and biogas fertilizer distribution, is achieved. This solves the problems of resource misallocation and low overall energy efficiency caused by independent decision-making of each path. By quantifying the carbon emission reduction benefits of the multi-path collaborative scheduling scheme into carbon benefit quantification data, and using the carbon benefit quantification data as the core constraint for dynamic trade-off and optimization correction of the multi-path collaborative scheduling scheme, the carbon emission reduction target is transformed from an external evaluation indicator into an internal optimization variable, realizing the simultaneous optimization of economic and environmental benefits in operational decision-making.

[0098] 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, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An optimized management system for the resource utilization of organic waste aimed at pollution reduction and carbon reduction, characterized in that: include, The formulation module analyzes the raw material characteristics of organic waste, obtains characteristic fingerprint data, analyzes the anaerobic digestion synergistic methanogenesis potential and acidification inhibition risk of different raw material combinations based on the characteristic fingerprint data, corrects the compatibility ratio, and outputs the waste feed formulation. The monitoring module performs formula-based co-conversion and state monitoring of organic waste according to the waste feed formula, and obtains material state monitoring data. The adjustment module compares and analyzes the deviation trend of material status monitoring data with preset material status index data in real time, generates status deviation data, performs dynamic strategy adjustment and command control on the status deviation data, and outputs dynamic adjustment commands. The scheme module executes dynamic adjustment commands, collects material bioconversion data and energy market information data after the execution of dynamic adjustment commands, and optimizes multi-path collaborative scheduling of material bioconversion data and energy market information data through linear programming algorithm, and outputs multi-path collaborative scheduling scheme. The execution module quantifies and evaluates the carbon emission reduction benefits of the multi-path collaborative scheduling scheme, generates carbon benefit quantification data, dynamically weighs and optimizes the carbon benefit quantification data and the multi-path collaborative scheduling scheme, outputs the carbon-energy collaborative scheduling scheme, issues instructions and executes the carbon-energy collaborative scheduling scheme, and outputs real-time resource allocation instructions and linked production plans.

2. The organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction as described in claim 1, characterized in that: The specific steps for analyzing the raw material characteristics of organic waste and obtaining characteristic fingerprint data are as follows: The physical and chemical properties of organic waste are detected by near-infrared spectroscopy, and raw material property detection data are output. The raw material characteristic test data are normalized and structured to form a set of raw material characteristic parameters; Raw material characteristics are extracted from the raw material characteristic parameter set, and the raw material characteristics are digitally encoded to output characteristic fingerprint data.

3. The organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction as described in claim 2, characterized in that: The specific steps of analyzing the anaerobic digestion synergistic methanogenesis potential and acidification inhibition risk of different raw material combinations based on characteristic fingerprint data, and adjusting the compatibility ratio to output waste feed formulation are as follows: The characteristic fingerprint data is dynamically matched and synergistic effect is quantitatively evaluated with a pre-set compatibility-metabolism association knowledge table to output the potential for synergistic methanogenesis in anaerobic digestion and the risk of acidification inhibition. Obtain the carbon intensity factor of the real-time energy market, and use a multi-objective programming algorithm to optimize the carbon-energy trade-off between the potential for co-methan production in anaerobic digestion, the risk of acidification inhibition, and the carbon intensity factor of the real-time energy market, and output a set of co-complementary ratios. The set of synergistic compatibility ratios is mapped to process control parameters, and the process control parameters are integrated to output the waste feed formulation.

4. The organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction as described in claim 3, characterized in that: The specific steps for performing formula-based co-conversion and state monitoring treatment of organic waste according to the waste feed formula, and obtaining material state monitoring data, are as follows: Organic waste is co-anaerobic digested according to the waste feed formula, and the output conversion process materials are produced. Real-time status monitoring of materials during the conversion process is performed to obtain real-time status monitoring data; Material status parameters are selected from real-time status monitoring data and then fused together to form material status monitoring data.

5. The organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction as described in claim 4, characterized in that: The specific steps for generating the state deviation data are as follows: The material status monitoring data is compared with the preset material status index data in real time to generate real-time material difference data. Statistical analysis of the time series deviation trend of real-time material status difference data, generating material deviation trend data; By comprehensively evaluating real-time material status difference data and material status deviation trend data, status deviation data is generated.

6. The organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction as described in claim 5, characterized in that: The specific steps for comprehensively evaluating real-time material state difference data and material state deviation trend data to generate state deviation data are as follows: Real-time difference data and deviation trend data are uniformly packaged and paired into time-series event stream data with timestamps; The event stream data is subjected to pattern recognition according to the preset cross-parameter collaborative deviation rules to identify the composite pattern of process semantics; Assign an importance score to the composite pattern of process semantics, determine the deviation level between the importance score and the composite pattern of process semantics, output the overall deviation level, and identify the deviation parameters. The overall deviation level and the level deviation parameter are integrated according to the timestamp to generate state deviation data.

7. The organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction as described in claim 1, characterized in that: The process involves using a linear programming algorithm to optimize the multi-path collaborative scheduling of material bioconversion data and energy market information data, outputting a multi-path collaborative scheduling scheme. The specific steps are as follows: The biotransformation data of materials and the information data of the energy market are standardized into unified quantitative parameters and integrated to generate multi-path scheduling data; The system calculates the operational revenue maximization solution from the multi-path scheduling data and outputs path load allocation data. Multi-path timing orchestration and execution instruction encapsulation are performed on the path load allocation data to output a multi-path collaborative scheduling scheme.

8. The organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction as described in claim 1, characterized in that: The quantitative evaluation of the carbon reduction benefits of the multi-path coordinated scheduling scheme, generating quantitative carbon benefit data, involves the following steps: Extract carbon emission parameters from the multi-path collaborative scheduling scheme, accumulate the carbon emission parameters of each path scheme in the multi-path collaborative scheduling scheme, and output the carbon emission reduction of the path. The carbon emission reductions from each pathway are structured and integrated to generate quantitative data on carbon benefits.

9. The organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction as described in claim 1, characterized in that: The specific steps for dynamically balancing and optimizing carbon benefit quantification data and multi-path collaborative scheduling schemes to output a carbon-energy collaborative scheduling scheme are as follows: The carbon benefit quantification data is compared with the preset carbon emission reduction target threshold in real time, and the real-time comparison results are converted into carbon constraints. The multi-path collaborative scheduling scheme is iteratively modified according to carbon constraints to generate a set of alternative scheduling schemes. Establish a comprehensive evaluation value system that integrates operational revenue with carbon emission reduction compliance, and use this system to weigh and select alternative scheduling schemes to output a carbon-energy coordinated scheduling scheme.

10. The organic waste resource utilization operation optimization management system for pollution reduction and carbon reduction as described in claim 1, characterized in that: The instruction issuance involves converting the carbon-energy coordinated scheduling scheme into control instructions and transmitting them to the corresponding organic waste resource production center. The coordinated execution refers to coordinating the synchronous operation of each organic waste resource production center in time and executing the control instructions in accordance with the control instructions.

Images