Method and system for calculating pollution-reducing and carbon-reducing synergistic effect of agricultural solid waste

Abstract

The invention provides an agricultural source solid waste pollution reduction and carbon reduction synergistic effect calculation method and system. According to the agricultural solid waste pollution reduction and carbon reduction synergistic effect calculation method, a livestock and poultry manure and straw mixture serves as a treatment object, an environmental parameter set of microbial metabolism intensity is obtained, and then a microbial agent feeding acceleration rate and a flora activity attenuation curve are dynamically matched. In the aerobic fermentation stage, through linkage of pile turning operation and a microbial inoculum adding strategy, activity balance of surface and deep flora is maintained, and a microbial inoculum adding time window is corrected in combination with historical data. And establishing a quantitative model with a carbon-nitrogen ratio imbalance threshold value as a constraint, iteratively optimizing a nonlinear association relationship between the inoculant feeding acceleration rate and the pile turning operation frequency, and outputting a full-period synergistic effect evaluation result. According to the technical scheme, the pollution reduction and carbon reduction synergistic effect of agricultural source solid waste treatment is improved, and the dual purposes of resource utilization and environmental protection are achieved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a calculation method and system for the synergistic effect of reducing pollution and carbon emissions from agricultural solid waste. Background Art

[0002] The treatment of agricultural solid waste (such as livestock manure and straw) is a key link in the prevention and control of agricultural non-point source pollution and resource utilization. Currently, how to achieve the synergistic effect of reducing pollution and carbon emissions during the treatment process has become the core technical requirement. Existing technologies need to solve problems such as the dynamic monitoring of key parameters inside the pile body, the precise regulation of operation strategies, and the quantitative evaluation of the benefits of reducing pollution and carbon emissions, in order to improve the treatment efficiency and reduce the environmental impact.

[0003] Currently, a composting optimization scheme based on simple parameter collection is applied to the treatment of agricultural solid waste. This scheme adjusts operation parameters by collecting basic data such as the temperature, humidity, and oxygen concentration of the pile body. For example, it adjusts according to temperature changes (such as turning the pile when the pile temperature > 60°C) and oxygen concentration (such as increasing the turning frequency when < 5%) to achieve the goals of reducing pollution and carbon emissions.

[0004] However, this scheme has the following deficiencies: First, relying only on simple parameters (such as temperature and humidity) cannot comprehensively reflect the complex biochemical processes inside the pile body, resulting in insufficient precision of operation strategies; second, it does not consider the synergistic optimization of the goals of reducing pollution and carbon emissions, which may lead to an imbalance in the treatment effect (such as excessive turning increasing energy consumption but not significantly improving the pollution reduction benefit). Summary of the Invention

[0005] The embodiments of this application provide a calculation method and system for the synergistic effect of reducing pollution and carbon emissions from agricultural solid waste, to solve the problem of poor calculation effect of the synergistic effect of reducing pollution and carbon emissions in the prior art.

[0006] In the first aspect, the embodiments of this application provide a calculation method for the synergistic effect of reducing pollution and carbon emissions from agricultural solid waste, including: During the treatment process of agricultural solid waste, taking the mixture of livestock manure and straw as the treatment object, by real-time monitoring the temperature gradient, pH value fluctuation range, and change amount of volatile organic compound concentration inside the pile body composed of the treatment object, to obtain an environmental parameter set reflecting the intensity of microbial metabolism; According to the change trend of the environmental parameter set, dynamically match the inoculant application rate with the attenuation curve of the microbial community activity, where the inoculant contains a composite microbial community of thermophilic cellulose-decomposing bacteria and nitrogen-fixing bacteria, and the application amount is adjusted in reverse compensation based on the oxygen diffusion efficiency inside the pile body and the carbon-nitrogen ratio imbalance threshold; During the aerobic fermentation stage of the heap body, through the linkage control of the turning operation and the inoculant dosing strategy, the balance of the microbial community activity distribution in the surface area and the deep area of the heap body is maintained. At the same time, combined with the historical data of the external environmental humidity and light intensity, the discrete interval of the inoculant dosing time window is corrected. Establish a quantitative model for the synergy effect of pollution reduction and carbon emission reduction with the carbon-nitrogen ratio imbalance threshold as a constraint condition, so as to iteratively optimize the non-linear correlation relationship between the inoculant dosing rate and the turning operation frequency through the quantitative model for the synergy effect of pollution reduction and carbon emission reduction, and output the effect evaluation results of the whole cycle of agricultural source solid waste treatment.

[0007] Optionally, it further includes: synchronously calculating the dynamic coupling coefficient of the organic matter degradation rate and the greenhouse gas emission reduction amount based on the balance of the microbial community activity distribution and the characteristics of the stage products of the heap body; Based on the dynamic coupling coefficient, adjust the non-linear correlation relationship between the inoculant dosing rate and the turning operation frequency to optimize the effect evaluation results of the whole cycle of agricultural source solid waste treatment.

[0008] Optionally, the dynamically matching the inoculant dosing rate with the microbial community activity decay curve according to the change trend of the environmental parameter set includes: Decompose the temperature gradient, pH value fluctuation range, and volatile organic compound concentration change amount in the environmental parameter set into a trend component and a fluctuation component respectively, where the trend component is characterized by the cumulative rate of parameter change within a sliding window, and the fluctuation component is extracted by the difference between the extreme points of the parameters within adjacent windows; Based on the cumulative rate change characteristics of the trend component, fit the microbial community activity decay curve of the composite microbial community in the heap body, where the activity decay curve calibrates the decay slope of the microbial community metabolism rate under different oxygen diffusion efficiencies through a preset inoculant dosing experiment; According to the decay slope of the microbial community activity decay curve and the extreme point distribution characteristics of the fluctuation component, establish a dynamic response model for the inoculant dosing rate, where an inverse proportional constraint relationship between the activity maintenance threshold and the decay slope is set in the dynamic response model, and a correction coefficient for the oxygen diffusion efficiency due to the change in the volume of the heap body is introduced; When the oxygen diffusion efficiency inside the heap body is lower than the preset critical threshold and the carbon-nitrogen ratio imbalance threshold reaches the segmented compensation interval, trigger the reverse compensation adjustment rule based on the dynamic response model, and dynamically adjust the increment gradient of the inoculant dosing rate; Through the coupling effect of the reverse compensation adjustment rule and the temperature gradient difference between the surface area and the deep area of the heap body, real-time update the dynamic control instruction of the inoculant dosing rate.

[0009] Optionally, the dynamic control instruction for the inoculant feeding rate is updated in real time through the coupling effect of the reverse compensation adjustment rule and the temperature gradient difference between the surface area and the deep area of the heap body, including: Stratified temperature monitoring nodes are respectively set in the surface area and the deep area of the heap body to obtain the surface temperature change sequence and the deep temperature change sequence, and the dynamic offset of the temperature gradient difference is calculated through the cumulative temperature difference within the sliding window; According to the dynamic offset and the preset equilibrium threshold of the microbial community activity distribution, the dynamic compensation coefficient in the reverse compensation adjustment rule is determined, where the dynamic compensation coefficient is secondarily weighted by the correction value of the oxygen diffusion efficiency due to the change in the volume of the heap body; Based on the correlation between the dynamic compensation coefficient and the carbon-nitrogen ratio imbalance threshold of the stage product characteristics of the heap body, a non-linear mapping relationship between the temperature gradient difference and the increment of the inoculant feeding rate is constructed, and the historical fluctuation data of the external environmental humidity is introduced to discretely calibrate the trigger time window of the mapping relationship; When the temperature gradient difference exceeds the critical interval of the activity distribution equilibrium threshold, the segmented compensation mechanism of the reverse compensation adjustment rule is activated, and the dynamic control instruction for the inoculant feeding rate is generated based on the non-linear mapping relationship, and the real-time monitoring data of the change amount of the volatile organic compound concentration in the heap body is coupled to update the weight distribution of the dynamic compensation coefficient; Through the segmented compensation mechanism and the real-time feedback of the oxygen diffusion efficiency of the heap body, the execution timing of the dynamic control instruction is iteratively adjusted, and the cumulative difference calculation period of the sliding window of the temperature gradient difference is synchronously corrected.

[0010] Optionally, during the aerobic fermentation stage of the heap body, the linkage control of the turning operation and the inoculant feeding strategy is used to maintain the balance of the microbial community activity distribution of the composite microbial community in the surface area and the deep area of the heap body, including: During the aerobic fermentation stage of the heap body, based on the real-time monitoring data of the temperature gradient difference and the change amount of the volatile organic compound concentration in the surface area and the deep area, the surface microbial community activity compensation coefficient and the deep oxygen diffusion correction coefficient are respectively calculated, where the surface microbial community activity compensation coefficient is dynamically calibrated by the cumulative temperature change rate within the sliding window and the decay slope of the preset microbial community activity decay curve; According to the non-linear correlation between the surface microbial community activity compensation coefficient and the deep oxygen diffusion correction coefficient, a collaborative control instruction for the turning operation trigger frequency and the inoculant feeding rate is generated to maintain the balance of the microbial community activity distribution of the composite microbial community in the surface area and the deep area of the heap body, where the weight distribution ratio of the surface and deep microbial community activities is set in the collaborative control instruction.

[0011] Optionally, combining the historical data of the external environmental humidity and light intensity simultaneously to correct the discrete interval of the microbial agent dosing time window, including: Extracting the historical data of the change rate of the external environmental humidity and the fluctuation amplitude of the light intensity, and calculating the offset of the discrete interval of the microbial agent dosing time window through the cumulative difference of the humidity and light coupling parameters within adjacent time windows, wherein the offset of the discrete interval is dynamically matched with the carbon-nitrogen ratio imbalance threshold of the stage product characteristics of the heap body; Based on the offset of the discrete interval and the extreme point distribution characteristics of the surface temperature change sequence, constructing a dynamic mapping relationship between the turning operation trigger moment and the microbial agent dosing time window, wherein an oxygen diffusion efficiency critical threshold is set as a segmented trigger condition in the dynamic mapping relationship, and the constraint boundary of the mapping relationship is updated by coupling the real-time feedback data of the change amount of the volatile organic compound concentration; When the deep oxygen diffusion efficiency of the heap body is lower than the preset critical value or the surface microbial community activity compensation coefficient deviates from the equilibrium interval, synchronously adjusting the increment gradient of the turning operation frequency and the compensation weight of the microbial agent dosing rate based on the dynamic mapping relationship, and iteratively updating the execution timing of the collaborative control instruction through the real-time correction value of the oxygen diffusion efficiency by the change of the heap body volume.

[0012] Optionally, iteratively optimizing the non-linear correlation relationship between the microbial agent dosing rate and the turning operation frequency through the pollution reduction and carbon reduction synergy effect quantification model, and outputting the effect evaluation results of the whole cycle of agricultural source solid waste treatment, including: Obtaining the microbial agent activity threshold, the turning mechanical energy efficiency coefficient, and the sudden drop threshold of the pH value in the deep area of the heap body through the sensors installed in the heap body and using them as the input parameters of the quantification model, and generating a candidate solution set of the microbial agent dosing rate and the turning operation frequency through the quantification model; Calculating the partial derivatives of the objective function of the microbial agent dosing rate and the turning operation frequency in the candidate solution set, and dynamically adjusting the microbial agent dosing rate and the turning operation frequency by combining the real-time data of near-infrared spectroscopy, gas chromatography, and mass spectrometry to generate a three-dimensional response surface characterizing the non-linear correlation relationship between the two; Fusing the three-dimensional response surface with the obtained unmanned aerial vehicle remote sensing and thermal infrared imaging data, screening abnormal working conditions deviating from the optimization path with the sudden drop threshold of the pH value in the deep area of the heap body as the constraint condition, and updating the discrete interval of the microbial agent dosing time window; Based on the updated discrete interval, using the dynamic programming algorithm to segmentally calculate the synergy index of the pollution reduction contribution degree and the carbon reduction contribution degree, and outputting the effect evaluation results optimized through multiple rounds of iteration, the results including the optimal microbial agent dosing rate, the turning operation frequency, and the synergy effect evaluation matrix.

[0013] In a second aspect, an embodiment of the present application provides a system for calculating the pollution reduction and carbon reduction synergy effect of agricultural source solid waste, including: A monitoring module, which is used to take the mixture of livestock manure and straw as the treatment object during the treatment process of agricultural source solid waste, and obtain a set of environmental parameters reflecting the intensity of microbial metabolism by real-time monitoring of the internal temperature gradient, pH value fluctuation range and volatile organic compound concentration change amount of the heap formed by the treatment object; A matching module, which is used to dynamically match the inoculant addition rate with the decay curve of the microbial community activity according to the change trend of the set of environmental parameters. The inoculant contains a composite microbial community of thermophilic cellulose-decomposing bacteria and nitrogen-fixing bacteria, and the addition amount is adjusted in reverse compensation based on the internal oxygen diffusion efficiency and the carbon-nitrogen ratio imbalance threshold of the heap; A control module, which is used to maintain the balance of the microbial community activity distribution in the surface area and the deep area of the heap through the linkage control of the turning pile operation and the inoculant addition strategy during the aerobic fermentation stage of the heap. At the same time, combined with the historical data of the external environmental humidity and light intensity, the discrete interval of the inoculant addition time window is corrected; An optimization module, which is used to establish a quantitative model of the synergistic effect of pollution reduction and carbon reduction with the carbon-nitrogen ratio imbalance threshold as the constraint condition, so as to iteratively optimize the non-linear correlation between the inoculant addition rate and the turning pile operation frequency through the quantitative model of the synergistic effect of pollution reduction and carbon reduction, and output the effect evaluation result of the whole cycle of agricultural source solid waste treatment.

[0014] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for calculating the synergistic effect of pollution reduction and carbon reduction of agricultural source solid waste as described in the first aspect above.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium, storing a computer program, and when the computer program is executed by a computer, it implements a method for calculating the synergistic effect of pollution reduction and carbon reduction of agricultural source solid waste as described in the first aspect.

[0016] In the embodiment of the present application, during the treatment process of agricultural source solid waste, taking the mixture of livestock manure and straw as the treatment object, by real-time monitoring the internal temperature gradient, pH value fluctuation range and volatile organic compound concentration change amount of the heap formed by the treatment object, to obtain an environmental parameter set reflecting the microbial metabolism intensity; according to the change trend of the environmental parameter set, dynamically matching the inoculant dosing rate with the decay curve of the microbial community activity, wherein the inoculant contains a composite microbial community of thermophilic cellulose-decomposing bacteria and nitrogen-fixing bacteria, and the dosing amount is reversely compensated and adjusted based on the internal oxygen diffusion efficiency of the heap and the carbon-nitrogen ratio imbalance threshold; in the aerobic fermentation stage of the heap, through the linkage control of the turning operation and the inoculant dosing strategy, maintaining the balance of the microbial community activity distribution in the surface area and the deep area of the heap, and at the same time combining the historical data of the external environmental humidity and light intensity to correct the discrete interval of the inoculant dosing time window; establishing a quantitative model for the synergistic effect of pollution reduction and carbon reduction with the carbon-nitrogen ratio imbalance threshold as the constraint condition, to iteratively optimize the non-linear correlation relationship between the inoculant dosing rate and the turning operation frequency through the quantitative model for the synergistic effect of pollution reduction and carbon reduction, and output the effect evaluation result of the whole cycle of agricultural source solid waste treatment.

[0017] The technical solution of the present application has the following beneficial effects: By real-time monitoring the internal temperature gradient, pH value fluctuation range and volatile organic compound concentration change amount of the heap, obtaining an environmental parameter set reflecting the microbial metabolism intensity, providing accurate data support for subsequent operation strategies, and ensuring the dynamic controllability of the treatment process. According to the change trend of the environmental parameters, dynamically matching the inoculant dosing rate with the decay curve of the microbial community activity, combining the oxygen diffusion efficiency and the carbon-nitrogen ratio imbalance threshold for reverse compensation adjustment, optimizing the inoculant dosing amount, maintaining the microbial metabolism efficiency inside the heap, and improving the organic matter degradation effect. In the aerobic fermentation stage, through the linkage control of the turning operation and the inoculant dosing strategy, maintaining the balance of the microbial community activity distribution in the surface and deep areas of the heap, avoiding the reduction of treatment efficiency caused by local hypoxia or uneven temperature, and at the same time combining the historical data of the external environmental humidity and light intensity to correct the inoculant dosing time window, enhancing the adaptability of the system to sudden working conditions. Establishing a quantitative model with the carbon-nitrogen ratio imbalance threshold as the constraint condition, and through iterative optimization of the non-linear correlation relationship between the inoculant dosing rate and the turning operation frequency, realizing the synergistic effect of pollution reduction and carbon reduction goals, outputting the whole cycle synergistic effect evaluation result, and providing a scientific basis for the resource utilization and environmental protection of agricultural source solid waste.

[0018] The present invention significantly improves the synergistic effect of pollution reduction and carbon reduction in the treatment of agricultural source solid waste through real-time monitoring, dynamic matching, linkage control and quantitative model optimization, and realizes the dual goals of resource utilization and environmental protection.

[0019] Furthermore, the activity threshold of the microbial agent, the energy efficiency coefficient of the turning machine, and the threshold of sudden pH drop in the deep area of the heap are obtained through sensors installed in the heap body, and used as input parameters for the quantification model to generate a candidate solution set of the microbial agent dosing rate and the turning operation frequency, providing an initial data basis for subsequent optimization and ensuring the scientificity and feasibility of the treatment strategy. By calculating the partial derivatives of the objective function of the microbial agent dosing rate and the turning operation frequency in the candidate solution set, and combining the real-time data of near-infrared spectroscopy, gas chromatography, and mass spectrometry, the operation parameters are dynamically adjusted to generate a three-dimensional response surface characterizing the non-linear correlation between the two, providing a visual basis for the optimization path and improving the accuracy of strategy adjustment. The three-dimensional response surface is fused with the data of drone remote sensing and thermal infrared imaging, and the abnormal working conditions deviating from the optimization path are screened with the threshold of sudden pH drop in the deep area of the heap as the constraint condition, and the discrete interval of the microbial agent dosing time window is updated to enhance the adaptability of the system to sudden working conditions and ensure the stability of the treatment process. Based on the updated discrete interval, the dynamic programming algorithm is used to calculate the synergy index of the pollution reduction contribution and the carbon reduction contribution in segments, and the effect evaluation results after multiple rounds of iterative optimization are output, including the optimal microbial agent dosing rate, the turning operation frequency, and the synergy effect evaluation matrix, providing a scientific basis for the resource utilization and environmental protection of agricultural source solid waste treatment.

[0020] Finally, through the acquisition of input parameters, the optimization of the objective function, the fusion of multi-modal data, and the dynamic programming algorithm, the iterative optimization of the microbial agent dosing rate and the turning operation frequency is realized, significantly improving the synergy effect of pollution reduction and carbon reduction in the treatment of agricultural source solid waste and ensuring the scientificity and stability of the treatment effect.

[0021] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 The flowchart of a method for calculating the synergy effect of pollution reduction and carbon reduction of agricultural source solid waste provided by the present application is shown; Figure 2 The structural schematic diagram of a method for calculating the synergy effect of pollution reduction and carbon reduction of agricultural source solid waste provided by the present application is shown; Figure 3 The structural schematic diagram of a computing device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application.

[0025] In some processes described in the specification, claims and above-mentioned drawings of this application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish each different operation, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0026] The present invention takes the mixture of livestock and poultry manure and straw as the treatment object, obtains a set of environmental parameters reflecting the microbial metabolic intensity by real-time monitoring of the temperature gradient inside the compost pile, the pH value fluctuation range and the change amount of volatile organic compound concentration, and provides accurate data support for subsequent operation strategies; according to the change trend of the environmental parameters, dynamically match the inoculant addition rate with the attenuation curve of the microbial community activity, and perform reverse compensation adjustment in combination with the oxygen diffusion efficiency and the carbon-nitrogen ratio imbalance threshold to optimize the inoculant addition amount and improve the organic matter degradation efficiency; in the aerobic fermentation stage, through the linkage control of the turning operation and the inoculant addition strategy, maintain the balance of the activity distribution of the composite microbial community in the surface and deep regions of the compost pile, and at the same time, combine the historical data of the external environmental humidity and light intensity to correct the inoculant addition time window and enhance the adaptability of the system to sudden working conditions; finally, establish a quantitative model for the synergistic effect of pollution reduction and carbon reduction with the carbon-nitrogen ratio imbalance threshold as the constraint condition, and output the evaluation results of the full-cycle synergistic effect by iteratively optimizing the non-linear correlation between the inoculant addition rate and the turning operation frequency, so as to achieve the synergistic effect of pollution reduction and carbon reduction and efficiency improvement in the treatment of agricultural solid waste.

[0027] The technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0028] Figure 1 For the embodiments of this application, a flowchart of a method for calculating the synergistic effect of pollution reduction and carbon reduction of agricultural solid waste is provided, as Figure 1 shown, the method includes: 101. During the treatment process of agricultural solid waste, taking the mixture of livestock manure and straw as the treatment object, the internal temperature gradient, pH value fluctuation range, and change amount of volatile organic compound concentration of the compost pile formed by the treatment object are monitored in real time to obtain an environmental parameter set reflecting the microbial metabolic intensity; In this step, the internal temperature gradient of the compost pile refers to the temperature difference formed in different regions inside the compost pile during the composting process due to heat generated by microbial metabolic activities. It is an important indicator to measure microbial activity and composting efficiency. The larger the temperature gradient, the more active the microbial metabolism and the more efficient the composting process; on the contrary, it may indicate insufficient microbial activity or poor composting conditions. By monitoring the temperature gradient, composting management can be optimized to ensure the full degradation of organic matter.

[0029] The pH value fluctuation range refers to the change range of the pH value of the compost pile over time or space during the composting process. The pH value directly affects the living environment and metabolic efficiency of microorganisms. An appropriate pH range (usually 6.5 - 8.5) is beneficial to microbial activities, while too acidic or too alkaline conditions will inhibit the degradation process. Monitoring the pH value fluctuation range helps to timely adjust the composting conditions, maintain microbial activity, and improve composting efficiency.

[0030] The change amount of volatile organic compound concentration refers to the change amount of the concentration of volatile organic compounds (VOCs) generated by microorganisms degrading organic matter over time or space during the composting process. The change in VOCs concentration directly reflects the rate and degree of organic matter degradation. A rapid decrease in concentration indicates efficient degradation, while too high a concentration may indicate poor composting conditions or odor problems. Monitoring the change amount of VOCs concentration helps to evaluate the composting process and optimize management measures.

[0031] The environmental parameter set refers to the data set obtained by monitoring the internal temperature gradient, pH value fluctuation range, and change amount of volatile organic compound concentration of the compost pile in real time, which is used to characterize the dynamic change of microbial metabolic intensity.

[0032] By deploying temperature sensors, pH probes, and volatile organic compound detectors, the internal temperature gradient of the compost pile (such as the temperature difference between the surface layer and the deep layer ≥ 15 °C), pH value fluctuation range (such as 6.5 - 8.2), and change amount of volatile organic compound concentration (such as the change in benzene series concentration) are collected in real time. The temperature gradient is calculated by the sliding window difference of multi-point temperature sensors, the pH value fluctuation range is dynamically calibrated by an embedded electrode array, and the volatile organic compound concentration is analyzed in real time by gas chromatography-mass spectrometry technology. Finally, the above data are integrated to generate an environmental parameter set, providing data support for subsequent operation strategies.

[0033] In the solid waste treatment of a large-scale farm, for the treatment of the stack of livestock manure and straw mixture, technicians evenly arranged 15 high-precision temperature sensors, 8 pH probes and 5 gas chromatography-mass spectrometry instruments at different depths (surface layer, middle layer and deep layer) inside the stack to comprehensively monitor the temperature gradient, pH value fluctuation range and volatile organic compound concentration change inside the stack. The temperature sensors adopt multi-point temperature measurement technology and collect data every 30 minutes, covering the temperature distribution of the stack from the surface layer to the deep layer; the pH probes record the pH value every 1 hour through an on-line monitoring system to ensure the real-time and accuracy of the data; the gas chromatography-mass spectrometry instruments analyze the gas samples inside the stack every 2 hours to detect the types and concentration changes of volatile organic compounds. After 10 days of continuous monitoring, the data shows that the maximum temperature difference between the surface layer and the deep layer of the stack can reach 30 °C (the surface layer temperature is 35 °C and the deep layer temperature is 65 °C), the pH value fluctuation range is between 6.5 and 8.5, and the volatile organic compound concentration change is between 0.5 and 3.0 mg / m³. The main components are organic acid substances such as acetic acid, propionic acid and butyric acid. After these data are preprocessed and integrated, an environmental parameter set is generated, providing accurate data support for the subsequent inoculant addition and turning pile operations, and ensuring the dynamic monitoring and optimized regulation of the microbial metabolic intensity.

[0034] 102. Dynamically match the inoculant addition rate with the decay curve of the microbial community activity according to the change trend of the environmental parameter set; In this step, the inoculant contains a composite microbial community of thermophilic cellulose-decomposing bacteria and nitrogen-fixing bacteria, and the addition amount is adjusted by reverse compensation based on the oxygen diffusion efficiency and the carbon-nitrogen ratio imbalance threshold inside the stack. Dynamic matching means adjusting the corresponding relationship between the inoculant addition rate and the decay curve of the microbial community activity according to the change trend of the environmental parameter set to ensure that the inoculant addition amount matches the microbial metabolic requirements.

[0035] The carbon-nitrogen ratio imbalance threshold refers to the critical value when the ratio of carbon element to nitrogen element inside the stack exceeds the suitable range for microbial metabolism (usually 20:1 to 30:1), which affects the microbial degradation efficiency.

[0036] Reverse compensation adjustment is a dynamic optimization strategy. According to the changes in oxygen diffusion efficiency and carbon-nitrogen ratio imbalance threshold, the inoculant addition rate is adjusted to ensure the stability and high efficiency of microbial metabolism.

[0037] The temperature gradient, pH value fluctuation range, and change amount of volatile organic compound concentration in the environmental parameter set are respectively decomposed into a trend component and a fluctuation component. The trend component is characterized by the cumulative rate of parameter change within a sliding window, and the fluctuation component is extracted by the difference between the extreme points of the parameter within adjacent windows. Based on the change characteristics of the cumulative rate of the trend component, the decay curve of the microbial community activity is fitted, and the decay slope is calibrated through a preset inoculant addition experiment. According to the decay slope and the distribution characteristics of the extreme points of the fluctuation component, a dynamic response model of the inoculant addition rate is established, a reciprocal constraint relationship between the activity maintenance threshold and the decay slope is set, and a correction coefficient of the oxygen diffusion efficiency due to the change in the volume of the compost pile is introduced. When the oxygen diffusion efficiency is lower than the preset critical threshold and the carbon-nitrogen ratio imbalance threshold reaches the segmented compensation interval, the reverse compensation adjustment rule is triggered to dynamically adjust the increment gradient of the inoculant addition rate.

[0038] Based on the environmental parameter set obtained from the previous case, the technician uses a machine learning algorithm (such as random forest regression) to predict the decay curve of the microbial community activity, and combines the data collected by the oxygen sensor and the carbon-nitrogen ratio detector (oxygen diffusion efficiency is 75%, carbon-nitrogen ratio imbalance threshold is 28:1), and dynamically adjusts the inoculant addition rate through the reverse compensation adjustment algorithm. In specific operations, the technician collects the oxygen diffusion efficiency and carbon-nitrogen ratio data every 2 hours, and combines the change trend of the environmental parameter set to adjust the inoculant addition rate in real time. For example, when the temperature of the compost pile reaches 55 °C, the inoculant addition rate is adjusted to 0.7 kg per hour; when the temperature drops to 45 °C, the addition rate is reduced to 0.5 kg per hour. At the same time, the technician adjusts the ratio of thermophilic cellulose-decomposing bacteria to nitrogen-fixing bacteria in the inoculant according to the change in the concentration of volatile organic compounds to ensure that the problem of carbon-nitrogen ratio imbalance is effectively controlled. After 21 days of dynamic adjustment, the activity of the composite microbial community is always maintained at the optimal level, and the microbial metabolism intensity inside the compost pile is stable, providing a good microbial community activity basis for the subsequent aerobic fermentation stage and significantly improving the treatment efficiency.

[0039] 103. During the aerobic fermentation stage of the compost pile, through the linkage control of the turning operation and the inoculant addition strategy, the balance of the microbial community activity distribution in the surface area and the deep area of the compost pile is maintained for the composite microbial community, and at the same time, combined with the historical data of the external environmental humidity and light intensity, the discrete interval of the inoculant addition time window is corrected; In this step, the aerobic fermentation stage refers to the stage in the compost pile treatment process where, with oxygen as the main condition, organic matter is decomposed into stable humus through the metabolic activities of aerobic microorganisms. This stage requires sufficient oxygen supply and appropriate temperature and humidity conditions to promote the efficient degradation of organic waste by microorganisms.

[0040] The inoculant dosing strategy refers to an optimized plan that dynamically adjusts the dosing rate, dosing location, and time window of the composite microbial community (such as thermophilic cellulose-decomposing bacteria and nitrogen-fixing bacteria) according to the distribution of microbial activity inside the compost pile and the changes in environmental parameters, aiming to maintain the balance and efficiency of microbial activity.

[0041] Interlocking control means maintaining the balance of the microbial community activity distribution in the surface and deep regions of the compost pile and correcting the dosing time window of the inoculant through the synergistic effect of turning the pile operation and the inoculant dosing strategy.

[0042] The balance of the microbial community activity distribution means that the activity difference of the composite microbial community between the surface and deep regions of the compost pile is controlled within a reasonable range, ensuring that the microbial metabolic intensity in each region of the compost pile is consistent, and avoiding the overall treatment efficiency being affected by too high or too low local activity.

[0043] In the aerobic fermentation stage, based on the temperature gradient difference and the change amount of volatile organic compound concentration between the surface and deep regions, the surface microbial community activity compensation coefficient and the deep oxygen diffusion correction coefficient are calculated respectively. The surface microbial community activity compensation coefficient is dynamically calibrated by the cumulative temperature change rate within the sliding window and the attenuation slope of the preset microbial community activity decay curve, and the deep oxygen diffusion correction coefficient is calculated by the correction value of the oxygen diffusion efficiency due to the change in the compost pile volume. According to the non-linear correlation between the two, a synergistic control instruction for the triggering frequency of the turning the pile operation and the inoculant dosing rate is generated, and the weight distribution ratio of the surface and deep microbial community activities is set. At the same time, combined with the historical data of the external environmental humidity and light intensity, the discrete interval offset of the inoculant dosing time window is calculated through the cumulative difference of the humidity and light coupling parameters within adjacent time windows, and a dynamic mapping relationship between the triggering moment of the turning the pile operation and the inoculant dosing time window is constructed based on the extreme point distribution characteristics of the surface temperature change sequence.

[0044] In the above scenario, during the aerobic fermentation stage of the compost pile, technicians perform a turning operation every 6 hours using an automated mechanical turning device and adjust the inoculant dosing strategy in combination with the distribution data of the microbial community activity. The turning operation uses fully automated equipment to ensure uniform oxygen distribution in the surface and deep regions of the compost pile. At the same time, by real-time monitoring the internal temperature gradient and pH value changes of the compost pile, the turning depth and frequency are optimized. The inoculant dosing strategy is dynamically adjusted according to the distribution data of the microbial community activity. When the microbial community activity in the surface region is lower than that in the deep region, the inoculant dosing amount in the surface region is increased, and the ratio of thermophilic cellulose-decomposing bacteria to nitrogen-fixing bacteria in the inoculant is adjusted. At the same time, technicians combine the historical data of the external environmental humidity (70%) and light intensity (5000 lux), and use a time series analysis algorithm to correct the discrete interval of the inoculant dosing time window, adjusting the inoculant dosing time window from once every 6 hours to once every 4 hours. After optimization, the difference in the distribution of microbial community activity between the surface and deep regions of the compost pile is controlled within 8%, achieving the balance of the distribution of microbial community activity, providing guarantee for the smooth progress of aerobic fermentation, and significantly shortening the treatment cycle.

[0045] 104. Establish a quantitative model for the synergistic effect of pollution reduction and carbon reduction with the carbon-nitrogen ratio imbalance threshold as a constraint condition, so as to iteratively optimize the non-linear correlation between the inoculant dosing rate and the turning operation frequency through the quantitative model for the synergistic effect of pollution reduction and carbon reduction, and output the effect evaluation results of the whole cycle of agricultural solid waste treatment.

[0046] In this step, the quantitative model for the synergistic effect of pollution reduction and carbon reduction refers to a mathematical model that takes the carbon-nitrogen ratio imbalance threshold as a constraint condition, iteratively optimizes the non-linear correlation between the inoculant dosing rate and the turning operation frequency, and outputs the evaluation results of the whole-cycle synergistic effect.

[0047] Obtain the inoculant activity threshold, turning mechanical energy efficiency coefficient, and the sudden pH drop threshold in the deep region of the compost pile through sensors as input parameters of the quantitative model, and generate a candidate solution set for the inoculant dosing rate and the turning operation frequency. Calculate the partial derivatives of the objective function of the inoculant dosing rate and the turning operation frequency in the candidate solution set, and dynamically adjust the operation parameters in combination with the real-time data of near-infrared spectroscopy, gas chromatography, and mass spectrometry to generate a three-dimensional response surface characterizing their non-linear correlation. Integrate the three-dimensional response surface with the data of unmanned aerial vehicle remote sensing and thermal infrared imaging, screen out abnormal working conditions deviating from the optimization path with the sudden pH drop threshold in the deep region of the compost pile as a constraint condition, and update the discrete interval of the inoculant dosing time window. Based on the updated discrete interval, use the dynamic programming algorithm to calculate the synergistic effect index of the pollution reduction contribution and the carbon reduction contribution in segments, and output the optimal inoculant dosing rate, turning operation frequency, and synergistic effect evaluation matrix.

[0048] Based on the carbon-nitrogen ratio imbalance threshold (28:1), technicians established a quantitative model for the synergistic effect of pollution reduction and carbon emission reduction, and used the NSGA-II multi-objective optimization algorithm to iteratively optimize the non-linear correlation between the inoculant application rate and the frequency of turning pile operations. In specific operations, technicians simulated the effects of different inoculant application rates and turning pile operation frequencies on pollution reduction and carbon emission reduction through the model, and corrected the parameters by combining actual monitoring data. For example, when the inoculant application rate is 0.7 kg per hour and the turning pile operation frequency is once every 6 hours, the pollution reduction and carbon emission reduction effects are the best, the treatment efficiency is increased by 30%, and the carbon emission is reduced by 20%. In practical applications, technicians adjusted the operation parameters according to the optimization results. Finally, the treatment cycle was shortened from the original 35 days to 25 days, the treatment efficiency was significantly improved, and the pollutant emissions were significantly reduced. In addition, technicians output the effect evaluation results of the whole cycle of agricultural solid waste treatment through the model, providing a scientific basis for the optimization of subsequent treatment processes and achieving a win-win situation of economic and environmental benefits.

[0049] Through the collaborative implementation of the above steps, the solid waste treatment of this large-scale farm realizes efficient and stable microbial metabolism regulation. Step 101 monitors the environmental parameter set in real time, providing accurate data support for subsequent steps; Step 102 dynamically matches the inoculant application rate to ensure that the microbial community activity is maintained at the best level; Step 103 realizes the balance of the microbial community activity distribution through the linkage control of the turning pile operation and the inoculant application strategy; Step 104 establishes a quantitative model for the synergistic effect of pollution reduction and carbon emission reduction, and optimizes the effect evaluation of the whole treatment cycle. Finally, the treatment cycle is shortened to 25 days, the treatment efficiency is increased by 30%, the carbon emission is reduced by 20%, and the pollutant emissions are significantly reduced at the same time, providing a scientific basis and a successful example for the resource utilization of agricultural waste, and achieving a win-win situation of economic and environmental benefits.

[0050] In order to realize the collaborative optimization of microbial metabolism efficiency and environmental benefits in the process of agricultural solid waste treatment, and solve the problems of low treatment efficiency and high carbon emission caused by uneven distribution of microbial community activity and difficult quantitative correlation between organic matter degradation rate and greenhouse gas emission reduction in traditional methods, this application synchronously calculates the dynamic coupling coefficient of organic matter degradation rate and greenhouse gas emission reduction based on the balance of microbial community activity distribution and the characteristics of stage products of the compost pile, and dynamically adjusts the non-linear correlation between the inoculant application rate and the frequency of turning pile operations based on this coefficient, so as to optimize the effect evaluation results of the whole treatment cycle and maximize the synergistic effect of pollution reduction and carbon emission reduction, providing a more scientific and refined solution for agricultural solid waste treatment.

[0051] In some embodiments, the method further includes: 201. Synchronously calculate the dynamic coupling coefficient of the organic matter degradation rate and the greenhouse gas emission reduction based on the balance of the microbial community activity distribution and the characteristics of the stage products of the compost pile; In this step, the balance of the microbial community activity distribution refers to whether the activity distribution of the microbial communities in different regions inside the compost pile is uniform during the composting process. A balanced microbial community activity distribution helps to improve the degradation efficiency of organic matter and avoid problems such as local overheating or incomplete degradation. By monitoring parameters such as temperature, pH value, and volatile organic compound concentration, the distribution of the microbial community activity can be evaluated.

[0052] The characteristics of the intermediate products of the compost pile refer to the types and concentration changes of the intermediate or final products generated by the compost pile at different stages during the composting process. For example, a large amount of volatile organic compounds may be generated in the initial stage of composting, while stable humus is generated in the later stage. These characteristics reflect the stages of the composting process and the degradation efficiency.

[0053] The organic matter degradation rate refers to the rate and degree of decomposition of the organic matter in the compost pile by microorganisms during the composting process. It is usually calculated by monitoring parameters such as the weight change of the compost pile, the concentration of volatile organic compounds, and the carbon-nitrogen ratio. A high degradation rate indicates an efficient composting process, and the organic matter is fully converted into stable products.

[0054] The greenhouse gas emission reduction amount refers to the amount of greenhouse gas (such as ) emissions reduced during the composting process through optimized management. By monitoring the gas emission concentration and combining it with the parameters of the compost pile, the emission reduction amount can be calculated. The higher the emission reduction amount, the better the environmental protection benefit of the composting process.

[0055] The dynamic coupling coefficient refers to the dynamic correlation parameter between the organic matter degradation rate and the greenhouse gas emission reduction amount, which is used to quantify the synergistic relationship between the degradation efficiency and the environmental protection benefit during the composting process. Calculated by establishing a mathematical model, this coefficient helps to optimize the composting management and achieve the dual goals of efficient degradation and low carbon emissions.

[0056] In the embodiments of the present application, first, by real-time monitoring parameters such as the temperature, pH value, and volatile organic compound concentration inside the compost pile, and combining with the data of the microbial community activity distribution, the balance of the microbial community activity distribution is evaluated. Based on the characteristics of the intermediate products of the compost pile (such as the concentration of volatile organic compounds, the generation amount of humus, and the change of the carbon-nitrogen ratio), the stages of the composting process and the degradation efficiency are determined. Then, a gas sensor is used to monitor the concentration of greenhouse gas (such as ) emitted by the compost pile, and the greenhouse gas emission reduction amount is calculated. A dynamic model of the organic matter degradation rate and the greenhouse gas emission reduction amount is established through a machine learning algorithm (such as multiple linear regression or neural network), and the dynamic coupling coefficient between the two is calculated. Finally, this coefficient is used to evaluate the comprehensive efficiency and environmental protection benefit of the composting process.

[0057] 202. Based on the dynamic coupling coefficient, adjust the non-linear correlation relationship between the inoculant addition rate and the turning frequency of the compost pile to optimize the evaluation result of the overall cycle of agricultural solid waste treatment.

[0058] In this step, the inoculant application rate refers to the frequency and amount of adding microbial inoculants to the compost pile during the composting process. The inoculant contains microorganisms that can efficiently degrade organic matter, and its application rate directly affects the microbial activity and degradation efficiency of the compost pile. By dynamically adjusting the inoculant application rate, the composting process can be optimized, the organic matter degradation rate can be increased, and greenhouse gas emissions can be reduced.

[0059] The frequency of turning the pile operation refers to the frequency of turning the compost pile during the composting process to improve aeration and uniformity. Turning the pile helps to regulate the temperature, humidity, and oxygen supply of the compost pile, promotes the uniform distribution and activity improvement of microorganisms. By optimizing the frequency of turning the pile, local overheating or anaerobic conditions can be avoided, and the composting efficiency can be improved.

[0060] The effect evaluation of the whole cycle of agricultural source solid waste treatment refers to the comprehensive evaluation of the efficiency, environmental protection benefits, and economy of each link in the entire composting process. It includes indicators such as the organic matter degradation rate, greenhouse gas emission reduction, energy consumption, and cost. By dynamically coupling the coefficient to optimize the inoculant application rate and the frequency of turning the pile operation, the overall effect of composting can be enhanced, and the goal of efficient, environmentally friendly, and economical waste treatment can be achieved.

[0061] In the embodiment of this application, first, according to the dynamic coupling coefficient obtained in step 201, the matching degree between the inoculant application rate and the frequency of turning the pile operation in the current composting process is analyzed. The inoculant application rate and the frequency of turning the pile operation are adjusted through an optimization algorithm (such as genetic algorithm or particle swarm optimization) to make them reach the best matching state. In specific implementation, the inoculant application rate is dynamically adjusted according to the compost pile temperature, pH value, and microbial community activity data, and the frequency of turning the pile operation is optimized according to the compost pile temperature gradient and the change amount of volatile organic compound concentration. Finally, through a real-time feedback mechanism, the composting process is continuously optimized to improve the treatment efficiency and environmental protection benefits.

[0062] The following is a specific example: In a certain agricultural source solid waste treatment site, the composting object is a mixture of livestock manure and straw. The scale of the compost pile is 100 cubic meters, and the composting cycle is 30 days. First, in step 201, the internal parameters of the compost pile are monitored in real time through temperature sensors, pH sensors, and gas sensors. Combining the data of the activity of the microbial community, the degradation rate of organic matter is calculated to be 75%, and the greenhouse gas emission reduction is 30%. A dynamic coupling coefficient of 0.85 is established between the two using a neural network model, indicating a strong positive correlation between the degradation rate of organic matter and the greenhouse gas emission reduction during the current composting process. Subsequently, in step 202, according to the dynamic coupling coefficient, the genetic algorithm is used to optimize the non-linear correlation between the inoculant addition rate and the turning frequency of the compost pile to optimize the effect evaluation results of the entire cycle of agricultural source solid waste treatment. Specifically, the genetic algorithm takes the dynamic coupling coefficient (0.85) as the benchmark, sets the optimization goal to improve the degradation rate of organic matter and the greenhouse gas emission reduction, while reducing energy consumption and costs. The inoculant addition rate and the turning frequency of the compost pile are used as optimization variables to establish a non-linear correlation model, and the optimal combination of the inoculant addition rate and the turning frequency of the compost pile is found through iterative calculations. According to the optimization results, the inoculant addition rate is adjusted to once every 24 hours to ensure the continuous and efficient activity of microorganisms; the turning frequency of the compost pile is adjusted to once every 48 hours to improve the aeration of the compost pile and avoid local overheating or anaerobic conditions. After implementing the optimization plan, the parameters of the compost pile are monitored again and the degradation rate of organic matter and the greenhouse gas emission reduction are calculated. The results show that the degradation rate of organic matter is increased to 85%, and the greenhouse gas emission reduction is increased to 40%. The comprehensive efficiency and environmental protection benefits of the composting process are significantly improved, providing a scientific basis and technical support for the resource utilization of agricultural source solid waste and greenhouse gas emission reduction.

[0063] To solve the problems of accelerated attenuation of microbial community activity, decreased treatment efficiency, and greenhouse gas emissions caused by uneven temperature distribution, low oxygen diffusion efficiency, and carbon-nitrogen ratio imbalance in the treatment of solid waste from large-scale farms, this method analyzes the variation laws of the temperature gradient, pH fluctuation, and volatile organic compound concentration in the compost pile, decomposes them into trend components and fluctuation components, fits the attenuation curve of microbial community activity based on the cumulative rate of the trend component, establishes a dynamic response model in combination with the carbon-nitrogen ratio imbalance threshold, and triggers a reverse compensation adjustment rule to dynamically adjust the inoculant addition rate. At the same time, the control instruction is updated in real time by coupling the temperature gradient difference between the surface layer and the deep layer. In the specific implementation, step 102 of dynamically matching the inoculant addition rate with the attenuation curve of microbial community activity according to the change trend of the environmental parameter set further includes: 301. Decompose the temperature gradient, pH value fluctuation range, and change amount of volatile organic compound concentration in the environmental parameter set into trend components and fluctuation components respectively; In step 301, the trend component refers to the changing trend of environmental parameters characterized by the cumulative rate of parameter changes within a sliding window. It is characterized by the cumulative rate of parameter changes within the sliding window and is used to reflect the long-term changing pattern of the microbial community activity.

[0064] The fluctuation component refers to the short-term fluctuations of environmental parameters extracted from the difference between the extreme value points of parameters in adjacent windows and is used to capture the impact of sudden operating conditions on the microbial community activity.

[0065] In the embodiments of the present application, by deploying temperature sensors, pH probes, and volatile organic compound detectors, the temperature gradient, the pH value fluctuation range, and the change amount of volatile organic compound concentration inside the compost pile are collected in real time. The sliding window method is used to calculate the trend component (such as the cumulative rate of temperature gradient ≥ 0.5 °C / h), and the fluctuation component is extracted through the difference between the extreme value points of adjacent windows (such as the fluctuation range of pH value ≥ 0.3). Finally, the environmental parameter set is decomposed into the trend component and the fluctuation component, providing a data basis for fitting the decay curve of the microbial community activity subsequently.

[0066] 302. Fit the decay curve of the microbial community activity of the composite microbial community in the compost pile based on the changing characteristics of the cumulative rate of the trend component; In step 302, the changing characteristics of the cumulative rate of the trend component refer to the long-term changing trend of environmental parameters during the composting process, which is characterized by the cumulative rate of parameter changes within a sliding window, reflecting the changing speed of parameters over time and providing data support for fitting the decay curve of the microbial community activity.

[0067] The decay curve of the microbial community activity refers to the curve fitted through the changing characteristics of the cumulative rate of the trend component and is used to characterize the changing pattern of the metabolic rate of the composite microbial community in the compost pile over time. Its decay slope is calibrated through a preset microbial agent dosing experiment.

[0068] In the embodiments of the present application, based on the trend components of the temperature gradient and pH value fluctuation, the least squares method is used to fit the decay curve of the microbial community activity, and the decay slopes under different oxygen diffusion efficiencies are calibrated through a preset microbial agent dosing experiment (such as when the oxygen diffusion efficiency < 5%, the decay slope is ). Finally, the decay curve of the microbial community activity is generated, providing a basis for establishing a dynamic response model.

[0069] 303. Establish a dynamic response model for the dosing rate of the microbial agent according to the decay slope of the decay curve of the microbial community activity and the distribution characteristics of the extreme value points of the fluctuation component.

[0070] In step 303, the dynamic response model refers to a mathematical model established based on the attenuation slope of the microbial community activity decay curve and the extreme point distribution characteristics of the fluctuation component, which is used to dynamically adjust the dosing rate of the microbial agent. The inverse proportional constraint relationship between the activity maintenance threshold and the attenuation slope ensures the stability of the microbial community activity, and the correction coefficient of the change in the pile volume on the oxygen diffusion efficiency is used to optimize the model accuracy. In the dynamic response model, the inverse proportional constraint relationship between the activity maintenance threshold and the attenuation slope is set, and the correction coefficient of the change in the pile volume on the oxygen diffusion efficiency is introduced.

[0071] In the embodiment of the present application, according to the attenuation slope of the microbial community activity decay curve (such as ), and the extreme point distribution characteristics of the fluctuation component (such as the pH value fluctuation range ≥ 0.3), the inverse proportional constraint relationship between the activity maintenance threshold (such as the microbial community activity ≥ 80%) and the attenuation slope is set, and the correction coefficient of the change in the pile volume on the oxygen diffusion efficiency (such as when the volume increases by 10%, the correction coefficient increases by 0.1) is introduced. Then, based on the above parameters and constraint relationships, a dynamic response model is established to provide support for the reverse compensation regulation.

[0072] 304. When the oxygen diffusion efficiency inside the pile is lower than the preset critical threshold and the carbon-nitrogen ratio imbalance threshold reaches the segmented compensation interval, trigger the reverse compensation regulation rule based on the dynamic response model, and dynamically adjust the increment gradient of the dosing rate of the microbial agent; In step 304, the segmented compensation interval refers to different adjustment ranges divided according to the degree of carbon-nitrogen ratio imbalance during the composting process. The carbon-nitrogen ratio is an important parameter affecting microbial activity and composting efficiency. When the carbon-nitrogen ratio is imbalanced, corresponding compensation measures need to be taken according to the degree of imbalance (such as mild, moderate, severe). The segmented compensation interval provides a clear adjustment basis for dynamically adjusting the dosing rate of the microbial agent, ensuring the stability and high efficiency of the composting process.

[0073] The reverse compensation regulation rule refers to the rule of dynamically adjusting the increment gradient of the dosing rate of the microbial agent when the oxygen diffusion efficiency inside the pile is lower than the preset critical threshold and the carbon-nitrogen ratio imbalance threshold reaches the segmented compensation interval, which is used to compensate for the attenuation of the microbial community activity.

[0074] In the embodiment of the present application, first, the oxygen diffusion efficiency and the change in the carbon-nitrogen ratio inside the pile are monitored in real time. When the oxygen diffusion efficiency is lower than the preset critical threshold (such as 5%) and the carbon-nitrogen ratio imbalance threshold reaches the segmented compensation interval (such as C / N ≥ 25), the reverse compensation regulation rule is triggered. Through the dynamic response model, the increment gradient of the dosing rate of the microbial agent is calculated (such as increasing by 0.5 L / h every 6 hours), and the dosing rate of the microbial agent is dynamically adjusted to ensure the stability of the microbial community activity. At the same time, in combination with the pile temperature gradient and the change amount of the volatile organic compound concentration, the increment gradient of the dosing rate of the microbial agent is further optimized to improve the composting efficiency and environmental protection benefits.

[0075] 305. Through the coupling effect of the reverse compensation adjustment rule and the temperature gradient difference between the surface area and the deep area of the heap body, the dynamic control instruction of the inoculant injection rate is updated in real time.

[0076] In step 305, the dynamic control instruction realizes the precise control of the inoculant injection rate by combining the reverse compensation adjustment rule with the temperature gradient difference of the heap body, and optimizes the distribution of the microbial community activity.

[0077] The coupling effect of the reverse compensation adjustment rule and the temperature gradient difference means taking the temperature gradient difference as an important parameter for dynamically adjusting the inoculant injection rate, and combining the reverse compensation adjustment rule to optimize the dynamic control instruction of the inoculant injection rate in real time.

[0078] In the embodiment of the present application, first, the temperature gradient difference between the surface area and the deep area of the heap body is monitored in real time. The surface area is in direct contact with air, so the temperature is relatively low and the oxygen supply is sufficient. While in the deep area, due to heat accumulation and limited oxygen diffusion, the temperature is relatively high and it may be in an oxygen-deficient state. This temperature gradient difference reflects the uneven distribution of heat and microbial activity inside the heap body. Subsequently, in combination with the reverse compensation adjustment rule, the temperature gradient difference is taken as an important parameter for dynamically adjusting the inoculant injection rate. When the temperature in the deep area is too high and the oxygen diffusion efficiency is insufficient, the inoculant injection rate is increased through the reverse compensation adjustment rule (such as increasing by 0.3 L / h every 4 hours), which promotes the microbial activity and improves the degradation efficiency in the deep area; at the same time, considering the characteristics of the relatively low temperature and sufficient oxygen supply in the surface area, the inoculant injection rate is appropriately adjusted (such as decreasing by 0.2 L / h every 6 hours) to avoid waste of resources caused by excessive injection. Through this coupling effect, the balanced distribution of the microbial community activity between the surface and deep areas of the heap body is realized, and the dynamic control instruction of the inoculant injection rate is updated in real time to ensure the overall efficiency and stability of the composting process.

[0079] The following is a specific example: At a certain agricultural source solid waste treatment site, the composting object is a mixture of livestock and poultry manure and straw. The scale of the compost pile is 100 cubic meters, and the composting cycle is 30 days. First, in step 301, the temperature gradient, pH value fluctuation range, and change amount of volatile organic compound concentration are decomposed by the sliding window method, and the trend component and fluctuation component are calculated. Subsequently, in step 302, based on the cumulative rate change characteristics of the trend component, the decay curve of the microbial community activity is fitted, and the decay slope is calibrated. In step 303, according to the decay slope and the extreme point distribution characteristics of the fluctuation component, a dynamic response model is established, and the inverse proportional constraint relationship between the activity maintenance threshold and the decay slope is set. In step 304, when the oxygen diffusion efficiency is lower than the critical threshold and the carbon-nitrogen ratio is imbalanced, the reverse compensation adjustment rule is triggered, and the dosing rate of the microbial agent is dynamically adjusted. Finally, in step 305, combined with the temperature gradient difference of the compost pile, the dynamic control instruction of the dosing rate of the microbial agent is updated in real time to ensure the stability of the microbial community activity and improve the composting efficiency.

[0080] Through the above steps, the precise control of the dosing rate of the microbial agent and the dynamic matching of the microbial community activity are realized during the treatment process of agricultural source solid waste. By real-time monitoring of the changes in environmental parameters, combining the dynamic response model and the reverse compensation adjustment rule, this method significantly improves the composting efficiency and the stability of the microbial community activity, providing a scientific basis and technical support for the efficient treatment of agricultural source solid waste.

[0081] In order to solve the problems of uneven distribution of microbial community activity, large fluctuations in the organic matter degradation rate, and excessive greenhouse gas emissions caused by significant temperature gradient differences between the surface and deep layers, limited oxygen diffusion efficiency due to the stacking density of materials, and carbon-nitrogen ratio imbalance during the composting process of the mixture of livestock and poultry manure and straw in large-scale farms, this method calculates the dynamic offset by layer-by-layer monitoring of the temperature changes in the compost pile and combines the activity balance threshold to determine the dynamic compensation coefficient (quadratic weighting after correcting the oxygen diffusion efficiency based on the change in the volume of the compost pile), constructs a non-linear mapping model between the temperature gradient difference and the increment of the microbial agent and calibrates the trigger time window, activates the segmented compensation mechanism to generate dynamic instructions when the temperature difference exceeds the limit, couples the real-time feedback of the volatile organic compound concentration to adjust the weight distribution, and optimizes the instruction timing and window calculation period through the feedback of the oxygen diffusion efficiency iteration to achieve precise control of the dosing rate of the microbial agent. In specific implementation, in step 305, the real-time update of the dynamic control instruction of the dosing rate of the microbial agent through the coupling effect of the reverse compensation adjustment rule and the temperature gradient difference between the surface area and the deep area of the compost pile further includes: 401. Layered temperature monitoring nodes are respectively set in the surface area and the deep area of the compost pile to obtain the surface temperature change sequence and the deep temperature change sequence, and the dynamic offset of the temperature gradient difference is calculated through the cumulative temperature difference within the sliding window; In step 401, the temperature change sequence refers to the sequence composed of the temperature data of the surface area and the deep area of the heap body at different time points, which is used to reflect the dynamic change of the internal temperature of the heap body.

[0082] The cumulative temperature difference within the sliding window refers to the cumulative value of the temperature difference between the surface layer and the deep layer of the heap body within the sliding time window, which is used to quantify the change trend of the temperature gradient difference. The obtained dynamic offset is used to characterize the imbalance of the internal heat distribution and the microbial activity distribution of the heap body.

[0083] The hierarchical temperature monitoring nodes refer to the temperature sensors distributed in the surface layer and the deep layer of the heap body, which are used to monitor the temperature change in real time; the dynamic offset is a parameter calculated through the cumulative temperature difference within the sliding window, which is used to quantify the change trend of the temperature gradient difference.

[0084] In the embodiment of the present application, first, hierarchical temperature monitoring nodes are respectively deployed in the surface layer and the deep layer of the heap body. For example, one temperature sensor is set per square meter to collect the surface temperature change sequence and the deep temperature change sequence in real time. Subsequently, the sliding window method (window length is 1 hour) is used to calculate the cumulative temperature difference between the surface layer and the deep layer within the window to obtain the dynamic offset. In specific implementation, the cumulative temperature difference within the sliding window is obtained by subtracting the deep layer temperature from the surface layer temperature at each time point within the window and then accumulating all the differences. The dynamic offset reflects the imbalance of the internal heat distribution and the microbial activity distribution of the heap body, providing data support for subsequent adjustment of the microbial agent injection rate.

[0085] 402. Determine the dynamic compensation coefficient in the reverse compensation adjustment rule according to the dynamic offset and the preset equilibrium threshold of the microbial community activity distribution, where the dynamic compensation coefficient is secondarily weighted by the correction value of the oxygen diffusion efficiency due to the change in the volume of the heap body; In step 402, the dynamic compensation coefficient refers to the parameter calculated according to the temperature gradient difference and the equilibrium threshold of the microbial community activity distribution, which is used to dynamically adjust the microbial agent injection rate; the correction value of the oxygen diffusion efficiency due to the change in the volume of the heap body is the parameter obtained by quantifying the influence of the change in the volume of the heap body on the oxygen diffusion efficiency.

[0086] In the embodiments of the present application, first, according to the dynamic offset and the preset balance threshold of the microbial community activity distribution (for example, compensation is triggered when the dynamic offset exceeds 5 °C), the dynamic compensation coefficient is calculated through a preset mathematical model. The mathematical model takes the dynamic offset as the input, combines the balance threshold of the microbial community activity distribution, and outputs the initial dynamic compensation coefficient. Then, in combination with the correction value of the oxygen diffusion efficiency due to the change in the volume of the compost pile, the dynamic compensation coefficient is weighted again. In specific implementation, the correction value of the oxygen diffusion efficiency due to the change in the volume of the compost pile is obtained by fitting experimental data, which is used to further optimize the accuracy of the dynamic compensation coefficient. Finally, the dynamic compensation coefficient is used to guide the dynamic adjustment of the inoculant application rate to ensure the uniform distribution of the microbial community activity in each region of the compost pile.

[0087] 403. Based on the correlation between the dynamic compensation coefficient and the carbon-nitrogen ratio imbalance threshold of the stage product characteristics of the compost pile, construct a non-linear mapping relationship between the temperature gradient difference and the increment of the inoculant application rate, and introduce the historical fluctuation data of the external environmental humidity to discretely calibrate the triggering time window of the mapping relationship; In step 403, the stage product characteristics refer to the types and concentration changes of intermediate products or final products generated in different stages of the composting process. For example, the concentration of volatile organic compounds is relatively high in the initial stage of composting and gradually decreases in the later stage, and stable humus is generated.

[0088] The non-linear mapping relationship refers to a complex correlation model between the temperature gradient difference and the increment of the inoculant application rate; the historical fluctuation data of the external environmental humidity refers to the historical change data of the humidity of the environment where the compost pile is located, which is used to calibrate the triggering time window of the mapping relationship.

[0089] The triggering time window refers to the time interval after calibrating the triggering time range of the non-linear mapping relationship based on the historical fluctuation data of the external environmental humidity, which is used to ensure the accuracy and applicability of the mapping relationship.

[0090] In the embodiments of the present application, first, by analyzing the correlation between the dynamic compensation coefficient and the carbon-nitrogen ratio imbalance threshold of the stage product characteristics of the compost pile, determine the non-linear influence law of the temperature gradient difference on the increment of the inoculant application rate. Then, use historical data to train a support vector machine or a neural network model, take the temperature gradient difference and the carbon-nitrogen ratio imbalance threshold as input features, and the increment of the inoculant application rate as the output target to construct a non-linear mapping relationship. Subsequently, in combination with the historical fluctuation data of the external environmental humidity, discretely calibrate the triggering time window of the mapping relationship. For example, when the humidity fluctuates greatly, shorten the triggering time window to 30 minutes to adapt to the rapidly changing environmental conditions. Finally, the calibrated non-linear mapping relationship is used to dynamically adjust the inoculant application rate to ensure the efficiency and stability of the composting process.

[0091] 404. When the temperature gradient difference exceeds the critical interval of the activity distribution equilibrium threshold, activate the piecewise compensation mechanism of the reverse compensation adjustment rule, generate a dynamic control instruction for the bacterial agent injection rate based on the non-linear mapping relationship, and couple the real-time monitoring data of the change amount of the volatile organic compound concentration in the compost pile to update the weight distribution of the dynamic compensation coefficient. In step 404, the piecewise compensation mechanism refers to an adjustment rule that adopts different compensation strategies according to different intervals of the temperature gradient difference; the dynamic control instruction is an adjustment instruction for the bacterial agent injection rate generated based on the non-linear mapping relationship.

[0092] The change amount of the volatile organic compound concentration in the compost pile refers to the change amount of the concentration of volatile organic compounds (VOCs) inside the compost pile over time or space during the composting process, which directly reflects the rate and degree of organic matter degradation.

[0093] In the embodiment of the present application, first, the temperature gradient difference is monitored in real time. When the temperature gradient difference exceeds the critical interval of the activity distribution equilibrium threshold (such as the dynamic offset exceeds 5 °C), activate the piecewise compensation mechanism of the reverse compensation adjustment rule. Subsequently, generate a dynamic control instruction for the bacterial agent injection rate based on the non-linear mapping relationship. For example, increase it by 0.3 L / h every 4 hours. At the same time, couple the real-time monitoring data of the change amount of the volatile organic compound concentration in the compost pile to update the weight distribution of the dynamic compensation coefficient. In specific implementation, the change amount of the volatile organic compound concentration is monitored in real time by a gas sensor and used as an important parameter for weight distribution to ensure that the bacterial agent injection rate is accurately matched with the actual needs of the compost pile.

[0094] 405. Through the real-time feedback of the piecewise compensation mechanism and the oxygen diffusion efficiency of the compost pile, iteratively adjust the execution timing of the dynamic control instruction, and synchronously correct the calculation period of the sliding window cumulative difference of the temperature gradient difference.

[0095] In step 405, the execution timing refers to the execution time arrangement of the dynamic control instruction; the calculation period of the sliding window cumulative difference refers to the time window length for calculating the dynamic offset.

[0096] In the embodiment of the present application, first, through the real-time feedback of the piecewise compensation mechanism and the oxygen diffusion efficiency of the compost pile, iteratively adjust the execution timing of the dynamic control instruction. For example, when the oxygen diffusion efficiency is lower than the preset critical threshold, shorten the execution interval of the dynamic control instruction to 2 hours. At the same time, synchronously correct the calculation period of the sliding window cumulative difference of the temperature gradient difference. For example, adjust the window length from 1 hour to 30 minutes to further improve the calculation accuracy of the dynamic offset and provide a reliable basis for the dynamic adjustment of the bacterial agent injection rate.

[0097] The following is a specific example: At a certain agricultural source solid waste treatment site, the composting object is a mixture of livestock manure and straw. The scale of the compost pile is 100 cubic meters, and the composting cycle is 30 days. First, in step 401, hierarchical temperature monitoring nodes are deployed in the surface layer and deep layer areas of the compost pile to collect the surface temperature change sequence and the deep temperature change sequence in real time, and the dynamic offset is calculated by the sliding window method. Subsequently, in step 402, according to the dynamic offset and the equilibrium threshold of the microbial community activity distribution, the dynamic compensation coefficient is determined, and the correction value of the oxygen diffusion efficiency is weighted twice in combination with the change in the volume of the compost pile. In step 403, based on the correlation between the dynamic compensation coefficient and the carbon-nitrogen ratio imbalance threshold of the characteristics of the stage products of the compost pile, a non-linear mapping relationship is constructed, and the historical fluctuation data of the external environmental humidity is introduced for calibration. In step 404, when the temperature gradient difference exceeds the critical interval, the segmented compensation mechanism is activated to generate dynamic control instructions, and the real-time monitoring data of the change in the concentration of volatile organic compounds is coupled to update the weight distribution of the dynamic compensation coefficient. Finally, in step 405, through the segmented compensation mechanism and the real-time feedback of the oxygen diffusion efficiency of the compost pile, the execution timing of the dynamic control instructions is iteratively adjusted, and the calculation period of the cumulative difference of the sliding window is corrected synchronously to ensure the precise control of the inoculant application rate and the dynamic optimization of the composting process.

[0098] Through the above steps, the precise control of the inoculant application rate and the optimization of the microbial community activity distribution during the composting process are realized, significantly improving the composting efficiency and stability. This method ensures the precise matching of the inoculant application rate with the actual needs of the compost pile by real-time monitoring the temperature gradient difference, the dynamic compensation coefficient and the non-linear mapping relationship, combined with the segmented compensation mechanism and the real-time feedback of the oxygen diffusion efficiency, ultimately achieving the comprehensive goals of improving the composting efficiency, reducing greenhouse gas emissions and optimizing the resource utilization rate.

[0099] In order to further improve the balance of the microbial community activity distribution and the composting efficiency during the composting process, during the aerobic fermentation stage of the compost pile, based on the real-time monitoring data of the temperature gradient difference and the change in the concentration of volatile organic compounds in the surface layer and deep layer areas, the surface microbial community activity compensation coefficient and the deep oxygen diffusion correction coefficient are calculated respectively. The surface microbial community activity compensation coefficient is dynamically calibrated by the cumulative temperature change rate within the sliding window and the decay slope of the preset microbial community activity decay curve; according to the non-linear correlation between the surface microbial community activity compensation coefficient and the deep oxygen diffusion correction coefficient, a coordinated control instruction for the turning frequency and the inoculant application rate is generated to maintain the balance of the microbial community activity distribution in the surface layer and deep layer areas of the compost pile. The weight distribution ratio of the surface and deep microbial community activities is set in the coordinated control instruction. By adjusting the turning operation and the inoculant application rate in real time, the uniform distribution of the microbial community activity in each area of the compost pile is ensured, and the composting efficiency and stability are improved.

[0100] In some embodiments, in the aerobic fermentation stage of the heap body, through the linkage control of the turning operation and the microbial agent dosing strategy, the balance of the microbial community activity distribution between the surface area and the deep area of the heap body is maintained, including: 501. In the aerobic fermentation stage of the heap body, based on the real-time monitoring data of the temperature gradient difference and the change amount of volatile organic compound concentration between the surface area and the deep area, calculate the surface microbial community activity compensation coefficient and the deep oxygen diffusion correction coefficient respectively; In step 501, the surface microbial community activity compensation coefficient is dynamically calibrated by the cumulative temperature change rate within the sliding window and the decay slope of the preset microbial community activity decay curve. The surface microbial community activity compensation coefficient is a parameter calculated based on the temperature gradient difference and the change amount of volatile organic compound concentration in the surface area, and is used to dynamically adjust the surface microbial community activity; the deep oxygen diffusion correction coefficient is a parameter calculated based on the deep oxygen diffusion efficiency, and is used to optimize the microbial community activity in the deep area; the cumulative temperature change rate within the sliding window refers to the cumulative rate of temperature change between the surface and the deep within a certain time window, and is used to reflect the change trend of the temperature gradient difference.

[0101] In the embodiment of the present application, first, temperature sensors and gas sensors are respectively deployed in the surface area and the deep area of the heap body to collect the temperature gradient difference and the change amount of volatile organic compound concentration in real time. Subsequently, the cumulative temperature change rate of the surface layer within the window is calculated by the sliding window method (window length is 1 hour), and combined with the decay slope of the preset microbial community activity decay curve, the surface microbial community activity compensation coefficient is dynamically calibrated. At the same time, based on the deep oxygen diffusion efficiency, the deep oxygen diffusion correction coefficient is calculated. Finally, the surface microbial community activity compensation coefficient and the deep oxygen diffusion correction coefficient are used to guide the coordinated control of the subsequent turning operation and the microbial agent dosing rate.

[0102] 502. According to the non-linear correlation relationship between the surface microbial community activity compensation coefficient and the deep oxygen diffusion correction coefficient, generate a coordinated control instruction for the turning operation trigger frequency and the microbial agent dosing rate to maintain the balance of the microbial community activity distribution between the surface area and the deep area of the heap body, wherein the coordinated control instruction sets the weight distribution ratio of the surface and deep microbial community activities.

[0103] In step 502, the coordinated control instruction refers to the adjustment instruction for the turning operation and the microbial agent dosing rate generated based on the non-linear correlation relationship between the surface microbial community activity compensation coefficient and the deep oxygen diffusion correction coefficient; the weight distribution ratio of the surface and deep microbial community activities refers to the weight distribution of the optimization objectives of the surface and deep area microbial community activities in the coordinated control instruction.

[0104] In the embodiments of the present application, first, based on the non-linear correlation between the surface flora activity compensation coefficient and the deep oxygen diffusion correction coefficient, a machine learning algorithm (such as a support vector machine or a neural network) is used to generate a collaborative control instruction for the turning operation trigger frequency and the microbial agent feeding rate. For example, when the surface flora activity compensation coefficient is relatively high, increase the turning operation trigger frequency (such as once every 12 hours) to improve the oxygen supply in the surface area; when the deep oxygen diffusion correction coefficient is relatively low, increase the microbial agent feeding rate (such as increasing by 0.2 L / h every 6 hours) to optimize the flora activity in the deep area. At the same time, set the weight distribution ratio of the surface and deep flora activities in the collaborative control instruction (such as 60% for the surface weight and 40% for the deep weight) to ensure uniform distribution of the flora activity in each area of the compost pile.

[0105] The following is a specific example: In a certain agricultural waste solid treatment site, the composting object is a mixture of livestock manure and straw, the scale of the compost pile is 100 cubic meters, and the composting period is 30 days. First, in step 501, temperature sensors and gas sensors are respectively deployed in the surface and deep areas of the compost pile to collect the temperature gradient difference and the change amount of volatile organic compound concentration in real time, and the cumulative change rate of the surface temperature is calculated by the sliding window method to dynamically calibrate the surface flora activity compensation coefficient; at the same time, based on the oxygen diffusion efficiency in the deep area, the deep oxygen diffusion correction coefficient is calculated. Subsequently, in step 502, based on the non-linear correlation between the surface flora activity compensation coefficient and the deep oxygen diffusion correction coefficient, a collaborative control instruction for the turning operation trigger frequency and the microbial agent feeding rate is generated, such as turning the pile once every 12 hours and increasing the microbial agent feeding rate by 0.2 L / h every 6 hours, and set the weight distribution ratio of the surface and deep flora activities to 60% and 40%. By adjusting the turning operation and the microbial agent feeding rate in real time, ensure uniform distribution of the flora activity in each area of the compost pile, and improve the composting efficiency and stability.

[0106] Through the above steps, the balance of the flora activity distribution and the significant improvement of the composting efficiency in the composting process are achieved. This method dynamically calibrates the surface flora activity compensation coefficient and the deep oxygen diffusion correction coefficient by real-time monitoring of the temperature gradient difference and the change amount of volatile organic compound concentration, and generates a collaborative control instruction for the turning operation and the microbial agent feeding rate to ensure uniform distribution of the flora activity in each area of the compost pile, and finally achieves the comprehensive goals of improving the composting efficiency, reducing greenhouse gas emissions and optimizing the resource utilization rate.

[0107] To solve the problems of carbon-nitrogen ratio imbalance, local anaerobic fermentation, and odor release caused by the sudden increase in the humidity of the compost pile and insufficient light during the rainy season in the southern agricultural waste treatment site, the present invention proposes a dynamic collaborative regulation method. By integrating the historical data of humidity fluctuations and intermittent light in the rainy season, the offset of the inoculant dosing window is calculated to match the stage characteristics of the compost pile. Based on the feedback of the deep oxygen diffusion efficiency threshold and the surface volatile organic compound concentration, an adaptive mapping relationship for turning pile triggering and inoculant compensation is constructed. When it is monitored that the oxygen mass transfer efficiency is lower than the critical value or the microbial community activity is abnormal, the turning pile gradient and the inoculant weight are adjusted synchronously, and the timing is iteratively controlled in real time using the change in the volume of the compost pile to solve the pain points such as overheating of the compost pile and oxygen mass transfer lag in a high-humidity environment. Finally, the dynamic collaboration of inoculant dosing and turning pile strategies is realized to improve the stability and degradation efficiency of composting. In specific implementation, it is reflected in combining the historical data of the external environment humidity and light intensity to correct the discrete interval of the inoculant dosing time window, including: 601. Extract the historical data of the change rate of the external environment humidity and the fluctuation amplitude of the light intensity, and calculate the offset of the discrete interval of the inoculant dosing time window through the cumulative difference of the humidity and light coupling parameters within adjacent time windows, where the discrete interval offset is dynamically matched with the carbon-nitrogen ratio imbalance threshold of the stage products characteristics of the compost pile; In step 601, the humidity and light coupling parameter refers to a dynamic correlation parameter generated by fusing the historical data of the change rate of the external environment humidity and the fluctuation amplitude of the light intensity, and is used to quantify the impact of environmental disturbances on the metabolism of the compost pile within adjacent time windows.

[0108] The discrete interval offset is the adjustment amount of the inoculant dosing time window calculated through the cumulative difference of the coupling parameters, and it is dynamically matched with the current carbon-nitrogen ratio imbalance threshold of the compost pile.

[0109] In the embodiment of the present application, first, the change rate of humidity and the fluctuation amplitude of light within the past 24 hours are collected through a temperature and humidity sensor and a light sensor. Then, the data is normalized, and a weighted sliding window method is used to generate the humidity and light coupling parameters. Then, the cumulative difference of the coupling parameters within adjacent time windows is calculated. When the difference exceeds a preset threshold, the calculation of the discrete interval offset is triggered, and the offset is dynamically adjusted according to the current carbon-nitrogen ratio imbalance threshold of the compost pile. Finally, the carbon-nitrogen ratio of the compost pile products is monitored in real time through an on-line near-infrared spectrometer. When the carbon-nitrogen ratio deviates from the target range, the offset parameter is automatically corrected.

[0110] 602. Based on the discrete interval offset and the extreme point distribution characteristics of the surface temperature change sequence, construct a dynamic mapping relationship between the turning pile operation trigger time and the inoculant dosing time window; In step 602, a critical threshold of oxygen diffusion efficiency is set in the dynamic mapping relationship as a piecewise trigger condition, and the constraint boundary of the mapping relationship is updated by coupling the real-time feedback data of the change amount of volatile organic compound concentration.

[0111] In the embodiment of the present application, first, an infrared thermal imager is used to collect the temperature sequence on the surface layer of the compost pile, and a filtering algorithm is used to smooth the data, and the temperature extreme points are extracted to determine the potential trigger moment of the turning operation. Then, the discrete interval offset and the temperature extreme points are input into the time series prediction model, and the dynamic mapping relationship between the turning trigger moment and the inoculant dosing window is output. During this process, the real-time change data of the volatile organic compound concentration is used as a feedback coefficient to dynamically adjust the constraint boundary of the mapping relationship. Then, when the dissolved oxygen in the deep layer of the compost pile is lower than the critical value, a forced turning instruction is triggered, and at the same time, the feedback coefficient is dynamically adjusted by the controller to ensure that the volatile organic compound concentration is within the target range. Finally, the dynamic mapping relationship is used to optimize the time window of the turning operation and the inoculant dosing, and improve the efficiency and stability of the composting process.

[0112] 603. When the oxygen diffusion efficiency in the deep layer of the compost pile is lower than the preset critical value or the surface layer microbial community activity compensation coefficient deviates from the equilibrium interval, based on the dynamic mapping relationship, synchronously adjust the increment gradient of the turning operation frequency and the compensation weight of the inoculant dosing rate, and iteratively update the execution timing of the collaborative control instruction through the real-time correction value of the oxygen diffusion efficiency by the change in the volume of the compost pile.

[0113] In step 603, the compensation weight refers to dynamically allocating the adjustment weights of the turning frequency increment and the inoculant dosing rate according to the deep oxygen mass transfer efficiency and the degree of abnormality of the microbial community activity.

[0114] The preset critical value refers to the lowest allowable value of the oxygen mass transfer efficiency in the deep layer of the compost pile, which is used to judge whether the compost pile is in an oxygen-deficient state, and is usually set according to the requirements of the composting process and the needs of microbial activity. For example, when the dissolved oxygen concentration in the deep layer is lower than 2 mg / L, the compensation mechanism is triggered.

[0115] The change in the volume of the compost pile refers to the shrinkage or expansion of the volume of the compost pile during the composting process due to factors such as material degradation and water evaporation, which is monitored in real time by a three-dimensional laser scanner and is used to evaluate the change in the internal pore structure and oxygen diffusion efficiency of the compost pile.

[0116] In the embodiments of the present application, first, when the deep oxygen mass transfer efficiency of the heap is lower than the preset critical value (such as the dissolved oxygen concentration is lower than 2 mg / L) or the microbial community activity deviates from the equilibrium range (such as the ATP concentration is lower than 1.2 or higher than 1.8), the compensation mechanism is triggered. Then, using the fuzzy control rule base, according to the oxygen mass transfer efficiency and the deviation of the microbial community activity, the weight ratio of the turning frequency increment and the microbial agent addition rate is output. Then, the volume change of the heap is monitored in real time by a three-dimensional laser scanner. For example, the volume of the heap shrinks by 8% due to material degradation, and the correction factor is calculated based on the regression model of the volume change and the oxygen diffusion efficiency. For example, the correction factor is 1.4. Finally, the execution timing of the coordinated control instruction is iteratively updated. For example, the turning interval time is compressed from 6 hours to 4.2 hours to ensure the stable and efficient metabolism process of the heap.

[0117] The following is a specific example: In a certain agricultural solid waste treatment site in the south, continuous rainfall during the rainy season caused the environmental humidity to soar from 50% to 85%, the light intensity to drop from 1000 lx to 200 lx, and the peak surface temperature of the heap to be delayed from 14:00 to 16:30. Due to the high humidity, the deep oxygen mass transfer efficiency of the heap decreased significantly, and the dissolved oxygen concentration dropped from 3.0 mg / L to 1.5 mg / L, lower than the preset critical value (2 mg / L), triggering the compensation mechanism. At the same time, the compensation coefficient of the microbial community activity deviated from the equilibrium range, and the ATP concentration dropped from 1.5 to 1.1, indicating that the microbial metabolic activity was inhibited.

[0118] Through real-time monitoring by a three-dimensional laser scanner, it was found that the volume of the heap shrank by 8% due to material degradation and water evaporation, and the internal pore structure changed, further affecting the oxygen diffusion efficiency. Based on the regression model of the volume change and the oxygen diffusion efficiency, the calculated correction factor was 1.4.

[0119] After the compensation mechanism is triggered, the system first dynamically allocates the weight ratio of the turning frequency increment and the microbial agent addition rate according to the oxygen mass transfer efficiency and the deviation of the microbial community activity using the fuzzy control rule base. For example, the turning frequency increases from once every 8 hours to once every 6 hours, the microbial agent addition rate increases from 0.5 L / min to 0.8 L / min, and the weight ratio is 7:3. Then, based on the correction factor of 1.4, the system compresses the execution interval of the coordinated control instruction from 6 hours to 4.2 hours to ensure that the heap quickly restores metabolic balance under the anoxic state.

[0120] In addition, the system also combines the feedback data of volatile organic compound (VOC) concentration to adjust the triggering moment of turning pile in real time. For example, when the VOC concentration rises from 800 ppm to 1200 ppm, a forced turning pile instruction is triggered, and the turning pile operation is advanced to 17:50 for execution. After turning the pile, the dissolved oxygen concentration in the deep layer of the pile gradually rises back to 2.8 mg / L, the surface temperature peak resumes to 14:30, and the microbial activity compensation coefficient returns to the equilibrium range (1.2 - 1.8), and the composting process re-enters a stable state.

[0121] Through the above dynamic adjustment, the pile body still maintains a high degradation rate in the high-humidity environment during the rainy season. The fluctuation range of the carbon-nitrogen ratio is reduced from ±2.5 to ±0.7, and the probability of anaerobic fermentation is reduced by 67%; the composting cycle is shortened from 45 days to 32 days, and the release amount of volatile organic compounds is reduced by 52%, significantly improving the stability and environmental adaptability of the composting process.

[0122] In this embodiment, by real-time monitoring of environmental parameters, pile volume changes and microbial activity, dynamically adjusting the dosing window of the microbial agent, the turning pile frequency and the execution timing of the collaborative control instructions, effectively solves the problems of oxygen deficiency, carbon-nitrogen ratio imbalance and excessive release of volatile organic compounds in the pile body under the high-humidity environment during the rainy season, significantly improves the composting efficiency and stability, and provides reliable technical support for the treatment of organic solid waste under complex environmental disturbances.

[0123] To solve the problems of carbon-nitrogen ratio imbalance, low oxygen mass transfer efficiency and poor adaptability to environmental disturbances caused by the static state of microbial agent dosing and turning pile operations in traditional composting processes, a collaborative control method based on dynamic environmental parameter coupling feedback is proposed. This method dynamically matches the historical data of humidity and light with the pile body characteristics to correct the offset of the microbial agent dosing window; based on the oxygen diffusion efficiency threshold and VOC feedback, constructs a dynamic mapping relationship for turning pile triggering and dosing; when the oxygen mass transfer is insufficient or the microbial activity is abnormal, adaptively adjusts the turning pile frequency and the weight of the microbial agent rate, and combines the pile volume change to iteratively control the timing in real time, realizing the precise coordination of microbial agent dosing and turning pile, and improving the composting efficiency and stability. In specific implementation, step 104 iteratively optimizes the non-linear correlation relationship between the microbial agent dosing rate and the turning pile operation frequency through the pollution reduction and carbon reduction collaborative effect quantification model, and outputs the effect evaluation results of the whole cycle of agricultural source solid waste treatment, including: 701. Obtain the microbial agent activity threshold, turning pile mechanical energy efficiency coefficient and the pH sudden drop threshold in the deep area of the pile body through the sensors installed in the pile body as the input parameters of the quantification model, and generate a candidate solution set of the microbial agent dosing rate and the turning pile operation frequency through the quantification model; In step 701, the solution set satisfies the carbon-nitrogen ratio imbalance threshold constraint and balances the weight conflict between the pollution reduction index and the carbon reduction index by the Lagrange multiplier method.

[0124] The microbial agent activity threshold refers to the minimum microbial agent concentration required for the microbial activity in the pile to achieve the optimal degradation efficiency, and is used to quantify the effectiveness of the microbial agent addition acceleration rate.

[0125] The mechanical energy efficiency coefficient of compost turning refers to the degree to which the compost turning operation improves the oxygen mass transfer efficiency of the pile body, and is used to evaluate the balance between compost turning frequency and energy consumption.

[0126] The sudden drop threshold of pH in the deep area of ​​the pile refers to when the pH value in the deep area of ​​the pile is lower than a certain critical value (such as pH <6.5), it indicates that the pile may enter an anaerobic fermentation state, which is used to screen abnormal operating conditions.

[0127] In the embodiment of the present application, firstly, the microbial agent activity threshold, the mechanical energy efficiency coefficient of compost turning and the sudden drop threshold of pH in the deep layer of the compost are collected in real time by deploying the microbial agent activity sensor, the compost turning mechanical energy efficiency sensor and the pH sensor in the compost body. Then, the above parameters are input into the quantitative model of the synergistic effect of pollution reduction and carbon reduction, and the candidate solution set of microbial agent injection acceleration rate and compost turning operation frequency is generated by genetic algorithm, for example, the microbial agent injection acceleration rate range is 0.5-1.0L / min, and the compost turning frequency range is once every 6-8 hours.

[0128] 702. Calculate the partial derivatives of the objective function of the microbial agent dosage acceleration rate and the compost turning operation frequency in the candidate solution set, dynamically adjust the microbial agent dosage acceleration rate and the compost turning operation frequency by combining the real-time data of near infrared spectroscopy and gas chromatography-mass spectrometry, and generate a three-dimensional response surface representing the nonlinear correlation between the two. In step 702, the partial derivative of the objective function refers to the local change rate of the synergistic effect of microbial agent injection acceleration rate and compost turning operation frequency on pollution reduction and carbon reduction in the candidate solution set, which is used to optimize the nonlinear correlation between the two.

[0129] The three-dimensional response surface refers to the three-dimensional spatial relationship between the acceleration rate of microbial agent addition, the frequency of compost turning operation and the synergistic effect of pollution reduction and carbon reduction, which is used to intuitively characterize the synergistic effect of the two.

[0130] In the embodiment of the present application, first, the partial derivative of the objective function (synergistic effect of pollution reduction and carbon reduction) in the candidate solution set is calculated, and the gradient descent method is used to optimize the microbial agent dosage acceleration rate and the frequency of compost turning operations. Then, the microbial agent dosage acceleration rate and the frequency of compost turning operations are dynamically adjusted by combining the real-time data of near infrared spectroscopy (NIR) and gas chromatography-mass spectrometry (GC-MS), and a three-dimensional response surface is generated. For example, the synergistic effect is best when the microbial agent dosage acceleration rate is 0.8L / min and the compost turning frequency is once every 7 hours.

[0131] 703. Fusing the three-dimensional response surface with the acquired UAV remote sensing and thermal infrared imaging data, using the pH sudden drop threshold in the deep layer of the pile as a constraint condition to screen abnormal conditions that deviate from the optimized path, and updating the discrete interval of the microbial agent addition time window; In step 703, the UAV remote sensing and thermal infrared imaging data refer to the data of the temperature distribution on the surface of the compost pile and the material degradation state obtained by the remote sensing sensor and the thermal infrared camera carried by the UAV, which are used to assist in screening abnormal working conditions.

[0132] In the embodiment of the present application, first, the three-dimensional response surface is fused with the UAV remote sensing and thermal infrared imaging data to identify the abnormal temperature area on the surface of the compost pile (such as temperature > 70 °C) and the uneven material degradation area. Then, taking the sudden drop threshold of pH in the deep area of the compost pile as a constraint condition, abnormal working conditions deviating from the optimized path are screened. For example, when pH < 6.5, the update time window of the inoculant dosing time is triggered.

[0133] 704. Based on the updated discrete interval, the dynamic programming algorithm is used to calculate the synergy index of the pollution reduction contribution degree and the carbon reduction contribution degree in segments, and the effect evaluation result after multiple rounds of iterative optimization is output. The result includes the optimal inoculant dosing rate, the turning frequency of the compost pile, and the synergy effect evaluation matrix.

[0134] In step 704, the pollution reduction contribution degree and the carbon reduction contribution degree refer to the contribution degree of the inoculant dosing and the turning operation of the compost pile to reducing pollutant emissions (such as VOCs) and reducing carbon emissions (such as CO2), which are used to quantify the synergy effect.

[0135] In the embodiment of the present application, first, based on the updated discrete interval, the dynamic programming algorithm is used to calculate the synergy index of the pollution reduction contribution degree and the carbon reduction contribution degree in segments. For example, when the inoculant dosing rate is 0.8 L / min and the turning frequency is once every 7 hours, the synergy index is the highest. Then, the effect evaluation result after multiple rounds of iterative optimization is output, including the optimal inoculant dosing rate, the turning frequency of the compost pile, and the synergy effect evaluation matrix, which is used to guide the optimization of the composting process.

[0136] The following is a specific example: In a certain agricultural solid waste treatment plant in the south, continuous rainfall during the rainy season caused the pH value in the deep area of the compost pile to drop suddenly from 7.0 to 6.3, triggering the abnormal working condition screening mechanism. Through the UAV remote sensing and thermal infrared imaging data, the abnormal temperature area on the surface of the compost pile (temperature > 70 °C) and the uneven material degradation area are identified. Based on the updated discrete interval, the dynamic programming algorithm is used to calculate the synergy index of the pollution reduction contribution degree and the carbon reduction contribution degree. Finally, the optimal inoculant dosing rate of 0.8 L / min and the turning frequency of once every 7 hours are output. The synergy effect evaluation matrix shows that the VOCs emissions are reduced by 52% and the CO2 emissions are reduced by 35%.

[0137] In summary, the solutions of steps 701 to 704 obtain sensor data, generate a candidate solution set, construct a three-dimensional response surface, screen abnormal working conditions, and perform dynamic programming calculations. This method significantly improves the co-efficiency of pollution reduction and carbon emission reduction in the composting process: the VOCs emissions are reduced by 52%, the CO2 emissions are reduced by 35%, the composting cycle is shortened to 32 days, realizing the precise collaborative optimization of inoculant dosing and turning operations, and providing efficient and reliable technical support for the treatment of agricultural solid waste.

[0138] Figure 2 FIG. is a schematic structural diagram of a calculation system for the co-effect of pollution reduction and carbon emission reduction of agricultural solid waste provided by an embodiment of the present application. As Figure 2 shown, the device includes: A monitoring module 21, which is used to take the mixture of livestock manure and straw as the treatment object during the treatment of agricultural solid waste, and obtain a set of environmental parameters reflecting the microbial metabolism intensity by real-time monitoring of the internal temperature gradient, pH value fluctuation range, and volatile organic compound concentration change amount of the heap formed by the treatment object; A matching module 22, which is used to dynamically match the inoculant dosing rate with the decay curve of the microbial community activity according to the change trend of the set of environmental parameters, wherein the inoculant contains a composite microbial community of thermophilic cellulose-decomposing bacteria and nitrogen-fixing bacteria, and the dosing amount is reversely compensated and adjusted based on the internal oxygen diffusion efficiency and the carbon-nitrogen ratio imbalance threshold of the heap; A control module 23, which is used to maintain the balance of the microbial community activity distribution in the surface area and the deep area of the heap through the linkage control of the turning operation and the inoculant dosing strategy during the aerobic fermentation stage of the heap, and at the same time, correct the discrete interval of the inoculant dosing time window by combining the historical data of the external environmental humidity and light intensity; An optimization module 24, which is used to establish a quantitative model for the co-effect of pollution reduction and carbon emission reduction with the carbon-nitrogen ratio imbalance threshold as a constraint condition, so as to iteratively optimize the non-linear correlation between the inoculant dosing rate and the turning operation frequency through the quantitative model for the co-effect of pollution reduction and carbon emission reduction, and output the effect evaluation result of the whole cycle of agricultural solid waste treatment.

[0139] Figure 2 The described device for calculating the co-effect of pollution reduction and carbon emission reduction of agricultural solid waste can execute Figure 1 the method for calculating the co-effect of pollution reduction and carbon emission reduction of agricultural solid waste described in the embodiment shown. The implementation principle and technical effects will not be elaborated. For the device for calculating the co-effect of pollution reduction and carbon emission reduction of agricultural solid waste in the above embodiment, the specific ways for each module and unit to execute operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0140] In a possible design, Figure 2A device for calculating the synergistic effect of reducing pollution and carbon emissions of agricultural solid waste in the illustrated embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0141] The processing component 32 is used for the above Figure 1 A method for calculating the synergistic effect of reducing pollution and carbon emissions of agricultural solid waste in the illustrated embodiment.

[0142] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0143] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0144] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0145] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0146] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0147] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above processing component, storage component, etc. may be basic server resources leased or purchased from a cloud computing platform.

[0148] The embodiment of the present invention also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above Figure 1A calculation method for the synergistic effect of reducing pollution and carbon emissions of agricultural solid waste in the illustrated embodiment.

[0149] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated here.

[0150] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0151] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A method for calculating the synergistic effect of pollution reduction and carbon reduction of agricultural solid waste, characterized in that: include: In the process of agricultural solid waste treatment, a mixture of livestock and poultry manure and straw is used as the treatment object, and the temperature gradient, pH fluctuation range and volatile organic compound concentration change inside the pile composed of the treatment object are monitored in real time to obtain a set of environmental parameters reflecting the intensity of microbial metabolism; According to the change trend of the set of environmental parameters, the microbial agent addition rate and the bacterial community activity decay curve are dynamically matched, wherein the microbial agent comprises a composite bacterial community of thermophilic cellulose decomposing bacteria and nitrogen-fixing bacteria, and the addition amount is reversely compensated and adjusted based on the oxygen diffusion efficiency inside the pile and the carbon-nitrogen ratio imbalance threshold; During the aerobic fermentation stage of the pile, the activity distribution balance of the composite bacterial community in the surface area and deep area of ​​the pile is maintained through the linkage control of the pile turning operation and the microbial agent addition strategy, and the discrete interval of the microbial agent addition time window is corrected in combination with the historical data of the external environmental humidity and light intensity; A quantitative model of the synergistic effect of pollution reduction and carbon reduction is established with the carbon-nitrogen ratio imbalance threshold as a constraint condition, so as to iteratively optimize the nonlinear correlation between the acceleration rate of microbial agent addition and the frequency of compost turning operation through the quantitative model of the synergistic effect of pollution reduction and carbon reduction, and output the effect evaluation results of the whole cycle of agricultural solid waste treatment.

2. The method according to claim 1, characterized in that Also includes: Based on the balance of the bacterial community activity distribution and the phased product characteristics of the pile, the dynamic coupling coefficient of the organic matter degradation rate and the greenhouse gas emission reduction is calculated synchronously; Based on the dynamic coupling coefficient, the nonlinear correlation between the microbial agent injection acceleration rate and the compost turning operation frequency is adjusted to optimize the effect evaluation results of the entire cycle of agricultural solid waste treatment.

3. The method according to claim 1, characterized in that: The dynamically matching the microbial agent injection acceleration rate and the bacterial colony activity decay curve according to the change trend of the environmental parameter set includes: Decomposing the temperature gradient, pH fluctuation range and volatile organic compound concentration change in the environmental parameter set into a trend component and a fluctuation component, respectively, wherein the trend component is characterized by the cumulative rate of parameter change in a sliding window, and the fluctuation component is extracted by the extreme point difference of the parameter in adjacent windows; Based on the cumulative rate variation characteristics of the trend component, fitting the bacterial community activity decay curve of the composite bacterial community in the pile, wherein the activity decay curve is calibrated by a preset bacterial agent addition experiment to determine the decay slope of the bacterial community metabolic rate under different oxygen diffusion efficiencies; According to the attenuation slope of the bacterial community activity attenuation curve and the extreme point distribution characteristics of the fluctuation component, a dynamic response model of the microbial agent dosage acceleration rate is established, wherein the inverse proportional constraint relationship between the activity maintenance threshold and the attenuation slope is set in the dynamic response model, and a correction coefficient of the pile volume change on the oxygen diffusion efficiency is introduced; When the oxygen diffusion efficiency inside the pile is lower than a preset critical threshold and the carbon-nitrogen ratio imbalance threshold reaches a segmented compensation interval, a reverse compensation adjustment rule based on the dynamic response model is triggered to dynamically adjust the incremental gradient of the microbial agent injection acceleration rate; The dynamic control instructions of the microbial agent injection acceleration rate are updated in real time through the coupling effect of the reverse compensation adjustment rule and the temperature gradient difference between the surface area and the deep area of ​​the pile.

4. The method according to claim 3, characterized in that The dynamic control instructions of the microbial agent injection acceleration rate are updated in real time through the coupling effect of the reverse compensation adjustment rule and the temperature gradient difference between the surface area and the deep area of ​​the pile body, including: Layered temperature monitoring nodes are respectively set in the surface area and deep area of ​​the pile body to obtain the surface temperature change sequence and the deep temperature change sequence, and the dynamic offset of the temperature gradient difference is calculated by the accumulated temperature difference in the sliding window; Determine the dynamic compensation coefficient in the reverse compensation adjustment rule according to the dynamic offset and the preset bacterial colony activity distribution equilibrium threshold, wherein the dynamic compensation coefficient performs secondary weighting on the correction value of the oxygen diffusion efficiency through the change of the stack volume; Based on the correlation between the dynamic compensation coefficient and the carbon-nitrogen ratio imbalance threshold of the phased product characteristics of the pile, a nonlinear mapping relationship between the temperature gradient difference and the microbial agent injection acceleration rate increment is constructed, and the historical fluctuation data of the external environmental humidity is introduced to discretize and calibrate the trigger time window of the mapping relationship; When the temperature gradient difference exceeds the critical interval of the activity distribution equilibrium threshold, the segmented compensation mechanism of the reverse compensation adjustment rule is activated, a dynamic control instruction of the microbial agent injection acceleration rate is generated based on the nonlinear mapping relationship, and the real-time monitoring data of the volatile organic matter concentration change of the pile body is coupled to update the weight distribution of the dynamic compensation coefficient; Through the segmented compensation mechanism and the real-time feedback of the stack oxygen diffusion efficiency, the execution timing of the dynamic control instruction is iteratively adjusted, and the sliding window cumulative difference calculation cycle of the temperature gradient difference is synchronously corrected.

5. The method according to claim 1, characterized in that In the aerobic fermentation stage of the pile, the activity distribution balance of the composite bacterial community in the surface area and the deep area of ​​the pile is maintained through the linkage control of the pile turning operation and the microbial agent addition strategy, including: During the aerobic fermentation stage of the pile, based on the temperature gradient difference between the surface area and the deep area and the real-time monitoring data of the change in volatile organic matter concentration, the surface bacterial community activity compensation coefficient and the deep oxygen diffusion correction coefficient are calculated respectively, wherein the surface bacterial community activity compensation coefficient is dynamically calibrated by the cumulative temperature change rate in the sliding window and the attenuation slope of the preset bacterial community activity attenuation curve; According to the nonlinear correlation between the surface bacterial community activity compensation coefficient and the deep oxygen diffusion correction coefficient, a coordinated control instruction of the pile turning operation trigger frequency and the microbial agent injection acceleration rate is generated to maintain the balanced distribution of the bacterial community activity of the composite bacterial community in the surface area and the deep area of ​​the pile body, wherein the weight distribution ratio of the surface and deep bacterial community activities is set in the coordinated control instruction.

6. The method according to claim 5, characterized in that The discrete interval of the time window for adding the microbial agent is corrected by combining the historical data of the external environment humidity and light intensity, including: The historical data of the rate of change of external environmental humidity and the amplitude of light intensity fluctuation are extracted, and the discrete interval offset of the inoculant addition time window is calculated by the cumulative difference of the humidity and light coupling parameters in adjacent time windows, wherein the discrete interval offset is dynamically matched with the carbon-nitrogen ratio imbalance threshold of the phased product characteristics of the pile; Based on the discrete interval offset and the extreme point distribution characteristics of the surface temperature change sequence, a dynamic mapping relationship between the triggering moment of the compost turning operation and the time window for adding the inoculant is constructed, wherein the critical threshold of the oxygen diffusion efficiency is set as a segmented trigger condition in the dynamic mapping relationship, and the constraint boundary of the mapping relationship is updated by coupling the real-time feedback data of the change in the concentration of volatile organic compounds; When the oxygen diffusion efficiency in the deep layer of the pile is lower than the preset critical value or the surface bacterial community activity compensation coefficient deviates from the equilibrium interval, the compensation weights of the incremental gradient of the pile turning operation frequency and the microbial agent injection acceleration rate are synchronously adjusted based on the dynamic mapping relationship, and the execution timing of the collaborative control instruction is iteratively updated through the real-time correction value of the oxygen diffusion efficiency due to the change in the pile volume.

7. The method according to claim 1, characterized in that The nonlinear correlation between the microbial agent dosage acceleration rate and the compost turning operation frequency is iteratively optimized through the quantitative model of the pollution reduction and carbon reduction synergistic effect, and the effect evaluation results of the whole cycle of agricultural solid waste treatment are output, including: The threshold of bacterial agent activity, the mechanical energy efficiency coefficient of compost turning and the sudden drop threshold of pH in the deep layer of the compost are obtained by sensors installed in the compost body and used as input parameters of the quantitative model, and a candidate solution set of bacterial agent injection acceleration rate and compost turning operation frequency is generated by the quantitative model; Calculate the partial derivatives of the objective function of the microbial agent dosage acceleration rate and the compost turning operation frequency in the candidate solution set, dynamically adjust the microbial agent dosage acceleration rate and the compost turning operation frequency by combining the real-time data of near infrared spectroscopy and gas chromatography-mass spectrometry, and generate a three-dimensional response surface representing the nonlinear correlation between the two; The three-dimensional response surface is integrated with the acquired UAV remote sensing and thermal infrared imaging data, and the abnormal working conditions that deviate from the optimized path are screened using the pH sudden drop threshold in the deep layer of the pile as a constraint condition, and the discrete interval of the microbial agent addition time window is updated; Based on the updated discrete intervals, a dynamic programming algorithm is used to calculate the synergistic effect index of pollution reduction contribution and carbon reduction contribution in segments, and the effect evaluation results after multiple rounds of iterative optimization are output, which include the optimal microbial agent addition acceleration rate, compost turning operation frequency and synergistic effect evaluation matrix.

8. A system for calculating the synergistic effect of pollution reduction and carbon reduction of agricultural solid waste, characterized in that: include: A monitoring module is used to obtain a set of environmental parameters reflecting the intensity of microbial metabolism by real-time monitoring of the temperature gradient, pH fluctuation range and volatile organic compound concentration change of the pile body formed by the agricultural solid waste treatment process, taking the mixture of livestock and poultry manure and straw as the treatment object; A matching module, for dynamically matching the microbial agent dosage rate and the bacterial community activity attenuation curve according to the change trend of the environmental parameter set, wherein the microbial agent comprises a composite bacterial community of thermophilic cellulose decomposing bacteria and nitrogen-fixing bacteria, and the dosage is reversely compensated and adjusted based on the oxygen diffusion efficiency inside the pile and the carbon-nitrogen ratio imbalance threshold; A control module is used to maintain the balance of bacterial community activity distribution in the surface area and deep area of ​​the pile body by linkage control of the pile turning operation and the microbial agent addition strategy during the aerobic fermentation stage of the pile body, and to correct the discrete interval of the microbial agent addition time window in combination with the historical data of the external environmental humidity and light intensity; The optimization module is used to establish a quantitative model of the synergistic effect of pollution reduction and carbon reduction with the carbon-nitrogen ratio imbalance threshold as a constraint condition, so as to iteratively optimize the nonlinear correlation between the acceleration rate of the microbial agent addition and the frequency of the compost turning operation through the quantitative model of the synergistic effect of pollution reduction and carbon reduction, and output the effect evaluation results of the whole cycle of agricultural solid waste treatment.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for calculating the synergistic effect of pollution reduction and carbon reduction of agricultural solid waste as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a method for calculating the synergistic effect of pollution reduction and carbon reduction of agricultural solid waste as described in any one of claims 1 to 7 is implemented.

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