Energy recovery method and system for agricultural non-point source pollution treatment and storage medium

Abstract

The invention is suitable for the field of digital control, and discloses an energy recovery method and system for agricultural non-point source pollution treatment and a storage medium. The energy recovery method for agricultural non-point source pollution treatment comprises the steps that if to-be-treated pollutants are detected, component information of the to-be-treated pollutants is detected, the component information comprises the carbon nitrogen ratio, the water content and the organic matter content, and the to-be-treated pollutants comprise at least one of livestock and poultry breeding waste, crop straw, rural domestic sewage and agricultural production wastewater; according to the component information, a target energy conversion process is selected, and the target energy conversion process comprises at least one of an anaerobic fermentation biogas preparation process, a biomass pyrolysis fuel gas production process, a sewage anaerobic electricity production process and a straw solidification heat production process; according to the target energy conversion process, the recovered energy is output, collected and stored, and the energy comprises at least one of methane, combustible mixed gas, electric energy and heat energy. The conversion efficiency of energy recovery is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of digital control, and particularly relates to an energy recovery method, system and storage medium for agricultural non-point source pollution treatment. BACKGROUND

[0002] In the agricultural non-point source pollution treatment technology, a common practice is to configure a fixed treatment process for a specific type of pollutant, for example, only build a biogas project for livestock and poultry manure, or only solidify the straw.

[0003] The rigid technical route cannot effectively cope with the realistic challenges of agricultural non-point source pollutants with a wide range of sources and complex and variable compositions, resulting in a mismatch between process selection and pollutant characteristics, and thus low energy recovery rate. A new technical means is needed to solve the above technical problems. SUMMARY

[0004] In view of this, the embodiments of the present application provide an energy recovery method, system and storage medium for agricultural non-point source pollution treatment, which can solve the problem of low energy recovery rate in related technologies.

[0005] The first aspect of the present application provides an energy recovery method for agricultural non-point source pollution treatment, the method comprising: If a to-be-treated pollutant is detected, the composition information of the to-be-treated pollutant is detected, wherein the composition information includes carbon-nitrogen ratio, water content, and organic matter content, and the to-be-treated pollutant includes at least one of livestock and poultry breeding waste, crop straw, rural domestic sewage, and agricultural production wastewater; According to the composition information, a target energy conversion process is selected, wherein the target energy conversion process includes at least one of an anaerobic fermentation biogas production process, a biomass pyrolysis fuel gas production process, a sewage anaerobic power generation process, and a straw solidification heat production process; According to the target energy conversion process, the recovered energy is output, collected and stored, wherein the energy includes at least one of methane, combustible mixed gas, electric energy and heat energy.

[0006] Optionally, in the first implementation manner of the first aspect of the present application, the step of selecting a target energy conversion process according to the composition information comprises: According to the composition information, a candidate energy conversion process is adapted, and an energy recovery rate is calculated according to the composition information; According to the energy recovery rate and the unit energy consumption of the candidate energy conversion process, the target energy conversion process with the highest comprehensive benefit value is determined.

[0007] Optionally, in the second implementation manner of the first aspect of the present application, the step of adapting a candidate energy conversion process according to the composition information comprises: Based on the carbon-nitrogen ratio, organic matter content, and moisture content in the component information, a set of candidate processes is obtained by matching them with predefined process applicability conditions, wherein the process applicability conditions define the range of adaptability of the preset process to the component parameters; Based on the physical form of the pollutants to be treated in the candidate process set, the candidate process set is screened to obtain the candidate energy conversion process, wherein the physical form includes solid, liquid or solid-liquid mixture.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the step of detecting the composition information of the pollutant to be treated if the pollutant to be treated is detected includes: Based on the pollutants to be treated, determine their source type and physical form; Based on the source type and physical morphology, an adaptive sampling strategy is determined to obtain representative samples. The adaptive sampling strategy dynamically adjusts the sampling point density and sampling frequency based on the pollutant distribution and uniformity of the pollutant to be treated. Based on the representative sample, a multi-sensor integrated system is controlled to measure the component information, wherein the multi-sensor integrated system includes a spectral analysis unit, a moisture detection unit, and an organic matter content detection unit.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the step of determining the source type and physical form based on the pollutant to be treated includes: The image features, spectral reflectance features, and near-field physical features of the pollutants to be treated are collected and multimodal data fusion analysis is performed to obtain preliminary classification results. The near-field physical features include density and viscosity. Based on the preliminary classification results and the pre-generated pollutant knowledge graph, a confidence assessment is performed to obtain the source type and the physical form. The confidence assessment is used to screen and correct contradictory classification information.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the step of performing confidence assessment based on the preliminary classification results and the pre-generated pollutant knowledge graph includes: Based on the feature weights corresponding to each feature in the preliminary classification results, a multi-dimensional confidence vector is calculated, wherein the feature weights are dynamically weighted based on the feature data; Based on the multi-dimensional confidence vector and the preset priority logic in the pollutant knowledge graph, conflict resolution and result arbitration are performed to obtain the source type and the physical form. The priority logic defines the priority adoption logic when there is a contradiction in the confidence vector.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, the step of outputting, collecting, and storing the recovered energy according to the target energy conversion process includes: Based on the type of the target energy conversion process, determine the corresponding energy recovery unit required; Based on real-time environmental conditions and preset energy output requirements, the control parameters of the energy recovery unit are dynamically adjusted. The control parameters include storage pressure, temperature control threshold, and energy conversion operating load. The energy conversion operating load is the amount of pollutants that the energy recovery unit needs to process per unit time and the energy input / output intensity corresponding to the processing amount.

[0012] Optionally, in a seventh implementation of the first aspect of the present invention, the step of determining the corresponding energy recovery unit according to the type of the target energy conversion process includes: Based on the energy type output by the target energy conversion process, a set of basic recovery units is matched to obtain the units required for energy recovery. The energy type includes at least one of gaseous methane, combustible gas mixture, liquid thermal energy, or electrical energy. The set of basic recovery units includes at least one of a gas storage device, a purification device, a heat exchanger, and an energy storage battery pack.

[0013] Secondly, embodiments of the present invention provide an energy recovery system for agricultural non-point source pollution control, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described energy recovery method for agricultural non-point source pollution control.

[0014] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described energy recovery method for controlling agricultural non-point source pollution.

[0015] Fourthly, embodiments of the present invention provide a computer program product that, when running on an energy recovery system for agricultural non-point source pollution control, causes the energy recovery system for agricultural non-point source pollution control to execute the aforementioned energy recovery method for agricultural non-point source pollution control.

[0016] The beneficial effects of this invention are: after detecting pollutants, its key component information is obtained first, and based on this component information, the most suitable target energy conversion process is dynamically selected from multiple candidate processes, overcoming the limitations of traditional single treatment modes. It can tap the energy conversion potential of different types and characteristics of pollutants, significantly improving the conversion efficiency of energy recovery. Attached Figure Description

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

[0018] Figure 1 This is a schematic diagram of an embodiment of the energy recovery method for agricultural non-point source pollution control in this invention. Figure 2 This is a schematic diagram of a specific embodiment of step S102 of the energy recovery method for agricultural non-point source pollution control in this invention. Figure 3 This is a schematic diagram of a specific embodiment of step S101 of the energy recovery method for agricultural non-point source pollution control in this invention. Figure 4 This is a schematic diagram of an embodiment of an energy recovery system for agricultural non-point source pollution control in this invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are protected by this invention.

[0020] It should be noted that the terms "comprising," "including," and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this invention, are intended to cover non-exclusive inclusion. For example, a process, method, terminal, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. In the claims, specification, and accompanying drawings of this invention, relational terms such as "first" and "second" are used merely to distinguish one entity / operation / object from another entity / operation / object, and do not necessarily require or imply any such immediate relationship or order between these entities / operations / objects.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] In agricultural non-point source pollution control technologies, a common practice is to configure fixed treatment processes for specific types of pollutants, such as building biogas projects only for livestock and poultry manure, or solidifying straw only.

[0023] Rigid technological approaches are ineffective in addressing the challenges posed by the wide range of agricultural non-point source pollutants and their complex and variable composition. This leads to a mismatch between process selection and pollutant characteristics, resulting in low energy recovery rates. A new technological approach is needed to solve these problems.

[0024] In view of this, embodiments of the present invention provide an energy recovery method, system, and storage medium for agricultural non-point source pollution control. Upon detecting pollutants, the method first acquires their key component information and then dynamically selects the most suitable target energy conversion process from multiple candidate processes based on this component information, overcoming the limitations of traditional single-treatment modes. This approach can tap into the energy conversion potential of different types and characteristics of pollutants, significantly improving the energy recovery conversion efficiency.

[0025] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0026] It is worth noting that the technical terms involved in this invention include, but are not limited to: Agricultural non-point source pollution: During agricultural production activities, pollutants enter water bodies, soil, or the atmosphere in a non-centralized and dispersed manner through surface runoff, groundwater seepage, and atmospheric deposition, causing environmental pollution. This is the target of this invention, distinguishing it from the centralized emission characteristics of industrial "point source pollution."

[0027] Pollutants to be treated: In this invention, pollutants that need to be treated and can be recycled for energy in agricultural non-point source pollution scenarios are specifically included in at least one of livestock and poultry breeding waste, crop straw, rural domestic sewage, and agricultural production wastewater, which are the raw material basis for energy recovery.

[0028] Carbon-to-nitrogen ratio: The mass ratio of carbon to nitrogen in pollutants, which directly affects the efficiency of microbial decomposition.

[0029] Moisture content: The percentage of water mass in the total mass of pollutants, which determines the applicability of the process.

[0030] Organic matter content: The mass percentage of organic matter in pollutants that can be decomposed or pyrolyzed by microorganisms is a key factor affecting energy recovery.

[0031] Adaptive sampling strategy: This strategy dynamically adjusts the sampling point density (e.g., increasing sampling points when the distribution is uneven) and sampling frequency (e.g., sampling at time intervals when continuously discharging wastewater) based on the source type, physical form, actual distribution characteristics, and uniformity of the pollutants to be treated. The aim is to obtain representative samples that can represent the overall characteristics of the pollutants and avoid detection bias caused by single sampling.

[0032] Multi-sensor integrated system: An integrated detection device for accurately measuring the composition information of pollutants to be treated.

[0033] Spectral analysis unit: Calculates elemental composition parameters such as carbon-nitrogen ratio by analyzing the spectral reflectance or absorption signals of pollutant samples; Moisture detection unit: Measures the moisture content of pollutants using techniques such as gravimetric and capacitance methods.

[0034] Organic matter content detection unit: Determines the proportion of organic matter in pollutants through chemical sensing, thermogravimetric analysis, and other methods.

[0035] Multimodal data fusion analysis: an analytical method for integrating and processing multimodal data (i.e., image features, spectral reflectance features, and near-field physical features of the pollutant to be processed). Image features refer to the appearance, color, and particle size of the pollutant; spectral reflectance features refer to the intensity of light reflected by the pollutant at different wavelengths; near-field physical features refer to physical properties measured through direct contact, including density and viscosity.

[0036] Pollutant Knowledge Graph: A pre-generated, structured database that stores information on agricultural non-point source pollutants, including typical characteristics of different pollutants, the relationship between source types and physical forms, etc., used to verify the rationality of preliminary classification results.

[0037] Multidimensional confidence vector: A numerical vector that reflects the reliability of different classification criteria, calculated based on the feature weights of each feature in the preliminary classification results. The feature weights refer to the importance coefficients of different modal features, which are dynamically adjusted according to the accuracy of the feature data. Each dimension in the vector corresponds to the confidence of a classification criterion.

[0038] Conflict resolution and outcome arbitration: When contradictions arise in the multi-dimensional confidence vectors, the process of selecting the more reliable classification result based on the pre-defined priority logic in the pollutant knowledge graph is used. The priority logic is a predefined rule used to determine the feature basis to be adopted first when there is a contradiction, and finally determine the source type and physical form of the pollutant.

[0039] Energy conversion process: A technical solution that converts organic matter in pollutants to be treated into usable energy.

[0040] Anaerobic fermentation biogas production process: In an anaerobic environment, microorganisms decompose organic matter in pollutants to produce biogas with methane as the main component, and the corresponding energy recovered is methane.

[0041] Biomass pyrolysis gas production process: Under anaerobic, oxygen-deficient, and high-temperature conditions, solid pollutants are pyrolyzed and decomposed to produce a combustible mixture mainly composed of hydrogen, carbon monoxide, and methane. The corresponding recovered energy is the combustible mixture.

[0042] Wastewater anaerobic power generation process: In an anaerobic environment, the organic energy in wastewater is converted into electrical energy through the metabolic action of microorganisms, and the corresponding recovered energy is electrical energy.

[0043] Straw curing and heat generation process: The process of compressing straw into shape and releasing heat energy through combustion or pyrolysis, thus recovering the energy as heat energy.

[0044] Process applicability conditions: Predefined range of component parameters for determining whether a certain type of energy conversion process is suitable for the pollutants to be treated. For example, the applicable conditions for anaerobic fermentation to produce biogas are "carbon-nitrogen ratio 20-30:1, moisture content 70%-90%, and organic matter content ≥20%". This is used to initially screen out a set of candidate processes that match the pollutant composition.

[0045] Energy recovery rate: Calculated based on the composition information of the pollutants to be treated, it is the percentage of recovered energy relative to the theoretically convertible energy of the organic matter in the pollutants, reflecting the energy conversion efficiency of the process.

[0046] Unit energy consumption: refers to the energy required to process a unit of pollutant during the operation of a certain type of energy conversion process. For example, the straw solidification and heat generation process requires 50 kWh of electricity to process 1 ton of straw. It is an indicator for evaluating the economic efficiency of the process.

[0047] Comprehensive benefit value: An indicator that measures the overall cost-effectiveness of energy conversion processes by combining energy recovery rate and unit energy consumption. The calculation logic is usually that the higher the energy recovery rate and the lower the unit energy consumption, the higher the comprehensive benefit value. In this invention, it is used to screen out the optimal target energy conversion process from candidate energy conversion processes.

[0048] Energy conversion operating load: refers to the processing capacity and energy input and output intensity of the unit required for energy recovery per unit time, including the amount of two pollutants treated (e.g., an anaerobic fermentation unit processes 10 tons of manure per hour) and the energy input and output intensity (e.g., when processing 10 tons of manure, the unit consumes 20 kWh of electricity and outputs 50 m³ of biogas per hour). It is the basis for dynamically adjusting process parameters.

[0049] Energy recovery units: The combination of equipment used to output, collect, and store recovered energy, determined according to the type of target energy conversion process (such as anaerobic fermentation to produce biogas, straw solidification to generate heat). It consists of the matching of equipment in the basic recovery unit set (such as the biogas production process requiring a gas storage device + purification device).

[0050] Basic recycling unit set: A predefined library of basic equipment that can be combined to form the units required for energy recovery.

[0051] Gas storage device: Equipment (such as gas storage tank) for storing gaseous energy (such as methane, combustible mixture).

[0052] Purification equipment: Equipment (such as desulfurization towers) that removes impurities (such as hydrogen sulfide in biogas) from gaseous energy sources.

[0053] Heat exchanger: A device that recovers and transfers heat energy (such as heating water with heat energy generated by burning straw).

[0054] Energy storage battery packs: devices that store electrical energy (such as storing electrical energy generated by anaerobic wastewater power generation processes in battery packs). Example 1:

[0055] Figure 1 The illustration shows a schematic diagram of the implementation process of an energy recovery method for agricultural non-point source pollution control provided by an embodiment of the present invention. This method can be applied to energy recovery systems for agricultural non-point source pollution control.

[0056] Specifically, the energy recovery method for agricultural non-point source pollution control may include the following steps S101 to S103.

[0057] Step S101: If a pollutant to be treated is detected, the composition information of the pollutant to be treated is detected. The composition information includes carbon-nitrogen ratio, moisture content, and organic matter content. The pollutant to be treated includes at least one of livestock and poultry breeding waste, crop straw, rural domestic sewage, and agricultural production wastewater.

[0058] The energy recovery system for agricultural non-point source pollution control includes a component detection module (responsible for pollutant characteristic collection, sampling control, and component parameter measurement), a process decision module (used for subsequent process selection), and an energy management module (used for subsequent energy recovery). The memory stores computer programs, detection data, and preset parameters, and the processor is the control center. By executing the computer program in the memory, instructions are sent to the component detection module, the system receives and analyzes the detection data fed back by the module, and simultaneously stores key data in the memory, forming a control flow of instruction issuance, data acquisition, and analysis feedback.

[0059] In a specific embodiment of the present invention, the energy recovery system for agricultural non-point source pollution control monitors the presence of pollutants to be treated in the environment in real time through its sensing unit. When the system detects the presence of pollutants to be treated (pollutants to be treated include at least one of livestock and poultry breeding waste, crop straw, rural domestic sewage, and agricultural production wastewater), it triggers a component detection process.

[0060] The component detection module is activated to perform component analysis on the pollutant to be treated in order to obtain its core component information, specifically including carbon-nitrogen ratio, water content and organic matter content.

[0061] Optionally, the system can perform component detection by using fixed-point sampling or regional scanning, depending on the distribution range of the pollutants, to adapt to the pollutant morphology in different scenarios.

[0062] Step S102: Select a target energy conversion process based on the composition information. The target energy conversion process includes at least one of the following: anaerobic fermentation to produce biogas, biomass pyrolysis to produce fuel gas, wastewater anaerobic power generation, and straw solidification to produce heat.

[0063] In a specific embodiment of the present invention, after receiving pollutant composition information from the component detection module, the system's process decision module filters and determines the target energy conversion process from a preset energy conversion process library based on this composition information. The preset energy conversion process library includes at least one of the following: anaerobic fermentation to produce biogas, biomass pyrolysis to produce fuel gas, wastewater anaerobic power generation, and straw solidification to generate heat. The system completes the selection by matching the composition information with the basic compatibility characteristics of each process.

[0064] Optionally, the system can standardize the component information and then compare it with the typical adaptation parameters of each process to improve the accuracy of process selection.

[0065] Step S103: According to the target energy conversion process, output, collect and store the recovered energy, wherein the energy includes at least one of methane, combustible gas mixture, electrical energy and thermal energy.

[0066] In a specific embodiment of the present invention, the system's energy management module initiates the corresponding energy recovery process based on the target energy conversion process determined by the process decision module. It controls the operation of the relevant equipment for this process, outputting the recovered energy; captures the output energy through a matching collection device; and transmits the captured energy to a suitable storage device for storage, thus completing the entire energy recovery process.

[0067] Optionally, the system selects the corresponding storage medium based on the form of energy (gaseous, liquid, or electrical energy), such as using pressure-resistant gas tanks to store gaseous energy and battery packs to store electrical energy.

[0068] The beneficial effects of this invention are as follows: After detecting pollutants, key component information is obtained first, and based on this component information, the most suitable target energy conversion process is dynamically selected from multiple candidate processes, overcoming the limitations of traditional single treatment modes. It can tap the energy conversion potential of different types and characteristics of pollutants, significantly improving the conversion efficiency of energy recovery. Example 2:

[0069] In agricultural non-point source pollution control technologies, the focus is often solely on the harmless treatment of pollutants such as livestock and poultry waste and rural domestic sewage, without considering the specific composition of the pollutants to select appropriate energy conversion processes. This leads to the waste of resources such as carbon and nitrogen contained in the pollutants that can be converted into energy. Furthermore, some treatment processes suffer from high energy consumption and a disconnect between treatment effectiveness and energy utilization due to incompatibility with pollutant composition, failing to achieve synergistic development of pollution control and energy recovery. Considering this deficiency, this invention proposes an optional embodiment. (Refer to...) Figure 2 , Figure 2 This is a schematic diagram of a specific embodiment of step S102 of the energy recovery method for agricultural non-point source pollution control in this invention. Step S102 further includes the following specific implementation methods: Step S1021: Based on the composition information, candidate energy conversion processes are adapted and the energy recovery rate is calculated based on the composition information.

[0070] In a specific embodiment of the present invention, information on the composition of the pollutant to be treated is received. Based on the composition information and preset process adaptation rules, candidate energy conversion processes that are compatible with the current pollutant composition are selected from anaerobic fermentation to produce biogas, biomass pyrolysis to produce fuel gas, wastewater anaerobic power generation, and straw solidification to generate heat. According to the composition information and combined with a preset energy conversion efficiency calculation model, the energy recovery rate of each candidate energy conversion process for the current pollutant is calculated.

[0071] Optionally, when calculating the energy recovery rate, the calculation results can be corrected by referring to the energy conversion data of pollutants of the same type in the historical database.

[0072] Step S1022: Based on the energy recovery rate and the unit energy consumption of the candidate energy conversion processes, determine the target energy conversion process with the highest comprehensive benefit value.

[0073] In a specific embodiment of the present invention, the unit energy consumption data of each candidate energy conversion process is obtained, and the energy recovery rate of each candidate process is correlated with the unit energy consumption according to the preset comprehensive benefit evaluation formula to obtain the comprehensive benefit value of each candidate process; the comprehensive benefit values ​​of all candidate processes are compared, and the candidate process with the highest comprehensive benefit value is selected and determined as the final target energy conversion process.

[0074] Optionally, during the calculation of the comprehensive benefit value, different weighting coefficients are set for the energy recovery rate and unit energy consumption according to the actual application scenario (such as energy demand priority and energy consumption control requirements) to match specific usage needs.

[0075] In this embodiment of the invention, after detecting pollutants such as livestock and poultry breeding waste and crop straw awaiting treatment, the composition information of the pollutants, such as carbon-nitrogen ratio, moisture content, and organic matter content, is obtained. Suitable target energy conversion processes, such as anaerobic fermentation to produce biogas or biomass pyrolysis to produce fuel gas, are then specifically selected. Ultimately, methane, combustible mixed gas, electricity, or heat energy are recovered and stored. This effectively solves the problem of agricultural non-point source pollution control and converts pollutants into usable energy, effectively avoiding the waste of pollutant resource value in traditional treatment methods. Example 3:

[0076] In traditional technologies, when screening candidate sets of energy conversion processes, only the matching of pollutant composition parameters with process applicability conditions is often considered, while neglecting the compatibility of pollutant physical forms with the process. For example, including straw solidification and heat generation processes suitable for solid pollutants in the candidate set of liquid rural domestic sewage processes leads to problems such as equipment failure to feed properly, a sharp drop in energy conversion efficiency, and even equipment damage during subsequent practical applications, affecting the practicality of the candidate processes and the accuracy of subsequent process selection. Considering this deficiency, the present invention proposes an optional embodiment. Step S1021 further includes the following specific implementation: Step S10211: Based on the carbon-nitrogen ratio, organic matter content and moisture content in the component information, match them with the predefined process application conditions to obtain a set of candidate processes. The process application conditions define the range of application of the preset process to the component parameters.

[0077] In a specific embodiment of the present invention, the composition information of the pollutant to be treated is obtained, and the pre-stored applicable process conditions are invoked.

[0078] The actual composition information of pollutants is compared with the applicable conditions of each preset process one by one. Preset processes in which all component parameters fall within their applicable range are selected and integrated to form a candidate process set.

[0079] Optionally, the predefined process application conditions are dynamically updated based on the differences in the composition and characteristics of agricultural non-point source pollutants in different regions, in order to improve the adaptability to regional pollutants.

[0080] Step S10212: Based on the physical form of the pollutants to be treated, the candidate process set is screened to obtain candidate energy conversion processes, wherein the physical form includes solid, liquid or solid-liquid mixture.

[0081] In a specific embodiment of the present invention, after obtaining the candidate process set, the physical form of the pollutant to be treated is obtained, and the adaptation requirements of each process in the candidate process set to the physical form of the pollutant are analyzed (e.g., straw solidification heat generation process is adapted to solid pollutants, and sewage anaerobic power generation process is adapted to liquid pollutants).

[0082] The process decision module removes processes from the candidate process set that are incompatible with the physical form of pollutants, and the remaining processes are the final candidate energy conversion processes.

[0083] Optionally, the determination of the physical form of pollutants can be combined with data from the simple morphology sensor on the system (such as whether it can flow or whether it has a fixed form) for auxiliary confirmation, so as to avoid screening errors caused by morphology judgment bias.

[0084] In this embodiment of the invention, by first generating a set of candidate processes based on the carbon-nitrogen ratio, organic matter content, and moisture content of pollutants and predefined process applicability conditions, and then screening the set of candidate processes in combination with the physical morphology of pollutants, it can be ensured that the final candidate energy conversion process is both adapted to the composition characteristics of pollutants and compatible with the physical morphology of pollutants, effectively avoiding the neglect of morphological compatibility due to only considering compositional compatibility. Example 4:

[0085] Traditional agricultural non-point source pollutant (NPP) component detection technologies often employ fixed sampling strategies, failing to simultaneously and accurately acquire multi-dimensional component information such as carbon-nitrogen ratio and organic matter content. This leads to component data bias, consequently affecting the rationality of subsequent energy conversion process selection. Considering this deficiency, this invention proposes an optional embodiment. (Refer to...) Figure 3 , Figure 3 This is a schematic diagram of a specific embodiment of step S101 of the energy recovery method for agricultural non-point source pollution control in this invention. Step S101 further includes the following specific implementation methods: Step S1011: Determine the source type and physical form of the pollutant to be treated.

[0086] In a specific embodiment of the present invention, after detecting the pollutant to be treated, the pollutant is initially identified, and its source type and physical form are analyzed and determined by combining information such as the appearance characteristics and generation scenario of the pollutant.

[0087] Optionally, a local database of common agricultural non-point source pollutants can be accessed to help determine the source type and physical form through feature comparison.

[0088] Step S1012: Determine an adaptive sampling strategy based on the source type and physical morphology to obtain representative samples. The adaptive sampling strategy dynamically adjusts the sampling point density and sampling frequency based on the pollutant distribution and uniformity of the pollutants to be treated.

[0089] In a specific embodiment of the present invention, based on the determined source type and physical form of the pollutant, the distribution and uniformity of the pollutant within the detection area are further analyzed (e.g., whether there are local differences in composition). The density of sampling points (e.g., increasing the number of sampling points when the distribution is scattered) and the sampling frequency (e.g., shortening the sampling interval when uniformity is poor) are dynamically adjusted according to the above data. Finally, sampling operations are performed according to this strategy to obtain representative samples that reflect the overall characteristics of the pollutant.

[0090] Optionally, if the pollutants are widely distributed, a regional sampling strategy can be implemented, with sampling parameters adjusted separately for each region, and the results can be aggregated to form a comprehensive representative sample.

[0091] Step S1013: Based on representative samples, control the multi-sensor integrated system to measure component information, wherein the multi-sensor integrated system includes a spectral analysis unit, a moisture detection unit, and an organic matter content detection unit.

[0092] In a specific embodiment of the present invention, after obtaining representative samples, a multi-sensor integrated system is activated and controlled to perform component analysis on the samples. This system includes a spectral analysis unit, a moisture detection unit, and an organic matter content detection unit. The spectral analysis unit is used to detect the carbon-to-nitrogen ratio of pollutants, the moisture detection unit is used to measure the water content, and the organic matter content detection unit is used to acquire organic matter content data. Each unit works collaboratively, synchronously collecting and integrating data to ultimately generate complete component information of the pollutants to be treated.

[0093] Optionally, if the sample contains impurities, the preprocessing unit can be controlled to filter and homogenize the sample before starting the multi-sensor integrated system for measurement, in order to avoid impurities affecting the accuracy of the composition information.

[0094] In this embodiment of the invention, by first determining the source type and physical form of pollutants, then formulating a targeted adaptive sampling strategy to obtain representative samples, and finally using a multi-sensor integrated system to accurately measure component information, the problem of inaccurate component information caused by "fixed sampling strategies and single detection methods" in traditional detection is effectively solved. Example 5:

[0095] In traditional technologies, the determination of the source type and physical form of agricultural non-point source pollutants often relies on a single feature (such as judging it as solid straw solely based on image features), ignoring the correlation between different features and easily leading to classification bias. Furthermore, there is a lack of effective conflict correction mechanisms. When classification results pointed to by different features conflict (such as liquid appearance with solid density), the actual characteristics of the pollutant cannot be accurately determined, resulting in subsequent sampling strategies and detection schemes based on the classification results being inconsistent with the actual situation of the pollutant, affecting the efficiency and accuracy of the overall treatment process. Considering this deficiency, the present invention proposes an optional embodiment. Step S1011 further includes the following specific implementation: Step S10111: Collect and perform multimodal data fusion analysis based on the image features, spectral reflectance features and near-field physical features of the pollutants to be treated to obtain preliminary classification results. The near-field physical features include density and viscosity.

[0096] In a specific embodiment of the present invention, image features (such as color, texture, and morphology) of pollutants are acquired by an image acquisition device, spectral reflectance features (such as reflectance at different wavelengths) of pollutants are acquired by a spectral sensor, and near-field physical features (including density and viscosity) of pollutants are acquired by a physical property detection component.

[0097] Through data calibration, feature association and other processing, feature information from different dimensions is integrated into a unified classification basis, and finally a preliminary classification result on the source type and physical form of pollutants is generated.

[0098] Optionally, if a certain type of feature data has slight noise interference, the data should be preprocessed by filtering and correction before participating in multimodal data fusion analysis.

[0099] Step S10112: Based on the preliminary classification results and the pre-generated pollutant knowledge graph, a confidence assessment is conducted to obtain the source type and physical form. The confidence assessment is used to screen and correct contradictory classification information.

[0100] In a specific embodiment of the present invention, a pre-generated pollutant knowledge graph in the memory is invoked, the preliminary classification results are compared with the standard information in the knowledge graph, and the confidence assessment process is initiated.

[0101] The confidence assessment process verifies the rationality of the preliminary classification results. If there are contradictions in the preliminary classification results (such as the image features of a pollutant pointing to solid straw, but the viscosity features pointing to liquid sewage), the contradictory information is filtered and corrected through the association rules in the knowledge graph, and finally the accurate source type and physical form of the pollutant are determined and output.

[0102] Optionally, the pre-generated pollutant knowledge graph is updated periodically or in real time based on the actual pollutant classification data accumulated during the long-term operation of the system.

[0103] In this embodiment of the invention, by collecting and fusing multimodal features such as images, spectral reflectance, and near-field physics of pollutants, the limitations of single-feature classification are effectively avoided. At the same time, by combining the pre-generated pollutant knowledge graph for confidence assessment, contradictory information in the preliminary classification results can be effectively screened and corrected, significantly improving the accuracy of pollutant source type and physical morphology judgment. Example 6:

[0104] In traditional technologies, the confidence assessment of pollutant classification results often uses fixed-weight calculations, which cannot adjust the weights according to the actual reliability of the feature data. This easily leads to unreliable features dominating the classification results. Furthermore, when different features point to contradictory classification results, there is a lack of clear priority rules for conflict resolution, requiring manual judgment. This is not only inefficient but also prone to biased classification results due to inconsistent judgment standards, affecting the rationality of subsequent sampling and detection processes. Considering these shortcomings, this invention proposes an optional embodiment. Step S10112 further includes the following specific implementation: Step S101121: Calculate the multi-dimensional confidence vector based on the feature weights corresponding to each feature in the preliminary classification results, wherein the feature weights are dynamically weighted based on the feature data.

[0105] In a specific embodiment of the present invention, preliminary classification results of the pollutants to be treated are obtained, and a preset feature weight allocation rule is invoked. The feature weight allocation rule dynamically weights each feature data based on its reliability to determine the feature weight of each type of feature in the classification results.

[0106] Based on the classification tendency of each feature and combined with the corresponding feature weights, a multi-dimensional confidence vector containing the classification confidence of each feature is generated through a preset vector calculation model. For example, if a feature has a weight of 0.6 and a classification confidence of 0.9, then the value of this dimension vector is 0.54, which intuitively quantifies the degree of support of different features for the classification results.

[0107] Optionally, the dynamic adjustment of feature weights can be based on the accuracy of each feature in historical classification data. If a feature has a high error rate in the classification of similar pollutants in the past, its weight can be reduced in real time.

[0108] Step S101122: Based on the multi-dimensional confidence vector and the preset priority logic in the pollutant knowledge graph, conflict resolution and result arbitration are performed to obtain the source type and physical form. The priority logic defines the priority adoption logic when there is a contradiction in the confidence vector.

[0109] In a specific embodiment of the present invention, a pre-generated pollutant knowledge graph in the memory is invoked, and preset priority logic is extracted from the graph. The values ​​of each dimension in the multi-dimensional confidence vector are compared. If there is a contradiction in the vector dimensions, the confidence dimension corresponding to the higher priority feature is adopted first according to the priority logic to resolve the contradictory information. Through arbitration, a unique and uncontradictory pollutant source type and physical form are determined, and the final confirmation of the classification result is completed.

[0110] Optionally, if there are no obvious contradictions in all dimensions of the multi-dimensional confidence vector, the classification tendency can be directly used as the final result without initiating the conflict resolution process.

[0111] In this embodiment of the invention, the multi-dimensional confidence vector is calculated by dynamic weighting, which avoids the problem of some unreliable features interfering with the classification results under the traditional fixed weight. At the same time, the priority logic of the pollutant knowledge graph is combined to resolve and arbitrate the contradictory confidence vectors, clarifying the judgment criteria when there is a feature conflict. This effectively solves the problem of classification ambiguity caused by multiple feature contradictions in the preliminary classification results, and significantly improves the reliability of the judgment of pollutant source type and physical form. Example 7:

[0112] In the energy recovery process of traditional agricultural non-point source pollution control, there are often problems with fixed recovery units and static control parameters, which can easily lead to unsafe energy storage, low recovery efficiency, or energy waste. Considering this deficiency, the present invention proposes an optional embodiment. Step S103 further includes the following specific implementation: Step S1031: Determine the corresponding energy recovery unit based on the type of the target energy conversion process.

[0113] In a specific embodiment of the present invention, the target energy conversion process type is received from the process decision module, and the corresponding energy recovery unit is selected and determined from the pre-configured recovery unit library of the process type based on the core energy output characteristics and recovery requirements of the process type.

[0114] Optionally, if the target energy conversion process is a composite process (i.e., it involves two or more energy outputs at the same time), multiple corresponding recycling units are combined and matched according to the priority of each energy output to form a recycling unit group adapted to the composite process.

[0115] Step S1032: Based on real-time environmental conditions and preset energy output requirements, dynamically adjust the control parameters of the energy recovery unit. The control parameters include storage pressure, temperature control threshold, and energy conversion operating load. The energy conversion operating load is the amount of pollutants that the energy recovery unit needs to process per unit time and the corresponding energy input and output intensity.

[0116] In a specific embodiment of the present invention, after determining the unit required for energy recovery, the current real-time environmental conditions (such as ambient temperature, atmospheric pressure, humidity, etc.) are monitored in real time, and the preset energy output requirement information in the memory is called up at the same time.

[0117] Based on the above real-time conditions and preset requirements, the control parameters of the energy recovery unit are dynamically adjusted. Among them, the storage pressure parameter is adjusted according to the storage safety and output stability requirements of gaseous energy, the temperature control threshold is set according to the thermal stability requirements of the energy storage process, and the energy conversion operation load (i.e. the amount of pollutants processed by the recovery unit per unit time and the corresponding energy input and output intensity) is adjusted according to the balance requirements of pollutant processing volume and energy output efficiency.

[0118] Optionally, a safe threshold range for parameter adjustment can be set. When sudden changes in real-time environmental conditions cause parameters to exceed the safe range, an early warning will be triggered. Adjustments can only be made after manual confirmation or automatic activation of the emergency plan to avoid equipment damage or safety risks.

[0119] In this embodiment of the invention, the appropriate energy recovery unit is first determined according to the target energy conversion process type; then, the control parameters are dynamically adjusted in combination with real-time environmental conditions and preset energy output requirements, which solves the problem that the recovery unit under traditional fixed parameters cannot adapt to environmental changes and fluctuations in output demand. Example 8:

[0120] In the energy recovery process of traditional agricultural non-point source pollution control, the appropriate recovery unit is often not selected based on the type of energy output from the energy conversion process. Instead, fixed, universal recovery units are used to process all types of energy. For example, a single gas storage device may be used to simultaneously attempt to recover electrical and thermal energy. This results in ineffective storage of electrical energy and significant loss of thermal energy, causing not only energy waste but also potential equipment failure due to conflicts between the unit and energy characteristics. Considering this deficiency, the present invention proposes an optional embodiment. Step S1031 further includes the following specific implementation: Step S10311: Match the basic recovery unit set according to the energy type output by the target energy conversion process to obtain the units required for energy recovery. The energy type includes at least one of gaseous methane, combustible gas mixture, liquid thermal energy or electrical energy. The basic recovery unit set includes at least one of gas storage device, purification device, heat exchanger and energy storage battery pack.

[0121] In a specific embodiment of the present invention, the type of energy output is analyzed and determined based on the output characteristics of the target energy conversion process type. Specifically, this includes at least one of gaseous methane, a combustible mixture, liquid thermal energy, or electrical energy.

[0122] Optionally, if the target energy conversion process involves multiple energy outputs, the specific type of each energy source should be identified to form a multi-energy type list.

[0123] Call the database of correspondence between energy types and basic recycling units. This database has pre-set basic recycling units adapted to different energy types (such as gas storage devices and purification devices for gaseous energy, energy storage battery packs for electrical energy, and heat exchangers for liquid thermal energy).

[0124] Based on the determined energy type, suitable units are selected from the set of basic recycling units; if there are multiple energy types, the corresponding basic units are matched and integrated to form the complete energy recycling units required.

[0125] Optionally, if a certain energy type has special requirements for the recovery unit (such as high-purity methane requiring multi-stage purification), then an auxiliary unit with corresponding functions (such as additional filtration and purification components) can be added on the basis of matching the basic unit.

[0126] In this embodiment of the invention, by first determining the type of energy output from the target energy conversion process and then specifically matching the set of basic recycling units, the problem of mismatch between unit functions and energy characteristics caused by the traditional unified unit recycling of multiple types of energy is effectively avoided, and the risk of leakage and loss of energy during the recycling process is effectively reduced.

[0127] like Figure 4 The diagram shown is a schematic representation of an energy recovery system for agricultural non-point source pollution control according to an embodiment of the present invention. This energy recovery system 400 for agricultural non-point source pollution control may include: a component detection module 401, a process decision module 402, an energy management module 403, a processor 404, a memory 405, and a computer program 406 stored in the memory 405 and executable on the processor 404, such as an energy recovery program for agricultural non-point source pollution control. When the processor 404 executes the computer program 406, it controls the component detection module 401, the process decision module 402, and the energy management module 403 to implement the steps described in the various embodiments of energy recovery for agricultural non-point source pollution control.

[0128] A computer program can be divided into one or more modules / units. One or more modules / units are stored in memory 405 and executed by processor 404 to complete the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions. These instruction segments describe the execution process of the computer program in an energy recovery system for agricultural non-point source pollution control.

[0129] An energy recovery system for agricultural non-point source pollution control may include, but is not limited to, a processor 404 and a memory 405. Those skilled in the art will understand that... Figure 4This is merely an example of an energy recovery system for agricultural non-point source pollution control and does not constitute a limitation on such systems. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, an energy recovery system for agricultural non-point source pollution control may also include input / output devices, network access devices, buses, etc.

[0130] The processor 404 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0131] The memory 405 can be an internal storage unit of the energy recovery system for agricultural non-point source pollution control, such as a hard drive or RAM. The memory 405 can also be an external storage device of the energy recovery system, such as a plug-in hard drive, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard. Furthermore, the memory 405 can include both internal and external storage units. The memory 405 is used to store computer programs and other programs and data required by the energy recovery system for agricultural non-point source pollution control. The memory 405 can also be used to temporarily store data that has been output or will be output.

[0132] It should be noted that, for the sake of convenience and brevity, the structure of the energy recovery system for agricultural non-point source pollution control described above can also be referred to the specific description of the structure in the method embodiment, and will not be repeated here.

[0133] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps in the above-described energy recovery method for controlling agricultural non-point source pollution.

[0134] This invention provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps in the energy recovery method for agricultural non-point source pollution control described above.

[0135] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0136] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for various specific applications, but such implementations should not be considered beyond the scope of this invention.

[0137] In the embodiments provided by this invention, it should be understood that the disclosed energy recovery system and method for agricultural non-point source pollution control can be implemented in other ways. For example, the embodiments of the energy recovery system for agricultural non-point source pollution control described above are merely illustrative. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0139] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0140] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0141] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An energy recovery method for agricultural non-point source pollution control, characterized in that, The method includes: If a pollutant to be treated is detected, the composition information of the pollutant to be treated is detected, wherein the composition information includes carbon-nitrogen ratio, moisture content, and organic matter content, and the pollutant to be treated includes at least one of livestock and poultry breeding waste, crop straw, rural domestic sewage, and agricultural production wastewater; Based on the component information, a target energy conversion process is selected, wherein the target energy conversion process includes at least one of the following: anaerobic fermentation to produce biogas, biomass pyrolysis to produce fuel gas, wastewater anaerobic power generation, and straw solidification to produce heat. According to the target energy conversion process, the recovered energy is output, collected and stored, wherein the energy includes at least one of methane, combustible gas mixture, electrical energy and thermal energy.

2. The energy recovery method for agricultural non-point source pollution control as described in claim 1, characterized in that, The step of selecting the target energy conversion process based on the component information includes: Based on the component information, candidate energy conversion processes are adapted, and the energy recovery rate is calculated based on the component information. Based on the energy recovery rate and the unit energy consumption of the candidate energy conversion processes, the target energy conversion process with the highest comprehensive benefit value is determined.

3. The energy recovery method for agricultural non-point source pollution control as described in claim 2, characterized in that, The step of adapting candidate energy conversion processes based on the component information includes: Based on the carbon-nitrogen ratio, organic matter content, and moisture content in the component information, a set of candidate processes is obtained by matching them with predefined process applicability conditions, wherein the process applicability conditions define the range of adaptability of the preset process to the component parameters; Based on the physical form of the pollutants to be treated in the candidate process set, the candidate process set is screened to obtain the candidate energy conversion process, wherein the physical form includes solid, liquid or solid-liquid mixture.

4. The energy recovery method for agricultural non-point source pollution control as described in claim 1, characterized in that, The step of detecting the composition information of the pollutant to be treated if it is detected includes: Based on the pollutants to be treated, determine their source type and physical form; Based on the source type and physical morphology, an adaptive sampling strategy is determined to obtain representative samples. The adaptive sampling strategy dynamically adjusts the sampling point density and sampling frequency based on the pollutant distribution and uniformity of the pollutant to be treated. Based on the representative sample, a multi-sensor integrated system is controlled to measure the component information, wherein the multi-sensor integrated system includes a spectral analysis unit, a moisture detection unit, and an organic matter content detection unit.

5. The energy recovery method for agricultural non-point source pollution control as described in claim 4, characterized in that, The step of determining the source type and physical form of the pollutant to be treated includes: The image features, spectral reflectance features, and near-field physical features of the pollutants to be treated are collected and multimodal data fusion analysis is performed to obtain preliminary classification results. The near-field physical features include density and viscosity. Based on the preliminary classification results and the pre-generated pollutant knowledge graph, a confidence assessment is performed to obtain the source type and the physical form. The confidence assessment is used to screen and correct contradictory classification information.

6. The energy recovery method for agricultural non-point source pollution control as described in claim 5, characterized in that, The step of conducting a confidence assessment based on the preliminary classification results and the pre-generated pollutant knowledge graph includes: Based on the feature weights corresponding to each feature in the preliminary classification results, a multi-dimensional confidence vector is calculated, wherein the feature weights are dynamically weighted based on the feature data; Based on the multi-dimensional confidence vector and the preset priority logic in the pollutant knowledge graph, conflict resolution and result arbitration are performed to obtain the source type and the physical form. The priority logic defines the priority adoption logic when there is a contradiction in the confidence vector.

7. The energy recovery method for agricultural non-point source pollution control as described in claim 1, characterized in that, The steps of outputting, collecting, and storing the recovered energy according to the target energy conversion process include: Based on the type of the target energy conversion process, determine the corresponding energy recovery unit required; Based on real-time environmental conditions and preset energy output requirements, the control parameters of the energy recovery unit are dynamically adjusted. The control parameters include storage pressure, temperature control threshold, and energy conversion operating load. The energy conversion operating load is the amount of pollutants that the energy recovery unit needs to process per unit time and the energy input / output intensity corresponding to the processing amount.

8. The energy recovery method for agricultural non-point source pollution control as described in claim 7, characterized in that, The step of determining the required energy recovery unit based on the type of the target energy conversion process includes: Based on the energy type output by the target energy conversion process, a set of basic recovery units is matched to obtain the units required for energy recovery. The energy type includes at least one of gaseous methane, combustible gas mixture, liquid thermal energy, or electrical energy. The set of basic recovery units includes at least one of a gas storage device, a purification device, a heat exchanger, and an energy storage battery pack.

9. An energy recovery system for agricultural non-point source pollution control, characterized in that, The method includes a component detection module, a process decision module, an energy management module, a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it controls the component detection module, the process decision module, and the energy management module to implement the steps of the energy recovery method for agricultural non-point source pollution control as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the energy recovery method for agricultural non-point source pollution control as described in any one of claims 1 to 8.

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