Hog house waste harmless treatment and resource utilization system and method

Abstract

The invention relates to the technical field of waste treatment, in particular to a pig house waste harmless treatment and resource utilization system and method, and the system comprises a component analysis and recognition module, an enzymolysis reaction regulation module, a microflora construction module, a resource conversion path module and a terminal product evaluation module. According to the method, accurate recognition of an organic component structure is achieved through area proportion analysis of a fiber image, egg white color order and lipid distribution, a compound enzyme combination is set by combining environmental parameters and enzyme dosage linkage, the reaction efficiency and the resource utilization rate are improved, flora screening is optimized and constructed based on a metabolic complementary relation, the degradation stability is enhanced, and the method is suitable for large-scale popularization and application. A resource conversion path is set according to cross grouping of nitrogen, organic carbon and water, functional distribution is achieved, terminal product evaluation is matched with ammonia nitrogen, gaseous components and particle size indexes, various use scenes are efficiently adapted, and the overall utilization value and the system adaptability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of waste treatment, and particularly to a harmless treatment and resource utilization system and method for pigsty waste. Background Art

[0002] The technical field of waste treatment includes technologies for scientifically and reasonably managing, treating, and resource-utilizing various types of waste, aiming to reduce environmental pollution caused by waste and promote the effective utilization of resources. This field involves multiple aspects such as garbage treatment, sewage treatment, and waste recycling, covering links such as waste classification, storage, transportation, treatment, and final harmless disposal. The core content of waste treatment technology includes technologies such as waste pretreatment, resource utilization, environmental protection, and energy efficiency optimization. With the continuous improvement of environmental protection requirements and the urgent need for resource recycling, waste treatment technology has been continuously innovated and improved, using various technical means to solve the environmental impact problems of waste and achieve the harmlessness, reduction, and resource utilization of waste.

[0003] Among them, the harmless treatment and resource utilization system for pigsty waste refers to a system that uses appropriate technical means to harmlessly treat the waste generated in pigsties and realizes its resource utilization. This patent theme focuses on the efficient treatment of pigsty waste, covering technical links such as waste collection, decomposition, and conversion. The system combines mechanical, physical, and biological treatment methods to ensure that pigsty waste is completely decomposed during the treatment process, avoiding environmental pollution. The system also includes measures for resource utilization of waste, such as using waste to produce organic fertilizers, energy and other products to achieve resource recycling.

[0004] In the process of treating pigsty waste in the prior art, traditional physical or simple biochemical means are often relied on for unified treatment, lacking accurate identification of the internal organic components of the waste, which easily leads to the deviation of subsequent reaction conditions from the actual composition, resulting in a decline in reaction efficiency and waste of resources. Fixed parameter configurations are used in the fermentation or enzymatic hydrolysis process, ignoring the dynamic coupling relationship between the enzyme dosage and environmental factors, and restricting the role of the composite enzyme system in multi-component complex waste. In the stage of introducing bacteria, the prior art directly adds general bacterial agents without screening dominant bacteria in combination with the degradation requirements of the target substrate, resulting in insufficient stability and metabolic consistency of the bacteria, affecting the sustainability and conversion efficiency of the fermentation reaction. When selecting the resource utilization path, there is a lack of in-depth analysis of the coupling characteristics between key indicators such as nitrogen, organic carbon, and moisture, and the selection of conversion modes often shows an empirical tendency, resulting in a single form of resource utilization and low conversion efficiency. The evaluation criteria for end products mostly rely on rough indicators, and an attribute matching mechanism corresponding to the target use has not been established, reducing the practical performance and market adaptability of the products in the agricultural or energy fields. Summary of the Invention

[0005] In order to solve the technical problems that in the process of treating pigsty waste in the prior art, traditional physical or simple biochemical means are often relied on for unified treatment, the accurate identification of the internal organic components of the waste is lacking, which easily leads to the deviation of the subsequent reaction conditions from the actual component composition, resulting in a decline in reaction efficiency and waste of resources. In the fermentation or enzymatic hydrolysis process, fixed parameter configurations are adopted, ignoring the dynamic coupling relationship between the enzyme dosage and environmental factors, and restricting the role of the complex enzyme system in multi-component complex waste. In the stage of introducing the microbial community, in the prior art, by directly adding a general microbial agent, the dominant microbial community is not screened in combination with the degradation requirements of the target substrate, and the microbial community stability and metabolic consistency are insufficient, affecting the sustainability and conversion efficiency of the fermentation reaction. When selecting the resource conversion path, there is a lack of in-depth analysis of the coupling characteristics between key indicators such as nitrogen, organic carbon, and moisture, and the selection of the conversion mode often shows an empirical tendency, resulting in a single form of resource utilization and low conversion efficiency. The evaluation criteria for the end products mostly rely on rough indicators, and an attribute matching mechanism corresponding to the target use has not been established, reducing the practical performance and market adaptability of the products in the agricultural or energy fields. The embodiments of the present invention provide a harmless treatment and resource utilization system and method for pigsty waste. The technical solutions are as follows:

[0006] On the one hand, a harmless treatment and resource utilization system for pigsty waste is provided. The system includes:

[0007] The component analysis and identification module, based on the pigsty waste sample data, including fiber particle images, protein residue color scales, and lipid microparticle distribution data, calculates the area ratio differences between the data to judge the organic component structure composition of the current waste, and generates the pigsty organic component ratio result;

[0008] The enzymatic hydrolysis reaction adjustment module calls the pigsty organic component ratio result, identifies the current dosage values of cellulase, protease, and lipase, and makes a joint adjustment through the matching relationship between the ratios of the three enzymes and environmental factors to set the optimal complex enzyme reaction combination, and generates the pigsty waste enzymatic hydrolysis configuration plan;

[0009] The microbial community construction module calls the pigsty waste enzymatic hydrolysis configuration plan, captures the microbial community structure data before and after enzymatic hydrolysis, identifies the distribution states of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria, and screens the matching microbial community composition of the strains to obtain the pigsty microbial community combination list;

[0010] The resource conversion path module calls the pigsty microbial community combination list, detects the nitrogen element, organic carbon, and water content value ranges in the current treated waste, and makes a grouped comparison to determine that the optimal material conversion directions are composting, fermentation, and carbonization, and obtains the pigsty resource conversion type identifier.

[0011] As a further solution of the present invention, the organic component ratio results of the pigsty include fiber content ratio, protein residue strength grade, and lipid particle distribution density. The enzymatic hydrolysis configuration scheme of the pigsty waste includes the composition of composite enzymes, enzyme concentration ratio parameters, and reaction adaptation environmental conditions. The pigsty flora combination list includes the types of core strains, the stability of the flora structure, and the proportional relationship of functional bacteria. The pigsty resource conversion type identifier includes the conversion path type code, the generation trend of the target substance, and the material conversion efficiency grade.

[0012] As a further solution of the present invention, the component analysis and identification module includes:

[0013] The particle identification sub-module is based on the pigsty waste sample data, including fiber particle image data. According to the particle shape edge contour information in the image, the particle area data is extracted, and the average area ratio of the particles is identified to obtain the fiber particle area ratio.

[0014] The color scale identification sub-module is based on the fiber particle area ratio, calls the protein residue color scale data in the waste sample, and screens the protein coverage area within the color scale range to obtain the protein residue area ratio.

[0015] The distribution operation sub-module calculates the ratio of the lipid coverage points to the total points in the image according to the protein residue area ratio, and combines the ratios of the fiber particle area and the protein residue area to generate the organic component ratio results of the pigsty.

[0016] As a further solution of the present invention, the enzymatic hydrolysis reaction regulation module includes:

[0017] The environmental factor identification sub-module calls the organic component ratio results of the pigsty, extracts the temperature, humidity, and pH value data in the pigsty fermentation reaction tank, analyzes the deviation between the data and the standard values, and combines the organic component ratio of the pigsty to obtain the environmental factor matching data.

[0018] The enzyme type identification sub-module is based on the environmental factor matching data, identifies the dosage of cellulase, protease, and lipase, adjusts the dosage ratio of each enzyme, and identifies the optimized dosage ratio of each enzyme to obtain the enzyme type dosage ratio.

[0019] The composite enzyme adjustment sub-module analyzes the matching relationship between the enzyme and the environment according to the enzyme type dosage ratio, adjusts the enzyme dosage ratio, and generates the enzymatic hydrolysis configuration scheme of the pigsty waste.

[0020] As a further solution of the present invention, the microbial community construction module includes:

[0021] Based on the enzymatic hydrolysis configuration plan of the pigsty waste, the microbial community data capture sub-module identifies the microbial community structure data before and after the enzymatic hydrolysis of the pigsty waste, captures the distribution status of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria during the enzymatic hydrolysis process, records the quantity and distribution location of each type of bacteria, and obtains the microbial community distribution data set;

[0022] Based on the microbial community distribution data set, the microbial community analysis sub-module identifies the matching degree of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria, analyzes the distribution of the three core strains in the pigsty waste, and identifies the proportion of the three strains in the microbial community to obtain the strain matching degree value;

[0023] According to the strain matching degree value, the microbial community combination construction sub-module performs weighted analysis on the distribution data of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria, screens the microbial community combination that meets the optimal match, and generates a list of pigsty microbial community combinations.

[0024] As a further solution of the present invention, the resource conversion path module includes:

[0025] Based on the microbial community types and proportion data in the pigsty microbial community combination list, the microbial community combination screening sub-module identifies the decomposition potential of the microbial community, screens the optimal microbial community combination that matches the current environment, and obtains the optimal microbial community combination coefficient;

[0026] According to the optimal microbial community combination coefficient, the material index detection sub-module detects the nitrogen element concentration, organic carbon concentration, and moisture content, identifies the distribution range of the indexes, and generates a cross-structure difference degree value;

[0027] The conversion type judgment sub-module calls the cross-structure difference degree value and the conversion path reference index, calculates the conversion path matching index, determines the path type of the index, analyzes the preferred conversion path category value, and matches it with the optimal microbial community combination coefficient to obtain the pigsty resource conversion type identifier.

[0028] As a further solution of the present invention, the conversion path matching index adopts the formula:

[0029]

[0030] Among them, G represents the conversion path matching index, P i represents the reference index value of the i-th path point, T i represents the real-time index value of the i-th path point, n represents the total number of path points, C avg represents the average value of the reference path category values, and D represents the optimal microbial community combination coefficient.

[0031] As a further solution of the present invention, the system further includes an end product evaluation module:

[0032] The end - product evaluation module calls the pigsty resource conversion type identifier, detects the ammonia - nitrogen residue, gaseous product component ratio and solid particle size in the product, and determines the applicable product category as fertilizer, gas fuel or soil conditioner according to the matching degree of the three indicators with the target object attributes, and obtains the classification of the resource utilization level of the pigsty products;

[0033] The classification of the resource utilization level of the pigsty products includes the fertilizer adaptation level, gas purity index and soil conditioning effect parameter.

[0034] As a further solution of the present invention, the end - product evaluation module includes:

[0035] The resource identification sub - module, based on the pigsty resource conversion type identifier, detects the ammonia - nitrogen residue, gaseous product component ratio and solid particle size of the product, analyzes the matching degree of the data with the target object attributes, compares the scores item by item, makes a determination according to the preset benchmark, and generates an evaluation result of the resource conversion effect;

[0036] The product detection sub - module, based on the evaluation result of the resource conversion effect, detects the ammonia - nitrogen residue, gaseous product component ratio and solid particle size in the product, performs a weighted calculation on the scores, screens the key indicators, and obtains a conversion product scoring coefficient;

[0037] The product classification sub - module classifies the product as fertilizer, gas fuel and soil conditioner according to the conversion product scoring coefficient, sorts and screens the scores according to the set standards, and obtains the classification of the resource utilization level of the pigsty products.

[0038] On the other hand, a method for harmless treatment and resource utilization of pigsty waste, the method for harmless treatment and resource utilization of pigsty waste is executed based on the above - mentioned system for harmless treatment and resource utilization of pigsty waste, and includes the following steps:

[0039] S1: Based on the pigsty waste sample data, extract the fiber particle image, protein residue color scale and lipid microparticle distribution data, calculate the area - to - ratio value, and perform component ratio analysis in combination with the sample distribution characteristics of the pigsty manure collection point to generate the pigsty organic component ratio data;

[0040] S2: Based on the pigsty organic component ratio data, extract the temperature, humidity and pH value in the pigsty fermentation tank, identify the dosing concentrations of cellulase, protease and lipase, and perform a matching degree judgment in combination with the environmental parameters, screen the optimal combination, and determine the compound dosing ratio of the three enzymes to obtain the enzyme hydrolysis ratio parameters of the pigsty waste;

[0041] S3: Based on the pigsty waste enzyme hydrolysis ratio parameters, compare the microbial community structure data before and after enzyme hydrolysis, identify the distribution states of ammonia - oxidizing bacteria, gas - producing bacteria and organic acid - converting bacteria, screen the matching composition structure, and obtain the pigsty microbial community composition list;

[0042] S4: Based on the pig house flora composition list, nitrogen, organic carbon and water content are extracted, grouped and compared, and the resource conversion path type is determined to obtain the pig house resource conversion path type;

[0043] S5: Based on the type of pig house resource conversion path, detect ammonia nitrogen residues, gaseous components and solid particle size, compare the attribute indicator categories, screen the corresponding matching categories, and obtain the pig house product resource utilization level classification.

[0044] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0045] By analyzing the area ratio of fiber particle images, protein residue color levels and lipid particle distribution data in piggery waste, the organic component structure can be accurately identified, the ability to distinguish differences in organic matter composition is enhanced, and the directional adjustment of subsequent treatment strategies is effectively supported. According to the obtained component ratio results, the temperature, humidity and pH parameters in the reaction chamber are collected in conjunction, and the current addition levels of three types of key enzymes are identified at the same time. The coupling setting is carried out through the adaptation relationship between the enzyme ratio and the environmental factors to improve the efficiency of the compound enzyme reaction and the precise utilization of enzyme resources. In the process of microbial community construction, a comparative analysis of the microbial community structure before and after enzymatic hydrolysis is introduced. Combined with the distribution status of ammonia oxidizing bacteria, organic acid conversion bacteria and gas-producing bacteria, a microbial community combination with high metabolic complementarity is screened to enhance the stability and metabolic efficiency of the target microbial community in the process of complex organic matter degradation. In the resource conversion path setting stage, the cross-value structure grouping of nitrogen, organic carbon and water is used to establish the optimal conversion direction under different material structures, and realize the functional utilization and diversion of piggery waste under the regulation of carbon-nitrogen ratio. The comprehensive matching analysis of ammonia nitrogen residue, gaseous product component ratio and solid particle size is introduced in the terminal product evaluation to adapt to different product usage scenarios with high precision, ensure the actual benefits of the final output in the fields of agriculture, fuel and soil improvement, and comprehensively improve the conversion efficiency, system adaptability and product practicality of pig house waste in the whole process from identification, treatment to utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 It is a schematic diagram of a system for harmless treatment and resource utilization of piggery waste provided by an embodiment of the present invention;

[0048] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0049] Figure 3 It is a flowchart of a method for harmless treatment and resource utilization of pigsty waste provided by an embodiment of the present invention. Detailed implementation manners

[0050] The following describes the technical solutions in the present invention with reference to the accompanying drawings.

[0051] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Precisely, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0052] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0053] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0054] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0055] The embodiments of the present invention provide a system for harmless treatment and resource utilization of pigsty waste, as Figure 1-2 shown in the schematic diagram of the system for harmless treatment and resource utilization of pigsty waste. The system includes:

[0056] Based on the pigsty waste sample data, including fiber particle images, protein residue color levels and lipid microparticle distribution data, the component analysis and identification module calculates the area ratio difference between the three data to judge the organic component structure composition of the current waste and generates the pigsty organic component ratio result;

[0057] The enzymatic hydrolysis reaction regulation module calls the pigsty organic component ratio result, extracts the temperature, humidity and pH value in the pigsty fermentation reaction tank, identifies the current dosage values of cellulase, protease and lipase, and makes a combined adjustment through the matching relationship between the ratios of the three enzymes and environmental factors to set the optimal composite enzyme reaction combination and generate the pigsty waste enzymatic hydrolysis configuration plan;

[0058] The microbial community construction module calls the enzymatic hydrolysis configuration plan for pigsty waste, captures the flora structure data before and after enzymatic hydrolysis, and screens the matching flora composition of three types of core strains by identifying the distribution states of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria, so as to obtain the pigsty flora combination list;

[0059] The resource conversion path module calls the pigsty flora combination list, detects the numerical ranges of nitrogen element, organic carbon, and water content in the current treated waste, groups and compares them according to the cross-structure of the three types of numerical values, and determines that the optimal material conversion directions are composting, fermentation, and carbonization, so as to obtain the pigsty resource conversion type identifier;

[0060] The end-product evaluation module calls the pigsty resource conversion type identifier, detects the ammonia nitrogen residue, gaseous product component ratio, and solid particle size in the product, and judges the applicable product category as fertilizer, gas fuel, or soil conditioner according to the matching degree of the three indicators with the target substance attributes, so as to obtain the classification of the pigsty product resource utilization level.

[0061] The results of the organic component ratio in the pigsty include the fiber content ratio, the protein residue strength grade, and the lipid particle distribution density. The enzymatic hydrolysis configuration plan for pigsty waste includes the composition of composite enzymes, the enzyme concentration ratio parameters, and the reaction adaptation environmental conditions. The pigsty flora combination list includes the types of core strains, the stability of the flora structure, and the proportional relationship of functional bacteria. The pigsty resource conversion type identifier includes the conversion path type code, the target substance generation trend, and the material conversion efficiency grade. The classification of the pigsty product resource utilization level includes the fertilizer adaptation grade, the gas purity index, and the soil conditioning effect parameters.

[0062] Specifically, as Figure 2 shown, the component analysis and identification module includes:

[0063] The particle identification sub-module is based on the pigsty waste sample data, including the fiber particle image data. According to the particle shape edge contour information in the image, the particle area data is extracted, and the average area ratio of the particles is identified to obtain the fiber particle area ratio value;

[0064] By obtaining the fiber particle images of the pigsty waste samples, through image preprocessing, including denoising, edge enhancement, contour extraction, etc., the outer contour of the particles is identified. Based on the contour information, the particle regions are extracted using image segmentation algorithms (such as threshold segmentation or edge detection, etc.), and the total area of the particles is calculated. This area is obtained through pixel-level calculations, based on the number of pixels contained in each particle region. Next, for all the identified particles, the area of each particle is calculated, and the average area of the particles is obtained through statistical analysis. The average area of the particles is compared with the total area of the image in the region to obtain the proportion of the average area of the particles. In a practical example, the total area of the image can be 1000 square pixels. If the total area of the identified particles is 250 square pixels, then the proportion of the average area of the particles is 25%, and this proportion value is used as the fiber particle area proportion value.

[0065] Based on the fiber particle area proportion value, the color level recognition sub-module calls the protein residue color level data in the waste sample, screens the protein coverage area within the color level range, and obtains the protein residue area proportion value;

[0066] The obtained fiber particle area proportion value is compared with the color level data of the protein residue in the waste sample. For different color level ranges, the color distribution algorithm (such as RGB or HSV color space conversion) is used to analyze the image, and the protein residue region is extracted. The specific method is to set a threshold range. For example, the threshold range of the protein residue color level is the region where the RGB value is between (100, 150, 200) and (180, 220, 255), indicating the presence of protein. All the pixel points in the image that meet this range are screened out, the total area of the points is calculated, and the proportion of the protein coverage area to the total image area is calculated. Through statistical analysis, the protein residue area proportion value is obtained. For example, assuming that the pixel area covered by protein is 200 square pixels and the total image area is 1000 square pixels, then the protein residue area proportion is 20%, and further analysis is carried out in combination with the particle area proportion value.

[0067] The distribution operation sub-module calculates the ratio of the lipid coverage points to the total points of the image according to the protein residue area proportion value, and generates the pigsty organic component proportion result in combination with the proportions of the fiber particle area and the protein residue area;

[0068] By performing grayscale detection and segmentation on the lipid regions in the image, assuming that the grayscale of the lipid regions is different from that of the protein residue regions, by setting another set of thresholds (for example, the region where the RGB values are between (50, 80, 100) and (120, 160, 200)), the lipid regions are extracted, and the total area of the lipid-covered points is calculated. Assuming the lipid coverage area is 150 square pixels, calculate the ratio of the lipid-covered points to the total points in the image. Assuming the total points in the image are 1000 and the lipid-covered points are 150, the ratio is 0.15, that is, 15% of the image is covered by lipids. Combining the previously calculated proportion of the fiber particle area (such as 25%) and the protein residue area (such as 20%), the proportion of different organic components in the pigsty is calculated through weighted average or summation. Specifically, a weight coefficient can be set. Assuming that the fiber particles, protein, and lipids each account for 40%, 30%, and 30% of the organic components by weight, combined with their respective proportions, the final result of the proportion of the organic components in the pigsty is generated. For example: the proportion of the organic components is 35% fiber particles, 25% protein residue, 15% lipids. The final proportion result reflects the distribution of various organic substances in the pigsty waste.

[0069] Specifically, as Figure 2 shown, the enzymatic reaction regulation module includes:

[0070] The environmental factor recognition sub-module calls the result of the proportion of the organic components in the pigsty, extracts the temperature, humidity, and pH value data in the pigsty fermentation reaction tank, analyzes the deviation between the data and the standard values, and combines the proportion of the organic components in the pigsty to obtain the environmental factor matching data;

[0071] Extract the temperature, humidity, and pH value data in the pigsty fermentation reaction tank. The data is monitored and collected in real time by sensors. The deviation between the collected data and the standard values is analyzed using data analysis methods. The standard values can be set according to relevant agricultural or breeding industry specifications. For example, the standard value of temperature is 25°C to 30°C, the standard value of humidity is 50% to 70%, and the standard value of pH is 6.5 to 7.5. The analysis method is to compare the actually collected data with the standard values and calculate the deviation value. For temperature, if the actual value is 28°C and the standard value is 25°C, the deviation is +3°C. If the humidity is 80% and the standard value is 65%, the deviation is +15%. If the pH value is 6.0 and the standard value is 7.0, the deviation is -1.0. Through the deviation value, it is possible to further analyze whether there are systematic problems. For example, too high humidity is an indication of poor ventilation in the fermentation reaction tank, and a low pH value indicates that the fermentation process is incomplete, resulting in an environment unsuitable for microbial activity. Combining the data of the proportion of the organic components in the pigsty, such as water content, carbon-nitrogen ratio, etc., further analyze the impact of different organic components on environmental factors, and finally through comprehensive analysis, obtain the environmental factor matching data.

[0072] The enzyme species recognition sub-module identifies the dosage of cellulase, protease, and lipase based on environmental factor matching data, adjusts the dosage ratio of each enzyme, identifies the optimized dosage ratio of each enzyme, and obtains the enzyme species dosage ratio.

[0073] The initial dosage of each enzyme is obtained through historical data or experiments. Assume the initial dosage of cellulase is 5 g / kg, protease is 3 g / kg, and lipase is 2 g / kg. By environmental factor matching data, such as temperature, humidity, pH value, etc., evaluate the impact of the environment on enzyme activity. For example, at a higher temperature, the enzyme activity increases, and the dosage needs to be appropriately reduced. When the humidity is high, the enzyme binds more tightly to water, resulting in insufficient activity, so the dosage needs to be increased. By combining the data, adjust the dosage ratio of each enzyme and identify the optimized dosage ratio. For example, if the environmental factor analysis results show that the temperature is too high and the humidity is too low, then the dosage ratio of lipase needs to be increased by adjustment because lipase has higher activity at higher temperatures, and at lower humidity, the low dosage of lipase limits its effect. Finally, the optimized dosage ratio of each enzyme is determined as cellulase 6 g / kg, protease 3.5 g / kg, and lipase 3 g / kg, and the enzyme species dosage ratio is obtained.

[0074] The compound enzyme adjustment sub-module analyzes the matching relationship between the enzyme and the environment according to the enzyme species dosage ratio, adjusts the enzyme dosage ratio, and generates an enzymatic hydrolysis configuration plan for pig house waste.

[0075] Analyze the matching relationship between the enzyme and environmental factors. For example, in an environment with a higher temperature, the activity of cellulase is higher, and the activity of protease decreases due to lower humidity. By analyzing the relationship between enzyme species and environmental factors through a data model, the impact of the enzyme species dosage ratio on the enzymatic hydrolysis efficiency of waste can be deduced, adjust the enzyme dosage ratio, and optimize the enzymatic hydrolysis plan. By repeatedly calculating the relationship between the change in the dosage ratio of each enzyme and the fluctuation of environmental factors. For example, assume that through experiments, it is found that the enzymatic hydrolysis effect of cellulase and protease is poor at lower temperatures, and the activity of lipase decreases in an environment with higher humidity. Therefore, according to the data comparison, by increasing the dosage ratio of cellulase and protease and reducing the dosage of lipase at the same time, an enzymatic hydrolysis configuration plan for pig house waste is generated, where the dosage of cellulase is 7 g / kg, the dosage of protease is 4 g / kg, and the dosage of lipase is 1.5 g / kg.

[0076] Specifically, as Figure 2 shown, the microbial community construction module includes:

[0077] The flora data capture sub-module identifies the flora structure data before and after enzymatic hydrolysis of pig house waste based on the enzymatic hydrolysis configuration plan for pig house waste, captures the distribution status of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria during the enzymatic hydrolysis process, records the quantity and distribution location of each bacterium, and obtains the flora distribution data set.

[0078] First, identify the changes in the microbial flora structure in the waste before and after enzymatic hydrolysis. By collecting waste samples from the pigsty and using high-throughput sequencing technology to analyze the microbial community before and after enzymatic hydrolysis, obtain the species and quantity distribution of microorganisms in each sample. Take samples of the pigsty waste to ensure the representativeness of the samples, and then use DNA extraction technology to extract microbial DNA from the samples. Through 16S rRNA gene sequencing technology, conduct sequence analysis on the extracted DNA samples to identify different microbial populations in the samples and classify them. According to the sample data before and after enzymatic hydrolysis, compare the changes in the species and quantities of microorganisms in the two groups of data. For example, if the number of ammonia-oxidizing bacteria in the sample after enzymatic hydrolysis increases significantly (such as from 1000 units to 5000 units), it indicates that the enzymatic hydrolysis process promotes the reproduction of ammonia-oxidizing bacteria. Based on the change data of the microbial flora, obtain the distribution state of the microbial flora and the relative quantities of different types of microbial flora. The data will provide a basis for subsequent microbial flora analysis, ensuring that the quantity of each type of bacteria and its distribution position in the sample can be recorded in detail, and obtaining a microbial flora distribution dataset.

[0079] Based on the microbial flora distribution dataset, the microbial flora analysis sub-module identifies the matching degrees of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria, analyzes the distribution of these three types of core strains in the pigsty waste, identifies the proportions of the three types of strains in the microbial flora, and obtains the strain matching degree values;

[0080] According to the quantity and distribution position of each type of bacteria, further analyze the proportions of the three types of core strains in the sample. By calculating the relative abundance of each microbial flora, that is, the percentage of the quantity of a certain microbial flora in the total quantity of the microbial flora in the sample. For example, if the number of ammonia-oxidizing bacteria in a sample is 5000 units and the total quantity of the microbial flora is 20000 units, then the relative abundance of ammonia-oxidizing bacteria is 25%. Then, based on the relative abundance data, calculate the matching degrees between different microbial floras. The calculation of the matching degree can be carried out through correlation analysis. For example, use the Pearson correlation coefficient to evaluate the relationship between ammonia-oxidizing bacteria and organic acid-converting bacteria. If the Pearson coefficient is close to 1, it indicates that the distributions of these two types of microbial floras in the sample are highly correlated, indicating a synergistic effect in the same environment. Through such analysis, the matching degree values of the three types of strains can be obtained, further revealing their distribution in the pigsty waste and providing data support for subsequent optimization of the microbial flora.

[0081] Based on the strain matching degree values, the microbial flora combination construction sub-module conducts weighted analysis on the distribution data of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria, screens out the microbial flora combinations that meet the optimal matching, and generates a list of pigsty microbial flora combinations;

[0082] Perform a weighted analysis on the distribution data of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria, and screen out the optimal microbial community combination that meets the best match. The specific steps of the weighted analysis include: First, set the weight of each microbial community in the overall microbial community. The weight can be set according to the functional importance of different microbial communities. For example, ammonia-oxidizing bacteria play an important role in the conversion of ammonia and can be assigned a higher weight (e.g., 0.5), while gas-producing bacteria only affect the degradation process of waste under specific conditions and can be assigned a lower weight (e.g., 0.2). According to the weight and matching degree of each microbial community, use the weighted average method to calculate the overall matching degree of various microbial communities. For example, if the matching degree of ammonia-oxidizing bacteria is 0.8, the matching degree of organic acid-converting bacteria is 0.6, and the matching degree of gas-producing bacteria is 0.7, and the weights are 0.5, 0.3, and 0.2 respectively, then the overall matching degree is 0.74. The optimal microbial community combination can be screened out, that is, select the microbial community combination with the highest overall matching degree. The microbial community combination will become the core component of the pigsty microbial community, and a list of pigsty microbial community combinations will be generated.

[0083] Specifically, as Figure 2 shown, the resource conversion path module includes:

[0084] The microbial community combination screening sub-module identifies the decomposition potential of the microbial community based on the microbial community types and their proportion data in the pigsty microbial community combination list, screens out the optimal microbial community combination that matches the current environment, and obtains the optimal microbial community combination coefficient;

[0085] First, it is necessary to identify the potential decomposition potential of the microbial community based on the microbial community types and their proportion data in the pigsty microbial community combination list. The specific process includes analyzing each item of the microbial community type in the list, evaluating its decomposition ability in the current environment, and calculating the adaptability and optimization degree of the microbial community in the environment. Finally, the most suitable microbial community combination is screened out. The microbial community types include bacteria, fungi, etc. Each type has specific decomposition characteristics and proportion values. The proportion values are obtained through real-time monitoring equipment to ensure data accuracy. For example, assume that the proportion of a certain bacterial group in the pigsty microbial community combination list is 30%, the proportion of a certain fungal group is 50%, and the proportion of other groups is relatively low. If the decomposition ability of the bacterial group is 0.7 and the organic carbon conversion potential is relatively high, then this group will be more likely to be selected as one of the optimal microbial communities during the screening process. Finally, the optimal microbial community combination coefficient is obtained, which is the value obtained by weighted averaging the proportion and decomposition potential of all microbial community types. This combination coefficient will be used for the detection of material indicators and the judgment of the conversion path in the subsequent steps.

[0086] The material indicator detection sub-module detects the nitrogen element concentration, organic carbon concentration, and moisture content according to the optimal microbial community combination coefficient, identifies the distribution range of the indicators, and generates a cross-structure difference degree value;

[0087] First, the environmental factors in the pigsty are actually detected according to the optimal microbial community combination coefficient. The key detections are the nitrogen element concentration, organic carbon concentration, and moisture content. The nitrogen element concentration can be detected by standard chemical analysis methods (such as the Kjeldahl method) and is measured in milligrams per liter (mg / L). Suppose the measured nitrogen concentration is 30 mg / L. The moisture content can be calculated by measuring the wet weight and dry weight of the sample, and suppose it is 25%. The organic carbon concentration is measured by the combustion method or acidification method, and suppose the result is 1.2%. Once the data is collected, the distribution range of each index needs to be identified, which can be achieved by comparing historical data or setting standard ranges. For example, the normal range of the nitrogen element concentration is 20–50 mg / L, the organic carbon concentration is 1.0%–2.0%, and the moisture content is 20%–30%. By comparing the data, the cross-structure difference degree value can be calculated to reflect the difference degree between different parameters. If the values of the indicators are relatively concentrated, the cross-structure difference degree value will be lower, otherwise it will be higher. Suppose the cross-structure difference degree value is 0.3, which means that the distribution of each indicator in the pigsty environment is relatively uniform.

[0088] The conversion type judgment sub-module calls the cross-structure difference degree value and the conversion path reference index, calculates the conversion path matching index, determines the path type of the index, analyzes the preferred conversion path category value, and matches it with the optimal microbial community combination coefficient to obtain the pigsty resource conversion type identifier;

[0089] The conversion path matching index adopts the formula:

[0090]

[0091] where G represents the conversion path matching index, P i represents the reference index value of the i-th path point, T i represents the real-time index value of the i-th path point, n represents the total number of path points, C avg represents the average value of the reference path category value, and D represents the optimal microbial community combination coefficient;

[0092] The main purpose of the formula is to calculate the conversion path matching index. By comparing the difference between the reference path and the actual path, and considering the influence of the conversion path category value and the optimal microbial community combination coefficient on path matching, the formula includes path difference degree calculation, category value calculation, and weighted adjustment coefficient;

[0093] Calculation of path point difference degree: P i represents the index value of the i-th reference path point, T i represents the index value of the i-th actual path point, P i and T iIt is obtained by monitoring the specific data of each key point on the path. The specific monitoring data includes the real-time collection of various parameters during the conversion process (such as conversion time, temperature, humidity, environmental factors, etc.). The number of path points n represents the total number of monitoring points on the path;

[0094] For example, in the pigsty resource conversion path, there are 5 monitoring points (n = 5), and the following data is obtained:

[0095] Reference path data: P i = [3.5, 4.2, 3.8, 4.0, 3.9];

[0096] Actual path data: T = [3.6, 4.1, 3.7, 3.9, 4.0];

[0097] Based on this, the difference of each path point can be calculated. The calculation formula is: |P i -T i |; The corresponding calculation results are: |3.5 - 3.6| = 0.1, |4.2 - 4.1| = 0.1, |3.8 - 3.7| = 0.1, |4.0 - 3.9| = 0.1, |3.9 - 4.0| = 0.1;

[0098] Sum of the squares of the path point difference degrees: Sum the squares of each difference to obtain the total path point difference degree: ∑ i n ＝1 (|P i -T i |) 2 = (0.1) 2 + (0.1) 2 + (0.1) 2 + (0.1) 2 + (0.1) 2 = 0.05;

[0099] Perform a square root operation on the total:

[0100] Calculation of the path matching index: The calculation formula of the path matching index is:

[0101] Category value and optimal flora combination coefficient:

[0102] C avg represents the average value of the reference path category values, and C avg is obtained by weighted average calculation of the data of all reference paths. For example, in a certain scenario, the category values of the reference paths are [4.5, 4.0, 4.2, 4.3, 4.6] respectively, and its average value is:

[0103] Let D represent the optimal flora combination coefficient, which is obtained by monitoring and experimenting on the combined effects of different flora. The value ranges from 0 to 1. Assume D = 0.85;

[0104] Substitute the above parameters into the formula:

[0105] The calculation results show that the conversion path matching index is 4.14. This value reflects the matching degree between the reference path and the actual path. A lower value indicates a smaller difference between the two and a higher matching degree;

[0106] Supplement and improve the acquisition process of each parameter and the process of dimension unification:

[0107] P i and T i Acquisition process: The path point values are monitored in real time through environmental sensors or monitoring devices (such as temperature and humidity sensors, light sensors, etc.). The data is collected and recorded automatically. The dimension unification requirement ensures that the units of all path points are consistent. Common units are degrees Celsius (°C), humidity percentage (%), time (hours), etc.;

[0108] C avg Acquisition process: The calculation of the category value is carried out by weighting different reference paths. The weight coefficients are set according to criteria such as the criticality and reliability of the path. The dimension of C avg is the corresponding path category value, which is a dimensionless pure numerical value;

[0109] Acquisition process of D: The optimal flora combination coefficient is obtained through laboratory data and on-site experiments, and it fluctuates between 0 and 1. The dimension of D is a dimensionless pure numerical value.

[0110] Specifically, as Figure 2 shown, the end product evaluation module includes:

[0111] The resource identification sub-module, based on the pigsty resource conversion type identifier, detects the ammonia nitrogen residue, gaseous product component ratio, and solid particle size of the product, analyzes the matching degree between the data and the target object attributes, compares the scores item by item, and makes a determination according to the preset benchmark to generate the resource conversion effect evaluation result;

[0112] First, relevant index data such as ammonia nitrogen residue, gaseous product component ratio, and solid particle size during the conversion process need to be obtained based on the pigsty resource conversion type identifier. For example, the ammonia nitrogen residue can be measured by water sample chemical analysis or gas chromatography to obtain the ammonia nitrogen concentration in the product. Suppose the measured value is 50 mg / L. The gaseous product component ratio is detected by a gas analyzer. For example, gas chromatography can distinguish the concentrations of different gases. For example, the proportion of nitrogen is 70%, the proportion of oxygen is 25%, and the proportion of carbon dioxide is 5%. The solid particle size is measured by a particle counting analyzer or sieving method. Suppose the obtained particle size range is 2 - 10 microns. The detected data is matched with the target object attributes, which include the optimal ammonia nitrogen concentration, organic composition, particle size requirements, etc. of the fertilizer. By comparing the differences between the actual data and the target values, the scores of each index are calculated item by item. For example, suppose the target ammonia nitrogen concentration is in the range of 40 - 60 mg / L, and the actual measured value is 50 mg / L. The score can be 1 point (within the preset range). By comparing the scores of each index item by item and then making a comprehensive judgment based on the preset criteria (such as the weights of ammonia nitrogen concentration and gaseous product component ratio are 0.4 and 0.6 respectively), the resource conversion effect evaluation result is finally generated. For example, the score of ammonia nitrogen residue is 0.8, the score of gaseous product component ratio is 0.9, the score of solid particle size is 1 point, and the total score is 0.9.

[0113] Based on the resource conversion effect evaluation result, the product detection sub-module detects the ammonia nitrogen residue, gaseous product component ratio, and solid particle size in the product, calculates the weighted scores, screens the key indicators, and obtains the conversion product scoring coefficient.

[0114] First, based on the resource conversion effect evaluation result, the ammonia nitrogen residue, gaseous product component ratio, and solid particle size are further detected. Suppose through repeated detection, the ammonia nitrogen residue is 48 mg / L, the gaseous product component ratio is 70% nitrogen, 28% oxygen, and 2% carbon dioxide, and the solid particle size is 4 microns. The detected data then needs to be compared with the previously obtained effect evaluation result. According to the preset weight parameters (suppose the weight of ammonia nitrogen residue is 0.3, the weight of gaseous product component ratio is 0.4, and the weight of solid particle size is 0.3), each index is weighted and calculated. By calculating the scores of each index and synthesizing them according to the weights, the conversion product scoring coefficient is obtained.

[0115] The product classification sub-module classifies the product into fertilizers, gas fuels, and soil conditioners according to the conversion product scoring coefficient, sorts and screens the scores according to the set criteria, and obtains the classification of the pigsty product resource utilization level.

[0116] The products are classified into three categories: fertilizers, gaseous fuels, and soil conditioners according to the conversion product scoring coefficients. For example, assuming the conversion product scoring coefficient is 0.89, according to the preset criteria, conversion products with a scoring coefficient higher than 0.8 can be classified as fertilizers, products with a scoring coefficient between 0.6 - 0.8 can be classified as gaseous fuels, and products with a scoring coefficient lower than 0.6 can be classified as soil conditioners. If the scoring coefficient is 0.89, then this product will be classified as a fertilizer. All product scores are sorted according to the set criteria (such as the scoring coefficient of fertilizers needs to be greater than 0.8, that of gaseous fuels needs to be greater than 0.6, etc.). For example, if there are five products with scoring coefficients of 0.95, 0.8, 0.75, 0.85, and 0.65 respectively, they will be arranged in descending order of the scoring coefficient, and finally the corresponding categories will be selected to obtain the classification of the resource utilization level of the pigsty products, which will help with the rational allocation and use of resources.

[0117] Please refer to Figure 3 , the harmless treatment and resource utilization method of pigsty waste is carried out based on the above-mentioned pigsty waste harmless treatment and resource utilization system, including the following steps:

[0118] S1: Based on the pigsty waste sample data, extract the fiber particle images, protein residue color levels, and lipid particle distribution data, calculate the area ratio, and conduct component ratio analysis in combination with the sample distribution characteristics of the pigsty manure collection points to generate the pigsty organic component ratio data;

[0119] S2: Based on the pigsty organic component ratio data, extract the temperature, humidity, and pH value in the pigsty fermentation tank, identify the dosing concentrations of cellulase, protease, and lipase, and conduct a matching degree judgment in combination with the environmental parameters, screen the optimal combination, and determine the compound dosing ratio of the three enzymes to obtain the enzyme hydrolysis ratio parameters of the pigsty waste;

[0120] S3: Based on the pigsty waste enzyme hydrolysis ratio parameters, compare the microbial community structure data before and after enzyme hydrolysis, identify the distribution states of ammonia-oxidizing bacteria, gas-producing bacteria, and organic acid-converting bacteria, screen the matching composition structure, and obtain the pigsty microbial community composition list;

[0121] S4: Based on the pigsty microbial community composition list, extract the nitrogen element, organic carbon, and moisture content, conduct grouped comparison, and judge the type of resource conversion path to obtain the pigsty resource conversion path type;

[0122] S5: Based on the pigsty resource conversion path type, detect the ammonia nitrogen residue, gaseous components, and solid particle sizes, compare with the attribute index categories, screen the corresponding matching categories, and obtain the classification of the resource utilization level of the pigsty products.

[0123] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims described above.

Claims

1. A harmless treatment and resource utilization system for pigsty waste, characterized in that, The system includes: Based on the data of pigsty waste samples, including fiber particle images, protein residue color levels, and lipid microparticle distribution data, the component analysis and identification module calculates the difference in area ratios between the data to determine the organic component structure of the current waste and generates the proportion results of pigsty organic components; The enzymatic hydrolysis reaction regulation module calls the proportion results of pigsty organic components, identifies the current dosage values of cellulase, protease, and lipase, and makes a combined adjustment through the matching relationship between the ratios of the three enzymes and environmental factors to set the optimal composite enzyme reaction combination and generate the enzymatic hydrolysis configuration plan for pigsty waste; The microbial community construction module calls the enzymatic hydrolysis configuration plan for pigsty waste, captures the bacterial community structure data before and after enzymatic hydrolysis, identifies the distribution states of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria, and screens the matching bacterial community composition of strains to obtain the pigsty bacterial community combination list; The resource conversion path module calls the pigsty bacterial community combination list, detects the nitrogen element, organic carbon, and water fraction value ranges in the current treated waste, makes a grouped comparison, and determines that the optimal material conversion directions are composting, fermentation, and carbonization to obtain the pigsty resource conversion type identifier.

2. The harmless treatment and resource utilization system for pig house waste according to claim 1, characterized in that: The proportion results of pigsty organic components include fiber content ratio, protein residue intensity level, and lipid particle distribution density. The enzymatic hydrolysis configuration plan for pigsty waste includes the composition of composite enzymes, enzyme concentration ratio parameters, and reaction-adapted environmental conditions. The pigsty bacterial community combination list includes the types of core strains, the stability of the bacterial community structure, and the proportional relationship of functional bacteria. The pigsty resource conversion type identifier includes the conversion path type code, the target product generation trend, and the material conversion efficiency level.

3. The harmless treatment and resource utilization system for pigsty waste according to claim 1, characterized in that: The component analysis and identification module includes: Based on the data of pigsty waste samples, including fiber particle image data, the particle identification sub-module extracts the particle area data according to the particle shape edge contour information in the image, identifies the average area ratio of the particles, and obtains the fiber particle area ratio value; Based on the fiber particle area ratio value, the color level identification sub-module calls the protein residue color level data in the waste sample, screens the protein coverage area within the color level range, and obtains the protein residue area ratio value; The distribution operation sub-module calculates the ratio of the lipid coverage points to the total points in the image according to the protein residue area ratio value, and combines the ratios of the fiber particle area and the protein residue area to generate the proportion results of pigsty organic components.

4. The harmless treatment and resource utilization system for pig house waste according to claim 3, characterized in that: The enzymatic hydrolysis reaction regulation module includes: The environmental factor identification sub-module calls the proportion results of pigsty organic components, extracts the temperature, humidity, and pH value data in the pigsty fermentation reaction chamber, analyzes the deviation between the data and the standard values, and combines the proportion of pigsty organic components to obtain the environmental factor matching data; Based on the environmental factor matching data, the enzyme type identification sub-module identifies the dosage of cellulase, protease, and lipase, adjusts the dosage ratio of each enzyme, and identifies the optimized dosage ratio of each enzyme to obtain the enzyme type dosage ratio value; The composite enzyme adjustment sub-module analyzes the matching relationship between the enzyme and the environment according to the enzyme type dosage ratio value, adjusts the enzyme dosage ratio, and generates the enzymatic hydrolysis configuration plan for pigsty waste.

5. The harmless treatment and resource utilization system for pig house waste according to claim 4, characterized in that: The microbial community construction module includes: The microbial flora data capture sub-module, based on the enzymatic hydrolysis configuration plan of the pigsty waste, identifies the microbial flora structure data before and after the enzymatic hydrolysis of the pigsty waste, captures the distribution status of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria during the enzymatic hydrolysis process, records the quantity and distribution location of each type of bacteria, and obtains the microbial flora distribution data set; The microbial flora analysis sub-module, based on the microbial flora distribution data set, identifies the matching degree of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria, analyzes the distribution of the three core strains in the pigsty waste, and identifies the proportion of the three strains in the microbial flora, obtaining the strain matching degree value; The microbial flora combination construction sub-module performs weighted analysis on the distribution data of ammonia-oxidizing bacteria, organic acid-converting bacteria, and gas-producing bacteria according to the strain matching degree value, screens the microbial flora combination that meets the optimal match, and generates a list of pigsty microbial flora combinations.

6. The harmless treatment and resource utilization system for pig house waste according to claim 5, characterized in that: The resource conversion path module includes: The microbial flora combination screening sub-module, based on the microbial flora type and proportion data in the pigsty microbial flora combination list, identifies the decomposition potential of the microbial flora, screens the optimal microbial flora combination that matches the current environment, and obtains the optimal microbial flora combination coefficient; The substance index detection sub-module, according to the optimal microbial flora combination coefficient, detects the nitrogen element concentration, organic carbon concentration, and moisture content, identifies the distribution range of the indexes, and generates the cross-structure difference degree value; The conversion type judgment sub-module calls the cross-structure difference degree value and the conversion path reference index, calculates the conversion path matching index, determines the path type of the index, analyzes the preferred conversion path category value, and matches it with the optimal microbial flora combination coefficient to obtain the pigsty resource conversion type identifier.

7. The harmless treatment and resource utilization system for pig house waste according to claim 6, characterized in that: The conversion path matching index adopts the formula: Among them, G represents the conversion path matching index, and P i represents the reference index value of the i-th path point, and T i represents the real-time index value of the i-th path point. n represents the total number of path points, and C avg represents the average value of the reference path category values, and D represents the optimal flora combination coefficient.

8. The harmless treatment and resource utilization system for pigsty waste according to claim 1, characterized in that: The system further includes a terminal product evaluation module: The terminal product evaluation module calls the pigsty resource conversion type identifier, detects the ammonia nitrogen residue, gaseous product component ratio, and solid particle size in the product, and determines the applicable category of the product as fertilizer, gas fuel, or soil conditioner according to the matching degree of the three indexes with the target substance attributes, and obtains the classification of the pigsty product resource utilization level; The classification of the pigsty product resource utilization level includes the fertilizer adaptation level, gas purity index, and soil conditioning effect parameter.

9. The harmless treatment and resource utilization system for pig house waste according to claim 8, characterized in that: The terminal product evaluation module includes: The resource identification sub-module, based on the pigsty resource conversion type identifier, detects the ammonia nitrogen residue, gaseous product component ratio, and solid particle size of the product, analyzes the matching degree of the data with the target substance attributes, compares the scores item by item, and makes a determination according to the preset benchmark, generating an evaluation result of the resource conversion effect; The product detection sub-module, based on the evaluation result of the resource conversion effect, detects the ammonia nitrogen residue, gaseous product component ratio, and solid particle size in the product, performs weighted calculation on the scores, and screens the key indexes to obtain the conversion product scoring coefficient; The product classification sub-module classifies the product as fertilizer, gas fuel, and soil conditioner according to the conversion product scoring coefficient, sorts and screens the scores according to the set criteria, and obtains the classification of the pigsty product resource utilization level.

10. A method for harmless treatment and resource utilization of pigsty waste, characterized in that, Executed according to the pigsty waste harmless treatment and resource utilization system as described in any one of claims 1-9, including the following steps: S1: Based on the sample data of pigsty waste, extract the fiber particle image, protein residue color scale, and lipid microparticle distribution data, calculate the area ratio, and conduct component ratio analysis in combination with the sample distribution characteristics of the pigsty manure collection points to generate the organic component ratio data of the pigsty. S2: Based on the organic component ratio data of the pigsty, extract the temperature, humidity, and pH value in the pigsty fermentation tank, identify the dosing concentrations of cellulase, protease, and lipase, and conduct a matching degree judgment in combination with environmental parameters, screen the optimal combination, determine the compound dosing ratio of the three enzymes, and obtain the enzymatic hydrolysis ratio parameters of pigsty waste. S3: Based on the enzymatic hydrolysis ratio parameters of pigsty waste, compare the bacterial community structure data before and after enzymatic hydrolysis, identify the distribution states of ammonia-oxidizing bacteria, gas-producing bacteria, and organic acid-converting bacteria, screen the matching composition structure, and obtain the list of pigsty bacterial community compositions. S4: Based on the list of pigsty bacterial community compositions, extract the nitrogen element, organic carbon, and moisture content, conduct grouped comparison, and judge the type of resource conversion path to obtain the pigsty resource conversion path type. S5: Based on the pigsty resource conversion path type, detect the ammonia nitrogen residue, gaseous components, and solid particle size, compare with the attribute index categories, screen the corresponding matching categories, and obtain the classification of the resource utilization level of pigsty products.

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