Comprehensive evaluation method for organic waste composting process and product quality

Abstract

The invention discloses a comprehensive evaluation method for an organic waste composting process and product quality. Comprising the following steps: constructing a layered index system covering a composting process and product quality; and performing interval intuition fuzzy evaluation on the first-level indexes through experts, determining an expert comprehensive weight coefficient, and obtaining a group comprehensive IVIFN opinion value of each first-level index according to the expert comprehensive weight coefficient. And determining the subjective weight of the first-level index based on the positive and negative ideal evaluation references. The method comprises the following steps: collecting a compost sample, detecting a measured value of a secondary index, calculating an objective weight of the secondary index by utilizing an entropy weight method, and obtaining a final weight by adopting a subjective and objective dynamic fusion method. And calculating a comprehensive quality index according to a weight system and actually measured data to realize scientific quantification and process optimization of compost quality. According to the method, expert cognition and data difference are comprehensively considered, the scientificity, objectivity and stability of evaluation are improved, and a reliable basis is provided for compost process control and product quality grading.

Description

Technical Field

[0001] This invention belongs to the field of organic solid waste treatment technology, specifically relating to a comprehensive evaluation method for the organic waste composting process and product quality. Background Technology

[0002] Organic waste composting is a typical multivariate, multi-objective coupled system. Its composting process involves complex interactions of material flow, energy flow, and biological information flow, exhibiting significant temporal and multi-scale coupling characteristics. For example, during composting, there are significant interactions between indicators such as temperature, moisture content, oxygen content, carbon-to-nitrogen ratio, pH, salinity, odor emissions, and greenhouse gas emissions. Different indicators contribute differently and have varying emphases on the efficiency and environmental impact of the final compost product. Since a single indicator cannot comprehensively reflect the overall performance of the composting system, and potential conflicts may exist between multiple objectives (such as the contradiction between treatment efficiency and environmental protection), modeling and optimizing the composting system using only a single indicator or simply considering all indicators often fails to balance the representativeness of the optimization objectives with the simplification requirements of the model. Therefore, there is an urgent need for an evaluation method that can integrate multiple indicators and provide a systematic, comprehensive, and scientific approach.

[0003] In the comprehensive evaluation of composting using multiple indicators, indicator weighting is a crucial step in achieving comprehensive evaluation. For example, Chinese patent CN117787760A discloses a fuzzy evaluation method and system for coal blending based on game theory-based combined weighting. This includes: constructing a hierarchical indicator system for the comprehensive evaluation of coal blending effects; acquiring coal blending data and screening evaluation features, standardizing the evaluation features to obtain the original evaluation matrix; using the analytic hierarchy process (AHP) to determine the subjective weights of each evaluation feature indicator, using the CRITIC weighting method to determine the objective weights of each evaluation feature indicator, and combining the two weights based on game theory set theory to obtain a comprehensive weight based on game theory combined weighting; using an improved fuzzy comprehensive evaluation method, determining the level of the coal blending scheme according to the maximum membership principle, and obtaining the score of each coal blending scheme to be evaluated through defuzzification, thus achieving the final evaluation of the coal blending effect.

[0004] However, this method has the following limitations: First, in terms of indicator selection, existing methods only select indicators from three dimensions: safety, economy, and environmental protection. However, the composting system involves a more complex range of indicators, including moisture, pH, EC, mechanical impurities, particle size, nutrients, heavy metals, pathogens, antibiotics and resistance genes, ammonia and odor emissions, and greenhouse gases. The contributions of different indicators to compost quality and environmental impact vary significantly, and a single-dimensional indicator system cannot comprehensively reflect the overall performance of composting. Second, in terms of data quality, actual composting monitoring data often suffers from missing or fluctuating data. Existing methods do not adequately model the impact of missing or uncertain data during the combination weighting and fuzzy evaluation process, and lack strategies for adjusting weights based on data completeness and reliability. Third, in terms of weighting methods, existing technologies use a linear combination of AHP and CRITIC for weight calculation, which is difficult to fully consider the fuzziness of expert subjective experience and objective data. It is also sensitive to the expert sample size and distribution, resulting in poor stability of the weighting results, which is easily affected by sampling bias and is not suitable for scenarios where expert opinions are ambiguous during the composting process. Therefore, there is an urgent need for a comprehensive evaluation method that can take into account multiple indicators of the composting process and products, balance expert experience with objective data characteristics, adapt to data gaps and fluctuations, and stably reflect expert consensus, so as to achieve scientific optimization of composting technology and product quality. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a comprehensive evaluation method for the composting process and product quality of organic waste.

[0006] The objective of this invention can be achieved through the following technical solutions: This invention provides a comprehensive evaluation method for the composting process and product quality of organic waste, comprising the following steps: Construct a hierarchical index system for evaluating the quality of composting processes and products, including primary indicators and several corresponding secondary indicators; Experts independently provide interval-based intuitive fuzzy evaluations of the primary indicators; Based on interval intuitionistic fuzzy evaluation, the expert comprehensive weight coefficient of each expert is calculated by using IVIFN and consensus opinions. Based on the comprehensive weight coefficients of each expert, the expert interval intuitive fuzzy evaluations of the first-level indicators are weighted and aggregated using the dual aggregation principle of complementary integration and strict consensus, so as to obtain the group comprehensive IVIFN opinion value of each first-level indicator. Based on interval intuitionistic fuzzy evaluation, positive and negative ideal evaluation references are constructed, and the subjective weights of each primary indicator are determined according to the positive and negative ideal evaluation references and the group comprehensive IVIFN opinion values ​​of each primary indicator. Compost samples were collected for evaluation and the measured values ​​of secondary indicators for each sample were obtained. The objective weights of the secondary indicators are calculated based on the dispersion of the measured values ​​of the secondary indicators using the entropy weight method. The subjective weights of each primary indicator are mapped and allocated to the secondary indicators according to the allocation rules of the secondary indicators contained under each primary indicator. The objective and subjective weights of the secondary indicators are integrated using a dynamic fusion method to obtain the weights of the secondary indicators. Based on the subjective weights of each primary indicator, the weights of secondary indicators, and the measured values ​​of secondary indicators, the comprehensive quality index of each compost sample is calculated by hierarchical weighted aggregation. The composting process parameters and operating conditions were optimized based on the comprehensive quality index of the compost samples.

[0007] Furthermore, the primary indicators include: basic characteristics, process, nutrient characteristics, environmental toxicity risk, degree of decomposition, and biological factors.

[0008] Furthermore, the aforementioned method of experts independently providing interval intuitive fuzzy evaluations of primary indicators specifically includes: Each expert provides an evaluation of each primary indicator using an interval intuitionistic fuzzy number representation, as follows: in, Indicates the first j The expert commented on the first i Interval intuitive fuzzy evaluation of each primary indicator Let be the membership interval, representing the th . j The expert believes that the first i Each primary indicator represents an important confidence interval. The interval is a non-membership interval, representing the first... j The expert believes that the first i Each primary indicator represents a confidence interval that is not considered significant. , The first j The expert commented on the first i The upper and lower values ​​of the membership interval for each primary indicator. , The first j The expert commented on the first i The upper and lower values ​​of the non-membership interval of each primary indicator; .

[0009] Furthermore, the interval-based intuitionistic fuzzy evaluation, through the calculation of expert weights using IVIFN and consensus opinions, calculates the comprehensive expert weight coefficient for each expert, specifically including: Based on interval intuitionistic fuzzy evaluation, the first... i The ideal solution for each primary indicator is determined by the arithmetic mean of expert opinions and the extreme values ​​of individual evaluations, using the following formula: in, , They represent the first i Positive and negative ideal solutions for each primary indicator , These represent the left-hand negative ideal solution and the right-hand negative ideal solution, respectively, used to characterize the distribution boundary of expert evaluation; n This indicates the total number of experts who participated in the evaluation; Based on the ideal solutions and distribution boundaries of each primary indicator, the similarity index of each expert for each primary indicator is calculated using the following formula: in, Indicates the first j The expert commented on the first i The similarity index among the consensus of each primary indicator. The Euclidean distance formula is used. Based on the differences in interval intuitive fuzzy evaluations among experts, the inter-expert proximity index is calculated using the following formula: in, Indicates the first j The experts and other experts in the i The inter-expert proximity index in the evaluation of primary indicators; For standardized distance formulas; Calculate the overall expert weighting coefficient for each expert by combining the similarity index and the proximity index among experts: in, For the first j The overall weighting coefficient of each expert. These are the fusion parameters.

[0010] Furthermore, the Euclidean distance formula is as follows: in, Indicates the first The expert and the first The experts at iEuclidean distance under each primary index; The standardized distance formula is as follows: in, Indicates the first The expert and the first The experts at i Standardized distance under each primary indicator.

[0011] Furthermore, based on the expert comprehensive weight coefficients of each expert, the intuitive fuzzy evaluations of each expert interval for the first-level indicators are weighted and aggregated using a dual aggregation principle of complementary integration and strict consensus, to obtain the group comprehensive IVIFN opinion value for each first-level indicator. The formula is as follows: in, Indicates the first i The overall IVIFN opinion value of each primary indicator. Let be the membership interval, representing the th . j The expert believes that the first i Each primary indicator represents an important confidence interval. The interval is a non-membership interval, representing the first... j The expert believes that the first i Each primary indicator represents a confidence interval that is not considered significant. For the first j The overall weighting coefficient of each expert. n This indicates the total number of experts who participated in the evaluation.

[0012] Furthermore, the construction of positive and negative ideal evaluation references based on interval intuitionistic fuzzy evaluation, and the determination of the subjective weights of each primary indicator based on the positive and negative ideal evaluation references and the group comprehensive IVIFN opinion values ​​of each primary indicator, specifically includes: Positive and negative ideal evaluation references are constructed based on the overall IVIFN opinion values ​​of the group for each primary indicator, using the following formula: in, The positive ideal solution represents the optimal reference IVIFN in the comprehensive evaluation; A negative ideal solution indicates the worst-case reference IVIFN in the overall evaluation; Based on the positive and negative ideal evaluation references and the group's comprehensive IVIFN opinion values ​​for each primary indicator, the relative closeness of each primary indicator is calculated according to the distance ratio, and then normalized to obtain the subjective weight: in, Indicates the first i The closer the relative approximation of each primary indicator is to the positive ideal solution, the larger the value. For the normalized first i Subjective weights of each primary indicator This is the Euclidean distance formula.

[0013] Furthermore, the method of using entropy weight to calculate the objective weight of the secondary indicators based on the dispersion of the measured values ​​of the secondary indicators specifically includes: The measured value of the standardized secondary indicator is given by the following formula: in, Indicates the first r The first compost sample was in the... i The first primary indicator The measured values ​​of each secondary indicator; This represents the standardized measured value; , These represent the minimum and maximum values ​​of the secondary indicator across all samples, respectively. Based on the measured values ​​of the standardized secondary indicators, the proportion of each secondary indicator in all samples is calculated using the following formula: in, Indicates the first r Each compost sample was in the secondary indicator The proportion of the surface N This represents the total number of compost samples. The entropy value of each secondary indicator is calculated based on its proportion in all samples, using the following formula: in, Indicates secondary indicators The entropy value, These are the normalization coefficients; The objective weight of each secondary indicator is calculated based on its entropy value, using the following formula: in, Indicates the first i The first-level indicator The objective weights of each secondary indicator; Indicates the first i The total number of secondary indicators under each primary indicator.

[0014] Furthermore, the method of fusion of objective and subjective weights of secondary indicators to obtain secondary indicator weights specifically includes: Determine weight parameters based on data quality. The weights of the secondary indicators are calculated based on the objective and subjective weights of the secondary indicators: in, Indicates the first i The first-level indicator The weights of the secondary indicators for each secondary indicator. , They represent the first i The first-level indicator The subjective and objective weights of each secondary indicator; This is a subjective weighting adjustment parameter, determined based on the data completeness and reliability of the measured values ​​of the secondary indicators. Higher data completeness results in higher... The larger the value, the more reliance on subjective weighting; when data is missing or fluctuates significantly, The smaller the value, the more it relies on objective entropy weight.

[0015] Furthermore, the comprehensive quality index of each compost sample is calculated by hierarchically weighted aggregation based on the subjective weights of each primary indicator, the weights of secondary indicators, and the measured values ​​of the secondary indicators. This specifically includes: Based on each compost sample r Standardized measured values ​​of secondary indicators Combined with the subjective weight of the primary indicators Weights of secondary indicators Calculate compost samples r Comprehensive quality index : in, Indicates compost sample r The overall quality index, The total number of primary indicators. Indicates the first i The total number of secondary indicators under each primary indicator For the normalized first i Subjective weights of each primary indicator Indicates the first i The first-level indicator The weights of the secondary indicators for each secondary indicator. This represents the standardized measured value.

[0016] Compared with the prior art, the present invention has the following advantages: (1) Actual monitoring data often contains missing, incomplete, and noisy data. If data quality is ignored during the weighting or fusion stage, it will lead to weight bias or evaluation distortion. Therefore, this invention explicitly uses data integrity and missing rate as explicit inputs for calculation and fusion: subjective weight adjustment parameters are introduced into the subjective-objective dynamic fusion method. u Furthermore, during project implementation, settings or calculations can be adaptively set or calculated based on the missing rate or data reliability. u This allows the fusion ratio of subjective / objective weights to be dynamically adjusted according to data quality, thereby realizing the impact of missing explicit modeling, reducing the misleading effect of subjective or objective weights caused by incomplete data, and improving the robustness and traceability of evaluation under incomplete data.

[0017] (2) Traditional weighting methods (such as using AHP or entropy weight alone) are difficult to simultaneously consider both expert fuzzy expressions and objective data characteristics, and are sensitive to expert samples, have poor weight stability, and are easily affected by sampling bias. This invention uses interval intuitionistic fuzzy numbers (IVIFN) to represent expert opinions and constructs a similarity between expert opinions and group consensus. Accessibility to experts The expert comprehensive weights are calculated by linear fusion of fusion parameters. In addition, geometric weighted closure aggregation of complementary integration and strict consensus is used to synthesize group opinions, thereby taking into account the ambiguity, expression range and complementarity among experts. It can naturally accommodate the range / fuzzy judgments of experts, improve the expressive power of fuzzy subjective information and reduce the impact of single expert outliers, and enhance the robust presentation of expert opinions by the weights.

[0018] (3) In the prior art, traditional subjective weighting methods usually rely on quantitative scoring or fuzzy language judgment by experts, which lacks an effective characterization of the uncertainty and hesitation in expert opinions. As a result, the expert judgment information is compressed into a single point value during the quantification process, which is difficult to truly reflect the overall cognitive differences and fuzzy interval characteristics of the expert group. At the same time, there are often disagreements or incomplete information among multiple expert opinions. Direct averaging or linear aggregation can easily amplify extreme opinions or cover up the judgments of a few strongly opposing opinions, resulting in unstable subjective weight results, poor interpretability, and difficulty in consistent integration with subsequent objective weight calculations. In order to address the above problems, this invention constructs positive and negative ideal evaluation references based on interval intuition fuzzy evaluation, and determines the subjective weight of each primary indicator based on the positive and negative ideal evaluation references and the group comprehensive interval intuition fuzzy number (IVIFN) opinion value of each primary indicator. In this way, the membership and non-membership interval information of expert opinions is completely preserved in the weight calculation, and the quantification and normalization of subjective weights are achieved through the ideal solution distance ratio method. This technical feature effectively solves the problems of uncertainty and ambiguity in expert judgment, takes into account the synergy and differences of multiple expert opinions, avoids interference from single or extreme opinions on the weighting results, realizes the authenticity, stability and interpretability of subjective weight calculation, significantly improves the accuracy of weight allocation in depicting the true cognition of the expert group, and provides a scientific and reliable foundation for subsequent subjective and objective integration and multi-indicator comprehensive evaluation.

[0019] (4) Existing combined weighting methods often employ fixed linear combinations or game theory to solve for weight combination and optimization, but lack adaptive strategies when data is missing or expert opinions fluctuate. The subjective-objective dynamic fusion method of this invention not only uses the entropy weight method to calculate objective weights, but also adaptively determines the fusion parameters based on data quality. u and, if necessary u The selection of fusion coefficients provides engineering rules (such as those based on missing rate intervals or cross-validation) to achieve a dynamic balance between subjective and objective weights, improve adaptability in different data quality scenarios, and avoid systematic biases caused by fixed fusion coefficients. Attached Figure Description

[0020] Figure 1 This is a flowchart of the comprehensive evaluation method according to an embodiment of the present invention; Figure 2 This is a diagram illustrating the weighting results of different weighting methods applied to the primary indicators in this embodiment of the invention. Figure 3 This is a schematic diagram illustrating the weight stability of different weighting methods in an embodiment of the present invention; Figure 4 This is a comparison chart of the overall weight stability of different weighting methods in embodiments of the present invention; Figure 5This invention provides a schematic diagram of the MDS dimensionality reduction plots of different methods, illustrating the distribution of sample points and inter-group differences. Figure 6 This is a network diagram showing the pairwise comparison results of the weighting method in this embodiment of the invention. Figure 7 This is a flowchart of the weight generation and compost quality assessment system according to an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] Example 1: This invention is applicable to organic solid waste treatment technologies in environmental engineering, specifically involving the application of multi-index comprehensive evaluation methods in the optimization of aerobic composting processes and a weight allocation system that integrates fuzzy mathematics and data-driven approaches. Based on a general multi-index evaluation method, this invention provides a compost-specific index system and weight generation process, along with engineering rules for handling data quality constraints (missing data, inconsistencies), thereby tightly coupling the algorithm with composting engineering scenarios. This invention addresses the comprehensive evaluation of the entire life cycle of aerobic composting, adapting to process monitoring and product quality assessment under different raw materials (kitchen waste, livestock and poultry manure, municipal sludge, garden waste, etc.) and different processes (windmill, trough, and closed reactors), primarily serving process operation optimization, anomaly early warning, product grading, and compliance review.

[0023] This embodiment provides a comprehensive evaluation method for the composting process and product quality of organic waste, such as... Figure 1 As shown, it includes the following steps: Step S1: Construct a hierarchical index system for evaluating the quality of the composting process and products, including primary indicators and several corresponding secondary indicators. Based on industry standards, scientific consensus, and practical application scenarios, determine key assessment indicators for compost quality such as moisture content, soluble organic matter, total nutrients, carbon-to-nitrogen ratio, and seed germination index. Based on the need for a balance between efficiency and environmental protection, incorporate technical process indicators such as greenhouse gas emissions, high-temperature duration, odor concentration, and oxygen content, as well as environmental risk indicators such as heavy metal content, microplastic content, and antibiotic resistance gene content. Finally, determine several (33) secondary indicators to construct the evaluation system and classify them as primary indicators, including basic characteristics, process flow, nutrient characteristics, environmental toxicity risk, maturity, and biological factors. Among these, basic characteristics include the following secondary indicators: moisture content... The composting process includes the following secondary indicators: pH value, electrical conductivity, mechanical impurities, and particle size; the process includes the following secondary indicators: greenhouse gas emissions, ammonia emissions, duration of high temperature period, oxygen concentration, odor emission intensity, initial carbon-nitrogen ratio, and composting duration; nutrient characteristics include the following secondary indicators: organic matter content, nitrogen content, total nutrient content, and easily oxidizable organic matter content; environmental toxicity risks include the following secondary indicators: heavy metal content, ascarid egg mortality rate, fecal coliform count, antibiotic and resistance gene content, chemical pollutant content, microplastic content, and chloride ion content; maturity includes the following secondary indicators: carbon-nitrogen ratio change, seed germination index, weed seed activity, humification index, aromatization index, respiration rate, ammonium nitrogen to nitrate nitrogen ratio, and humic acid content; biological factors include the following secondary indicators: microbial diversity and enzyme activity.

[0024] Step S2: Experts independently provide interval intuitive fuzzy evaluations of the primary indicators, specifically including: Assemble at least 10 (14) field experts (standard: having participated in at least national-level research projects, published articles in renowned academic journals, or possessing over 20 years of industry experience), provide them with standardized questionnaires and evaluation guidance, and have the experts independently conduct interval intuitive fuzzy evaluations of the importance of each primary indicator, formalizing the evaluation into... Among them, satisfying .

[0025] Step S3: Based on interval intuitionistic fuzzy evaluation, calculate the comprehensive expert weight coefficient for each expert by using IVIFN and consensus opinions as expert weights. Specifically, this includes: Based on interval intuitionistic fuzzy evaluation, the first... i The ideal solution for each primary indicator is determined by the arithmetic mean of expert opinions and the extreme values ​​of individual evaluations, using the following formula: in, , They represent the first i Positive and negative ideal solutions for each primary indicator , These represent the left-hand negative ideal solution and the right-hand negative ideal solution, respectively, used to characterize the distribution boundary of expert evaluation; n This indicates the total number of experts who participated in the evaluation; Based on the ideal solutions and distribution boundaries of each primary indicator, the similarity index of each expert for each primary indicator is calculated using the following formula: in, Indicates the first j The expert commented on the first i The similarity index among the consensus of each primary indicator. The Euclidean distance formula is used. Based on the differences in interval intuitive fuzzy evaluations among experts, the inter-expert proximity index is calculated using the following formula: in, Indicates the first j The experts and other experts in the i The inter-expert proximity index in the evaluation of primary indicators; For standardized distance formulas; Calculate the overall expert weighting coefficient for each expert by combining the similarity index and the proximity index among experts: in, For the first j The overall weighting coefficient of each expert. These are the fusion parameters.

[0026] The Euclidean distance formula is as follows: in, Indicates the first The expert and the first The experts at i Euclidean distance under each primary index; The standardized distance formula is as follows: in, Indicates the first The expert and the first The experts at i Standardized distance under each primary indicator , They represent the first i The minimum and maximum Euclidean distances between all pairwise evaluations by experts for each primary indicator.

[0027] In existing technologies, expert evaluations of primary indicators often exhibit subjective differences and ambiguity, making it difficult to obtain reliable weight allocations by relying solely on individual expert opinions. This can lead to evaluation results that deviate from group consensus, affecting the scientific evaluation of the composting process and product quality. To address this issue, this invention introduces an expert consensus analysis method based on Interval Intuitive Fuzzy Numbers (IVIFN) in step S3. By defining positive ideal solutions, negative ideal solutions, and left and right negative ideal solutions for each primary indicator, the central trend and distribution boundaries of expert evaluations are characterized, thus comprehensively reflecting both group opinions and extreme individual opinions. The method calculates the similarity index of each expert relative to the ideal solution, as well as the indirect proximity index based on the differences in evaluations among experts, and combines these two in a weighted combination according to fusion parameters to obtain the comprehensive weight coefficient for each expert. This method quantifies expert evaluation differences using Euclidean distance and eliminates the influence of different indicator dimensions and scales using standardized distance, thereby fully considering both group consensus and individual evaluation characteristics in the weight calculation, achieving objectivity, stability, and scientification of expert weight allocation. This not only improves the reliability of the subjective weights of the primary indicators, but also enhances the representativeness and accuracy of the multi-indicator comprehensive evaluation of the composting process and product quality, providing a solid foundation for the subsequent weighting of secondary indicators and the calculation of the comprehensive composting quality index.

[0028] Step S4: Based on the expert comprehensive weight coefficients of each expert, the expert interval intuitive fuzzy evaluations of the first-level indicators are weighted and aggregated using the dual aggregation principle of complementary integration and strict consensus to obtain the group comprehensive IVIFN opinion value for each first-level indicator. The formula is as follows: in, Indicates the first i The overall IVIFN opinion value of each primary indicator. Let be the membership interval, representing the th . j The expert believes that the first i Each primary indicator represents an important confidence interval. The interval is a non-membership interval, representing the first... j The expert believes that the first i Each primary indicator represents a confidence interval that is not considered significant. For the first j The overall weighting coefficient of each expert. n This indicates the total number of experts who participated in the evaluation.

[0029] This confirms that the aggregation method is a closure operation within the IVIFN set. This aggregation method exhibits the following characteristics: a complementary integration strategy is used for membership degrees, emphasizing the synergistic support of multiple opinions; a strict consensus strategy is used for non-membership degrees, ensuring conservative estimation of non-membership degrees.

[0030] In existing technologies, simply using individual expert evaluations or simple arithmetic averages cannot fully reflect the overall trend of expert group opinions. This easily overlooks the differences in importance among expert evaluations or fails to adequately address extreme opinions, resulting in an unscientific and unsound comprehensive evaluation of primary indicators. To address this issue, in step S4 of this invention, based on the comprehensive weight coefficients of each expert, a dual aggregation principle of complementary integration and strict consensus is adopted to weight and aggregate the inter-expert intuitionistic fuzzy evaluations of the primary indicators. Specifically, a complementary integration strategy is applied to the membership part, effectively strengthening the synergistic support of multiple opinions through power-weighted accumulation of each expert's support. A strict consensus strategy is applied to the non-membership part, ensuring conservative estimation through power-weighted multiplication of weights and avoiding high-risk biases. The resulting comprehensive IVIFN opinion value is mathematically a closure operation of the IVIFN set, maintaining the comparability and standardization of evaluation results while fully considering the differences and consensus among experts. This achieves a scientific, objective, and stable comprehensive expression of the group opinions on primary indicators, providing a reliable foundation for subsequent subjective weight calculations and multi-indicator fusion.

[0031] Step S5: Construct positive and negative ideal evaluation references based on interval intuitionistic fuzzy evaluation, and determine the subjective weights of each primary indicator based on the positive and negative ideal evaluation references and the group comprehensive IVIFN opinion values ​​of each primary indicator. Specifically, this includes: Positive and negative ideal evaluation references are constructed based on the overall IVIFN opinion values ​​of the group for each primary indicator, using the following formula: in, The positive ideal solution represents the optimal reference IVIFN in the comprehensive evaluation; A negative ideal solution indicates the worst-case reference IVIFN in the overall evaluation; Based on the positive and negative ideal evaluation references and the group's comprehensive IVIFN opinion values ​​for each primary indicator, the relative closeness of each primary indicator is calculated according to the distance ratio, and then normalized to obtain the subjective weight: in, Indicates the first i The closer the relative approximation of each primary indicator is to the positive ideal solution, the larger the value. For the normalized firsti Subjective weights of each primary indicator This is the Euclidean distance formula.

[0032] In existing technologies, the subjective weights of primary indicators are often determined through simple expert scoring or the analytic hierarchy process (AHP). This approach is easily influenced by the extreme opinions of individual experts, making it difficult to scientifically reflect the consensus of the expert group. Furthermore, it lacks a relative reference for the merits of the indicators, resulting in an unreasonable allocation of subjective weights. To address this problem, in step S5 of this invention, positive and negative ideal evaluation references are constructed based on the group's comprehensive IVIFN opinion values ​​for each primary indicator, where the positive ideal solution... This represents the optimal reference IVIFN in the comprehensive evaluation, and the negative ideal solution. The worst-case reference (IVIFN) is represented, while also considering the weights of each expert to comprehensively reflect the boundaries and trends of the group opinion. Subsequently, based on the proportion of distances between the group opinion and the positive and negative ideal solutions for each indicator, the relative closeness of each primary indicator is calculated, and these are normalized to obtain the subjective weights. Through this method, the present invention achieves a scientific, quantitative, and stable allocation of subjective weights. It not only takes into account the overall consensus of the expert group, but also clearly reflects the importance of each indicator relative to the optimal and worst-case scenarios, thereby improving the rationality and reliability of indicator weights and providing a solid foundation for multi-indicator comprehensive evaluation.

[0033] Step S6: Collect compost samples for evaluation and obtain the measured values ​​of the secondary indicators for each sample; Step S7: Using the entropy weight method, calculate the objective weight of the secondary indicators based on the dispersion of the measured values ​​of the secondary indicators. Specifically, this includes: The measured value of the standardized secondary indicator is given by the following formula: in, Indicates the first r The first compost sample was in the... i The first primary indicator The measured values ​​of each secondary indicator; This represents the standardized measured value; , These represent the minimum and maximum values ​​of the secondary indicator across all samples, respectively. Based on the measured values ​​of the standardized secondary indicators, the proportion of each secondary indicator in all samples is calculated using the following formula: in, Indicates the first r Each compost sample was in the secondary indicator The proportion of the surface N This represents the total number of compost samples. The entropy value of each secondary indicator is calculated based on its proportion in all samples, using the following formula: in, Indicates secondary indicators The entropy value, These are the normalization coefficients; The objective weight of each secondary indicator is calculated based on its entropy value, using the following formula: in, Indicates the first i The first-level indicator The objective weights of each secondary indicator; Indicates the first i The total number of secondary indicators under each primary indicator.

[0034] Step S8: Map and allocate the subjective weights of each primary indicator to the secondary indicators according to the allocation rules of the secondary indicators contained under each primary indicator. This includes: Determine the rules for allocating the relative importance of each secondary indicator within each primary indicator. These rules can be set based on expert experience, industry standards, or the technical characteristics of the secondary indicators. The first i The subjective weight of each primary indicator is mapped to the corresponding secondary indicator according to the allocation ratio of each secondary indicator under the primary indicator, thus obtaining the subjective weight of each secondary indicator.

[0035] Step S9: Use the subjective-objective dynamic fusion method to fuse the objective and subjective weights of the secondary indicators to obtain the weights of the secondary indicators, specifically including: Determine weight parameters based on data quality. The weights of the secondary indicators are calculated based on the objective and subjective weights of the secondary indicators: in, Indicates the first i The first-level indicator The weights of the secondary indicators for each secondary indicator. , They represent the first i The first-level indicator The subjective and objective weights of each secondary indicator; This is a subjective weighting adjustment parameter, determined based on the data completeness and reliability of the measured values ​​of the secondary indicators. Higher data completeness results in higher... The larger the value, the more reliance on subjective weighting; when data is missing or fluctuates significantly, The smaller the value, the more it relies on objective entropy weight.

[0036] Step S10: Based on the subjective weights of each primary indicator, the weights of secondary indicators, and the measured values ​​of secondary indicators, calculate the comprehensive quality index of each compost sample by hierarchical weighted aggregation, specifically including: Based on each compost sample r Standardized measured values ​​of secondary indicators Combined with the subjective weight of the primary indicators Weights of secondary indicators Calculate compost samples r Comprehensive quality index : in, Indicates compost sample r The overall quality index, The total number of primary indicators. Indicates the first i The total number of secondary indicators under each primary indicator For the normalized first i Subjective weights of each primary indicator Indicates the first i The first-level indicator The weights of the secondary indicators for each secondary indicator. This represents the standardized measured value.

[0037] Step S11: Optimize composting process parameters and operating conditions based on the comprehensive quality index of compost samples.

[0038] Example 2: like Figure 2 The chart shown is a display of the weighting results of the first-level indicators using different weighting methods in this embodiment. The chart uses a stacked bar chart to show the original weighting results of different weighting methods, and a scatter plot to show the distribution of the resampling results of different methods at each indicator level. Different colors represent different indicators, the scatter plot represents the average weight of each method on the corresponding indicator during multiple resampling processes, and the error bar represents the resampling confidence interval (2.5% and 97.5% quantiles of the resampling results).

[0039] like Figure 3 The figure shows the stability of the weights of each indicator in resampling using different weighting methods in this embodiment. The chart uses the standard deviation (a) and the confidence interval width (b) to measure the weight stability of different methods on each indicator in resampling. Different colors represent different weighting methods. The standard deviation and the confidence interval width are reversed so that the points with smaller values ​​are distributed on the side of each axis away from the center.

[0040] like Figure 4The diagram shows the MDS dimensionality reduction and multi-group difference test results of this embodiment. The scatter plot represents the relative position distribution of the original samples and resampled samples after dimensionality reduction from the original space to a two-dimensional plane using different weighting methods. Different colors represent different weighting methods. Among the same color, opaque scatter points represent the original samples, and other transparent scatter points represent resampled samples. The box plot shows the distribution of sample points of each method on the horizontal and vertical axes after MDS dimensionality reduction. The ac value represents the "additive constant," corresponding to Cailliez's additive constant. c , representing the minimum constant that needs to be added to the original distance matrix in a non-Euclidean distance matrix to make the distance matrix satisfy Euclidean property (i.e., all eigenvalues ​​are non-negative). The GOF value is the ratio of the sum of the first k eigenvalues ​​of the adjusted distance matrix to the sum of all eigenvalues, given the selected number of dimensions (k=2). It is generally considered to be the ratio that explains the original variance and measures the explanatory power of the selected dimensions for the original distance matrix.

[0041] like Figure 5 The diagram shows the pairwise comparison results of different weighting methods in this embodiment. Different rectangular boxes represent different methods, and the gray lines between the boxes indicate that there is no significant difference between the results. >0.05), the blue line represents a significant difference between the two results ( <0.05), a statistical measure of the difference between line thickness and measurement method. Proportional.

[0042] like Figure 6 The diagram shown is a double-ring donut chart illustrating the subjective weights of the primary and secondary indicators derived from IVIFN-TOPSIS in this embodiment. The inner ring displays the six primary indicators and their normalized subjective weights. The outer ring displays the secondary indicators, whose weights are calculated using IVIFN-TOPSIS. Colors associate the secondary indicators with their respective primary indicators. Percentage labels display the corresponding weights; the total weight within each ring is 100%. This visualization summarizes the subjective importance used in calculating the Compost Quality Index (CQI).

[0043] like Figure 7 The diagram shows the workflow of the Compost Quality Index (CQI) framework in this embodiment; the index system includes primary and secondary indicators. Expert opinions provide four subjective weighting schemes (IVIFN-VIKOR, IVIFN-TOPSIS, PCM-AHP, and ERM-OPA). Observational data are weighted objectively using the entropy weighting method (EW). The methods are compared using the bootstrap method - ProDiCT. Subsequently, the subjective and objective weights are integrated to calculate the Compost Quality Index (CQI), supporting downstream applications: quality quantification and comparison, process optimization objectives, scientific labeling of compost products, and extension of environmental impact assessment.

[0044] Example 3: This embodiment provides a method for comparing inter-group differences in the results of different weighting methods for multiple compost indicators, also known as the Prouk distance-based component difference test (ProDiCT). Its specific implementation steps include: Step 1: Bootstrap Resampling Bootstrap resampling is performed on the expert team, and the weighting process is repeated for the samples obtained in each resampling to obtain the weighted results.

[0045] Step 2: Component Data Transformation To avoid the spurious correlation problem caused by the definite summation constraint (closure) of the weighted data itself, and to adapt to the actual situation where certain methods may assign zero weights, a high-dimensional spherical transformation is used to... m Dimensional component data Convert to m-1 dimensional spherical coordinate data .

[0046] Step 3: Determine the sample center 1. Define the distance metric: After coordinate transformation, the data points are distributed on an m-1 dimensional hypersphere. In this case, a generalized geodesic distance is used. Measuring the differences between sample points: 2. Sample Center Calculation: Based on the Bootstrap samples and the original samples, the Karcher mean for each method is obtained using the iterative projection method. As a sample center: Step 4: Method point cloud manifold alignment and difference test 1. Rotation Matrix Calculation: To quantify the differences in variation patterns of outcomes from different methods in resampling the same potential decision-maker population, the Bootstrap point clouds generated by any two methods are used... (Including original samples and resampled samples), using a rotation matrix Alignment eliminates systematic differences between groups: It has been proven that in obtaining Using Euclidean distance or geodesic distance as the optimization objective is equivalent in the process, therefore the rotation matrix calculation has a closed-form solution.

[0047] 2. Alignment residual calculation: using the root mean square (RMS) residual of the geodesic distance between corresponding points in the point cloud. As a measure of point cloud shape differences: This reflects the degree of heterogeneity of intragroup variation between groups a and b during the bootstrap process. If this value is small (approaching the noise level), it indicates that the bootstrap variation patterns within different methods are statistically homogeneous, that is, the magnitude of the impact of bootstrap perturbation on different methods is close to the distribution direction of the variation introduced by multiple bootstrap perturbations in the principal component space.

[0048] 3. Calculation of difference statistics: Definition of the statistic Used to measure the degree of inherent difference between methods under controlled bootstrap mutation mode: It is the multiple of the geodesic distance between the centers of the two sets of point clouds relative to the difference in the shape of the point clouds, which comprehensively reflects the degree of difference between the methods.

[0049] 4. Significance Test: The significance of the statistic is verified by a permutation test controlling for the bootstrap order. The main steps include: Calculate the raw statistic ; Merge all samples from the groups to be compared, and randomly shuffle the method labels while controlling the bootstrap order; The same procedure is used to calculate the permutation statistic for each permutation dataset. After repeating this process multiple times, the empirical distribution of the permutation statistic constitutes the null distribution for the difference significance test problem. The p-value is obtained based on the quantile of the original statistic in the zero distribution and is used as the basis for significance judgment.

[0050] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0051] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A comprehensive evaluation method for the composting process and product quality of organic waste, characterized in that, Includes the following steps: Construct a hierarchical index system for evaluating the quality of composting processes and products, including primary indicators and several corresponding secondary indicators; Experts independently provide interval-based intuitive fuzzy evaluations of the primary indicators; Based on interval intuitionistic fuzzy evaluation, the expert comprehensive weight coefficient of each expert is calculated by using IVIFN and consensus opinions. Based on the comprehensive weight coefficients of each expert, the expert interval intuitive fuzzy evaluations of the first-level indicators are weighted and aggregated using the dual aggregation principle of complementary integration and strict consensus, so as to obtain the group comprehensive IVIFN opinion value of each first-level indicator. Based on interval intuitionistic fuzzy evaluation, positive and negative ideal evaluation references are constructed, and the subjective weights of each primary indicator are determined according to the positive and negative ideal evaluation references and the group comprehensive IVIFN opinion values ​​of each primary indicator. Compost samples were collected for evaluation and the measured values ​​of secondary indicators for each sample were obtained. The objective weights of the secondary indicators are calculated based on the dispersion of the measured values ​​of the secondary indicators using the entropy weight method. The subjective weights of each primary indicator are mapped and allocated to the secondary indicators according to the allocation rules of the secondary indicators contained under each primary indicator. The objective and subjective weights of the secondary indicators are integrated using a dynamic fusion method to obtain the weights of the secondary indicators. Based on the subjective weights of each primary indicator, the weights of secondary indicators, and the measured values ​​of secondary indicators, the comprehensive quality index of each compost sample is calculated by hierarchical weighted aggregation. The composting process parameters and operating conditions were optimized based on the comprehensive quality index of the compost samples.

2. The comprehensive evaluation method for the composting process and product quality of organic waste according to claim 1, characterized in that, The primary indicators include: basic characteristics, process, nutrient characteristics, environmental toxicity risk, degree of decomposition, and biological factors.

3. The comprehensive evaluation method for the composting process and product quality of organic waste according to claim 1, characterized in that, The process of experts independently providing interval-based intuitive fuzzy evaluations of primary indicators specifically includes: Each expert provides an evaluation of each primary indicator using an interval intuitionistic fuzzy number representation, as follows: in, Indicates the first j The expert commented on the first i Interval intuitive fuzzy evaluation of each primary indicator Let be the membership interval, representing the th . j The expert believes that the first i Each primary indicator represents an important confidence interval. The interval is a non-membership interval, representing the first... j The expert believes that the first i Each primary indicator represents a confidence interval that is not considered significant. , The first j The expert commented on the first i The upper and lower values ​​of the membership interval for each primary indicator. , The first j The expert commented on the first i The upper and lower values ​​of the non-membership interval of each primary indicator; .

4. A comprehensive evaluation method for the composting process and product quality of organic waste according to claim 1 or 3, characterized in that, The interval-based intuitionistic fuzzy evaluation calculates the comprehensive expert weight coefficient for each expert by using IVIFN and consensus opinions as expert weights. Specifically, this includes: Based on interval intuitionistic fuzzy evaluation, the first... i The ideal solution for each primary indicator is determined by the arithmetic mean of expert opinions and the extreme values ​​of individual evaluations, using the following formula: in, , They represent the first i Positive and negative ideal solutions for each primary indicator , These represent the left-hand negative ideal solution and the right-hand negative ideal solution, respectively, used to characterize the distribution boundary of expert evaluation; n This indicates the total number of experts who participated in the evaluation; Based on the ideal solutions and distribution boundaries of each primary indicator, the similarity index of each expert for each primary indicator is calculated using the following formula: in, Indicates the first j The expert commented on the first i The similarity index among the consensus of each primary indicator. The Euclidean distance formula is used. Based on the differences in interval intuitive fuzzy evaluations among experts, the inter-expert proximity index is calculated using the following formula: in, Indicates the first j The experts and other experts in the i The inter-expert proximity index in the evaluation of primary indicators; For standardized distance formulas; Calculate the overall expert weighting coefficient for each expert by combining the similarity index and the proximity index among experts: in, For the first j The overall weighting coefficient of each expert. These are the fusion parameters.

5. A comprehensive evaluation method for the composting process and product quality of organic waste according to claim 4, characterized in that, The Euclidean distance formula is as follows: in, Indicates the first The expert and the first The experts at i Euclidean distance under each primary index; The standardized distance formula is as follows: in, Indicates the first The expert and the first The experts at i Standardized distance under each primary indicator.

6. The comprehensive evaluation method for the composting process and product quality of organic waste according to claim 1, characterized in that, Based on the expert comprehensive weight coefficients of each expert, the intuitive fuzzy evaluations of each expert interval for the first-level indicators are weighted and aggregated using the dual aggregation principle of complementary integration and strict consensus, to obtain the group comprehensive IVIFN opinion value for each first-level indicator. The formula is as follows: in, Indicates the first i The overall IVIFN opinion value of each primary indicator. Let be the membership interval, representing the th . j The expert believes that the first i Each primary indicator represents an important confidence interval. The interval is a non-membership interval, representing the first... j The expert believes that the first i Each primary indicator represents a confidence interval that is not considered significant. For the first j The overall weighting coefficient of each expert. n This indicates the total number of experts who participated in the evaluation.

7. The comprehensive evaluation method for the composting process and product quality of organic waste according to claim 1, characterized in that, The construction of positive and negative ideal evaluation references based on interval intuitionistic fuzzy evaluation, and the determination of the subjective weights of each primary indicator based on the positive and negative ideal evaluation references and the group comprehensive IVIFN opinion values ​​of each primary indicator, specifically includes: Positive and negative ideal evaluation references are constructed based on the overall IVIFN opinion values ​​of the group for each primary indicator, using the following formula: in, The positive ideal solution represents the optimal reference IVIFN in the comprehensive evaluation; A negative ideal solution indicates the worst-case reference IVIFN in the overall evaluation; Based on the positive and negative ideal evaluation references and the group's comprehensive IVIFN opinion values ​​for each primary indicator, the relative closeness of each primary indicator is calculated according to the distance ratio, and then normalized to obtain the subjective weight: in, Indicates the first i The closer the relative approximation of each primary indicator is to the positive ideal solution, the larger the value. For the normalized first i Subjective weights of each primary indicator This is the Euclidean distance formula.

8. The comprehensive evaluation method for the composting process and product quality of organic waste according to claim 1, characterized in that, The method employing entropy weighting calculates the objective weights of secondary indicators based on the dispersion of their measured values. Specifically, this includes: The measured value of the standardized secondary indicator is given by the following formula: in, Indicates the first r The first compost sample was in the... i The first primary indicator The measured values ​​of each secondary indicator; This represents the standardized measured value; , These represent the minimum and maximum values ​​of the secondary indicator across all samples, respectively. Based on the measured values ​​of the standardized secondary indicators, the proportion of each secondary indicator in all samples is calculated using the following formula: in, Indicates the first r Each compost sample was in the secondary indicator The proportion of the surface N This represents the total number of compost samples. The entropy value of each secondary indicator is calculated based on its proportion in all samples, using the following formula: in, Indicates secondary indicators The entropy value, These are the normalization coefficients; The objective weight of each secondary indicator is calculated based on its entropy value, using the following formula: in, Indicates the first i The first-level indicator The objective weights of each secondary indicator; Indicates the first i The total number of secondary indicators under each primary indicator.

9. A comprehensive evaluation method for the composting process and product quality of organic waste according to claim 1, characterized in that, The method of combining objective and subjective weights of secondary indicators using a dynamic fusion of subjective and subjective factors to obtain the weights of the secondary indicators specifically includes: Determine weight parameters based on data quality. The weights of the secondary indicators are calculated based on the objective and subjective weights of the secondary indicators: in, Indicates the first i The first-level indicator The weights of the secondary indicators for each secondary indicator. , They represent the first i The first-level indicator The subjective and objective weights of each secondary indicator; This is a subjective weighting adjustment parameter, determined based on the data completeness and reliability of the measured values ​​of the secondary indicators. Higher data completeness results in higher... The larger the value, the more reliance on subjective weighting; when data is missing or fluctuates significantly, The smaller the value, the more it relies on objective entropy weight.

10. A comprehensive evaluation method for the composting process and product quality of organic waste according to claim 1, characterized in that, The comprehensive quality index of each compost sample is calculated by hierarchically weighting and aggregating the subjective weights of each primary indicator, the weights of secondary indicators, and the measured values ​​of the secondary indicators. Specifically, this includes: Based on each compost sample r Standardized measured values ​​of secondary indicators Combined with the subjective weight of the primary indicators Weights of secondary indicators Calculate compost samples r Comprehensive quality index : in, Indicates compost sample r The overall quality index, The total number of primary indicators. Indicates the first i The total number of secondary indicators under each primary indicator For the normalized first i Subjective weights of each primary indicator Indicates the first i The first-level indicator The weights of the secondary indicators for each secondary indicator. This represents the standardized measured value.

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